Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-22-074 JUNE 2022 

Facilitating Analysts’ Use of Traffic 

Data from the Long-Term Pavement 

Performance (LTPP) Program
FOREWORD 

Traffic data are an integral part of the Long-Term Pavement Performance (LTPP) database. This 

report describes the data analysis study performed to help LTPP database users navigate and 

understand LTPP traffic data and select traffic parameters that best fit their project needs. This 

study included the development of analysis-ready traffic datasets to support a broad variety of 

analysis projects that require quality traffic data. 

Emergence of new-generation pavement analysis and design methods that are incorporated into 

AASHTOWare® Pavement ME DesignTM software require traffic parameters that LTPP 

database tables were not originally designed to store. This study addressed LTPP database users’ 

needs for traffic inputs in the AASHTOWare Pavement ME Design software format by 

developing computational methodologies and producing computed parameter tables containing 

necessary traffic inputs in this format for all LTPP sites. 

Methodologies presented in this report can be applied to help highway agencies compute traffic 

statistics necessary to support pavement design, research, management, and forensic 

investigations. 

Cheryl Allen Richter, Ph.D., P.E. 

Director, Office of Infrastructure 

Research and Development 

Notice 

This document is disseminated under the sponsorship of the U.S. Department of Transportation 

(USDOT) in the interest of information exchange. The U.S. Government assumes no liability for 

the use of the information contained in this document.

The U.S. Government does not endorse products or manufacturers. Trademarks or 

manufacturers’ names appear in this report only because they are considered essential to the 

objective of the document. 

Quality Assurance Statement 

The Federal Highway Administration (FHWA) provides high-quality information to serve 

Government, industry, and the public in a manner that promotes public understanding. Standards 

and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its 

information. FHWA periodically reviews quality issues and adjusts its programs and processes to 

ensure continuous quality improvement. 

Recommended citation: Facilitating Analysts’ Use of Traffic Data from the Long-Term 

Pavement Performance (LTPP) Program (Washington, DC: 2022) 

https://doi.org/10.21949/1521878.
TECHNICAL REPORT DOCUMENTATION PAGE 

1. Report No. 

FHWA-HRT-22-074

2. Government Accession No. 3. Recipient’s Catalog No. 

4. Title and Subtitle 

Facilitating Analysts’ Use of Traffic Data from the LongTerm Pavement Performance (LTPP) Program 

5. Report Date 

June 2022 

6. Performing Organization Code: 

7. Author(s) 

O.I. Selezneva and M.E. Hallenbeck (ORCID: 0000-0002-

2298-0666) 

8. Performing Organization Report No. 

9. Performing Organization Name and Address 

Applied Research Associates, Inc. 

7184 Troy Hill Drive, Suite N 

Elkridge, MD 21075-7056 

10. Work Unit No. 

11. Contract or Grant No. 

DTFH61-14-C-00018 

12. Sponsoring Agency Name and Address 

Office of Infrastructure Research and Development 

Federal Highway Administration 

6300 Georgetown Pike 

McLean, VA 22101-2296 

13. Type of Report and Period Covered 

Final Report; July 2014–May 2021 

14. Sponsoring Agency Code 

HRDI-30

15. Supplementary Notes 

Deborah Walker (ORCID: 0000-0002-3684-5190; HRDI-30) served as the Contracting Officer’s Representative. 

16. Abstract 

This report examines Long-Term Pavement Performance (LTPP) database users’ needs for traffic data and 

parameters and the extent of traffic data availability in the LTPP database. It identifies and recommends new 

analysis-ready traffic parameters and traffic data–usability indices for inclusion in the LTPP database. 

This report also presents a methodology for computation of analysis-ready traffic parameters selected by the 

Federal Highway Administration for this study and a methodology for assessing reasonableness of historical axle 

loading data included in LTPP database tables for sites that do not have sufficient information about the accuracy 

of equipment used to collect these data. This report includes a description of LTPP database tables that include 

newly computed analysis-ready traffic parameters. For LTPP sites that do not have traffic data, this report 

explores alternative data sources and provides recommendations for using these data sources. This report includes 

an overview of the User Guide for Selecting and Using Long-Term Pavement Performance Traffic Data, which 

aims to help LTPP database users navigate and understand LTPP traffic data and parameters and select or 

compute traffic parameters that best fit their project or analysis needs.(1)

17. Key Words 

Traffic data, traffic loading, pavement design, 

Long-Term Pavement Performance, MEPDG, 

WIM, weigh-in-motion, HPMS 

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161. 

http://www.ntis.gov 

19. Security Classif. (of this report) 

Unclassified 

20. Security Classif. (of this page) 

Unclassified 

21. No. of Pages 

307 

22. Price 

N/A 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
ii

SI* (MODERN METRIC) CONVERSION FACTORS

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol

LENGTH

in inches 25.4 millimeters mm

ft feet 0.305 meters m

yd yards 0.914 meters m

mi miles 1.61 kilometers km

AREA

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha

mi2 square miles 2.59 square kilometers km2

VOLUME

fl oz fluid ounces 29.57 milliliters mL

gal gallons 3.785 liters L

ft3 cubic feet 0.028 cubic meters m3

yd3 cubic yards 0.765 cubic meters m3

NOTE: volumes greater than 1,000 L shall be shown in m3

MASS

oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2,000 lb) 0.907 megagrams (or “metric ton”) Mg (or “t”)

TEMPERATURE (exact degrees)

°F Fahrenheit 5 (F-32)/9 Celsius °C or (F-32)/1.8

ILLUMINATION

fc foot-candles 10.76 lux lx

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS

lbf poundforce 4.45 newtons N

lbf/in2 poundforce per square inch 6.89 kilopascals kPa

APPROXIMATE CONVERSIONS FROM SI UNITS

Symbol When You Know Multiply By To Find Symbol

LENGTH

mm millimeters 0.039 inches in

m meters 3.28 feet ft

m meters 1.09 yards yd

km kilometers 0.621 miles mi

AREA

mm2 square millimeters 0.0016 square inches in2

m2 square meters 10.764 square feet ft2

m2 square meters 1.195 square yards yd2

ha hectares 2.47 acres ac

km2 square kilometers 0.386 square miles mi2

VOLUME

mL milliliters 0.034 fluid ounces fl oz

L liters 0.264 gallons gal

m3 cubic meters 35.314 cubic feet ft3

m3 cubic meters 1.307 cubic yards yd3

MASS

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or “t”) megagrams (or “metric ton”) 1.103 short tons (2,000 lb) T

TEMPERATURE (exact degrees)

°C Celsius 1.8C+32 Fahrenheit °F

ILLUMINATION

lx lux 0.0929 foot-candles fc

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS

N newtons 2.225 poundforce lbf

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
iii

TABLE OF CONTENTS 

CHAPTER 1. INTRODUCTION ................................................................................................ 1

BACKGROUND ..................................................................................................................... 1

Challenges with Traffic Loading Data Collected at LTPP Sites ........................................ 1

Benefits of WIM Traffic Data from Transportation Pooled Fund Study (TPF), TPF5(004) .................................................................................................................................. 4

STUDY OBJECTIVES........................................................................................................... 4

STUDY SCOPE ....................................................................................................................... 5

STUDY OUTCOMES ............................................................................................................. 6

REPORT OVERVIEW .......................................................................................................... 7

CHAPTER 2. TRAFFIC DATA AND PARAMETERS USED FOR PAVEMENT 

STUDIES ....................................................................................................................................... 9

OVERVIEW OF TRAFFIC PARAMETERS USED FOR PAVEMENT 

ANALYSIS, DESIGN, AND MANAGEMENT ................................................................... 9

BASIC TRAFFIC DATA AND LOW-LEVEL SUMMARY STATISTICS ................... 10

DETAILED TRAFFIC DATA AND PARAMETERS ...................................................... 10

Traffic Parameters for Pavement Response Prediction Based on Mechanistic 

Models............................................................................................................................... 10

Traffic Parameters for Mechanistic–Empirical Pavement Performance Prediction ......... 11

SUMMARY TRAFFIC PARAMETERS USED IN EMPIRICAL PAVEMENT 

STUDIES AND TRADITIONAL PAVEMENT DESIGN AND MANAGEMENT 

APPLICATIONS .................................................................................................................. 12

Challenges with Using a Single-Value Traffic Loading Summary Statistic .................... 12

ESAL as a Traditional Traffic Loading Summary Statistic .............................................. 13

General ESAL ................................................................................................................... 13

Traffic and Truck Volume Summary Parameters ............................................................. 14

Relative Pavement Performance Impact Factor (RPPIF) ................................................. 14

Annual Total Load and CTL ............................................................................................. 14

Traffic Load Grouping Statistic ........................................................................................ 14

Average GVW .................................................................................................................. 15

Recommendation for Selecting a Traffic Loading Summary Statistic ............................. 15

TRAFFIC PARAMETERS USED IN AASHTOWARE PAVEMENT ME 

DESIGN SOFTWARE ......................................................................................................... 22

MEPDG Traffic Parameters.............................................................................................. 22

Traffic Loading Defaults for MEPDG .............................................................................. 23

MEPDG Parameters Requiring Special Input Formats and Tools.................................... 24

Accuracy of Weight Data.................................................................................................. 24

CHAPTER 3. ASSESSMENT OF LTPP USERS’ NEEDS AND AVAILABLE LTPP 

TRAFFIC DATA SOURCES..................................................................................................... 25

PURPOSE OF LTPP TRAFFIC DATA AND LTPP USERS’ PRIORITIES ................ 25

GOAL OF LTPP TRAFFIC DATA AVAILABILITY ASSESSMENT.......................... 25

AVAILABILITY OF BASIC AND DETAILED TRAFFIC DATA AND 

PARAMETERS..................................................................................................................... 26

LTPP Data Sources for Basic and Detailed Traffic Data and Parameters ........................ 26
iv

Availability Assessment and Recommendations for Basic and Detailed Traffic Data 

and Parameters .................................................................................................................. 28

AVAILABILITY OF ANALYSIS-READY TRAFFIC PARAMETERS ........................ 29

Need for Analysis-Ready Traffic Datasets ....................................................................... 29

Why Analysis-Ready Traffic Parameters Should Be a Priority for LTPP........................ 29

How Analysis-Ready Traffic Data and Parameters Are Used .......................................... 30

LTPP Traffic Data Sources to Support Mechanistic Pavement Response Prediction 

and Modeling .................................................................................................................... 30

Availability Assessment and Recommendations for Parameters Supporting 

Mechanistic Pavement Response Prediction and Modeling ............................................. 32

LTPP Traffic Data Sources to Support Mechanistic–Empirical Pavement 

Performance Prediction..................................................................................................... 33

Availability Assessment and Recommendations for Parameters Supporting 

Mechanistic–Empirical Pavement Performance Prediction.............................................. 35

LTPP Traffic Data Sources to Support MEPDG Applications with AASHTOWare 

Pavement ME Design Software ........................................................................................ 35

Availability Assessment and Recommendations for Parameters Supporting 

MEPDG Applications with AASHTOWare Pavement ME Design Software.................. 39

LTPP Traffic Data Sources Supporting Empirical and High-Level Pavement 

Management Analyses ...................................................................................................... 40

Availability Assessment and Recommendations for Parameters Supporting 

Empirical and High-Level Pavement Management Analyses .......................................... 43

LTPP Traffic Data Availability Assessment and Recommendations for Parameters 

Supporting Model Development for HPMS ..................................................................... 43

AVAILABILITY OF LTPP TRAFFIC METADATA PARAMETERS ......................... 45

What Are Metadata? ......................................................................................................... 45

Traffic Data Collection Equipment ................................................................................... 45

Accuracy of Traffic Data Collection Equipment .............................................................. 46

Traffic Data Availability ................................................................................................... 46

Shared LTPP Traffic Sites ................................................................................................ 46

Linking LTPP Traffic Data Tables to LTPP Sites ............................................................ 46

Methods Used for Computing LTPP Traffic Parameters ................................................. 46

Limitations of and Concerns Related to LTPP Traffic Metadata ..................................... 47

Recommendations Related to Traffic Metadata Parameters ............................................. 47

Proposed Traffic Data–Usability Indices .......................................................................... 47

RECOMMENDATIONS BASED ON ASSESSMENT OF LTPP USER NEEDS 

AND CURRENT TRAFFIC DATA AVAILABILITY ..................................................... 48

Analysis-Ready Traffic Parameters .................................................................................. 48

Core Traffic Parameters .................................................................................................... 49

Analysis-Ready Traffic Parameters for Direct Input to AASHTOWare Pavement 

ME Design Software ......................................................................................................... 58

Traffic Parameters Beneficial for LTPP Analyses but Not Included in the LTPP 

Database ............................................................................................................................ 60

CHAPTER 4. ALTERNATIVE TRAFFIC DATA SOURCES.............................................. 77

PURPOSE OF ALTERNATIVE DATA SOURCES ......................................................... 77

AADT ..................................................................................................................................... 77
v

Best Alternative Data Source ............................................................................................ 77

Other Options .................................................................................................................... 80

AADTT................................................................................................................................... 81

Best Alternative Data Source ............................................................................................ 81

Other Options .................................................................................................................... 81

TRUCK VOLUMES BY FHWA VEHICLE CLASSIFICATION .................................. 82

Best Alternative Data Source ............................................................................................ 82

MONTHLY TRUCK VOLUMES BY FHWA VEHICLE CLASS ................................. 83

Best Alternative Data Source ............................................................................................ 83

Secondary Data Source ..................................................................................................... 84

HOURLY TRUCK VOLUME DISTRIBUTIONS ............................................................ 84

Best Alternative Data Source ............................................................................................ 85

ANNUAL TRUCK (AXLE) WEIGHT DISTRIBUTIONS .............................................. 85

Best Alternative Data Source ............................................................................................ 85

MONTHLY TRUCK (AXLE) WEIGHT DISTRIBUTIONS .......................................... 87

CHAPTER 5. ANALYSIS-READY TRAFFIC PARAMETERS SELECTED FOR 

THIS STUDYAND GENERAL APPROACH FOR PARAMETER COMPUTATION 

AND DELIVERY TO USERS ................................................................................................... 89

ANALYSIS-READY TRAFFIC PARAMETERS SELECTED FOR THIS 

STUDY ................................................................................................................................... 89

GENERAL APPROACH FOR PARAMETER COMPUTATION ................................. 94

Challenges Using Available Traffic Data ......................................................................... 94

Addressing Missing or Questionable Traffic Data ........................................................... 94

Estimating Traffic Changes over Time ............................................................................. 94

Development of Representative Traffic Values ................................................................ 95

Development of Input Values for AASHTOWare Pavement ME Design software ......... 96

DELIVERY OF COMPUTED PARAMETERS TO LTPP USERS ................................ 96

CPTs.................................................................................................................................. 96

Use InfoPave to Access New CPTs .................................................................................. 96

User Guide for Selecting and Using LTPP Traffic Data .................................................. 96

CHAPTER 6. ESTIMATING PARAMETERS DESCRIBING TRUCK VOLUMES 

AND TRUCK VOLUME CHANGES OVER TIME .............................................................. 97

AADTT FOR EACH IN-SERVICE YEAR, ALL TRUCKS COMBINED .................... 97

Computed Parameter Description ..................................................................................... 97

Data Sources Used for Parameter Computation ............................................................... 98

Methodology for Parameter Computing or Estimating .................................................... 99

Computational Procedure or Algorithm ............................................................................ 99

Examples of Parameter Estimating ................................................................................. 103

AADTT FOR EACH TRUCK CLASS AND EACH IN-SERVICE YEAR .................. 108

Computed Parameter Description ................................................................................... 108

Data Sources used for Parameter Computation .............................................................. 108

Methodology for Parameter Computing or Estimating .................................................. 109

Computational Procedure or Algorithm .......................................................................... 113

Examples of Parameter Estimating ................................................................................. 117

TOTAL ANNUAL TRUCK VOLUME ............................................................................ 125

Computed Parameter Description ................................................................................... 125
vi 

Data Sources used for Parameter Computation .............................................................. 125

Methodology and Computational Procedure for Parameter Computing or 

Estimating ....................................................................................................................... 125

Examples of Parameter Estimating ................................................................................. 126

TOTAL ANNUAL TRUCK VOLUME FOR EACH TRUCK CLASS (FHWA 

VEHICLE CLASSES 4–13) ............................................................................................... 126

Computed Parameter Description ................................................................................... 126

Data Sources used for Parameter Computation .............................................................. 127

Methodology and Computational Procedure for Parameter Computing ........................ 127

Examples of Parameter Estimating ................................................................................. 127

CUMULATIVE TOTAL TRUCK VOLUME FOR VEHICLES IN FHWA 

CLASSES 4–13 COMBINED, ESTIMATED AND COMPOUNDED ANNUALLY .. 128

Computed Parameter Description ................................................................................... 128

Data Sources used for Parameter Computation .............................................................. 128

Methodology and Computational Procedure for Parameter Computing ........................ 128

Examples of Parameter Estimating ................................................................................. 128

Cumulative Truck Volume FOR EACH VEHICLE CLASS (FHWA CLASSES 4–

13), ESTIMATED AND COMPOUNDED ANNUALLY ............................................... 130

Computed Parameter Description ................................................................................... 130

Data Sources used for Parameter Computation .............................................................. 131

Methodology and Computational Procedure for Parameter Computing ........................ 131

Examples of Parameter Estimating ................................................................................. 131

Producing the TRF_TREND CPT .................................................................................. 132

MEPDG TRUCK VOLUME GROWTH PARAMETERS ............................................ 132

Computed Parameter Description ................................................................................... 132

Data Sources Used for Parameter Computation ............................................................. 133

Methodology and Computational Procedure for Parameter Computing ........................ 133

Examples of Parameter Estimating ................................................................................. 135

CHAPTER 7. ESTIMATING REPRESENTATIVE TRUCK VOLUMES AND 

TRUCK VOLUME DISTRIBUTIONS BY VEHICLE CLASS .......................................... 143

REPRESENTATIVE AADTT FOR LTPP LANE, FHWA VEHICLE CLASSES 

4–13 COMBINED ............................................................................................................... 143

Computed Parameter Description ................................................................................... 143

Data Sources used for Parameter Computation .............................................................. 143

Methodology for Parameter Computing or Estimating .................................................. 143

Computational Procedure or Algorithm.......................................................................... 144

Examples of Parameter Estimating ................................................................................. 145

REPRESENTATIVE PERCENTILE VEHICLE CLASS DISTRIBUTION FOR 

FHWA CLASSES 4–13 IN LTPP LANE ......................................................................... 148

Computed Parameter Description ................................................................................... 148

Data Sources used for Parameter Computation .............................................................. 148

Methodology for Parameter Computation or Estimation................................................ 148

Computational Procedure or Algorithm.......................................................................... 149

Examples of Parameter Estimating ................................................................................. 150
vii 

CHAPTER 8. ESTIMATING REPRESENTATIVE AXLE LOADING 

DISTRIBUTION FACTORS BY VEHICLE CLASS AND AXLE GROUP FOR 

MEPDG USE ............................................................................................................................. 153

COMPUTED PARAMETER DESCRIPTION ................................................................ 153

DATA SOURCES USED FOR PARAMETER COMPUTATION................................ 153

NUMBER OF LTPP SITES REQUIRING PARAMETER ESTIMATION ................ 154

METHODOLOGY FOR PARAMETER COMPUTATION OR ESTIMATION ....... 154

Hierarchical Approach for WIM Data Selection and Usability Code Assignment 

Based on Data Quality Considerations ........................................................................... 155

WIM Data Rationality and Data Reasonableness Checks .............................................. 156

WIM Data Availability Criteria ...................................................................................... 159

Handling Atypical Axle Groupings ................................................................................ 160

Method 1—Develop ALDF Based on Site-Specific WIM Data .................................... 161

Method 2—Estimate ALDF Using Alternative Data Sources or Defaults ..................... 162

Storing and Using ALDF ................................................................................................ 163

PROCEDURES FOR ALDF COMPUTATION .............................................................. 163

Procedure to Review Axle Loading Data Availability, Accuracy, and Rationality 

and Assign the ALDF Computation Method .................................................................. 163

Procedure to Assess Rationality and Identify Loading Pattern Using Annual NALS 

of Limited Accuracy ....................................................................................................... 171

Procedure to Compute ALDF Based on Site-Specific WIM Data Using Method 1 ...... 175

Procedure to Estimate ALDF for Sites with Insufficient or no Site-Specific WIM 

Data Using Method 2 ...................................................................................................... 177

EXAMPLES OF ALDF ESTIMATION ........................................................................... 180

ALDF with Unique Values for Each Calendar Month Based on Site-Specific WIM 

Data (Method 1—Monthly) ............................................................................................ 180

ALDF Representing a Typical Day of a Year Based on Site-Specific WIM Data 

(Method 1—Annual) ....................................................................................................... 184

ALDF Representing a Typical Day of a Year Based on Site-Specific WIM Data 

(Method 1—Annual Combined) ..................................................................................... 186

ALDF Estimated for a Site with Insufficient Site-Specific WIM Data (Method 2—

Limited Data) .................................................................................................................. 189

ALDF Estimated for Site with No Usable Site-Specific WIM Data (Method 2—No 

Usable Data).................................................................................................................... 190

CHAPTER 9. ESTIMATING REPRESENTATIVE NUMBER OF AXLES PER 

TRUCK BY VEHICLE CLASS .............................................................................................. 193

COMPUTED PARAMETER DESCRIPTION ................................................................ 193

DATA SOURCES USED FOR PARAMETER COMPUTATION................................ 193

METHODOLOGY FOR PARAMETER COMPUTING OR ESTIMATING ............. 193

COMPUTATIONAL PROCEDURE ................................................................................ 194

Procedure to Compute Representative APT Based on Site-Specific WIM Data............ 194

Procedure to Estimate Representative APT for Sites with No Site-Specific WIM 

Data ................................................................................................................................. 196

EXAMPLES OF ESTIMATING REPRESENTATIVE APTs....................................... 197

Estimating Representative APTs Based on Site-Specific WIM Data ............................. 197

Estimating Representative APT for Sites with No Site-Specific WIM Data .................. 200
viii 

CHAPTER 10. ESTIMATING PARAMETERS DESCRIBING TRUCK LOADING 

AND TRUCK LOADING CHANGES OVER TIME ........................................................... 203

ESAL FOR EACH IN-SERVICE YEAR, ALL TRUCKS COMBINED ...................... 203

Computed Parameter Description ................................................................................... 203

Data Sources Used for Parameter Computation ............................................................. 203

Methodology for Parameter Computation or Estimation ................................................ 203

Procedure for Parameter Computation ............................................................................ 205

Example of Parameter Estimating .................................................................................. 209

GESAL FOR EACH IN-SERVICE YEAR, ALL TRUCKS COMBINED ................... 213

Computed Parameter Description ................................................................................... 213

Data Sources Used for Parameter Computation ............................................................. 214

Methodology and Computational Procedure for Parameter Computing ........................ 214

Example of Parameter Estimating .................................................................................. 216

ANNUAL TOTAL GVW FOR EACH IN-SERVICE YEAR, ALL TRUCKS 

COMBINED ........................................................................................................................ 221

Computed Parameter Description ................................................................................... 221

Data Sources used for Parameter Computation .............................................................. 221

Methodology and Computational Procedure for Parameter Computing ........................ 221

Example of Parameter Estimating .................................................................................. 223

CHAPTER 11. DESIGN OF THE CPTS ............................................................................... 231

TRF_TREND....................................................................................................................... 231

TRF_REP............................................................................................................................. 233

MEPDG_TRUCK_VOL_PARAMETERS....................................................................... 237

MEPDG_AXLE_LOAD_DIST_FACTOR ....................................................................... 239

MEPDG_AXLE_PER_TRUCK ........................................................................................ 242

CODES DESCRIBING DATA AND METHODS USED FOR PARAMETER 

COMPUTATION, AND PARAMETER USABILITY ................................................... 243

CHAPTER 12. OVERVIEW OF THE USER GUIDE FOR SELECTING AND 

USING LONG-TERM PAVEMENT PERFORMANCE TRAFFIC DATA ...................... 253

PURPOSE OF THE GUIDE .............................................................................................. 253

OVERVIEW OF THE GUIDE .......................................................................................... 253

CHAPTER 13. SUMMARY OF STUDY RESULTS ............................................................ 255

OVERVIEW OF STUDY OUTCOMES .......................................................................... 255

NEW TRAFFIC PARAMETERS DEVELOPED IN THIS STUDY ............................. 255

AVAILABILITY OF ANALYSIS-READY TRAFFIC PARAMETERS FOR 

LTPP TRAFFIC SITES ..................................................................................................... 256

Truck Volume, Growth, and Vehicle Classification Parameters for AASHTOWare 

Pavement ME Design Software Input ............................................................................. 256

Axle Loading Distribution Factors for AASHTOWare Pavement ME Design 

Software Input ................................................................................................................. 258

Axles Per Truck Parameters for AASHTOWare Pavement ME Design Software 

Input ................................................................................................................................ 260

Traffic Trend Parameters for LTPP Analyses ................................................................ 260

Representative Traffic Parameters for High-Level Pavement Performance Analyses ... 261
ix

APPENDIX A. SCREENSHOTS OF AASHTOWARE PAVEMENT ME DESIGN 

SOFTWARE GUIS FOR TRAFFIC INPUTS ....................................................................... 263

APPENDIX B. LTPP WIM SITES CONSIDERED FOR ALDF COMPUTATION ........ 275

REFERENCES .......................................................................................................................... 291
x

LIST OF FIGURES 

Figure 1. Graphs. Illustration of questionable tandem axle loads reported in SDR 27. ................. 2

Figure 2. Charts. Comparison of distress prediction using questionable axle weight data. 

Challenges with Developing Traffic Inputs for Pavement Analyses ........................................ 3

Figure 3. Flowchart. Work execution sequence.............................................................................. 6

Figure 4. Photo. Example of an LTPP test section located on a divided roadway where that 

side of the roadway is not used in the HPMS shape file. ........................................................ 79

Figure 5. Photo. Example of an SPS-8 site location relative to the nearest HPMS roadway 

segment. .................................................................................................................................. 80

Figure 6. Graph. Monitored AADTT by year at LTPP traffic site 320100. ............................... 106

Figure 7. Chart. Monthly NALS for LTPP traffic site 060200 FHWA Class 9 tandem axles 

from 2008 to 2015. ................................................................................................................ 181

Figure 8. Chart. Average monthly NALS for LTPP traffic site 060200 FHWA Class 9 

tandem axles.......................................................................................................................... 182

Figure 9. Chart. Percentages of heavily loaded FHWA Class 9 tandem axles by month for 

LTPP traffic site 060200. ...................................................................................................... 183

Figure 10. Chart. Annual NALS for LTPP traffic site 090960 FHWA Class 9 tandem axles 

from 2002 to 2004. ................................................................................................................ 185

Figure 11. Chart. Annual NALS for LTPP traffic site 060200 FHWA Class 13 tridem axles 

from 2008 to 2015. ................................................................................................................ 187

Figure 12. Chart. Axle load distribution for LTPP traffic site 060500 FHWA Class 13 

tridem axles based on data from 2008 to 2015 combined. ................................................... 188

Figure 13. Chart. Axle load distribution for LTPP traffic site 135023 FHWA Class 9 

tandem axles based on data from 2014 and LTPP Heavy 2 default distribution. ................. 190

Figure 14. Chart. Axle load distribution for LTPP traffic site 131003 FHWA Class 9 

tandem axles based on data from 1994 and 1996. ................................................................ 191

Figure 15. Chart. Axle load distribution selected for LTPP traffic site 131003 FHWA 

Class 9 tandem axles based on the LTPP PLUG “Moderate” default. ................................. 192

Figure 16. Screenshot. Traffic inputs main screen. .................................................................... 265

Figure 17. Screenshot. Vehicle class distribution and growth inputs. ........................................ 267

Figure 18. Screenshot. Monthly adjustments inputs. .................................................................. 267

Figure 19. Screenshot. APT inputs. ............................................................................................ 268

Figure 20. Screenshot. AADTT, traffic capacity, axle configuration, lateral wander, and 

wheelbase inputs. .................................................................................................................. 269

Figure 21. Screenshot. ALDF inputs. ......................................................................................... 271

Figure 22. Screenshot. Hourly adjustment inputs. ...................................................................... 273
xi 

LIST OF TABLES 

Table 1. Functional performance requirements and error tolerances for 95 percent 

compliance for WIM systems per ASTM E1318-09. ............................................................... 2

Table 2. LTPP WIM data accuracy criteria for research-quality data. ........................................... 4

Table 3. Definition of axle loading categories based on percent of heavy loads in load 

spectrum. ................................................................................................................................. 15

Table 4. Definition of truck loading categories. ........................................................................... 15

Table 5. Traffic summary parameters. .......................................................................................... 18

Table 6. Traffic input parameters required by AASHTOWare Pavement ME Design 

software. .................................................................................................................................. 22

Table 7. Availability of basic and detailed traffic data and parameters in LTPP database. ......... 26

Table 8. Traffic parameters for mechanistic pavement response modeling and LTPP 

sources..................................................................................................................................... 30

Table 9. Traffic parameters for generic mechanistic–empirical pavement performance 

modeling and LTPP sources. .................................................................................................. 34

Table 10. MEPDG traffic input parameters and LTPP sources assessment using SDR 27.......... 36

Table 11. Traffic summary parameters and LTPP data sources. .................................................. 40

Table 12. HPMS traffic parameters and available LTPP data sources. ........................................ 43

Table 13. Identified core traffic parameters. ................................................................................. 49

Table 14. Availability of core traffic parameters by LTPP traffic site and year count................. 56

Table 15. MEPDG traffic input parameters supported by LTPP traffic data. .............................. 59

Table 16. Traffic parameters not included in the LTPP database but used for pavement 

applications. ............................................................................................................................ 62

Table 17. Analysis-ready traffic parameters approved by FHWA. .............................................. 89

Table 18. AADTT estimates for LTPP traffic site 040100......................................................... 103

Table 19. AADTT estimates for LTPP traffic site 320100......................................................... 105

Table 20. AADTT estimates for LTPP traffic site 321020......................................................... 107

Table 21. AADTTij for each vehicle class for LTPP traffic site 040100. .................................... 118

Table 22. Percentage of trucks in each vehicle class for LTPP traffic site 040100. ................... 118

Table 23. Computed truck volumes and percentages by truck class for LTPP traffic site 

040100 in 2002 and 1993...................................................................................................... 119

Table 24. AADTTij by FHWA truck class at LTPP traffic site 321020. ..................................... 120

Table 25. Percent of AADTTij by FHWA truck class by year at LTPP traffic site 321020. ....... 120

Table 26. CPT for AADTTij by FHWA vehicle classification for FHWA Classes 4–13 at 

LTPP traffic site 321020. ...................................................................................................... 122

Table 27. AADTTij for each vehicle class for LTPP traffic site 320100. .................................... 124

Table 28. Percent of AADTT by FHWA truck class at LTPP traffic site 320100. .................... 124

Table 29. Computed AADTTij values by FHWA truck class for missing years at LTPP 

traffic site 320100. ................................................................................................................ 125

Table 30. Total annual truck volume estimates for LTPP traffic site 040100. ........................... 126

Table 31. Total annual truck volume estimates for FHWA Class 9 for LTPP traffic site 

040100................................................................................................................................... 127

Table 32. Cumulative total truck volume for LTPP traffic site 040100. .................................... 129

Table 33. Cumulative total truck volume for LTPP traffic site 321020: GPS-1 experiment. .... 129

Table 34. Cumulative total truck volume for LTPP traffic site 321020: GPS-6 experiment. .... 130
xii 

Table 35. Cumulative total truck volume for FHWA Class 9 trucks for LTPP traffic site 

040100................................................................................................................................... 131

Table 36. AADTTij for each vehicle class for LTPP traffic site 040100. .................................... 136

Table 37. Growth function attributes for each vehicle class for LTPP traffic site 040100. ....... 136

Table 38. AADTTij for each vehicle class for LTPP traffic site 320100. .................................... 137

Table 39. Growth function characteristics for each vehicle class for LTPP traffic site 

320100................................................................................................................................... 137

Table 40. AADTTij for each FHWA truck class for LTPP traffic site 321020, GPS-1 

experiment............................................................................................................................. 139

Table 41. Growth function characteristics for each FHWA truck class for LTPP traffic site 

321020, GPS-1 experiment. .................................................................................................. 139

Table 42. AADTTij for each FHWA truck class for GPS site 321020. ....................................... 140

Table 43. Growth Function Characteristics for Each FHWA Truck Class for GPS Site 

321020................................................................................................................................... 141

Table 44. Computed parameter AADTTi values for LTPP traffic site 040100. .......................... 145

Table 45. Computed parameter AADTTi values for LTPP traffic site 320100. .......................... 146

Table 46. Computed parameter AADTTi values for LTPP traffic site 321020. .......................... 147

Table 47. Percentage of trucks in each vehicle class for LTPP traffic site 040100. ................... 150

Table 48. Percentage of trucks in each vehicle class for LTPP traffic site 321020. ................... 151

Table 49. Mean percentage of trucks in each vehicle class for LTPP traffic site 321020. ........ 152

Table 50. Loading category assignment for Type II or low-precision WIM data. ..................... 174

Table 51. Unloaded to loaded peak ratios in tandem axle load distributions for different 

loading categories. ................................................................................................................ 175

Table 52. Recommended LTPP default axle loading categories for FHWA Class 9 

vehicles based on road functional use, truck volume, and vehicle class distribution 

information............................................................................................................................ 179

Table 53. ALDF table populated with records for FHWA Class 9 tandem axles for LTPP 

traffic site 060200. ................................................................................................................ 183

Table 54. ALDF table populated with records for FHWA Class 9 tandem axles for LTPP 

traffic site 090960. ................................................................................................................ 185

Table 55. ALDF table populated with records for FHWA Class 13 tridem axles for LTPP 

traffic site 060200. ................................................................................................................ 188

Table 56. Typical APT ranges for vehicles in FHWA Classes 4–13.......................................... 195

Table 57. FHWA vehicle classification definitions. ................................................................... 195

Table 58. LTPP PLUG default APT values. ............................................................................... 197

Table 59. Annual APT values for LTPP traffic site 245807 for years used in ALDF 

computation........................................................................................................................... 198

Table 60. AASHTOWare Pavement ME Design software APT input and total axles per 

truck values for LTPP traffic site 245807. ............................................................................ 199

Table 61. Flagged atypical values for average total axles for the LTPP site 245807. ................ 200

Table 62. APT records from the TRF_MEPDG_APT table for LTPP traffic site 245807. ....... 200

Table 63. APT values assigned for LTPP traffic site 011021..................................................... 201

Table 64. ESAL-per-truck statistics for design lanes with different percentages of trucks in 

FHWA Classes 7 and 9–13. .................................................................................................. 204

Table 65. ESAL-per-truck statistics for design lane for different road functional 

classification. ........................................................................................................................ 204
xiii 

Table 66. Annual ESAL values from multiple sources for LTPP GPS-3 site 047613. .............. 210

Table 67. ESAL-per-truck values by year from multiple sources for LTPP GPS-3 

site 047613. ........................................................................................................................... 211

Table 68. Annual ESAL trend estimates for LTPP GPS-3 site 047613. .................................... 213

Table 69. ALDF_METHOD records from the ALDF_ASSIGNMENT_METHOD table for 

LTPP traffic site 271085. ...................................................................................................... 216

Table 70. GESAL-per-vehicle class computed using data for individual ALDF years for 

LTPP site 271085. ................................................................................................................. 217

Table 71. GESAL-per-vehicle class based on representative values for ALDF years for 

LTPP site 271085. ................................................................................................................. 218

Table 72. GESAL-per-vehicle-class values for ALDF years for LTPP site 271085. ................. 219

Table 73. GESAL-per-vehicle-class values for non-ALDF years for LTPP site 271085. ........ 219

Table 74. Truck volume, AADTT, and daily and annual GESAL trend values for LTPP site 

271085................................................................................................................................... 219

Table 75. Annual GESAL trend and data source code values for LTPP site 271085. .............. 220

Table 76. Annual total axle loads by vehicle class and axle group for LTPP site 271085. ........ 223

Table 77. Total annualized GVW for ALDF years for LTPP site 271085. ................................ 225

Table 78. Annual total GVW for ALDF years, prorated for partial years for LTPP site 

271085................................................................................................................................... 226

Table 79. AADTT by vehicle class for LTPP site 271085. ........................................................ 227

Table 80. Representative GVW in pounds by vehicle class for LTPP site 271085.................... 227

Table 81. Daily total GVW estimate for each non-ALDF year for LTPP site 271085. ............ 228

Table 82. Annual total GVW estimate for each non-ALDF year for LTPP site 271085. ......... 228

Table 83. Annual total GVW trend for LTPP site 271085. ........................................................ 229

Table 84. Field names and descriptions for the TRF_TREND table. ......................................... 231

Table 85. Field names and descriptions for the TRF_REP table. ............................................... 233

Table 86. Field names and descriptions for the MEPDG_TRUCK_VOL_PARAMETERS 

table. ...................................................................................................................................... 238

Table 87. Field names and descriptions for the MEPDG_AXLE_LOAD_DIST_FACTOR 

table. ...................................................................................................................................... 239

Table 88. Field names and descriptions for the MEPDG_AXLE_PER_TRUCK table. ............ 243

Table 89. CPT fields describing sources of data and methods used to develop different 

traffic parameters. ................................................................................................................. 243

Table 90. Codes for AADTT_SOURCE and VEH_CLASS_SOURCE fields. ......................... 244

Table 91. Codes for ESALS_SOURCE, GESALS_SOURCE, and GVW_SOURCE fields. .... 244

Table 92. Codes for the ESALS_SOURCE field. ....................................................................... 245

Table 93. Codes for the GESALS_SOURCE field..................................................................... 245

Table 94. Codes for the GVW_SOURCE field. ......................................................................... 246

Table 95. Codes for the REP_AADTT_USE_RATING field. ................................................... 246

Table 96. Codes for REP_VEH_CLASS_USE_RATING and 

VEH_CLASS_GROWTH_USE_RATING fields. ............................................................... 247

Table 97. Codes for the ALDF_USE_RATING field. ............................................................... 247

Table 98. Codes for the APT_USE_RATING field. .................................................................. 249

Table 99. Codes for the REP_LOAD_USE_RATING field. ..................................................... 251

Table 100. ALDF assessment results. ......................................................................................... 260

Table 101. LTPP WIM sites considered for ALDF computation. .............................................. 275
xiv 

LIST OF ACRONYMS AND ABBREVIATIONS 

AADT average annual daily traffic 

AADTT average annual daily truck traffic 

AASHTO American Association of State Highway and Transportation Officials 

AC asphalt concrete 

ALDF axle load-distribution factor

APT axles per truck 

ATL annual total load 

CPT computed parameter table 

CTL cumulative traffic load 

CTV cumulative truck volume 

DOT department of transportation 

DOW day of week 

ESAL equivalent single axle load 

ETG Expert Task Group 

FHWA Federal Highway Administration 

GESAL general equivalent single axle load 

GIS geographic information system 

GPS General Pavement Study

GUI graphic user interface 

GVW gross vehicle weight 

HPMS Highway Performance Monitoring System 

IVR individual vehicle record 

JPCP jointed plain concrete pavement 

KESAL kilo equivalent single axle load 

LEF load equivalency factor 

LTAS Long-Term Pavement Performance Traffic Analysis Software 

LTPP Long-Term Pavement Performance 

MEPDG Mechanistic–Empirical Pavement Design Guide

NALS normalized axle load spectra 

NCHRP National Cooperative Highway Research Program 

PCC portland cement concrete 

PLUG Pavement Loading User Guide

QA quality assurance 

QC quality control 

RPPIF relative pavement performance impact factor 

SDR Standard Data Release 

SHRP Strategic Highway Research Program 

SPS Specific Pavement Study

TMAS Travel Monitoring Analysis System 

TMG Traffic Monitoring Guide

TPF Transportation Pooled Fund Study 

TRB Transportation Research Board 

TSSC Technical Support Services Contractor 

WIM weigh-in-motion
1

CHAPTER 1. INTRODUCTION

BACKGROUND

A key outcome of the Long-Term Pavement Performance (LTPP) program is the delivery of a 

comprehensive, easily used database allowing for a wide range of pavement performance 

research and analysis. Using LTPP data, researchers can develop knowledge, relationships, and 

models to facilitate improved pavement design and reliable performance predictions. Traffic data 

are an integral part of the LTPP database. In the past, when researchers wanted to obtain and use 

these traffic data in their analyses, they were intimidated by the size of the dataset, the variety of 

data sources and data collection methods, and the different parameters used to characterize traffic 

data at LTPP sites.

This data analysis study was designed to help LTPP database users navigate and understand 

LTPP traffic data and select traffic parameters that best fit their project needs, including 

development of analysis-ready traffic datasets that could support a variety of LTPP analyses.

Challenges with Traffic Loading Data Collected at LTPP Sites

When the Strategic Highway Research Program (SHRP) initiated the LTPP program, little was 

known about the selection, installation, and operation of weigh-in-motion (WIM) and other 

automated truck traffic data collection equipment. As the LTPP program progressed, the LTPP 

program management team discovered that field conditions at many LTPP sites and the selected 

WIM equipment were not suitable for accurate WIM measurements, and the traffic data supplied 

by State and Provincial agencies lacked detailed information on truck traffic characteristics at 

those sites.

In some cases, inaccurate, poor-quality data were not included in the LTPP database. In other 

cases, inaccurate, poor-quality data were questioned during the initial LTPP quality 

assurance/quality control (QA/QC) process but ultimately included in the LTPP database

because the lack of information on truck traffic characteristics could neither confirm nor deny the 

accuracy of data. After 20 yr of additional data collection, it became apparent that some data 

accepted as valid in the early years were invalid measures of truck traffic characteristics. Thus, 

several LTPP test sites are either missing traffic data elements or burdened by inaccurate, poorquality data.

ASTM E1318-09, Standard Specification for Highway Weigh-In-Motion (WIM) Systems with 

User Requirements and Test Methods, is a standard specification widely used in the United 

States to evaluate WIM performance and measurement accuracy.(11) This specification provides 

WIM measurement error tolerances for different types of WIM systems, as shown in table 1. 

Rating of LTPP WIM data quality using measurement accuracies provided for the three ASTM 

WIM types would be beneficial for LTPP traffic data users. However, only a minority of LTPP 

WIM sites have WIM measurement accuracy information available.
2

Table 1. Functional performance requirements and error tolerances for 95 percent 

compliance for WIM systems per ASTM E1318-09. 

Function WIM Type I WIM Type II WIM Type III 

Wheel load 25 percent N/A 20 percent 

Axle load 20 percent 30 percent 15 percent 

Axle group load 15 percent 20 percent 10 percent 

Gross vehicle weight 10 percent 15 percent 6 percent 

Speed 1 mph 1 mph 1 mph 

Axle spacing and wheelbase 0.5 ft 0.5 ft 0.5 ft 

N/A = not available. 

Report No. FHWA-RD-03-094, Estimating Cumulative Traffic Loads, Volume II: Traffic Data 

Assessment and Axle Load Projection for the Sites with Acceptable Axle Weight Data, Final 

Report for Phase 2, indicated that axle loading characteristics for more than 70 percent of 

General Pavement Study (GPS) WIM sites were not suitable for use in the development of 

pavement performance models due to their lack of accuracy.(2) For example, figure 1 illustrates 

annual normalized Class 9 tandem axle load spectra extracted from the January 2013 LTPP 

database Standard Data Release (SDR) 27 for several sites in Georgia. For the three LTPP sites 

shown in figure 1-A, a large percentage of the tandem axles are estimated to weigh more than 

50,000 lb and as much as 80,000 lb, meaning the gross vehicle weight (GVW) for the trucks is 

over 100,000 lb and potentially twice as much as the legal load limit for FHWA Class 9 vehicles. 

Although these weights are highly unlikely and would cause significant damage to the truck if 

carried routinely. Therefore, data are likely erroneous, either because of errors in data 

transmission or the result of poor equipment performance. For the five LTPP sites shown in 

figure 1-B, loads are atypically low, indicating a classification error or a data collection period 

that was too short to capture representative truck weight distribution.

Source: FHWA.

A. Example of atypically high axle loads. 

Source: FHWA.

B. Example of atypically low axle loads. 

Figure 1. Graphs. Illustration of questionable tandem axle loads reported in SDR 27.
3

Figure 2-A and figure 2-B illustrate the differences in pavement distress prediction based on 

these axle load spectra for asphalt concrete (AC) and jointed plain concrete pavements (JPCPs).

Questionable data could lead to different pavement distress prediction outcomes and may 

jeopardize the validity of conclusions from analyses using these data. In addition, several LTPP 

sites, particularly GPS sites, are completely missing traffic loading data. 

 

Source: FHWA. Source: FHWA. 

A. Example of total rut prediction. B. Example of slab cracking. 

Figure 2. Charts. Comparison of distress prediction using questionable axle weight data. 

Challenges with Developing Traffic Inputs for Pavement Analyses 

LTPP traffic data tables were designed to provide a comprehensive view of traffic loading

patterns at each LTPP test site. The downside of this design is that available traffic data are more 

complex and comprehensive than necessary for some types of pavement research analyses. This 

complexity is aggravated by missing and/or invalid data noted previously. In addition, the 

emergence of new-generation pavement analysis and design methods using AASHTOWare® 

Pavement ME DesignTM software requires use of traffic parameters LTPP database tables were 

not originally designed to produce.(3,15) As a result, to enter LTPP traffic data into 

AASHTOWare Pavement ME Design software, a researcher must analyze and summarize 

available data to develop detailed traffic inputs representing the analysis base year conditions 

and traffic growth pattern for each LTPP site. Given the complexity of data in the LTPP 

database, such an analysis and summary significantly burden researchers. 

AASHTOWare Pavement ME Design software requires a specially formatted data file for 

entering traffic loading inputs, such as axle load distributions representing a typical day of each 

calendar-month for the base design or analysis year. Prior to this data analysis study, the LTPP 

database did not have the capability to generate these files at the user’s request. 
4

Benefits of WIM Traffic Data from Transportation Pooled Fund Study (TPF), TPF-5(004) 

Recognizing the limitations of traffic data collected during early years of the LTPP program and 

the need for research-quality data to support the development of improved pavement 

performance prediction models, the program initiated the LTPP Specific Pavement Study (SPS) 

Traffic Data Collection Transportation Pooled Fund Study, TPF-5(004). TPF-5(004) focused on 

installing highly reliable, permanent WIM systems to collect axle loading data. Using a uniform 

vehicle classification scheme (i.e., LTPP vehicle classification scheme), developed for that study 

based on FHWA 13-category classification rule set, and rigorous QC procedures, TPF-5(004) 

produced research-quality data (e.g., traffic volume counts by vehicle classification and traffic 

loading data) to support LTPP analysis projects. TPF-5(004) was designed with the support of 

the Transportation Research Board (TRB) Expert Task Group (ETG) on Traffic Data Collection 

and Analysis. The effort consisted of two principal elements: shifting data collection from 

highway agencies to a national, centralized effort; and standardizing data collection equipment 

and procedures. Additionally, guidelines for pavement smoothness, equipment calibration 

checks, equipment model specifications, and the LTPP vehicle classification scheme were 

developed and implemented for TPF-5(004) WIM sites. 

Since 2003, 26 LTPP WIM sites were installed in 22 different States as part of the TPF-5(004) 

study to collect research-quality traffic loading data at select SPS-1, -2, -5, and -6 sites. To meet 

the research-quality standards of TPF-5(004), data of known calibration, meeting LTPP’s WIM 

data accuracy requirements—for steering and tandem axles, GVW, bumper-to-bumper vehicle 

length, vehicle speed, and axle spacing—must be collected for 210 d within a year.(12) Details 

about LTPP SPS WIM equipment, installation, calibration, and accuracy requirements are 

documented in the LTPP Field Operations Guide for SPS WIM Sites.

(13) Table 2 shows the 

criteria used to evaluate if errors observed in WIM data collected during field validations meet 

the criteria for the research-quality data. 

Table 2. LTPP WIM data accuracy criteria for research-quality data. 

Parameter 95 Percent Confidence Interval of Error 

Steering axles ±20 percent 

Tandem axles ±15 percent 

GVW ±10 percent 

Vehicle length ±3.0 percent (or 2.2 ft) 

Axle length ± 0.5 ft 

The LTPP program used data from the TPF-5(004) study to develop several axle loading defaults 

compatible with AASHTOWare Pavement ME Design software.(4) These defaults can be used 

for LTPP sites that have no axle loading data or insufficient data (i.e., lacking quality and/or 

quantity), and several of them were adopted by AASHTOWare Pavement ME Design software. 

STUDY OBJECTIVES 

This data analysis study was designed to enhance the experience of LTPP users by improving 

traffic data included in the LTPP database and making it easier for researchers to select traffic 

data needed for their specific analyses. This was accomplished by developing analysis-ready 

traffic parameters that could support a variety of pavement analysis objectives listed in the 
5

Strategic Plan for LTPP Data Analysis, including analyses using AASHTOWare Pavement ME 

Design software.(15,16)

To assist in decision-making regarding usability of a given computed parameter, special 

indicators explaining the nature and applicability of computed traffic parameters were provided 

for each LTPP site and computed traffic parameter. These indicators were developed based on 

the assessed quality of data used to compute the parameter and reflect how well these data 

represent traffic conditions at LTPP sites. In addition, these indicators help to identify LTPP sites 

that are based on high-quality traffic loading data. 

The forthcoming User Guide for Selecting and Using Long-Term Pavement Performance Traffic 

Data (1)(henceforth referred to as the “Guide”) which walks pavement researchers through the 

selection of the most appropriate traffic statistics for the pavement analyses they are performing, 

is a product of this analysis study. The Guide contains examples of how to select the appropriate 

traffic parameter for pavement analysis or design as well as extract data from LTPP computed 

parameter tables (CPTs) using InfoPaveTM. 

STUDY SCOPE 

This data analysis study has three phases, which are described as follows: 

• Phase I: Assessment of data needs and availability, including identification of 

traffic-computed parameters to be developed by the LTPP program and approaches for 

users to access and use these parameters. 

• Phase II: Development of methodologies and processes for computing or estimating 

traffic parameters to meet LTPP traffic data needs defined in Phase I. 

• Phase III: Development of traffic CPTs and the Guide.

Figure 3 is a flowchart sequencing specific task executions for each phase. Deliverables 

associated with each phase are shown in blocks with heavy borders. 
6

Source: FHWA. 

Figure 3. Flowchart. Work execution sequence.

STUDY OUTCOMES 

The primary outcomes of this study include the following: 

• CPTs meeting many traffic data users’ needs not previously addressed in the LTPP 

database. 

• Traffic data–usability indices providing information to users about data and methods used 

to develop different analysis-ready CPTs. 
7

• The Guide describing how to select LTPP traffic parameters for different types of 

analyses. 

• Final study report summarizing research outcomes.

CPTs and the Guide produced under this study aim to help reduce the time pavement researchers 

spend identifying and selecting LTPP traffic data and parameters, thus reducing costs of future 

research while improving the consistency of results and meeting objectives listed in the Strategic 

Plan for LTPP Data Analysis.

(16)

A secondary outcome from the improved traffic data for LTPP sites will be lower costs and more 

effective State-specific calibration of pavement performance prediction models for the 

AASHTOWare Pavement ME Design software process. For most States, data from LTPP sites 

within their jurisdiction are the first choice of data for local calibration of the pavement 

performance prediction models for AASHTOWare Pavement ME Design software. Improved 

availability of traffic parameters for those LTPP sites significantly increases the use of LTPP 

data in local calibration efforts. Moreover, by providing guidelines, procedures, and tools for 

traffic estimation, the LTPP program aids States in reviewing and improving traffic data for their 

non-LTPP sites. 

REPORT OVERVIEW 

This report provides a summary of the data analysis study’s objectives, methods, and findings. 

Contents of this report are organized into the following chapters: 

• Chapter 1 contains a description of the research study background, objectives, scope, and 

outcomes. 

• Chapter 2 provides an overview of traffic data needed to support various pavement 

studies (e.g., research, analysis, design, and management). 

• Chapter 3 summarizes findings from the assessment of available LTPP traffic data to 

support user needs, describes gaps between needs and available traffic data, and provides 

recommendations for developing analysis-ready LTPP traffic CPTs meeting most user 

needs. 

• Chapter 4 assesses availability of alternative data sources to help produce LTPP traffic 

CPTs given limitations in data availability and/or quality in the LTPP database. 

• Chapter 5 provides information about analysis-ready traffic parameters selected for this 

study and describes the general approach for developing computed parameters. 

• Chapters 6–10 describe methodologies for developing analysis-ready traffic parameters 

selected for this study, including examples of parameter computation. 

• Chapter 11 contains a description of new LTPP traffic CPTs containing analysis-ready 

traffic parameters. 

• Chapter 12 provides an overview of the Guide. 

• Chapter 13 provides a summary of the results. 

9

CHAPTER 2. TRAFFIC DATA AND PARAMETERS USED FOR PAVEMENT 

STUDIES 

This chapter synthesizes information about traffic data used in various pavement studies, 

including pavement-related research, analysis, design, and management. 

OVERVIEW OF TRAFFIC PARAMETERS USED FOR PAVEMENT ANALYSIS, 

DESIGN, AND MANAGEMENT 

The types of traffic data and parameters used in pavement engineering applications are 

categorized by the following: 

• Basic traffic data and rudimentary statistics. 

• Detailed traffic data and parameters for characterization or study of traffic loading effects 

on pavement structure, such as pavement analysis, research, mechanistic and 

mechanistic–empirical pavement modeling, and forensic studies. 

• Summary traffic parameters for high-level analyses, empirical pavement design and 

performance modeling, pavement maintenance, and management applications.(5)

• Computed parameters for use in specialized pavement analysis and design software, such 

as the Mechanistic–Empirical Pavement Design Guide (MEPDG).(3)

• Traffic metadata parameters explaining how data were collected and how parameters 

were computed. 

Basic traffic data include either actual counts of vehicle volume, records of axle weight, or lowlevel aggregated statistics, such as daily and monthly traffic volume or truck volume by vehicle 

class summaries, or axle load count by load bin summaries reported for each sampling period. 

The basic data and parameters serve as an input to compute summary statistics and analysisready traffic parameters used in pavement engineering and management applications. 

Parameters providing detailed characterizations of traffic loading are used for mechanistic and 

mechanistic–empirical pavement response and performance modeling. Analysis and modeling of 

pavement response require accurate information about wheel and axle load magnitude, load 

position and configuration (i.e., axle configuration and position of wheels on the pavement), area 

of load application or tire footprint, load duration, and time history of load application (i.e., 

changes in load magnitude over time). For pavement performance modeling, traffic loading 

history for the entire analysis period is needed (i.e., the number and magnitudes of loads reported 

for specified time increments used in the analysis). This information is typically collected or 

estimated by the traffic data collection staff within a State or Provincial highway agency. 

Summary traffic parameters are useful in empirical pavement response and performance analysis 

and modeling, empirical pavement design procedures, and high-level analyses of pavement 

management models and decision support tools. For these analyses and procedures, researchers 

desire a single traffic summary statistic, such as the equivalent single axle load (ESAL), average 

annual daily truck traffic (AADTT), cumulative truck volume (CTV), or cumulative traffic load 

(CTL). These summary statistics are also used to identify and group road segments in categories 

representing different levels of traffic. 
10

Another traffic data category contains traffic parameters used as a direct input for specialized 

pavement analysis or design, such as those found in the American Association of State Highway 

and Transportation Officials (AASHTO) Guide for Design of Pavement Structures 1993 

pavement design methodology and the newer MEPDG design and analysis software, 

AASHTOWare Pavement ME Design software.(3, 5,15)

BASIC TRAFFIC DATA AND LOW-LEVEL SUMMARY STATISTICS 

Basic traffic data are collected to support development of the traffic parameters used in pavement 

applications. These data typically contain actual counts of vehicle volume aggregated to different 

levels or individual vehicle records. These data may be collected over a short period (typically 

2 d) or longer period (continuous counting using automated data collection equipment) and 

reported in 15-min, hourly, or daily increments. 

Basic traffic volume data include the following: 

• Total vehicle volume. 

• Total truck volume. 

• Truck volume by vehicle class (typically FHWA Classes 4–13).(14)

Another type of basic traffic data are individual vehicle records (IVRs) from WIM systems. 

IVRs contain detailed information about each vehicle passage, including the following basic 

traffic data elements: 

• Vehicle class. 

• Time of each vehicle passage. 

• Axle-to-axle spacing. 

• Weight of each axle. 

• Vehicle speed (available in some formats). 

Basic traffic data have limited direct application in pavement analyses but are necessary to

compute analysis-ready traffic summary parameters. Because these data have no or low levels of 

aggregation, they require a large storage space and may be challenging for researchers to 

download and analyze. However, these data are necessary for researchers to understand specific 

traffic characteristics and data samples used to compute aggregated summary statistics and for 

researchers focusing on investigating effects of different traffic characteristics on pavement 

response and performance. 

DETAILED TRAFFIC DATA AND PARAMETERS 

Traffic Parameters for Pavement Response Prediction Based on Mechanistic Models 

As pavement engineering evolves from empirical to mechanistic–empirical, and then to fully 

mechanistic methods, the demand for more accurate and detailed traffic loading characterization 

continues to rise. The emerging mechanistic pavement response analysis and modeling studies 

focus on stresses, strains, and deflections pavements experience under each traffic load 
11

application. Pavement responses are predicted using static or dynamic mechanistic modeling 

methods. 

Pavement responses predicted by static models (e.g., elastic, viscoelastic, elastoplastic) depend 

on the following traffic loading parameters: 

• Load magnitude. 

• Load configuration (i.e., location and number of wheel loads simultaneously applied on 

the pavement surface). 

• Sequence of loads. 

• Time and date of load application. 

• Area of load application and shape of load distribution under each wheel (e.g., over the 

tire footprint). 

• Position of wheels and axles relative to pavement or concrete slab edges. 

Pavement responses predicted by dynamic models consider the dynamic effect of the applied 

loads. In addition to the parameters in the previous list for static modeling, dynamic models 

require the following additional inputs: 

• Load duration. 

• Rate of load application (i.e., number of load applications per time unit measure). 

• Time history of load application (i.e., change in load magnitude or pressure under tire 

footprint over time as each wheel passes over the specific pavement location). 

Existing traffic monitoring technology, especially WIM, can provide most but not all of these

parameters. New advancements are required to take WIM measurement ability beyond the 

estimation of the static equivalent of axle or truck load weight, to accurate recording and 

reporting of the full time history of load application, including accurate measurement of the 

dynamic forces applied by the tire to the pavement and quantification of the exact area and 

duration of load application (e.g., load or tire footprint) and position of each tire footprint for 

each truck, relative to the pavement edge. 

Traffic Parameters for Mechanistic–Empirical Pavement Performance Prediction 

Changes in Loading over Time 

Mechanistic–empirical pavement performance analysis and modeling focuses on predicting 

pavement distress that develops over time. Most pavement distress (e.g., cracking, rutting, 

faulting) develops from incremental or cumulative changes in pavement structure due to material 

aging, environmental impacts, and traffic loading. Therefore, for traffic loading characterization, 

not only must information about individual traffic loads be known, but also the sequence and 

cumulative number of traffic load applications leading to pavement deterioration over time. Such 

detailed characterization of traffic loading allows modeling of pavement responses and 

performance using methods where each axle load application on the pavement—expected or 

observed during the analysis period—is modeled and its effect on pavement response and 

performance predicted. 
12

In addition to axle load frequency, information about the relative position of axle loads on 

pavement is also important, especially for jointed rigid pavements.

Axle Loading Distribution or Axle Load Spectrum

To provide a means for tracking and summarizing traffic load applications over time, traffic 

loads are summarized in an axle load spectrum (also called axle load distribution in some 

pavement applications).

(7) The axle load spectrum represents a frequency distribution of axle 

loads, where counts of axle load applications, observed during a specified period, are summed 

and reported using predefined load bins. Recognizing the importance of load configuration, 

separate axle load spectra are used to summarize axle load counts for typical axle load groups: 

single, tandem, tridem, and quad. Depending on the intended application, load spectra could be 

created for an individual truck class or for all truck classes combined. In summary, the axle load

spectrum input provides information about axle load magnitudes, number of axle load

applications over a specified period, and load configuration. If no site-specific axle vehicle 

weight data are available to compute axle load spectrum, default axle weights can be selected for 

each vehicle class based on the primary road use.

Normalized Axle Load Spectra

Axle load spectra may express frequency of distribution by specifying axle counts or percentages 

of total loads in each load bin. If percentages are used to express load frequency distribution, 

load spectra are called normalized axle load spectra (NALS).

SUMMARY TRAFFIC PARAMETERS USED IN EMPIRICAL PAVEMENT STUDIES 

AND TRADITIONAL PAVEMENT DESIGN AND MANAGEMENT APPLICATIONS

Traditionally, pavement engineering relied on an empirically derived relationship between traffic 

summary statistics and pavement performance. Many studies of pavement response and 

performance still use empirical methods or statistical models to correlate pavement performance 

parameters (e.g., road roughness) monitored over time with traffic and environmental loads, site 

conditions, material properties, and construction practices. These studies frequently use a single 

traffic summary parameter to describe traffic and may require a complete history of changes in 

the selected traffic summary parameter (computed annually for the duration of analysis period), a 

single cumulative value aggregated over the analysis period, or one representative traffic 

summary value. The most frequently used traffic summary parameters are AADTT and ESAL.

(6)

Challenges with Using a Single-Value Traffic Loading Summary Statistic

The definition and use of a single traffic loading summary statistic for pavement performance 

analyses presents several challenges. First, the relationship between traffic load magnitude and 

pavement performance is nonlinear, and the degree of this nonlinearity changes for different 

types of pavements and pavement distresses. A single-number cumulative traffic loading statistic 

cannot provide information if the total load is a result of many light axle loads or a few heavy

axle loads that produce similar cumulative values.
13

The distance between truck axles plays an important role in the stress and strain distribution 

through different pavement layers; therefore, the number and location of simultaneously applied 

axle loads is also important and should be accounted for in the traffic loading summary statistic.

Conventional parameters based on simple summation or averaging all applied traffic loads do not 

provide an effective means for correlating truck traffic loading and pavement performance. For 

example, two axle load spectra with the same mean load or cumulative load value may have 

different effects on pavement damage and associated pavement distress based on different 

percentages of heavy loads in the load frequency distribution and different axle configurations 

(i.e., different load distribution over the pavement). The limitations described in this section 

do not preclude using a single-value traffic loading summary statistic but should be considered 

when selecting an applicable parameter traffic loading.

ESAL as a Traditional Traffic Loading Summary Statistic

ESAL has been used as a traffic loading summary statistic for pavement design and analysis 

applications since the 1960s.(6) ESAL is a concept developed from data collected during the 

AASHO Road Test to establish a damage relationship for comparing effects of axles carrying 

different loads. In ESAL computation, load equivalency factors (LEFs) are used to convert a 

mixed stream of traffic consisting of different axle loads and configurations predicted over a 

design or analysis period into an equivalent number of 18,000-lb, single-axle load applications 

summed over that period. Thus, ESAL is a cumulative traffic loading summary statistic. 

Although general understanding and consensus exist in the pavement engineering community 

that neither ESALs nor LEFs precisely describe the relationship between axle load and specific 

pavement distresses like rutting or cracking, ESAL is a convenient statistic for sizing and 

quantifying traffic loading levels for empirical pavement analysis and design.

The ESAL value depends not only on traffic but on pavement type and thickness and a 

compound measure of road condition expressed through a subjective pavement serviceability 

index. For example, ESAL values representing the same traffic stream can change because the 

pavement type changes, or the pavement is rehabilitated and pavement thickness and/or 

roughness changes.

General ESAL

General ESAL (GESAL) is a parameter computed similarly to ESAL using LEF values for 

flexible pavements with a structural number of 5 and a terminal serviceability index of 2.5.(7)

Because LEF values are set to a constant, GESALs are independent of pavement type and 

thickness and level and type of pavement distress. Therefore, any changes in GESAL values can 

be attributed directly to changes in traffic loads. This makes GESAL a more desired traffic 

loading summary statistic for comparison of loads or effects of loads on pavement performance 

between different sites. GESAL is more sensitive to the importance of heavy loads on pavement 

performance compared to average or total load summary statistics. However, use of the constant 

LEF parameters makes GESAL not applicable as a direct input to pavement design.
14

Traffic and Truck Volume Summary Parameters 

Average annual daily traffic (AADT) and AADTT are the most widely used traffic volume 

parameters for pavement analyses. AADTT is more relevant for pavement analysis and 

management applications than AADT because trucks contribute more to pavement damage than 

lighter vehicles that make up most of the AADT number. 

Other traffic volume statistics used in pavement analyses include the following: 

• Total annual truck volume. 

• Annual truck volume by vehicle class. 

• Cumulative volume of Class 9 vehicles. 

• Cumulative volume of heavy vehicles (FHWA Classes 4 and 6–13). 

Relative Pavement Performance Impact Factor (RPPIF) 

Recent developments regarding traffic loading summary statistics include RPPIF.(4) RPPIF is 

computed similarly to ESAL, but instead of LEFs based on data from the AASHO Road Test, it 

utilizes W-factors (W stands for “weight”) determined through MEPDG analysis using globally 

calibrated distress prediction models included in National Cooperative Highway Research 

Program (NCHRP) Report 1-37A, Guide for Mechanistic–Empirical Design of New and 

Rehabilitated Pavement Structures.

(9) The purpose of the RPPIF statistic is to compare axle 

loading distributions between different sites. As MEPDG models evolve, W-factors used for 

RPPIF computation may need updating to reflect the importance of different axle groups and 

axle load magnitudes for pavement performance. As with GESAL, RPPIF values are 

independent of pavement type and thickness and pavement distress. RPPIF is used to identify 

and group sites or road segments with similar traffic loading levels. 

Annual Total Load and CTL 

Annual total load (ATL) and CTL are estimates or summaries of all traffic loads accumulated 

over 1 yr (for ATL) or over the analysis period (for CTL).(8) An advantage of these statistics is 

that they are independent of any empirically derived relationships that relate load to damage. 

However, ATL and CTL cannot be used to infer whether trucks are empty or loaded and whether 

ATL or CTL values are affected by the number or weight of trucks (i.e., a small number of heavy 

trucks and a large number of light trucks may produce the same ATL or CTL values). 

Traffic Load Grouping Statistic 

Some pavement analyses need only a general traffic loading characterization to rank traffic 

loading at a site, such as light, moderate, or heavy loading categories. A recent LTPP data 

analysis study used MEPDG simulations to analyze pavement performance under different axle 

load spectra.(4) Differences in predicted pavement performance were then used to define loading 

categories and sort axle load spectra. Based on the researchers’ findings, NALS assigned to these 

traffic loading categories are likely to lead to different prediction outcomes for pavement 

thickness (difference of 0.5 inch) or service life (difference of 20 percent), assuming similar 

truck types and volumes. The traffic loading categories are shown in table 3. Sites with axle 
15

loads in the same loading category can be grouped together based on similar per-truck or 

per-axle loading. 

Table 3. Definition of axle loading categories based on percent of heavy loads in load 

spectrum.

Traffic loading Category or 

MEPDG Default Category 

Singles ≥ 15 kip 

(Percent) 

Tandems ≥ 26 kip 

(Percent) 

Tridems ≥ 39 kip 

(Percent) 

Quads ≥ 54 kip 

(Percent) 

Very light (VL) <3 0 N/A N/A 

Light (L) <10 <10 N/A N/A 

Moderate (M) 10–30 10–30 N/A N/A 

Heavy (H) >30 30–50 <50 <30

Very heavy (VH) N/A >50 >50 >30

1 kip = 4,448.2216 N; N/A = not applicable. 

Average GVW 

A distribution of average GVW for all classes combined, or GVW spectrum, can be used to 

identify the truck loading category for a site, as shown in table 4.

Table 4. Definition of truck loading categories. 

Truck Loading Category Heavy Trucks (≥60,000 lb) in GVW Spectrum 

(Percent) 

Very light (VL) <3

Light (L) <10

Moderate (M) 10–30

Heavy (H) 30–50

Very heavy (VH) >50

If sites have similar vehicle class distributions with one dominant heavy truck class (e.g., FHWA 

Class 9), average GVW can be used to identify sites with similar or different traffic loading. For 

example, average GVW can be used to identify if trucks are primarily empty, primarily loaded, 

or have moderate loading (i.e., half of the trucks are loaded, and half are empty). 

Recommendation for Selecting a Traffic Loading Summary Statistic 

Selecting a traffic summary statistic should be based on the analysis intent and the perceived 

relationship between load and pavement distress. No single traffic loading summary parameter 

exists that works equally well for different LTPP analysis applications. The main reason is 

differences in sensitivity of pavement distresses to load magnitude and the number of axle load 

applications. 

Weighted summary parameters like ESAL, GESAL, and RPPIF generally have a stronger 

correlation with load-related pavement distresses than parameters based on simple load 

summation or averaging. For pavement performance measures where the number of load 

applications is more important than the relative distribution of loads between trucks, parameters 

based on simple summation or averaging of loads (like CTL and ATL) provide a viable 

alternative, especially when used for roads with similar trucks that serve similar purposes. 
16

Parameters based on total truck volume are a poor choice for studying distresses sensitive to 

truck loads. If no loading information is available, total volume of trucks in heavy vehicle classes 

(FHWA Classes 4 and 6–13) is more appropriate than total traffic volume or total truck volume 

statistics. 

The following recommendations are provided to aid in selecting a traffic loading summary 

parameter for use in empirical analyses, based on the type of pavement distress being studied: 

• If pavement distress is primarily caused by repeated load applications (e.g., raveling), a 

summary loading statistic that accurately describes the number of load applications 

should be used, such as AADTT, CTV, CTL, or total number of axle loads for vehicles in 

FHWA Classes 4–13. 

• If pavement distress is primarily caused by repeated heavy axle loads (e.g., fatigue 

cracking), a summary loading statistic that properly accounts for the number of heavy 

load applications should be used, such as ESAL, GESAL, or RPPIF. Alternative statistics 

that can be used are the number and average weight of Class 9 vehicles or combination 

unit trucks.

• If pavement distress is caused by overloaded trucks or axles (e.g., rigid pavement slab 

cracking), a summary loading statistic that accurately describes the number or percentage 

of fully loaded and overloaded axles should be used. Alternative statistics that can be 

used are the number and average weight of heavy trucks or RPPIF. 

• If the cause of pavement distress is not known but perceived to be load-related and a 

single traffic loading summary parameter is desired in the analysis, GESAL is an 

appropriate statistic. GESAL has a formulation and meaning similar to ESAL, thus is 

easily understood by pavement engineers. However, unlike ESAL, GESAL is 

independent of pavement-related variables and recognizes the higher significance of 

heavier traffic loads for pavement performance. GESAL is computed based on an actual 

axle load spectrum and considers truck volume (i.e., number of axle load applications). 

GESAL should not be used as a direct input for pavement design. 

• If a user needs a traffic loading statistic free of any adjustments with respect to 

significance of load to pavement damage development, ATL or CTL should be used. 

Alternatively, if the road use is dominated by one heavy truck class (e.g., FHWA Class 

9), average GVW in combination with the total number of FHWA Class 9 vehicles could 

be used. 

• If performing high-level empirical analyses where the specific mechanism of 

deterioration is of lesser importance and the focus is on evaluating the whole road 

network or parts of the network serving specific functions (e.g., evaluation of the 

difference in performance between freight routes and routes primarily used for local 

services and delivery of goods), ATL or CTL may be used (annual total load values or 

cumulative load over the analysis period). 
17

• If no site-specific vehicle weight data are available to compute axle or truck weight for 

use as summary loading statistic, default axle weights can be selected for individual truck 

classes (i.e., FHWA Classes 4–13) based on the primary road facility use. The LTPP 

Pavement Loading User Guide (PLUG) contains a database of default values.(10) These 

default weights, in combination with site-specific truck volume and vehicle classification 

data, can be used to estimate traffic loads and compute the traffic loading summary 

statistic of choice for any LTPP site. 

Table 5 summarizes different traffic summary parameters and specifies the types of analyses for 

which these parameters are appropriate.
18

Table 5. Traffic summary parameters. 

Parameter Description or Meaning Recommended Use and Limitations 

AADT Basic traffic summary statistic to measure 

average daily road use by all vehicular traffic. 

Use to quantify road importance for pavement 

management applications, to quantify road utilization, 

and as an input for safety and congestion studies. 

AADTT Traffic summary statistic to measure average 

daily road use by trucks and buses 

(FHWA Classes 4–13). 

Use to quantify road importance for pavement 

management and freight planning applications. Not 

sufficient as a single summary statistic for evaluating 

effects of traffic loads on pavement performance. 

CTV Traffic summary statistic to measure total road 

use by heavy vehicles (FHWA Classes 4–13). 

Can be cumulated and reported annually or for 

the analysis duration. 

Use in analyses of effects of traffic on nonstructural 

pavement distresses or as a supplemental input in 

analyzing effects of traffic on structural pavement 

response and performance. Not sufficient as a single 

summary statistic for evaluating effects of traffic on 

structural pavement performance. 

Cumulative volume of 

FHWA Class 9 trucks 

Traffic summary statistic used to measure road 

use by FHWA Class 9 trucks. Can be cumulated 

and reported annually or for the analysis 

duration. 

Use in analyses of pavement performance as a ranking 

measure to identify roads with low, medium, and high 

volume of FHWA Class 9 trucks. Not sufficient as a 

single summary statistic for evaluating effects of traffic 

on structural pavement performance. 

Cumulative volume of 

heavy trucks 

(FHWA Classes 4 and 6–

13) 

Traffic summary statistic used to measure road 

use by vehicles in FHWA Classes 4 and 6–13. 

Can be cumulated and reported annually or for 

the analysis duration. 

Use in analyses of pavement performance as a ranking 

measure to identify roads with low, medium, and high 

volume of heavy loaded trucks. Not sufficient as a 

single summary statistic for evaluating effects of traffic 

on structural pavement performance. 

Annual ESAL Basic traffic loading summary statistic that uses 

LEF developed from the AASHO Road Test to 

convert traffic stream to an equivalent number 

of 18,000-lb single axle loads (based on 

expected effects on pavement serviceability). 

Historically used as a primary traffic loading parameter 

to relate pavement performance with traffic loading. 

Caution: ESAL is affected by nontraffic parameters 

(e.g., pavement structure, thickness, and serviceability). 

If used as a direct input to analyze pavement response 

or performance, the analyst must consider limitations 

associated with ESAL. 
19

Parameter Description or Meaning Recommended Use and Limitations 

Cumulative ESAL Basic traffic loading summary statistic using 

LEF developed from the AASHO Road Test to 

convert traffic stream to an equivalent number 

of 18,000-lb single axle loads accumulated over 

time (i.e., analysis or design period). 

Historically used as a primary traffic loading parameter 

to relate pavement performance with traffic loading or 

for design pavement. Used as a traffic input for the 

AASHTO Guide for Design of Pavement Structures.

Caution: ESAL is affected by nontraffic parameters 

(e.g., pavement structure, thickness, and serviceability). 

If used as a direct input to analyze pavement response 

or performance, the analyst must consider limitations 

associated with ESAL. 

Annual GESAL GESAL is similar to ESAL but with pavement 

structure and condition inputs set to constant 

values. Therefore, GESAL is not affected by 

changes in pavement type, thickness, or 

serviceability parameters. 

Use to relate pavement performance with traffic loading 

or to compare traffic loading between different sites. If 

used in analysis, the analyst must consider limitations 

and assumptions associated with GESAL formulation.

Cumulative GESAL Cumulative GESAL is a traffic loading 

summary statistic similar to cumulative ESAL 

but with pavement structure and condition 

inputs set to constant values. Cumulative 

GESAL is accumulated over time (i.e., analysis 

or design period). 

Use to relate pavement performance with traffic loading 

or to compare traffic loading between different sites. If 

used in analysis, the analyst must consider limitations 

and assumptions associated with GESAL formulation.

Annual RPPIF RPPIF is computed similar to ESAL but with 

factors estimated based on MEPDG simulations 

and normalized to fully loaded 34,000-lb

tandem axle loads instead of 18,000-lb single 

axle loads. 

Use as an input parameter to relate pavement 

performance with traffic loading, especially for 

distresses sensitive to heavy axle load applications. If 

used in analysis, the analyst must consider limitations 

and assumptions associated with annual RPPIF. 

Cumulative RPPIF Cumulative RPPIF is computed similar to 

cumulative ESAL but with LEF estimations 

based on MEPDG simulations and normalized 

to fully loaded 34,000-lb tandem axle loads 

instead of 18,000-lb single axle loads. 

Use to relate pavement performance with traffic 

loading, especially for distresses sensitive to heavy axle 

load applications. If used in analysis, the analyst must 

consider limitations and assumptions associated with 

cumulative RPPIF. 
20

Parameter Description or Meaning Recommended Use and Limitations 

ATL An estimate of the total weight of all vehicles in 

FHWA Classes 4–13 applied to pavement 

during the year. 

Use to relate pavement performance with traffic 

loading. If used in analysis, the analyst must consider 

limitations and assumptions associated with ATL. 

CTL An estimate of the total weight of all vehicles in 

FHWA Classes 4–13 applied to pavement over 

time.

Use to relate pavement performance with traffic 

loading. If used in analysis, the analyst must consider 

limitations and assumptions associated with CTL. 

Average GVW for each 

vehicle class 

(FHWA Classes 4–13) 

Average GVW for each vehicle in 

FHWA Classes 4–13, considering all available 

axle weight data. 

Use to identify or group LTPP sites with similar 

per-truck loading for specific types of trucks. Average 

GVW for each vehicle class does not contain 

information about truck volume or total loading 

experienced by an LTPP site. In combination with 

AADTT by vehicle class information, average GVW for 

each vehicle class can be used to define average daily 

traffic loading associated with each vehicle class (i.e., 

FHWA Classes 4–13) at an LTPP site. 

Average GVW 

(FHWA Classes 4–13 

combined) 

Average GVW for an LTPP site for vehicles in 

FHWA Classes 4–13 combined, considering all 

available axle weight data. 

Used to identify or group LTPP sites with similar 

per-truck loading. Average GVW does not contain 

information about truck volume or total loading 

experienced by an LTPP site. In combination with 

AADTT information, average GVW can be used to 

characterize average daily truck traffic loading or total 

truck traffic loading (i.e., FHWA Classes 4–13 

combined) at an LTPP site. 
21

Parameter Description or Meaning Recommended Use and Limitations 

Cumulative number of 

heavy axle load 

applications for FHWA 

Classes 4–13 combined 

Cumulative number (i.e., axle count) of axles 

weighing over 75 percent of Federal legal load 

limit per LTPP site, considering all available 

axle weight data.

Use to characterize exposure of an LTPP site to heavy 

axle loads, compare traffic loading between LTPP sites, 

or group LTPP sites with similar traffic loading (i.e., 

similar exposure to heavy axle loads). Cumulative 

number of heavy axle load applications for all vehicle 

classes combined is useful for analyses where pavement 

responses and performance are highly affected by heavy 

loads and may not be applicable for analyses where the 

total number of loads is more important than the total 

load. 

Average percent of heavy 

axle load applications for 

FHWA Classes 4–13 

combined 

Average percentage of axles weighing over 

75 percent of Federal legal load limit per LTPP 

site, considering all available axle weight data. 

Use to characterize exposure of an LTPP site to heavy 

traffic loads, compare traffic loading between LTPP 

sites, or group LTPP sites with similar traffic loading. 

Average percent of heavy axle load applications for all 

vehicle classes combined is useful for analyses where 

pavement responses and performance are highly 

affected by heavy loads and may not be applicable for 

analyses where the total number of loads is more 

important than the total load. 

Axle loading category for 

each vehicle class 

(FHWA Classes 4–13) 

and for each axle 

type/group (i.e., single, 

tandem, tridem, quad, 

penta+).

Axle loading category for each vehicle class 

(FHWA Classes 4–13) and for each axle 

type/group (single, tandem, tridem, quad, 

penta+) based on a percentage of heavy axles 

(i.e., light, moderate, heavy, very heavy). 

Use to identify or group sites with similar per-axle 

loading for specific types of trucks. Allows to identify 

heaviest axles by class and axle group. Axle loading 

category for each vehicle class and for each axle 

type/group describes how heavy different truck axles 

are but does not contain information about truck 

volume. 

Truck loading category Truck loading category is determined based on 

the percentage of heavy trucks in the combined 

GVW distribution of vehicles in 

FHWA Classes 4–13: very light, light, 

moderate, heavy, or very heavy. 

Use to identify or group LTPP sites with similar 

percentage of heavy trucks or similar average per-truck 

loading. Truck-loading category does not provide 

information about how many trucks contributed to this 

distribution. 
22

TRAFFIC PARAMETERS USED IN AASHTOWARE PAVEMENT ME DESIGN 

SOFTWARE 

MEPDG Traffic Parameters 

Pavement engineering is undergoing a paradigm shift from empirical to mechanistic–empirical 

design methods with the goal of eventually developing mechanistic design methods. In contrast 

with empirical design methods that included only one traffic summary parameter (ESAL) for 

over 50 yr, mechanistic–empirical design methods require extensive use of a large number of 

traffic input parameters. 

Many mechanistic–empirical pavement performance analyses use the MEPDG method and 

products like AASHTOWare Pavement ME Design software, which uses a defined set of traffic 

input parameters in a specific format. Table 6 describes the traffic parameters required for 

analyses and design based on the MEPDG method.

Table 6. Traffic input parameters required by 

AASHTOWare Pavement ME Design software. 

MEPDG Input Parameter Parameter Description 

ALDF ALDF represent a percentile axle load distribution for a typical day 

of each calendar month for a typical design/analysis year. One set 

of ALDF are provided for each vehicle class (FHWA Classes 4–

13), axle group type (i.e., single, tandem, tridem, quad), and 

calendar month. ALDF remain constant between analysis years. 

Vehicle class volume 

distribution 

One representative percentile distribution of truck volume by 

vehicle class (FHWA Classes 4–13) is provided to represent a 

vehicle class distribution for the base design or analysis year. 

Monthly adjustment 

factors 

One representative set of 12 monthly coefficients is provided for 

each vehicle class (FHWA Classes 4–13) to represent differences 

in truck volumes between different calendar months for the base 

design or analysis year. 

Hourly distribution factors One representative set of 24 hourly factors showing the average 

percentage of total daily truck volume for each hour. Values are 

the same for all truck classes. Hourly distribution factors only 

apply to portland cement concrete (PCC) pavements. 

Number of axles per truck One representative set of values showing the average number of 

single, tandem, tridem, and quad axles for each truck class 

(FHWA Classes 4–13). 

Base (first) year AADTT 

for design lane 

One value representing average annual daily volume of vehicles in 

FHWA Classes 4–13 for the base design or analysis year. If base 

(first) year AADTT for design lane is used in AASHTOWare 

Pavement ME Design software in place of two-way AADTT, also 

enter the following values: percent trucks in design direction is

100, and percent trucks in design lane is 100. Alternative inputs 

include: MEPDG base (first) year two-way AADTT, percent 

trucks in design direction, and percent trucks in design lane. 
23

MEPDG Input Parameter Parameter Description 

Base (first) year two-way 

AADTT 

Two-way AADTT computed for the base design or analysis year. 

Percent of trucks in design 

direction 

Percent of trucks in design direction for the base design or analysis 

year. 

Percent of trucks in design 

lane 

Percent of trucks in design lane for the base design or analysis 

year. 

Vehicle class annual 

volume growth rate for 

each truck class 

(FHWA Classes 4–13) 

Growth rate (percent) for each truck class (FHWA Classes 4–13). 

Used together with the growth function (linear or compound) to 

estimate truck volume over the analysis or design period from the 

base design or analysis year AADTT values. 

Vehicle class growth 

function 

Type of truck volume growth function (linear or compound) for 

each vehicle class (FHWA Classes 4–13). Applied together with 

the growth rate to estimate truck volume over the analysis or 

design period from the base design or analysis year AADTT 

values. 

Operational speed Average speed in mph of heavier trucks through the project limits. 

If not available, use defined as-posted speed limit value. 

Axle spacing Average representative axle spacing in inches for tandem, tridem, 

and quad axles. 

Average wheelbase length 

and corresponding 

percentage of trucks 

Average wheelbase length in feet and corresponding percentages 

of trucks with wheelbases that fall in the following three 

categories: short (≤12 ft), medium (>12 ft and ≤15 ft), and long 

(>15 ft and ≤20 ft). For multiunit and combination trucks, only 

wheelbase of the truck power-unit (i.e., first unit) is considered. 

Used for top-down JPCP cracking model only. 

Average axle width Distance in feet between two outside edges of an axle. Constant 

between all truck classes. Only needed for rigid pavement designs. 

Mean wheel location Mean distance in inches from the outer edge of the wheel to the 

pavement marking. Mean wheel location is constant between all 

truck classes and does not change with time. 

Truck wander standard 

deviation 

Standard deviation from the mean wheel location. Computed in 

inches based on measurements from the lane marking. 

Dual tire spacing Dual tire spacing is constant between all truck classes and does not 

change with time. 

ALDF = axle load-distribution factors. 

Traffic Loading Defaults for MEPDG 

Recognizing the emerging state of WIM technology, the limited number of sites reporting WIM 

data, and the need for research-quality WIM data to support LTPP research, the LTPP program 

installed and maintained WIM equipment at SPS WIM sites in 22 States. This effort proved that 

collecting consistently high-quality WIM data (satisfying the performance requirements of 

Type I WIM systems for ASTM E1318-09 over long periods (10 yr or more) is possible with 

proper maintenance and calibration.

(11, 12, 13) Data from LTPP SPS WIM sites have been used to 

develop new-generation traffic loading defaults for use with the MEPDG method.(4) These 

defaults were included in AASHTOWare Pavement ME Design software for national and 
24

international use. FHWA developed LTPP PLUG to help users select and use these defaults.(10)

LTPP is expanding its WIM program to cover more sites across the United States as part of its 

Warm-Mix Asphalt Overlay of Asphalt Pavements Experiment. Several States are also working 

on or have completed developing their own MEPDG traffic loading defaults. 

MEPDG Parameters Requiring Special Input Formats and Tools 

MEPDG input for ALDF requires a table with over 19,000 data entries representing normalized 

axle load-distribution for a typical day for each of the 12 calendar months for a base analysis 

year. This distribution is required for each vehicle class (FHWA Classes 4–13) and each axle 

type/group (i.e., single, tandem, tridem, and quad). Due to the large size of MEPDG input for 

ALDF, the preferable way to enter it into AASHTOWare Pavement ME Design software is to 

upload an axle load-distribution file in .xml format. Like axle load distribution, a monthly traffic 

volume adjustment input required by AASHTOWare Pavement ME Design software requires 

120 entries and can be imported in .txt format. 

Accuracy of Weight Data 

Emerging mechanistic and MEPDG methods demand accurate measurements of traffic loads. To 

provide accurate predictions of stresses, strains, and deflections in a pavement structure, weight 

measurements should be as accurate as those used for weight enforcement. For MEPDG 

methods, accuracy of WIM data should satisfy the performance requirements of Type I WIM 

systems for ASTM E1318-09.(11)
25

CHAPTER 3. ASSESSMENT OF LTPP USERS’ NEEDS AND AVAILABLE LTPP 

TRAFFIC DATA SOURCES 

This chapter identifies traffic data and computed parameters necessary to support different types 

of LTPP data analyses, specifies whether these parameters were computed and stored by the 

LTPP program at the beginning of this study, and describes the benefits of these parameters. 

Recommendations are provided at the end of this chapter for additional traffic data and/or 

parameters that should be made available to LTPP users. 

PURPOSE OF LTPP TRAFFIC DATA AND LTPP USERS’ PRIORITIES 

To better understand the traffic data and computed parameters needs of LTPP users, a review 

was conducted of previously published LTPP documentation, including LTPP experiment design 

documents, the Strategic Plan for Long-Term Pavement Performance Data Analysis, selected 

LTPP and NCHRP data analysis reports, and reports and documentation generated under 

TPF-5(004).(16,17)

In addition, a series of discussions took place between the study research team with members of 

the LTPP team, LTPP Customer Support Service Center (CSSC) representatives, TRB ETG for 

LTPP Special Activities members, and LTPP traffic data collectors and data analysis contractors. 

Topics discussed included types of traffic data and parameters needed, data quality and quantity, 

data storage and accessibility, and the minimum data availability requirements to support 

different LTPP experiments and analyses. As a result of these discussions, the following LTPP 

traffic data uses were identified and are listed in order of importance: 

• To support the analysis plan outlined in the Strategic Plan for Long-Term Pavement 

Performance Data Analysis, including development of analysis-ready traffic datasets. 

• To support current and future pavement design procedures used by State and Provincial 

highway agencies, such as development of enhanced mechanistic–empirical pavement 

design models and local calibration of these models, including development of traffic 

inputs required for these analyses, such as MEPDG traffic inputs and traffic loading 

summary statistics like ESAL. 

• To support studies aimed at improving pavement performance and cost allocation models 

supporting the Highway Performance Monitoring System (HPMS) by providing traffic 

inputs compatible with HPMS traffic data requirements. 

• To promote and highlight the LTPP program’s importance through sharing and 

expanding research-quality traffic data collected by the LTPP program.

GOAL OF LTPP TRAFFIC DATA AVAILABILITY ASSESSMENT 

The LTPP traffic data availability assessment activity focused on the availability of traffic 

parameters needed to support LTPP data analyses rather than data available to compute traffic 

parameters. Traffic parameters needed to support various LTPP data analyses were identified in 

chapter 2. 
26

AVAILABILITY OF BASIC AND DETAILED TRAFFIC DATA AND PARAMETERS 

LTPP Data Sources for Basic and Detailed Traffic Data and Parameters 

Table 7 summarizes basic traffic data and parameters and identifies corresponding LTPP data 

sources.

Table 7. Availability of basic and detailed traffic data and parameters in LTPP database. 

Parameter Description and Use LTPP Data Source 

Hourly distribution of daily 

truck traffic volume for each 

monitoring day or hourly 

truck volumes 

Use for developing MEPDG 

inputs and defaults. Limited direct 

application. 

Data from LTAS table 

HH_CL_CT can be used to 

compute this parameter. 

Daily total traffic volume 

for each monitoring day 

Use to compute AADT. Limited 

direct application. 

LTAS table DD_VOL has this 

parameter. 

Daily truck traffic volume 

for each monitoring day 

Use to compute AADTT. Limited 

direct application. 

Data from LTAS table 

DD_CL_CT can be used to 

compute this parameter. 

Daily truck traffic volume 

by vehicle class (FHWA 

Classes 

4–13) for each monitoring 

day

Use to compute AADTT by 

vehicle class and MEPDG 

normalized vehicle class 

distribution. Limited direct 

application. 

LTAS table DD_CL_CT has 

this parameter. 

Monthly truck volume by 

vehicle class for each 

monitoring month 

Use to compute MEPDG monthly 

truck volume adjustment factors. 

Data from LTAS table 

MM_CT can be used to 

compute this parameter. 

Monthly ADTT volume by 

class for each monitoring 

month 

Use to compute AADTT by 

vehicle class. 

Data from LTAS table 

MM_CT can be used to 

compute this parameter. 

Percent trucks (FHWA 

Classes 

4–13) for each monitoring 

year 

Use to estimate AADTT from 

AADT when no vehicle 

classification is available or in 

studies that require truck volume 

but truck volume data are not 

available for each year. Limited 

direct application. 

Data from LTAS table 

YY_CT (where 

TRF_data_type is 4) can be 

used to compute this 

parameter. 

Axle weight distribution in 

the form of a daily axle load 

spectrum for each 

monitoring day for each 

vehicle class (FHWA 

Classes 4–13) and each axle 

group type (i.e., single, 

tandem, tridem, quad, 

penta+) 

Use to develop representative 

monthly NALS for MEPDG 

input. Use to develop MEPDG 

defaults. Limited direct 

application. 

LTAS table DD_AX has this 

parameter. 
27

Parameter Description and Use LTPP Data Source 

Axle weight distribution in 

the form of monthly axle 

load spectra for each 

monitoring month with at 

least 7 DOW data available 

for each vehicle class 

(FHWA Classes 4–13) and 

each axle group type (i.e., 

single, tandem, tridem, 

quad, penta+) 

Use to develop representative 

monthly NALS for MEPDG 

input. Use to develop MEPDG 

defaults. Limited direct 

application. 

Data from LTAS table 

DD_AX or MM_AX can be 

used to compute this 

parameter. 

Axle weight distribution in 

the form of annual axle load 

spectra for each monitoring 

year for each vehicle class 

(FHWA Classes

4–13) and each axle group 

type (i.e., single, tandem, 

tridem, quad, penta+) 

Use to compute annual ESAL. 

Limited direct use of this statistic. 

LTAS table YY_AX and SDR 

table 

TRF_MONITOR_AXLE_DIS

TRIB have this parameter.

Monthly normalized axle 

weight distribution for each 

monitoring month with at 

least 7 DOW data available 

for each vehicle class 

(FHWA Classes 4–13) and 

each axle group type (i.e., 

single, tandem, tridem, 

quad, penta+) 

Use to develop representative 

monthly NALS for MEPDG 

input. Use to develop MEPDG 

defaults. Limited direct use of this 

statistic. 

Data from SDR table 

TRF_MEPDG_AX_DIST or 

LTAS table DD_AX or 

MM_AX can be used to 

compute this parameter. 

Annual normalized axle 

weight distribution for each 

monitoring year for each 

vehicle class (FHWA 

Classes 4–13) and each axle 

group type (i.e., single, 

tandem, tridem, quad, 

penta+) 

Use to develop MEPDG defaults. 

Limited direct use of this statistic. 

SDR table 

TRF_MEPDG_AX_DIST_A

NL has this parameter. 

Average number of axles of 

each axle group type (i.e., 

single, tandem, tridem, 

quad, penta+) per vehicle 

class (FHWA Classes 4–13) 

for each year with WIM 

data 

Use to develop MEPDG inputs 

and defaults. Limited direct use of 

this statistic. 

SDR table 

TRF_MEPDG_AX_PER_TR

UCK has this parameter. 
28

Parameter Description and Use LTPP Data Source 

Axle-to-axle spacing for 

tandem axle configurations 

based on WIM data for each 

year 

Use to develop MEPDG axle 

spacing inputs and defaults. 

Limited direct use of this statistic. 

No LTPP table available but 

can be computed from IVR 

weight files.

Axle-to-axle spacing for 

tridem axle configurations 

based on WIM data for each 

year 

Use to develop MEPDG axle 

spacing inputs and defaults. 

Limited direct use of this statistic. 

No LTPP table available but 

can be computed from IVR 

weight files.

Axle-to-axle spacing for 

quad+ axle configurations 

for each year with WIM 

data 

Use to develop MEPDG axle 

spacing inputs and defaults. 

Limited direct use of this statistic. 

No LTPP table available but 

can be computed from IVR 

weight files.

Average axle spacing and 

percentile distribution of the 

tractor unit wheelbase of 

tractor–semitrailer 

combination trucks (FHWA 

Classes 8–13) in three 

categories: short (≤12 ft), 

medium (>12 and ≤15 ft), 

and long (>15 ft and ≤20 ft) 

for each year with WIM 

data 

Use to develop MEPDG 

wheelbase input and defaults. 

Limited direct use of this statistic. 

No LTPP table available but 

can be computed from IVR 

weight files.

Average axle spacing and 

percentile distribution of all 

axle spacings from vehicles 

in FHWA Classes 4–13 in 

three categories: short (≤12 

ft), medium (>12 and ≤15 

ft), and long (>15 ft and 

≤20 ft) for each year with 

WIM data 

Use to develop MEPDG axle 

spacing inputs and defaults. 

Limited direct use of this statistic. 

No LTPP table available but 

can be computed from IVR 

weight files.

Average and standard 

deviation of outer wheel 

location from pavement lane 

marking based on all axle 

passages for vehicles in 

FHWA Classes 4–13

Use to develop MEPDG inputs 

and defaults. Wheel location is 

measured in inches from the outer 

edge of the wheel to the pavement 

marking for each axle and vehicle 

passage. Limited direct use of this 

statistic. 

Not available. This 

information is not collected 

by the LTPP program.

DOW = day of week; LTAS = Long-Term Pavement Performance Traffic Analysis Software. 

Availability Assessment and Recommendations for Basic and Detailed Traffic Data and 

Parameters 

As shown in table 7, most basic traffic parameters are directly available in LTAS or SDR 

database tables while other parameters can be computed based on LTPP traffic data. LTPP data 
29

users can obtain these parameters either by request through the LTPP CSSC or ltppinfo@dot.gov 

or directly through InfoPave. 

Gaps in the availability of detailed parameters identified in table 7 include lack of information 

about load position and configuration, tire footprint information, time history of each load 

application (i.e., changes in location and magnitude of loads over time as truck tires roll over a 

pavement section), and the magnitude of dynamic forces applied to the pavement. These 

parameters are likely to become more important as pavement design moves to more advanced 

mechanistic modeling. The research study team recommends that the LTPP program investigate 

the feasibility of collecting these data elements in the future. 

AVAILABILITY OF ANALYSIS-READY TRAFFIC PARAMETERS 

Need for Analysis-Ready Traffic Datasets 

Assessment of LTPP database user needs and review of available LTPP traffic data (using 

SDR 27) indicated that the LTPP database contained insufficient analysis-ready traffic 

parameters. Analysis-ready traffic parameters are summary statistics representing traffic in the 

form of computed values used as direct inputs to analysis models. In addition, analysis-ready 

traffic parameters are used as direct traffic inputs for pavement design and analysis tools, such as 

the AASHTO Guide for Design of Pavement Structures and AASHTOWare Pavement ME 

Design software. Analysis-ready traffic parameters are the most frequently requested data from 

the LTPP database and represent a high priority for LTPP database users. The main analysisready traffic parameters missing from or incomplete in the LTPP database are those used directly 

with AASHTOWare Pavement ME Design software, including axle loading frequency 

distributions, truck traffic growth parameters, and parameters providing information about 

relative position of axle loads on the pavement. 

Analysis-ready traffic parameters contain information relevant to achieving LTPP analysis 

objectives (e.g., AADTT for each analysis year and cumulative ESAL computed for years 

corresponding to distress data collection years over the analysis period). To compute analysisready parameters, systematic procedures are applied to available data, including review of data 

from multiple sampling periods and multiple data sources, identification and investigation of 

outliers, estimation of missing traffic data, and computation or estimation of traffic parameters 

suitable for a direct analysis input. 

Why Analysis-Ready Traffic Parameters Should Be a Priority for LTPP 

Previous LTPP studies involving pavement response and performance prediction required 

significant effort to develop analysis-ready traffic input parameters. Efforts included reviewing 

available traffic data, identifying questionable data and outliers (either due to limited samples or 

traffic monitoring equipment lacking calibration or improperly calibrated), rationalizing 

unexpected traffic trends, and projecting traffic parameters over the pavement analysis period 

from limited and sometimes contradictory site-specific data samples. 

Many LTPP sites did not have any traffic loading information, thus limiting their use in analyses. 

In the past, this situation led to two outcomes: either a portion of the research effort was diverted 

from the analysis of pavement performance to development of comprehensive traffic inputs (as 
30

in cases of analyses requiring MEPDG-type traffic inputs) or the analysis was not executed to its 

full potential because traffic data availability was limited. The latter led to either a reduced 

number of LTPP sites used in analyses or use of traffic parameters that were available but did not 

have a direct correlation with pavement performance, such as using traffic volume data in lieu of 

insufficient traffic loading data. 

How Analysis-Ready Traffic Data and Parameters Are Used 

Analysis-ready traffic parameters are provided in a ready-to-use format as a direct input in the 

majority of LTPP analyses. These parameters are summary statistics or application-specific input 

parameters supporting a broad range of research topics related to pavement response and 

performance modeling and analysis. Analysis-ready traffic parameters are also used as inputs in 

the AASHTO Guide for Design of Pavement Structures and AASHTOWare Pavement ME 

Design software.(3,5)

Based on the intended application, analysis-ready traffic parameters can be categorized as 

follows: 

• Parameters for detailed characterization of traffic loading for mechanistic and 

mechanistic–empirical pavement response and performance analysis and modeling. 

• Summary traffic parameters for empirical pavement response and performance analysis 

and modeling and for use in high-level analyses. 

Detailed review of these parameters is provided in chapter 2. The following section summarizes 

the availability assessment of analysis-ready traffic parameters in the LTPP database. 

LTPP Traffic Data Sources to Support Mechanistic Pavement Response Prediction and 

Modeling 

Review of available LTPP traffic data sources shows that, while raw data are available to 

compute most needed parameters, these parameters are not available as analysis-ready values. As 

such, significant effort is needed to compute traffic inputs for pavement response analyses. 

Table 8 summarizes traffic parameters necessary for mechanistic pavement response prediction 

and modeling and LTPP data sources that can be used to obtain these parameters. 

Table 8. Traffic parameters for mechanistic pavement response modeling and LTPP 

sources. 

Input Parameter Parameter Description LTPP Data Source 

Vehicle class and axle 

configuration for each 

vehicle passage 

Description of a vehicle class 

(FHWA Classes 4–13), truck 

body type, and axle 

configuration for each vehicle 

passage, including number and 

spacing of axles. 

LTPP WIM IVR files stored 

offline. 
31

Input Parameter Parameter Description LTPP Data Source 

Axle-to-axle spacing for 

each vehicle passage or 

annual average for each 

vehicle class (FHWA 

Classes 4–13) 

Distance between pairs of 

consecutive vehicle’s axles for 

each vehicle passage. 

LTPP WIM IVR files stored 

offline. 

Axle load, both static and 

dynamic, for each axle for 

each vehicle passage or 

annual average for each 

vehicle class (FHWA 

Classes 4–13) 

Static weight estimated by 

WIM system for each axle for 

each vehicle passage. 

LTPP WIM IVR files stored 

offline. 

Wheel load for each axle 

for each vehicle passage or 

annual average for each 

vehicle class (FHWA 

Classes 4–13) 

Static weight estimated by 

WIM system for each axle for 

each vehicle passage. 

This information is available 

from WIM controller but is not 

stored by the LTPP program. 

Duration of each axle or 

wheel load application 

Time during which axle load 

was applied on monitored 

pavement section. Used for 

dynamic pavement response 

modeling only. 

This information is not collected 

by the LTPP program but can be 

obtained from WIM controller 

records. 

Time history of changes in 

load magnitude for each 

axle or wheel passage 

Dynamic load magnitude 

estimated based on WIM 

signal for each millisecond 

during axle passage over the 

WIM sensor. Used for 

dynamic pavement response 

modeling only. 

This information is not collected 

by the LTPP program but can be 

obtained from WIM controller 

records. 

Wheel location on the 

pavement associated with 

each axle and vehicle 

passage or annual average 

for each vehicle class 

(FHWA Classes 4–13) 

Wheel location measured in 

inches from the outer edge of 

the wheel to the pavement 

marking for each axle and 

vehicle passage. 

This information is not collected 

by the LTPP program but can be 

obtained from specially 

configured quartz-piezo sensor 

arrays. 

Truck wander Standard deviation from the 

mean wheel location, 

computed in inches based on 

measurements from the lane 

marking. 

This information is not collected 

by the LTPP program but can be 

computed from wheel location 

records. 

Tire footprint area for each 

axle associated with each 

vehicle passage or annual 

average for each vehicle 

class (FHWA Classes 4–13) 

Tire footprint area of each axle 

and vehicle passage. 

This information is not collected 

by the LTPP program. 
32

Input Parameter Parameter Description LTPP Data Source 

Axle width with each 

vehicle passage or annual 

average for each vehicle 

class (FHWA Classes 4–13) 

Distance in feet between two 

outside edges of an axle. 

This information is not collected 

by the LTPP program but can be 

collected using staggered WIM 

sensor arrays. The default value 

may be appropriate due to 

expected low variability of this 

parameter. 

Dual tire spacing with each 

vehicle passage or annual 

average for each vehicle 

class (FHWA Classes 4–13) 

Distance in feet between two 

tires. 

This information is not collected 

by the LTPP program. The 

default value may be appropriate 

due to expected low variability of 

this parameter. 

Tire pressure for wheel of 

each vehicle or annual 

average for each vehicle 

class (FHWA Classes 4–13) 

Tire pressure can be used as an 

alternative means for 

computing size of tire 

footprint. 

This information is not collected 

by the LTPP program. 

Truck speed for each 

vehicle passage or annual 

average for each vehicle 

class (FHWA Classes 4–13) 

Truck speed in mph. This information is not 

collected/reported by the LTPP 

program but can be obtained 

from available LTPP WIM IVR 

for each vehicle passage. 

Availability Assessment and Recommendations for Parameters Supporting Mechanistic 

Pavement Response Prediction and Modeling 

Many of the parameters presented in table 8 are not collected or reported by the LTPP program, 

mainly because the program was designed for long-term monitoring of pavement performance 

rather than investigating pavement responses immediately after each load application. Some 

traffic parameters used for pavement response modeling can be obtained from IVR collected by 

WIM systems, such as estimate of static axle and/or wheel loads for each vehicle, axle spacing, 

and vehicle speed. Other parameters can be computed using time histories of the changes in 

WIM sensor signals collected by WIM data-processing units. 

Additional data collection is required to accurately measure the location of wheel load with 

respect to pavement edge, size of the tire footprint, and distribution of loads under the tire 

footprint. Tire footprint position can be obtained using currently available technologies by using 

specially configured quartz-piezo WIM sensor arrays. WIM sensors technologies capable of 

measuring tire footprint or estimating tire pressure are being developed by WIM sensor 

manufacturers and should be coming to the market in future years. 

Based on analysis of available LTPP data and the design of LTPP experiments, a very limited 

number of LTPP sites can support fundamental research of mechanistic pavement response 

modeling. In mechanistic pavement response modeling and analysis, pavement responses due to 

traffic loads are predicted using laws and principles of the mechanics of materials and compared 

to the same responses measured in the field. Mechanistic pavement responses are characterized 

by stresses, strains, or deflections induced in pavement layers under traffic loading. Only those 
33

LTPP sites where these pavement responses are monitored under actual known traffic loading 

can be used for development or verification of mechanic pavement response models. In addition 

to traffic parameters, mechanistic pavement response modeling requires extensive pavement 

material characterization (i.e., extensive testing and coring); in-situ measurement of stresses, 

strains, and deflections in pavement layers; and in-situ environmental data collection. Only two 

LTPP dynamic load response sites, one each in in Ohio and North Carolina, have the necessary 

measured pavement response information to support fundamental research of mechanistic 

pavement response modeling. 

The effort required to collect data necessary for mechanistic pavement response modeling may 

be too high compared to possible benefits and contributions to overall LTPP program objectives; 

thus, these parameters represent a low priority compared to other traffic data needs, such as 

development of analysis-ready traffic parameters for use in AASHTOWare Pavement ME 

Design software. 

LTPP Traffic Data Sources to Support Mechanistic–Empirical Pavement Performance 

Prediction 

Although pavement response analysis and modeling studies focus on stresses, strains, and 

deflections that pavement experience under each traffic load application, pavement performance 

analysis and modeling studies focus on pavement distresses (e.g., cracking, rutting, faulting) that 

develop over time. To provide a means for tracking and summarizing traffic load applications 

over time, traffic loads are summarized in an axle load spectrum. The axle load spectrum input 

provides information about axle load magnitudes, the number of axle load applications over a 

specified period at different magnitudes, and load configuration (i.e., number of axles in each 

axle load group). In addition to the axle load spectrum, information about the relative position of 

axle loads on the pavement is also important for pavement performance modeling.

Table 9 summarizes traffic parameters necessary for mechanistic–empirical pavement 

performance modeling and LTPP data sources that can be used to obtain these parameters. 
34

Table 9. Traffic parameters for generic mechanistic–empirical pavement performance 

modeling and LTPP sources. 

Input 

Parameter Parameter Description LTPP Data Source 

Axle load 

spectrum 

Axle load spectrum is a frequency distribution of 

the number of axle load applications by load bins 

(or load ranges) where a number of axle load 

applications observed during a specified period is 

reported using predefined load bins. Separate axle 

load spectra are used to summarize axle loading 

for typical axle load groups: single, tandem, 

tridem, and quad+. Axle load spectra can be 

created for an individual truck class or for all 

truck classes combined. Axle load spectra can 

represent daily, monthly, or annual traffic loading 

summaries. This input must cover the whole 

analysis period, using time increments specified 

for analysis, so the number of axle load 

application can be used to model incremental 

changes in pavement structure over the selected 

analysis period. 

This parameter is available in 

LTPP database tables 

DD_AX, MM_AX, YY_AX, 

and 

TRF_MONITOR_AXLE_DI

STRIB for each year with 

WIM data. 

Number of 

axles per 

truck

Average number of single, tandem, tridem, and 

quad axles for each truck class (FHWA Classes 

4–13). 

This parameter is available in 

the LTPP database table 

TRF_MEPDG_AX_PER_TR

UCK for each year with 

sufficient WIM data. 

Axle 

spacing 

Average axle spacing in inches for tandem, 

tridem, and quad axles. 

This parameter is not 

available in the LTPP 

database but can be computed 

from LTPP WIM IVR files. 

Axle 

spacing 

distribution 

Frequency of longitudinal spacing of consecutive 

axles in feet, excluding spacing within multi-axle 

groups. Use to model locations of loads for JPCP 

pavements. 

This parameter is not 

available in the LTPP 

database but can be computed 

from LTPP WIM IVR files. 

Average 

axle width 

Distance in feet between two outside edges of an 

axle. Use for rigid pavement analysis only.

This information is not 

collected by the LTPP 

program but can be collected 

using staggered WIM sensor 

arrays, video samples, or 

truck fleet surveys. 
35

Input 

Parameter Parameter Description LTPP Data Source 

Operational 

speed 

Posted speed limit or average speed (in mph) of 

heavier trucks (FHWA Classes 6–13) through 

project limits. 

This information is not 

collected/reported by the 

LTPP program but can be 

obtained from IVR records 

following the new FHWA 

Traffic Monitoring Guide 

(TMG) format. 

Dual tire 

spacing 

Distance in inches from the center of one tire to 

the next for the tire assemblies located on the 

same axle and in the same wheel path. 

This information is not 

collected by the LTPP 

program. 

Tire 

pressure 

One value representing hot tire inflation pressure. This information is not 

collected by the LTPP 

program. 

Mean wheel 

location 

Distance in inches from the outer edge of the 

wheel to the pavement marking. Use to model the 

location of the load. 

This information is not 

collected by the LTPP 

program but can be collected 

using specially configured 

quartz-piezo WIM sensor 

arrays or from video samples. 

Truck 

wander 

Standard deviation from the mean wheel location, 

computed in inches, based on wheel location 

measurements from the lane marking. Use to 

model the location of the load. 

This information is not 

collected by the LTPP 

program. 

Availability Assessment and Recommendations for Parameters Supporting 

Mechanistic–Empirical Pavement Performance Prediction 

Although some parameters identified in table 9 are readily available in LTPP database tables, 

significant effort is required for users to process data and compute other parameters (e.g., axle 

spacing and axle spacing distribution). Yet, for a few parameters (e.g., truck wander, mean wheel 

location, operational speed), the LTPP database contains no information. 

The LTPP program should consider computing the parameters identified in table 9 if supporting 

data are available (See the LTPP Data Source column), and storing the parameters in LTPP 

database tables. 

LTPP Traffic Data Sources to Support MEPDG Applications with AASHTOWare 

Pavement ME Design Software 

A complete assessment of traffic parameters required for all MEPDG analyses involving 

AASHTOWare Pavement ME Design software is shown in table 10. The assessment presented 

reflects the status of LTPP data as of SDR 27. As a result of this study, the most critical input 

parameters listed in table 10 were created in a format compatible with AASHTOWare Pavement 

ME Design software and are available in the LTPP database as analysis-ready CPTs, as 

described in chapter 11. 
36

Table 10. MEPDG traffic input parameters and LTPP sources assessment using SDR 27. 

MEPDG Input 

Parameter Parameter Description LTPP Data Source 

MEPDG ALDF One representative NALS for each 

vehicle class (FHWA Classes 4–

13), axle group type (i.e., single, 

tandem, tridem, quad), and 

calendar month. NALS represent 

an axle load distribution for a 

typical day for each calendar 

month for a base design/analysis 

year and remains constant for the 

analysis or design period. 

Information to compute this 

parameter is available in the LTPP 

database in monthly and annual 

summary statistics for each year with 

data satisfying the data availability 

requirements of NCHRP 1-37A. 

These data are not readily applicable,

as AASHTOWare Pavement ME 

Design software input requires one 

representative distribution instead of 

by-year data. 

MEPDG vehicle 

class volume 

distribution 

One representative normalized 

distribution of vehicles in 

FHWA Classes 4–13 to represent 

an average vehicle class 

distribution for the base 

design/analysis year. 

Information to compute this 

parameter is available in the LTPP 

database in annual summary statistics 

for each year with data satisfying the 

data availability requirements of 

NCHRP 1-37A. These data are not 

readily applicable, as AASHTOWare 

Pavement ME Design software input 

requires one representative 

distribution instead of by-year data. 

MEPDG monthly 

adjustment factors 

One representative set of 12 

monthly coefficients is provided 

for each vehicle class (FHWA 

Classes 4–13) to represent 

differences in truck volumes 

between different calendar months 

for the base design/analysis year. 

The sum for all months for one 

truck class is 12. 

Information to compute this 

parameter is available in the LTPP 

database in monthly summary 

statistics for each year with data. 

These data are not readily applicable,

as AASHTOWare Pavement ME 

Design software input requires one 

representative set of factors for each 

LTPP site. 

MEPDG hourly 

distribution factors 

One representative set of 24 

hourly factors showing the 

percentage of daily total truck 

traffic for each hour. Values are 

the same for all truck classes and 

only apply to truck volume. This 

input parameter only applies to 

PCC pavements. 

Information to compute this 

parameter is available in the LTPP 

database for each year with data. 

These data are not readily applicable,

as AASHTOWare Pavement ME 

Design software input requires one 

representative set of factors for each 

LTPP site. 
37

MEPDG Input 

Parameter Parameter Description LTPP Data Source 

MEPDG number of 

axles per truck 

One representative set of values 

showing the number of axles per 

truck class (FHWA Classes 4–13), 

which provides the average 

number of single, tandem, tridem, 

and quad axles for each truck 

class (FHWA Classes 4–13). 

Information to compute this 

parameter is available in the LTPP 

database. These data are not readily 

applicable, as AASHTOWare 

Pavement ME Design software input 

requires one representative set of 

factors for each LTPP site. 

MEPDG base (first) 

year AADTT for 

LTPP lane 

One value representing average 

annual daily volume of vehicles in 

FHWA Classes 4–13 for the base 

design/analysis year. Use this 

value in AASHTOWare Pavement 

ME Design software in 

combination with the following 

MEPDG parameters: percent 

trucks in design direction is

100 percent, and percent trucks in 

design lane is 100 percent. 

Information to compute this 

parameter is available in the LTPP 

database for most LTPP sites. 

Additional effort is required from 

LTPP users to determine if available 

AADTT values accurately represent 

desired base year conditions. 

MEPDG base (first) 

year two-way 

AADTT 

Two-way AADTT computed for 

the base design/analysis year. 

Information to compute this 

parameter is available in the LTPP 

database for limited years for most 

LTPP sites. Additional effort is 

required from LTPP users to 

determine if available AADTT values 

accurately represent desired base year 

conditions. 

MEPDG base (first) 

year two-way 

AADT

Two-way AADT computed for 

the base design/analysis year. This 

parameter and percent trucks are 

only included in MEPDG 

software for agencies that do not 

compute AADTT. 

Information to compute this 

parameter is available in the LTPP 

database for limited years for most 

LTPP sites. Additional effort is 

required from LTPP users to 

determine if available AADT values 

accurately represent desired base year 

conditions. 

Number of lanes in 

design direction 

Number of lanes in design 

direction (i.e., direction of LTPP 

lane). 

This parameter is available in LTPP 

database table SHRP_INFO. 

Percent of trucks in 

design direction 

Percent of trucks in design 

direction (i.e., direction of LTPP 

lane) for the base design/analysis 

year. 

This parameter is not available in the 

LTPP database. Because the LTPP 

database already contains design lane 

AADTT values, this parameter is not 

necessary. 
38

MEPDG Input 

Parameter Parameter Description LTPP Data Source 

Percent of trucks in 

design lane 

Percent of trucks in design lane 

(i.e., LTPP lane) for the base 

design/analysis year. 

This parameter is not available in the 

LTPP database. Because the LTPP 

database already contains design lane 

AADTT values, this parameter is not 

necessary. 

MEPDG vehicle 

class annual volume 

growth rate by 

vehicle class 

Growth rate in percent for each 

truck class (FHWA Classes 4–13) 

from the base design/analysis 

year. 

This parameter is not available in the 

LTPP database. 

MEPDG truck 

traffic growth 

function by vehicle 

class

Linear or composite growth 

function by vehicle class (FHWA 

Classes 4–13) from the base 

design/analysis year. 

This parameter is not available in the 

LTPP database. 

Operational speed Posted speed limit or average 

speed of heavier trucks (FHWA 

Classes 6–13) through the project 

limits in mph. 

This parameter is not available in the 

LTPP database. 

MEPDG axle 

spacing for tandem, 

tridem, and quad 

axles 

Average representative axle 

spacing in inches for tandem, 

tridem, and quad axles. 

This parameter is not available in the 

LTPP database but can be computed 

from WIM IVR files for LTPP WIM 

sites. 

MEPDG average 

wheelbase length 

and corresponding 

percentage of trucks 

with wheelbases in 

the following 

categories: short, 

medium, and long 

Average wheelbase length in feet 

and the corresponding percentages 

of trucks with wheelbases that fall 

in the following categories: short 

(≤12 ft), medium (>12 ft and ≤15 

ft), and long (>15 ft and ≤20 ft). 

For multiunit and combination 

trucks, only the wheelbase of the 

truck power-unit (i.e., first unit) is 

considered. Use for top–down 

JPCP cracking model only. 

Current MEPDG model uses 

values computed for 

FHWA Classes 8–13 only. 

This parameter is not available in the 

LTPP database but can be computed 

from WIM IVR files for LTPP WIM 

sites. 

MEPDG average 

axle width 

Distance in feet between two 

outside edges of an axle. This 

parameter is constant between all 

truck classes. Use for rigid 

pavement designs only. 

This parameter is not available in the 

LTPP database. 
39

MEPDG Input 

Parameter Parameter Description LTPP Data Source 

MEPDG mean 

wheel location 

Distance in inches from the outer 

edge of the wheel to the pavement 

marking. This parameter is 

constant between all truck classes 

and does not change over time. 

This parameter is not available in the 

LTPP database. 

MEPDG truck 

wander standard 

deviation 

Standard deviation from the mean 

wheel location, computed in 

inches, based on measurements 

from the lane marking. 

This parameter is not available in the 

LTPP database. 

MEPDG dual tire 

spacing 

This parameter is constant 

between all truck classes and does 

not change over time. 

This parameter is not available in the 

LTPP database. 

MEPDG tire 

pressure 

One value representing hot tire 

inflation pressure. This parameter 

is constant between all truck 

classes and does not change over 

time. 

This parameter is not available in the 

LTPP database. 

Availability Assessment and Recommendations for Parameters Supporting MEPDG 

Applications with AASHTOWare Pavement ME Design Software 

The Phase I review of available LTPP data sources indicated that some input parameters required 

by AASHTOWare Pavement ME Design software are not provided in LTPP SDRs, while others 

are provided in a format not readily applicable as an AASHTOWare Pavement ME Design 

software input. 

None of the parameters included in the LTPP TRF_MEPDG series of tables can be used directly 

with AASHTOWare Pavement ME Design software, mainly because these tables contain values 

from multiple years, while AASHTOWare Pavement ME Design software requires one 

representative input for each parameter. This input is typically computed based on data from 

years that passed QC checks for vehicle classification and heavy axle loading data accuracy or 

reasonableness (when accuracy cannot be quantified). Currently, LTPP database users rely on 

personal judgement to determine data suitability when computing representative values. This 

requires additional effort and professional judgement, especially when large deviations in traffic 

parameters exist between different years of data. 

The parameters presented in table 10 are the most frequently requested LTPP traffic parameters, 

as many State and Provincial highway agencies are implementing the MEPDG method and 

conducting local calibrations of pavement performance models. These efforts require extensive 

use of traffic inputs that States/Provinces do not have; the LTPP database is the only available 

national traffic data source with the necessary information to compute these inputs. The 

availability of AASHTOWare Pavement ME Design software traffic inputs is beneficial to all 

State/Provincial LTPP users as well as LTPP researchers using AASHTOWare Pavement ME 

Design software for their analyses. 
40

The LTPP program should consider computing the parameters identified in table 10 if supporting 

data are available (See the LTPP Data Source column) and storing the parameters in LTPP 

database tables. In addition, due to the anticipated high demand for these data, these parameters 

should be given a high implementation priority for display or access within InfoPave. For 

parameters that do not have supporting LTPP data, the LTPP program should consider 

conducting a limited data sampling and compute LTPP defaults or providing references to 

national defaults or default values assigned to LTPP sites. 

LTPP Traffic Data Sources Supporting Empirical and High-Level Pavement Management 

Analyses 

Typically, a traffic summary parameter is a one-value summary statistic providing information 

about traffic or truck volume or loading. Traffic summary parameters are typically used to study 

the empirical relationship between traffic volume and/or load and pavement performance. Traffic 

summary parameters are also used as an input for decision support or prioritization algorithms 

with higher-level pavement maintenance and management modeling. These traffic summary 

parameters were discussed in detail in chapter 2. 

Table 11 summarizes different traffic summary parameters and specifies LTPP data sources that 

were available in SDR 27 during Phase I of this study. In Phase III of this study, many of the 

parameters listed in table 11 were developed and are currently available in InfoPave. These 

traffic summary parameters are presented in chapter 11. 

Table 11. Traffic summary parameters and LTPP data sources. 

Parameter Parameter Description LTPP Data Source 

AADT Basic traffic summary statistic to measure 

average daily road use by all vehicular 

traffic. 

TRF_HIST_VOLUME_COUNT, 

TRF_MON_EST_ESAL, and 

TRF_HIST_EST_ESAL tables. 

AADTT Traffic summary statistic to measure 

average daily road use by trucks and 

buses (FHWA Classes 4–13). 

TRF_MON_EST_ESAL, 

TRF_HIST_EST_ESAL, and 

TRF_HIST_VOLUME_COUNT

tables.

Cumulative truck 

traffic volume 

Traffic summary statistic to measure total 

road use by heavy vehicles (FHWA 

Classes 4–13). Can be cumulated and 

reported annually or for the analysis 

duration. 

This parameter is not available in 

the LTPP database, but an annual 

cumulative total can be computed 

using data from the 

TRF_MONITOR_LTPP_LANE 

table. At some sites, this 

parameter can be computed for 

the whole pavement service life. 
41

Parameter Parameter Description LTPP Data Source 

Cumulative 

volume of 

FHWA Class 9 

trucks 

Traffic summary statistic used to measure 

road use by FHWA Class 9 trucks. Can 

be cumulated and reported annually or for 

the analysis duration. 

This parameter is not available in 

the LTPP database, but an annual 

cumulative total can be computed 

using data from the 

TRF_MONITOR_LTPP_LANE 

table. At some sites, this 

parameter can be computed for 

the whole pavement service life. 

Cumulative 

volume of heavy 

trucks (FHWA 

Classes 4 and 6–

13) 

Traffic summary statistic used to measure 

road use by vehicles in FHWA Classes 4 

and 6–13. Can be cumulated and reported 

annually or for the analysis duration. 

This parameter is not available in 

the LTPP database, but an annual 

cumulative total can be computed 

using data from the 

TRF_MONITOR_LTPP_LANE 

table. At some sites, this 

parameter can be computed for 

the whole pavement service life. 

Annual ESAL Basic traffic loading summary statistic 

that uses LEF developed from the 

AASHO Road Test to convert traffic 

stream to an equivalent number of 

18,000-lb single axle loads (based on 

expected effects on pavement 

serviceability). 

TRF_ESAL_COMPUTED, 

TRF_MON_EST_ESAL, and 

TRF_HIST_EST_ESAL tables. 

Cumulative 

ESAL 

Basic traffic loading summary statistic 

that provides ESALs accumulated over 

time (i.e., analysis or design period). 

This parameter is not available in 

the LTPP database but can be 

computed using data from 

TRF_ESAL_COMPUTED, 

TRF_MON_EST_ESAL, and

TRF_HIST_EST_ESAL tables. 

Annual GESAL GESAL is similar to ESAL but with 

pavement structure and condition inputs 

set to constant values. Therefore, GESAL 

is not affected by changes in pavement 

type, thickness, or serviceability 

parameters. 

This parameter is not available in 

the LTPP database. 

Cumulative 

GESAL 

Cumulative GESAL is a traffic loading 

summary statistic similar to cumulative 

ESAL but with pavement structure and 

condition inputs set to constant values. 

Cumulative GESAL is accumulated over 

time (i.e., analysis or design period). 

This parameter is not available in 

the LTPP database. 
42

Parameter Parameter Description LTPP Data Source 

Annual RPPIF RPPIF is computed similar to ESAL but 

with LEF estimations based on MEPDG 

simulations and normalized to fully 

loaded 34,000-lb tandem axle loads 

instead of 18,000-lb single axle loads. 

At the time of Phase I of this 

study, this parameter was under 

development by the LTPP 

program (See the 

RPPIF_NALS_ANNUAL table). 

Cumulative 

RPPIF 

Cumulative RPPIF is computed similar to 

cumulative ESAL but with LEF 

estimations based on MEPDG simulations 

and normalized to fully loaded 34,000-lb

tandem axle loads instead of 18,000-lb

single axle loads. 

This parameter is not available in 

the LTPP database. 

ATL An estimate of the total weight of all 

vehicles in FHWA Classes 4–13 applied 

to pavement during the year. 

This parameter is not available in 

the LTPP database. 

CTL An estimate of a total weight of all 

vehicles in FHWA Classes 4–13 applied 

to pavement over time. 

This parameter is not available in 

the LTPP database. 

Average GVW 

for each vehicle 

class (FHWA 

Classes 4–13) 

Average GVW for each vehicle in 

FHWA Classes 4–13, considering all 

available axle weight data. 

This parameter is not available in 

the LTPP database. 

Average GVW 

(FHWA Classes 

4–13 combined) 

Average GVW for an LTPP site for 

vehicles in FHWA Classes 4–13 

combined, considering all available axle 

weight data. 

This parameter is not available in 

the LTPP database. 

Cumulative 

number of heavy 

axle load 

applications for 

all vehicle 

classes combined 

Cumulative number (i.e., axle count) of 

axles weighing over 75 percent of Federal 

legal load limit per LTPP site, 

considering all available axle weight data. 

This parameter is not available in 

the LTPP database. 

Average percent 

of heavy axle 

load applications 

for all vehicle 

classes combined 

Average percentage of axles weighing 

over 75 percent of Federal legal load limit 

per LTPP site, considering all available 

axle weight data. 

This parameter is not available in 

the LTPP database. 

Axle loading 

category for each 

vehicle class 

(FHWA Classes

4–13) and for 

each axle 

type/group 

Axle loading category for each vehicle 

class (FHWA Classes 4–13) and for each 

axle type/group based on a percentage of 

heavy axles (i.e., light, moderate, heavy, 

very heavy). 

This parameter is not available in 

the LTPP database. 
43

Parameter Parameter Description LTPP Data Source 

Truck loading 

category 

Truck loading category is determined 

based on the percentage of heavy trucks 

in the combined GVW distribution of 

vehicles in FHWA Classes 4–13 

(i.e., very light, light, moderate, heavy, or 

very heavy). 

This parameter is not available in 

the LTPP database. 

Availability Assessment and Recommendations for Parameters Supporting Empirical and 

High-Level Pavement Management Analyses 

The LTPP program should consider expanding the available LTPP traffic loading summary 

parameters to include, at a minimum, GESAL, ATL, average GVW, axle and truck loading 

category. This will provide LTPP users with options to characterize traffic loading using singlevalue summary statistics beyond ESAL. 

LTPP Traffic Data Availability Assessment and Recommendations for Parameters 

Supporting Model Development for HPMS 

Most HPMS traffic parameters are not currently available in the LTPP database. The main 

challenge in adding these parameters to the LTPP database is that HPMS parameters are 

computed and reported for the entire roadway section, while LTPP traffic parameters are 

computed and reported for the LTPP lane only. For two-directional roads, HPMS requires all 

traffic statistics be reported as bidirectional values (i.e., traffic data collected for individual lanes 

in the same direction of travel are combined, and values characterizing each direction of travel 

are provided in the database). 

To accommodate the use of LTPP data for future enhancement of pavement performance models 

used by HPMS, such as cost allocation models, the HPMS traffic parameters listed in table 12

are needed for LTPP sites and experiments that are likely to be used for enhancements of HPMS 

models. In addition to HPMS needs, these parameters can be used for LTPP analyses where 

correlations between traffic observed in the LTPP lane and overall roadway traffic are 

considered as well as for analyses, where knowledge about pavement performance in the LTPP 

lane alone is not enough to draw conclusions about expected roadway performance. 

Table 12. HPMS traffic parameters and available LTPP data sources. 

Parameter Descriptions and Instructions LTPP Data Availability 

AADT for the 

roadway 

All AADTs must reflect DOW, seasonal, 

and axle correction factors, as necessary; 

no other adjustment factors can be used. 

Growth factors should be applied if AADT 

is not derived from current year counts. 

AADT is an average daily value 

representing all days of the reporting year. 

This parameter is available in 

TRF_HIST_VOLUME_COUNT, 

TRF_MON_EST_ESAL, and 

TRF_HIST_EST_ESAL tables. 
44

Parameter Descriptions and Instructions LTPP Data Availability 

Single-unit 

truck and bus 

AADT for the 

roadway 

Location-specific measured values are 

requested based on traffic counts taken on 

a minimum 3-yr cycle. 

This parameter is not available in 

the LTPP database but can be 

computed based on available 

data. Additional data requests or 

data expansion techniques may 

be necessary to expand data from 

the LTPP lane to the roadway. 

Peak hour 

single-unit truck 

and bus volume 

as a percentage 

of total AADT 

for the roadway 

Calculated by dividing the number of 

single-unit trucks and buses (FHWA 

Classes 4–7) during the hour with the 

highest total volume (i.e., peak hour for 

total traffic, not the hour with the most 

truck traffic) by AADT (i.e., total daily 

traffic). 

This parameter is not available in 

the LTPP database but can be 

computed based on available 

data. Additional data requests or 

data expansion techniques may 

be necessary to expand data from 

the LTPP lane to the roadway. 

Combination 

truck AADT for 

the roadway 

Combination trucks are defined as vehicles 

in FHWA Classes 8–13. AADT values are 

updated annually to represent current year 

data. 

This parameter is not available in 

the LTPP database but can be 

computed based on available 

data. Additional data requests or 

data expansion techniques may 

be necessary to expand data from 

the LTPP lane to the roadway. 

Percent peak 

combination 

trucks as a 

percentage of 

the applicable 

roadway 

section’s AADT 

This value is calculated by dividing the 

number of combination trucks during the 

hour with the highest total volume (i.e.,

peak hour for total traffic, not the hour 

with the most truck traffic) by AADT (i.e.,

total daily traffic). 

This parameter is not available in 

the LTPP database. Additional 

data requests may be necessary. 

K-factor Peak hour volume as a percentage of 

AADT for the roadway. 

This parameter is not available in 

the LTPP database but can be 

computed based on available raw 

traffic data. Additional data 

requests or data expansion may 

be necessary. 

Directional 

factor 

Percent of design hour volume (30th 

largest hourly volume for a given calendar 

year if deriving from continuous traffic 

monitoring site, or peak hour volume if 

deriving from short-term counts) flowing 

in the higher volume direction (i.e., peak 

direction). 

This parameter is not available in 

the LTPP database. Additional 

data requests or data collection 

may be necessary. 

Future AADT 

for the roadway 

A 20-yr forecast of two-way AADT (oneway for one-directional roads). This parameter is not available in 

the LTPP database. Additional 

data requests may be necessary. 
45

Parameter Descriptions and Instructions LTPP Data Availability 

Capacity of a 

roadway 

The capacity of the roadway as estimated 

by the State/Provincial or local agency. 

The capacity of a roadway facility is the 

maximum reasonable hourly rate at which 

vehicles can be expected to transverse a 

point or a uniform section of lane or 

roadway. 

This parameter is not available in 

the LTPP database. Additional 

data requests may be necessary. 

Truck location 

indicator 

(the National 

Truck Network) 

Indicator of a roadway section that is a 

component of the National Truck 

Network. 

This parameter is not available in 

the LTPP database. Additional 

data requests may be necessary. 

DOW = day of week. 

Currently, LTPP traffic data are not available to develop most of HPMS traffic parameters. To 

make these parameters available through the LTPP database, additional data should be acquired 

from State/Provincial highway agencies. This would require sending additional traffic data 

requests to participating State/Provincial agencies to identify data availability for LTPP site 

locations, additional traffic data processing by LTPP analysts, and development of the new LTPP 

traffic data tables or data fields in existing tables. Costs and benefits of these recommendations 

should be weighed against LTPP program—and broader FHWA—objectives. 

An alternative approach is presented in Estimating Design Lane Truck Volumes from HPMS 

Traffic Data for Long-Term Pavement Performance Analyses, which shows how HPMS traffic 

data parameters can be converted to design lane estimates, consistent with truck volume and 

vehicle class parameters reported for LTPP sites. With this approach, current LTPP pavement 

performance models can be used with HPMS traffic data. 

AVAILABILITY OF LTPP TRAFFIC METADATA PARAMETERS 

What Are Metadata? 

Metadata are used to reference documentation or methodologies explaining how LTPP traffic 

data were collected (including measurement accuracy of equipment) and processed (i.e., quality 

checked and summarized) and how traffic parameters were computed. These data provide 

supporting information that helps LTPP analysts understand traffic parameters and evaluate their 

applicability based on analysis objectives. LTPP database metadata parameters include codes, 

values, or references to established procedures or documentation. Available sources of LTPP 

traffic metadata are described in the following paragraphs. 

Traffic Data Collection Equipment 

The SITE_EQUIPMENT_INFO table contains information about traffic monitoring equipment 

used at LTPP sites. The SITE_EQUIPMENT_INFO table identifies the type of data collected 

(e.g., volume, classification, and weight) and the traffic monitoring equipment, type of sensors, 

and vehicle classification scheme used. The SITE_EQUIPMENT_INFO table does not provide 

information on installation, maintenance, validation, or calibration; this information is entered in 

TRF_CALIBRATION_AVC, TRF_CALIBRATION_WIM, and TRF_EQUIPMENT_MASTER 
46

tables. Equipment installation and maintenance information remains offline as a hard copy using 

Traffic Data Sheets 14 and 15. 

Accuracy of Traffic Data Collection Equipment 

The following tables contain information about calibration of traffic monitoring equipment used 

at LTPP sites, including measurement error statistics used to infer measurement accuracy: 

• TRF_CALIBRATION_AVC—calibration of automated vehicle classifiers. 

• TRF_CALIBRATION_WIM—calibration of WIM devices. 

In addition, Traffic Data Sheet 11 contains information about calibration of traffic volume 

counters. 

Traffic Data Availability 

The following tables contain information about traffic data availability (based on the number of 

days data were collected during each monitored year for which traffic data were accepted): 

• TRF_Monitor_LTPP_Ln—shows the number of days with vehicle classification data in 

the COUNT_DAYS field.

• TRF_Monitor_LTPP_Ln—shows the number of days with vehicle/axle weight data in the 

WEIGHT_DAYS field. 

Shared LTPP Traffic Sites 

For some LTPP sites, no traffic data are provided in LTPP traffic tables because one traffic site is 

used for multiple LTPP sites. The record of shared data is maintained in the SHRP_INFO table. 

The CLASS_SITE field in the SHRP_INFO table identifies the reference LTPP site for vehicle 

classification and volume information, and the WIM_SITE field identifies the reference LTPP 

site for axle loading and truck weight information. 

Linking LTPP Traffic Data Tables to LTPP Sites 

Traffic data source (or traffic site ID) is identified in the LTPP database by STATE_CODE and 

SHRP_ID fields in the SHRP_INFO table. If the traffic data source is site-specific, traffic site ID 

is the same as LTPP site ID for a given LTPP site. If the traffic data source is site-related, traffic 

site ID is different from LTPP site ID for a given LTPP site. In the latter case, the traffic data 

source is referencing a different LTPP site ID. Therefore, one traffic site ID can be a source of 

traffic data for more than one LTPP site. Thus, the number of the LTPP traffic site IDs is smaller 

in comparison to the number of LTPP site IDs provided in the LTPP database. 

Methods Used for Computing LTPP Traffic Parameters 

The following tables contain information about computing different LTPP traffic parameters: 
47

• SITE_FACTOR_INFO—contains factors used to convert samples of monitored vehicle 

volume data to annual estimates based on the practices implemented by different 

State/Provincial and local highway agencies. These data are derived from LTPP 

transmittal sheets submitted by highway agencies. 

• TRAFFIC_CLASS_CONVERT—document vehicle classification schemes used for data 

within LTAS. The TRAFFIC_CLASS_CONVERT table provides information about 

conversion from a classification scheme used by State/Provincial and local highway 

agencies to that described in the FHWA TMG, if a conversion must be done. 

• TRF_ESAL_INPUTS_SUMMARY—contains factors used in annual ESAL 

computations. 

Limitations of and Concerns Related to LTPP Traffic Metadata 

During the literature search for this study, no LTPP documents or database reference tables were 

found containing information about methods and procedures used to compute several traffic 

parameters currently available in the LTPP database, including the following: 

• MEPDG monthly and annual axle load distributions. 

• MEPDG hourly volume factors. 

• MEPDG monthly volume factors. 

• MEPDG axle per class coefficients. 

This raises a concern about the accuracy and applicability of parameters that may be based on 

partial data, where the estimation process was used to compute monthly or annual summary 

statistics, and parameters that may be based on data from poorly calibrated WIM devices.

Recommendations Related to Traffic Metadata Parameters 

The LTPP program should consider documenting the formulas and procedures used for 

computing all traffic parameters (e.g., AADT, AADTT, ESAL, annual vehicle class distribution, 

annual axle load spectra). This information should be available, upon request, to LTPP users. 

The program should also include references to these metadata sources in the Guide.

(1)

Proposed Traffic Data–Usability Indices 

Metadata can help LTPP users determine the applicability of different traffic parameters for 

specific pavement analysis applications by providing information about equipment used to 

collect data, data sampling, and the data estimation process. For example, WIM data collected 

using portable WIM systems with piezo-polymer sensors are likely to be less accurate (due to 

technology and sampling duration limitations) than data collected by a permanently installed and 

calibrated load cell WIM system. Traffic loading parameters computed based on data from a 

portable WIM system may not be applicable for direct use in MEPDG analyses but can be used 

to make an informed selection of MEPDG traffic loading defaults. Although pieces of supporting 

information can be found in several LTPP tables, there is no way for pavement researchers who 
48

are not familiar with traffic loading data collection and parameter estimation processes to judge 

how accurate and applicable traffic loading parameters computed based on these data are. 

One of the objectives of this study was to develop traffic data–usability indices to help pavement 

analysts assess the accuracy of LTPP traffic parameters and their applicability for selected 

analysis methods. 

While developing an approach for computing these indices, initially the research team 

considered indices providing an estimate of statistical confidence associated with a given 

parameter. However, given the need to combine multiple years of data into a single set of 

estimates (e.g., providing only one representative axle load spectra or ESAL value cumulated 

over multiple years per site), the need to estimate missing/unreliable values at some sites based 

on little direct knowledge, and the lack of information about equipment used to collect data and 

unknown performance characteristics of that equipment, it was concluded this approach cannot 

be successfully implemented. Therefore, the decision was made to use a set of descriptive indices 

containing parameter usage recommendations based on available information about data 

collected at each LTPP site. These indices are set on a simple ordinal scale where lower index 

values indicate better data availability and quality and wider usability of the parameter for 

pavement analysis applications. 

The proposed site-level traffic data–usability indices are described in chapter 10. These indices 

are assigned to each LTPP site included in analysis-ready CPTs described in chapter 7 and stored 

in these CPTs. These indices are intended to help LTPP database users identify sites with traffic 

data meeting their analysis criteria based on data quantity and quality—or data rationality when

data quality cannot be determined. 

In addition to site-level traffic data–usability indices, it is recommended that LTPP traffic data 

managers assign similar indices, as applicable, to each LTPP site and each month with WIM data 

reported in the LTPP database. These indices should indicate whether reported WIM data 

summaries are Type I, Type II, or unknown based on ASTM E1318-09 data accuracy criteria. 

For unknown WIM types, it would beneficial to identify whether any data rationality rules were 

broken for each month with WIM data. 

RECOMMENDATIONS BASED ON ASSESSMENT OF LTPP USER NEEDS AND 

CURRENT TRAFFIC DATA AVAILABILITY 

This section summarizes recommendations developed by the research team. 

Analysis-Ready Traffic Parameters 

Many traffic parameters used for pavement analysis were identified through the user needs 

assessment. These parameters were discussed in detail in chapter 2. Some of these parameters 

support the original research of pavement response and performance modeling, while other 

parameters are used as inputs into pavement analysis and design tools that were created as a 

result of the original LTPP research efforts. With LTPP program maturity, higher emphasis is 

being placed on data analysis and model development using LTPP data. Therefore, analysisready traffic parameters are of the most interest and use to LTPP researchers.
49

The research team recommends the LTPP program make analysis-ready traffic parameters 

described in this report available to LTPP users, to the extent possible, especially parameters 

serving a large pool of LTPP users, such as those needed for AASHTOWare Pavement ME 

Design software traffic inputs and other core parameters described in the next section.

Core Traffic Parameters 

Traffic parameters presented in tables 5 through 11 support different LTPP analysis needs. Some 

were further designated by the research team as core parameters. These core parameters,

presented in table 13, are analysis-ready traffic parameters likely to be used in many types of 

LTPP analyses and likely to have a significant effect on analysis outcomes. Parameters identified 

as new or revised in table 13 require developing new or revised LTPP data tables. Parameters 

identified as existing have missing values in LTPP data tables for some pavement in-service 

years and require estimating those values to fill in the blanks.

Table 13. Identified core traffic parameters.

Core Item 

No. Core Parameter 

Used for these 

Analyses Description 

New or Existing LTPP 

Parameter or 

Indicator 

1 AADTT LTPP empirical 

analyses where 

truck volume is 

important, networklevel pavement 

management 

analyses, and 

analyses where 

traffic is used as a 

secondary 

parameter. 

Vehicle classes 

used: 

FHWA Classes 4–

13 combined. 

Based on measured 

or estimated data 

for each year 

pavement site was 

in service up to the 

end of site 

participation in the 

LTPP experiment. 

Existing for some 

years. 

New estimated 

parameter for years 

without monitored 

or historical data 

(approximately 

25 percent of all 

years). 

2 AADTT by vehicle 

class 

LTPP empirical 

analyses where 

truck volume for 

specific truck 

classes is important, 

analyses where 

truck volume is 

aggregated for 

specific truck 

classes, and 

analyses where 

traffic is used as a 

secondary 

parameter. 

Applies to each 

FHWA 

vehicle Class 4–13. 

Based on measured 

or estimated data 

for each year 

pavement site was 

in service up to the 

end of site 

participation in the 

LTPP experiment. 

New. 
50

Core Item 

No. Core Parameter 

Used for these 

Analyses Description 

New or Existing LTPP 

Parameter or 

Indicator 

3 Annual ESAL LTPP empirical 

analyses where a 

single-value traffic 

loading statistic is 

desired, networklevel pavement 

management 

analyses, empirical 

analyses of loadrelated distresses, 

and pavement 

design using the 

AASHTO Guide for 

Design of Pavement 

Structures (1993 or 

earlier versions). 

Vehicle classes 

used: 

FHWA Classes 4–

13 combined. 

Based on measured 

or estimated data 

for each year 

pavement site was 

in service up to the 

end of site 

participation in the 

LTPP experiment. 

Existing for some 

years. 

New for years 

without monitored 

or historical data. 

4 Annual GESAL LTPP empirical 

analyses where a 

single-value traffic 

loading statistic is 

desired for 

quantifying and 

comparing traffic 

loads between sites. 

Vehicle classes 

used: 

FHWA Classes 4–

13 combined. 

Based on measured 

or estimated data 

for each year 

pavement site was 

in service up to the 

end of site 

participation in the 

LTPP experiment. 

New. 

5 Annual total load LTPP empirical 

analyses where a 

single-value traffic 

loading statistic is 

desired for 

quantifying and 

comparing traffic 

loads between sites. 

Vehicle classes 

used: 

FHWA Classes 4–

13 combined. 

Based on measured 

or estimated data 

for each year 

pavement site was 

in service up to the 

end of site 

participation in the 

LTPP experiment. 

New. 
51

Core Item 

No. Core Parameter 

Used for these 

Analyses Description 

New or Existing LTPP 

Parameter or 

Indicator 

6 MEPDG ALDF Analyses using 

MEPDG models 

and software tools 

and analyses for 

enhancing, 

developing, and 

calibrating MEPDG 

models. 

Applies to each 

FHWA vehicle 

Class 4–13 and axle 

group (single, 

tandem, tridem, and 

quad). 

One representative 

set of normalized 

ALDF for each 

LTPP site in 

MEPDG format. 

These values 

represent axle load 

distribution for a 

typical day for each 

of 12 calendar 

months. These 

factors should be 

computed 

separately for each 

vehicle class 

(FHWA Classes 4–

13) and each axle 

type or group (i.e., 

single, tandem, 

tridem, and quad). 

These parameters 

can be computed 

from measured data 

or from LTPP 

NALS default 

values. 

New or revised. 

The existing 

TRF_MEPDG_AX

_DIST_ANL table 

provides values by 

year. Further 

summarization and 

analysis is required 

to compute 

representative 

monthly MEPDG 

input in a format 

compatible with 

AASHTOWare 

Pavement ME 

Design software. 
52

Core Item 

No. Core Parameter 

Used for these 

Analyses Description 

New or Existing LTPP 

Parameter or 

Indicator 

7 MEPDG truck 

volume growth rate 

Analyses using 

MEPDG models 

and software tools; 

analyses for 

enhancing, 

developing, and 

calibrating MEPDG 

models; and all 

LTPP analyses 

requiring truck 

traffic volume 

estimation for the 

analysis period. 

Applies to each 

FHWA vehicle 

Class 4–13. 

One value 

computed 

separately for each 

vehicle class 

(FHWA Classes 4–

13) and for FHWA 

Classes 4–13 

combined from the 

first year since the 

LTPP site opened to 

traffic to each year 

with a change in 

pavement structure 

or change in 

experiment type up 

to the end of site 

participation in the 

LTPP experiment. 

New. 

8 MEPDG truck 

volume growth 

function 

Analyses using 

MEPDG models 

and software tools; 

analyses for 

enhancing, 

developing, and 

calibrating MEPDG 

models; and all 

LTPP analyses 

requiring truck 

traffic volume 

estimation for the 

analysis period. 

Applies to each 

FHWA vehicle 

Class 4-13. 

Descriptive 

parameter 

indicating either 

linear or compound 

function associated 

with the MEPDG 

truck volume 

growth rate 

parameter for each 

vehicle class 

(FHWA Classes 4–

13) and for FHWA 

Classes 4–13 

combined. 

New. 
53

Core Item 

No. Core Parameter 

Used for these 

Analyses Description 

New or Existing LTPP 

Parameter or 

Indicator 

9 MEPDG vehicle 

class volume 

distribution 

Analyses using 

MEPDG models 

and software tools; 

analyses for 

enhancing, 

developing, and 

calibrating MEPDG 

models; and 

analyses focusing 

on effects of 

different truck types 

on pavement 

performance. 

Vehicle classes 

used: 

FHWA Classes 4–

13. 

One representative 

normalized vehicle 

volume by class 

distribution in 

MEPDG format. 

These parameters 

can be computed 

from measured data 

or use NCHRP 1-

37A MEPDG 

defaults. 

New or revised. 

The existing 

TRF_MEPDG_VE

H_

CLASS_DIST table 

provides values by 

year. Further 

summarization and 

analysis is required 

to compute 

representative 

MEPDG input. 

10 MEPDG number of 

axles per truck 

Analyses using 

MEPDG models 

and software tools 

and analyses for 

enhancing, 

developing, and 

calibrating MEPDG 

models. 

Applies to each 

FHWA vehicle 

Class 4–13 and axle 

group (single, 

tandem, tridem, 

quad). 

One set of 

representative 

numbers of axles 

per truck. These 

parameters can be 

computed from sitespecific measured 

total truck and total 

axle count data or 

assigned using 

LTPP MEPDG 

default values. 

New or revised. 

The existing 

TRF_MEPDG_AX

_

PER_TRUCK table 

provides values by 

year. Further 

summarization and 

analysis is required 

to compute 

representative 

MEPDG input. 
54

Core Item 

No. Core Parameter 

Used for these 

Analyses Description 

New or Existing LTPP 

Parameter or 

Indicator 

11 MEPDG monthly 

adjustment factors 

Analyses using 

MEPDG models 

and software tools 

and analyses for 

enhancing, 

developing, and 

calibrating MEPDG 

models. Primarily 

used for the sites 

with data 

supporting analysis 

of seasonal 

changes. 

One set of 

representative 

monthly truck 

volume adjustment 

factors for each 

vehicle class 

(FHWA Classes 4–

13) for sites with 

sufficient seasonal 

truck volume data. 

A default value of 

“1” should be used 

for sites with 

insufficient 

seasonal data. 

New or revised. 

Similar parameters 

are provided in the 

TRF_MEPDG_ 

MONTH_ADJ_FA

CTR table by year. 

Further 

summarization and 

analysis is required 

to compute 

representative 

MEPDG input. 

12 Monthly NALS Development of 

LTPP traffic 

loading defaults and 

studies of loadrelated pavement 

distress prediction 

models for 

situations where 

seasonal changes in 

pavement response 

and performance 

are considered. 

Also used to 

compute sitespecific MEPDG 

ALDF. 

Monthly estimates 

of normalized 

monthly axle load 

distribution and 

total number of 

axles for each 

FHWA 

vehicle Class 4–13 

and each axle type 

(i.e., single, tandem, 

tridem, and quad) 

for LTPP sites with 

WIM systems 

performing within 

ASTM E1318-09 

accuracy 

requirements for 

Type I WIM 

sensors. Provide for 

each month with 

data satisfying 

ASTM E1318-09 

accuracy 

requirements for 

Type I WIM 

sensors. 

New or revised. 

Similar parameters 

are provided in 

table MM_AX by 

DOW but require 

further 

summarization to 

obtain normalized 

monthly axle load 

distribution for each 

month. 
55

Core Item 

No. Core Parameter 

Used for these 

Analyses Description 

New or Existing LTPP 

Parameter or 

Indicator 

13 Annual axle loading 

distribution 

Analyses involving 

development or 

enhancement or 

mechanistic–

empirical pavement 

performance 

models. Also used 

to develop default 

axle loading 

distributions or 

default axle load 

spectra for 

AASHTOWare 

Pavement ME 

Design software 

analyses. 

Annualized 

frequency 

distribution of the 

number of axle load 

applications by load 

bins (or load 

ranges) where a 

number of axle load 

applications of 

different 

magnitudes 

observed or 

estimated over a 

year is summarized 

and reported using 

predefined load 

bins. Reported 

separately for axle 

load groups (i.e., 

single, tandem, 

tridem, and quad) 

and for individual 

truck classes 

(FHWA Classes 4–

13). 

Existing for some 

years. These data 

are actual counts 

and not the 

projected full-year 

loading summary 

(i.e., computed only 

for dates when 

actual axle weight 

data were 

collected). 

This parameter is 

available in the 

YY_AX and 

TRF_MONITOR_

AXLE_DISTRIB 

tables for each year 

with WIM data. 
56

Core Item 

No. Core Parameter 

Used for these 

Analyses Description 

New or Existing LTPP 

Parameter or 

Indicator 

14 Traffic data–

usability indices 

Provides 

information about 

WIM data quantity 

and quality—or 

rationality if quality 

cannot be 

quantified—to help 

users make 

informed decisions 

about traffic loading 

data applicability 

for analysis. Used 

to identify LTPP 

sites with loading 

data acceptable as 

site-specific inputs 

(i.e., MEPDG Level 

1) and sites that 

may be better 

served by default 

values. 

Indicator on an 

ordinal scale linked 

to the table 

providing WIM 

data quantity and 

quality or 

rationality 

information for 

each indicator 

value. Also contain 

recommendations 

about applicability 

of traffic loading 

parameter for 

pavement analyses. 

New. 

DOW = day of week. 

Data Availability for Core Traffic Parameters 

The research team further assessed the availability of core parameters for LTPP traffic sites. 

Using SDR 28 (the latest SDR available at the time of the assessment), 847 unique LTPP traffic 

sites were identified. These traffic sites provide data for one or more LTPP GPS pavement sites 

and LTPP SPS sections. Results of the core data availability assessment are summarized in 

table 14. For each LTPP traffic site, traffic data availability for different traffic parameters was 

assessed for each year a site was in service. The results are reported as percentages of all siteyears either with or without a given traffic parameter. 

Table 14. Availability of core traffic parameters by LTPP traffic site and year count.

Core 

Item 

No. Traffic Parameter 

Number of LTPP Traffic Sites 

Percent of Site-Years for All 

LTPP Traffic Sites Combined 

With 

Parameter 

Without 

Parameter 

With 

Parameter 

Without 

Parameter 

1 AADTT 840 7 97 percent 3 percent 

2 AADTT by vehicle class** 0 0 54 percent 46 percent 

3 Annual ESAL 765 82 55 percent 45 percent 

4 Annual GESAL** N/A N/A 0 percent 100 percent 

5 Annual total load** N/A N/A 21 percent 79 percent 

6 MEPDG ALDF (monthly)** N/A N/A N/A N/A 
57

Core 

Item 

No. Traffic Parameter 

Number of LTPP Traffic Sites 

Percent of Site-Years for All 

LTPP Traffic Sites Combined 

With 

Parameter 

Without 

Parameter 

With 

Parameter 

Without 

Parameter 

7 MEPDG truck volume growth 

rate (percent)** 

N/A N/A N/A N/A 

8 MEPDG truck volume growth 

function**

N/A N/A N/A N/A 

9 MEPDG vehicle class volume 

distribution*

707 140 N/A N/A 

10 MEPDG number of axles per 

truck*

438 409 N/A N/A 

11 MEPDG monthly adjustment 

factors*

606 241 N/A N/A 

12 Monthly NALS or axle weight 

distributions*

290 557 10 percent 90 percent 

13 Annual axle weight 

distributions**

724 123 27 percent 73 percent 

14 Traffic loading data indicator N/A N/A N/A N/A 

*Further summarization of available LTPP traffic data is required to compute this parameter. 

**New proposed parameter. 

N/A = not applicable: for MEPDG parameters, only one set of representative values is needed instead of values 

reported by year. 

Addressing Missing Core Traffic Parameters 

Table 14 indicated several LTPP traffic sites are missing core traffic parameters identified in this 

study. Some parameters are not reported in the LTPP database, and others have missing values 

for some years. To ensure the maximum number of LTPP sites are available for pavement 

analyses, the research team recommends estimating parameters for missing years using data from 

available years or by assigning default values. The research team recommends including these 

estimated parameters and defaults in the LTPP database on an LTPP site ID level to facilitate 

traffic data extraction queries. For parameters currently not included in the LTPP database, the 

research team recommends developing new CPTs. 

Use of Traffic Estimates for Years without Traffic Data 

For most LTPP traffic sites, core traffic parameters are available only for selected years of 

pavement service life and do not cover all years from site construction to the end of site 

participation in the LTPP experiment. These parameters were developed for years when data 

were collected or when the State/Provincial highway agencies reported their estimates. Most 

analyses of long-term pavement performance require traffic inputs for each year in pavement 

service life. One of the gaps identified through the data availability assessment was a lack of 

information or tools available for analysts to estimate core traffic parameters for the years prior 

to each site’s incorporation into the LTPP experiment. 
58

Traffic Data–Usability Indices

Although a considerable amount of axle loading data were found in LTPP database tables, there 

are concerns with WIM data quality for LTPP sites not equipped with Type I WIM sensors (most 

LTPP WIM sites) and appropriate use of parameters computed based on WIM data. Specific 

concerns include parameters computed based on data collected using WIM equipment that was 

out of calibration or by WIM equipment with piezo-ceramic or piezo-polymer sensors prone to 

calibration drift over time and additional errors due to temperature change sensitivity. Only 178 

of 847 identified WIM sites have sensors satisfying ASTM E1318-09 accuracy requirements for 

Type I WIM systems, and even these systems need periodic calibration to ensure accurate data 

collection. To ensure appropriate use of WIM-based parameters, all available annual and 

monthly axle loading distributions should be assessed for data accuracy (where supporting 

measurement accuracy and sample size information is available) or data rationality (where 

supporting measurement accuracy information is not available). Based on the results of this 

assessment, data–usability indices should be assigned to all available axle loading distributions 

to provide guidance to pavement analysts on applicable data use.

Analysis-Ready Traffic Parameters for Direct Input to AASHTOWare Pavement ME 

Design Software

Another major limitation found by the research team through the review of the available LTPP 

traffic parameters is a lack of analysis-ready traffic parameters that can be used as a direct input 

into AASHTOWare Pavement ME Design software. Most input requirements for AASHTOWare 

Pavement ME Design software were developed after the LTPP traffic database and 

data-processing tools were designed. As a result, many of traffic parameters currently included in 

the TRF_MEPDG series of tables require further summarization and analysis to be used as 

AASHTOWare Pavement ME Design software inputs.

AASHTOWare Pavement ME Design software is the primary tool for conducting MEPDG 

analyses using LTPP data. Therefore, development of analysis-ready traffic parameters for direct 

input into AASHTOWare Pavement ME Design software is recommended by the research team 

as a high priority for the LTPP program. Table 15 summarizes MEPDG inputs that can be 

developed using currently available traffic data.
59

Table 15. MEPDG traffic input parameters supported by LTPP traffic data. 

MEPDG Input 

Parameter Parameter Description LTPP Data Source 

ALDF One representative NALS for a 

typical day for each calendar month 

(January to December) for a base 

design/analysis year to be provided 

for each vehicle class 

(FHWA Classes 4–13), axle group 

type (i.e., single, tandem, tridem, 

quad), and month of a calendar year. 

NALS represent the axle load 

distribution with percentage of loads 

reported by load bins. Due to the 

large size of this input parameter 

table, MEPDG software accepts 

uploads of specially formatted input 

files.

Information to compute this 

parameter is available in the LTPP 

database in daily, monthly, and 

annual axle load distributions for 

each year with data but is not readily 

applicable as an AASHTOWare 

Pavement ME Design software 

input. One representative 

distribution should be provided for 

each LTPP site. For LTPP sites 

where computation of individual 

monthly NALS is not feasible, the 

same values should be provided for 

each month of a calendar year. 

Vehicle class 

volume 

distribution 

One representative normalized 

distribution of vehicles in FHWA 

Classes 4–13 is used to represent an 

average vehicle class distribution for 

the base design/analysis year. The 

sum for all truck classes is 100.

Information to compute this 

parameter is available in the LTPP 

database in annual distributions for 

each year with data but is not readily 

applicable as an AASHTOWare 

Pavement ME Design software 

input. One representative 

distribution should be provided for 

each LTPP site. 

Monthly 

adjustment factors 

One representative set of 12 monthly 

coefficients is provided for each 

vehicle in FHWA Classes 4–13 to 

represent differences in truck 

volumes between different months 

for the base design/analysis year. The 

sum for all months for one truck class 

is 12. 

Information to compute this 

parameter is available in the LTPP 

database in monthly factors for each 

year with data but is not readily 

applicable as an AASHTOWare 

Pavement ME Design software 

input. One representative set of 

factors should be provided for each 

LTPP site. 

Hourly 

distribution 

factors 

One representative set of 24 hourly 

factors showing the percentage of 

total truck traffic for each hour. The 

sum for all hours is equal to 100. 

Values are the same for all truck 

classes and only apply to truck 

volume. This input parameter only 

applies to PCC pavements. 

Information to compute this 

parameter is available in the LTPP 

database for each year with 

monitoring data but is not readily 

applicable as an AASHTOWare 

Pavement ME Design software 

input. One representative set of 

factors should be provided for each 

LTPP site. 
60

MEPDG Input 

Parameter Parameter Description LTPP Data Source 

Number of axles 

per truck 

One representative set of values 

showing the number of axles per 

truck class and (FHWA Classes 4–

13) provides the average number of 

single, tandem, tridem, and quad 

axles for each truck class (FHWA 

Classes 

4–13). 

Information to compute this 

parameter is available in the LTPP 

database but is not readily applicable 

as AASHTOWare Pavement ME 

Design software input. One 

representative set of factors should 

be provided for each LTPP site. 

Base year 

(first year) 

AADTT (LTPP 

lane) 

One value representing the average 

annual daily volume of vehicles in 

FHWA Classes 

4–13 for the base design/analysis 

year. Use this value in the MEPDG 

in combination with the following 

MEPDG parameters: percent trucks 

in design direction is 100 percent,

and percent trucks in design lane is

100 percent. 

Information to compute this 

parameter is available in the LTPP 

database for most years for most 

LTPP sites. Additional effort is 

required from an analyst to 

determine if available AADTT 

values accurately represent desired 

base year conditions. If the base year 

is not in the LTPP database, an 

analyst must “backcast” that year’s 

AADTT from available data. 

Axle spacing for 

tandem, tridem, 

and quad axles 

Average representative axle spacing 

in inches for tandem, tridem, and 

quad axles. 

This parameter is not available in the 

LTPP database but can be computed 

from WIM IVR files. 

Average 

wheelbase length 

and percentage of 

trucks with 

wheelbases that 

fall in short, 

medium, and long 

categories 

Average wheelbase length and the 

corresponding percentages of trucks 

with wheelbases that fall in the 

following categories: short (≤12 ft), 

medium (>12 ft and ≤15 ft), and long 

(>15 ft and ≤20 ft). For multiunit and 

combination trucks, only the 

wheelbase of the truck power-unit 

(i.e., first unit) is considered. Use for 

top–down JPCP cracking model only. 

Current MEPDG model uses values 

computed for vehicles in 

FHWA Classes 8–13 only. 

This parameter is not available in the 

LTPP database but can be computed 

from WIM IVR files. 

Traffic Parameters Beneficial for LTPP Analyses but Not Included in the LTPP Database 

Table 16 lists traffic parameters identified though the LTPP user needs assessment that are not 

currently available in the LTPP database. These parameters serve many of the LTPP strategic 

analysis objectives identified in table 16. Additionally, table 16 includes a column identifying if 

the parameter is considered by LTPP users to be core and likely to have high influence on 

pavement analysis outcomes.
61

The research team recommends that the parameters listed in table 16 be made available, to the 

extent possible, in the LTPP database. Availability of these parameters will enhance LTPP traffic 

data usability. As a minimum, the research team recommends including in the LTPP database 

new traffic parameters, identified in table 16, as core traffic parameters for all LTPP sites that 

have sufficient nontraffic data for use in future pavement performance studies.
62

Table 16. Traffic parameters not included in the LTPP database but used for pavement applications. 

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

MEPDG base (first) 

year AADTT LTPP 

lane 

AADTT for the first full 

year the pavement was 

first open to traffic and 

for each year where a 

major structural 

pavement rehabilitation 

event took place. This 

value can be either 

estimated (i.e., 

backcasted from later 

data) or computed based 

on available traffic 

monitoring data. This is 

a major traffic input 

required by 

AASHTOWare 

Pavement ME Design 

software. Truck 

volumes inside 

AASHTOWare 

Pavement ME Design 

software cannot be 

estimated without this 

value. 

Use in all LTPP 

pavement performance 

analyses using the 

MEPDG method and in 

analyses for enhancing, 

developing, and 

calibrating MEPDG 

models. 

Use in all LTPP 

analyses requiring a 

time history of truck 

volumes or CTV values. 

When a base year 

AADTT value for the 

LTPP lane is used in 

AASHTOWare 

Pavement ME Design 

software, the percent 

trucks in the direction of 

travel variable and the 

percent trucks in the 

design lane values 

should be set to 100

percent. 

1, 5, 6, 7, 8 Yes LTPP data and 

supplemental sources 

(e.g., HPMS) are 

available to help 

compute this parameter. 

Analytical effort is 

required to review 

available annual 

AADTT values and 

compute base year 

values for each LTPP 

site. 
63

MEPDG vehicle class

growth function 

Either a linear or 

compound growth curve 

that best describes 

changes in truck volume 

over the pavement’s 

service life from the 

first year the pavement 

was open to traffic to 

the end of the 

experiment or to the last 

year with data (if still in 

the experiment) and, 

where applicable, from 

each year where major 

structural pavement 

rehabilitation took place 

to the end of that 

experiment or to the last 

year with data (if still in 

the experiment). This is 

a major traffic input 

required by 

AASHTOWare 

Pavement ME Design 

software. Truck 

volumes inside 

AASHTOWare 

Pavement ME Design 

software cannot be 

estimated without this 

function. This 

parameter is also used 

to estimate CTV. 

Use in all LTPP 

pavement performance 

analyses based on the 

MEPDG method and in 

analyses for enhancing, 

developing, and 

calibrating MEPDG 

models. 

Use in all LTPP 

analyses requiring

estimates of truck 

volumes by year or 

CTV over an analysis 

period. 

1, 5, 6, 7, 8 Yes LTPP data and 

supplemental sources 

(e.g., HPMS) are 

available to compute 

this parameter. 

Analytical effort is 

required to develop the 

appropriate growth 

trend for each LTPP 

site. 
64

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

MEPDG vehicle class 

growth rate 

The annual growth rate 

(percent) for each truck 

class (FHWA Classes 

4–13) from the year 

pavement was first open 

to traffic to the end of 

the experiment or to the 

last year with data (if 

still in the experiment) 

and, where applicable, 

from each year where 

major structural 

pavement rehabilitation 

took place to the end of 

that experiment or to the 

last year with data (if 

still in the experiment). 

This is a major traffic 

input required by 

AASHTOWare 

Pavement ME Design 

software. Truck 

volumes inside 

AASHTOWare 

Pavement ME Design 

software cannot be 

estimated without this 

function. 

Use in all LTPP 

pavement performance 

analyses based on the 

MEPDG method and in 

analyses for enhancing, 

developing, and 

calibrating MEPDG 

models. 

Use in all LTPP 

analyses requiring 

estimates of truck 

volumes by year or 

CTVs over an analysis 

period. 

1, 5, 6, 7, 8 Yes LTPP data and 

supplemental sources 

(e.g., HPMS) are 

available to compute 

this parameter. 

Analytical effort is 

required to develop the 

appropriate growth 

trend for each LTPP 

site. 
65

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

MEPDG vehicle class 

volume distribution 

One normalized 

distribution of vehicles 

in FHWA Classes 4–13 

is used to represent 

average vehicle class 

distribution for the base 

design/analysis year. 

The sum for all truck 

classes is 100. This is a 

major traffic input 

required by 

AASHTOWare 

Pavement ME Design 

software. Truck 

volumes and axle loads 

associated with 

individual vehicle 

classes inside 

AASHTOWare 

Pavement ME Design 

software cannot be 

estimated without this 

value. 

Use in all LTPP 

pavement performance 

analyses based on the 

MEPDG method and in 

analyses for enhancing, 

developing, and 

calibrating MEPDG 

models.

Use in LTPP analyses 

where truck type and 

configuration effect 

pavement response and 

performance. 

1, 5, 6, 7, 8 Yes Annual values are 

available for some years 

in the 

TRF_MEPDG_VEH_ 

CLASS_DIST table. 

Analytical effort is 

required to review 

available annual values 

and compute one 

representative 

distribution for each 

LTPP site. 
66

MEPDG ALDF One representative 

normalized distribution 

(i.e., axle load 

spectrum) is provided 

for each vehicle class 

(FHWA Classes 4–13), 

axle group type (i.e., 

single, tandem, tridem, 

quad), and each 

calendar month 

(January to December) 

to represent loads for a 

typical day for each 

calendar month for a 

base design/analysis 

year for each LTPP site. 

Due to the large size, 

the primary method of 

uploading this input 

parameter into 

AASHTOWare 

Pavement ME Design 

software is by a 

specially formatted 

input file. This is a 

major traffic input 

required by 

AASHTOWare 

Pavement ME Design 

software. Truck loads 

associated with 

individual vehicle 

classes inside 

Use in all LTPP 

pavement performance 

analyses based on the 

MEPDG method and in 

analyses for enhancing, 

developing, and 

calibrating MEPDG 

models.

Use to compute ESAL 

and other summary 

loading statistics. 

Use to assess how 

heavy truck loads are 

for a given LTPP sites. 

Use for load-related 

pavement response and 

performance analysis. 

1, 5, 6, 7, 8 Yes Annual values are 

available in the 

TRF_MEPDG_AX_DI

ST_ANL table, and 

monthly DOW 

summaries are available 

in the MM_AX table to 

compute representative 

values for the 

AASHTOWare 

Pavement ME Design 

software input. 

Analytical effort is 

required to review 

available annual and, 

where available, 

monthly values and 

compute base year 

values for each LTPP 

site. Significant effort is 

needed to identify and 

remove data collected 

by low accuracy or 

uncalibrated WIM 

systems from the 

summarization effort.

Default values are 

assigned to sites with 

insufficient or poor 

site-specific WIM data. 
67

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

AASHTOWare 

Pavement ME Design 

software cannot be 

estimated without this 

value. 

MEPDG number of 

axles per truck 

One set of 

representative values 

describing number of 

axles per truck is used 

to estimate the number 

of axle loads for each of 

the 10 vehicle classes 

(FHWA Classes 4–13) 

for each LTPP site. This 

is a major traffic input 

required by 

AASHTOWare 

Pavement ME Design 

software. Number of 

axle loads associated 

with individual vehicle 

classes cannot be 

estimated without this 

value inside 

AASHTOWare 

Pavement ME Design 

software. 

Use in all LTPP 

pavement performance 

analyses based on the 

MEPDG method and in 

analyses for enhancing, 

developing, and 

calibrating MEPDG 

models. 

1, 5, 6, 7, 8 Yes Annual values are 

available in the 

TRF_MEPDG_AX_PE

R_

TRUCK table to 

compute representative 

values for the 

AASHTOWare 

Pavement ME Design 

software input. Also, 

these parameters can be 

computed from sitespecific measured total 

truck and total axle 

count data or assigned 

using LTPP MEPDG 

default values. 
68

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

MEPDG monthly 

adjustment factors 

One set of 

representative monthly 

truck volume 

adjustment factors is 

provided for each 

vehicle class (FHWA 

Classes 4–13) for each 

LTPP site with 

sufficient seasonal truck 

volume data. A default 

value of “1” for each 

month is used for sites 

with insufficient 

seasonal data. 

Use for analyses using 

MEPDG models and 

software tools and for 

analyses for enhancing, 

developing, and 

calibrating MEPDG 

models for LTPP sites 

with data supporting 

analysis of seasonal 

changes. 

1, 3, 5, 6, 7, 8 No Similar parameters are 

provided in the 

TRF_MEPDG_MONT

H_

ADJ_FACTR table by 

year. Further 

summarization and 

analysis is required to 

compute one 

representative 

distribution for each 

LTPP site. 

MEPDG hourly 

distribution factors 

One representative set 

of 24 hourly factors 

showing the percentage 

of total truck traffic 

(FHWA Classes 4–13 

combined) occurring in 

each hour. The sum for 

all hours is equal to 100. 

This distribution is used 

to prorate truck volumes 

for hours when concrete 

slab curling or warping 

is observed. 

Use for analyses using 

MEPDG models and 

software tools and for 

analyses for enhancing, 

developing, and 

calibrating MEPDG 

models for LTPP sites 

with JPCP. 

1, 5, 6, 7, 8 No Information to compute 

this parameter is 

available in the 

TRF_MEPDG_HOURL

Y 

_DIST table for each 

year with monitored 

truck volume. Further 

summarization is 

required to compute one 

representative set of 

factors for each LTPP 

site. 
69

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

Axle spacing for 

tandem, tridem, and 

quad axles 

One representative 

average axle spacing in 

inches is input for 

tandem, tridem, and 

quad axles. This statistic 

is used to calculate 

pavement responses in 

mechanistic models. 

Use for mechanisticbased pavement 

response and 

performance modeling, 

including the MEPDG 

method.

1, 5, 6, 7, 8 No This parameter is not 

available in the LTPP 

database but can be 

computed from WIM 

IVR files stored in the 

LTPP database. 

Average wheelbase and 

corresponding 

percentage of vehicles 

in FHWA Classes 

8–13 with a 

tractor-semitrailer 

power-unit in short, 

medium, and long 

wheelbase categories, 

plus additional 

percentage of other 

trucks with axle spacing 

in these ranges 

Average wheelbase and 

corresponding 

percentages of trucks 

with a 

tractor-semitrailer 

power-unit that falls in 

the following categories 

(determined based on 

concrete slab length): 

short (≤12 ft), medium 

(>12 ft and ≤15 ft), and 

long (>15 ft and ≤20 ft) 

axles. Also included is 

the percentage of other 

trucks with axle spacing 

in these ranges, 

excluding axle spacing 

within multi-axle 

groups. 

Use for mechanisticbased pavement 

response and 

performance modeling 

of JPCP, including the 

MEPDG method. 

1, 8 No Significant data 

processing effort is 

needed to extract and 

summarize data from 

WIM IVR files stored in 

the LTPP database. 
70

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

Operational speed Posted speed limit in 

mph or average speed of 

heavier trucks (FHWA 

Classes 6–13) through 

the project limits. 

Use for mechanisticbased pavement 

response and 

performance modeling 

of AC pavements, 

including the MEPDG 

method.

1, 2, 5, 6, 7, 8 No This information can be 

obtained from IVR 

records submitted using 

the TMG IVR record 

format or from LTPP 

Inventory Sheets. 

MEPDG base (first) 

year two-way AADT 

(AASHTOWare 

Pavement ME Design 

software base year twoway AADT) 

Two-way AADT for the 

first full year pavement 

was open to traffic and 

for each year where 

major structural 

pavement rehabilitation 

took place. This value 

can either be estimated 

(i.e., backcasted from 

later data) or computed 

based on available 

traffic monitoring data. 

An alternative 

parameter is MEPDG 

base (first) year 

AADTT for the LTPP 

lane. 

Use in combination 

with the percentage of 

trucks to estimate 

AADTT. When 

available, AADTT 

parameter is preferred 

over AADT for LTPP 

studies.

1, 8 No This parameter is used 

only when LTPP lane 

AADTT values are not 

available. 

Additional analytical 

effort is required to 

determine if available 

AADT values 

accurately represent, or 

can be used to 

characterize, base year 

conditions. 
71

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

Percent of trucks in 

AADT volume 

Average percent of 

trucks in the two-way 

AADT estimate. 

Use in combination 

with the two-way 

AADT parameter to 

estimate AADTT.

When available, 

AADTT parameter is 

preferred over AADT 

and percent trucks. 

1, 8 No This parameter is used 

only when LTPP lane 

AADTT values are not 

available. 

Percent of trucks in 

design direction 

Average percent of 

trucks in the design 

direction (i.e., direction 

of travel of the LTPP 

lane) for the base 

design/analysis year. 

Use in combination 

with the two-way 

AADT parameter to 

estimate LTPP lane 

AADTT inside 

AASHTOWare 

Pavement ME Design 

software. 

1, 8 No This parameter is used 

only when LTPP lane 

AADTT values are not 

available. Raw data may 

exist to compute this 

parameter. 

Percent of trucks in 

design lane 

The percentage of 

vehicles in the two-way 

AADT estimate that are 

trucks (i.e., FHWA 

Classes 4–13). 

Use in combination 

with the two-way 

AADT, to compute 

AADTT for the LTPP 

lane. Use in high-level 

analyses to identify 

LTPP sites with similar 

levels of truck usage. 

1, 5, 6, 8 No This parameter is 

needed primarily when 

LTPP lane AADTT 

values are not available. 

Information to compute 

this parameter is 

available in the YY_CT

table.
72

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

MEPDG mean and 

standard deviation of 

wheel location 

Mean distance and its 

standard deviation in 

inches from the outer 

edge of the wheel to the 

pavement marking. 

These are 

AASHTOWare 

Pavement ME Design 

software inputs. 

Use in combination 

with axle width, axle 

spacing, and wheelbase 

parameters to model 

location of the load on 

the pavement. Use for 

mechanistic pavement 

response and 

performance analysis 

and modeling. 

1, 5, 7, 8 No This information is not 

collected by the LTPP 

program. It can be 

collected using specially 

configured quartz-piezo 

WIM sensor arrays or 

from video samples. 

Dual tire spacing This is an 

AASHTOWare 

Pavement ME Design 

software input. 

Use in mechanistic 

pavement response and 

performance analysis 

and modeling to 

determine the location 

of the load on the 

pavement. 

1, 5, 7, 8 No This information is not 

collected by the LTPP 

program. It can be 

collected using specially 

configured quartz-piezo 

WIM sensor arrays or 

from video samples. 

Tire pressure One value representing 

hot tire inflation 

pressure. This is an 

AASHTOWare 

Pavement ME Design 

software input. 

Use to compute the 

pavement/tire 

interaction area for 

mechanistic pavement 

response and 

performance analysis. 

1, 5, 7, 8 No This information is not 

collected by the LTPP 

program. 

Average axle width Distance in feet 

between the two outside 

edges of an axle. Use 

for rigid pavement 

analysis only. This is an 

AASHTOWare 

Pavement ME Design 

software input. 

Use in mechanistic 

pavement response and 

performance analysis 

and modeling to 

determine the location 

of the load on the 

pavement. 

1, 5, 7, 8 No This information is not 

collected by the LTPP 

program. It can be 

collected using 

staggered WIM sensor 

arrays or from video 

samples or truck fleet 

surveys. 
73

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

Annual GESAL Based on the measured 

or estimated axle 

loading data in the 

LTPP lane for FHWA 

Classes 4–13 combined.

This value is computed 

for each year the 

pavement site was in 

service up to the end of 

the site’s participation 

in the LTPP experiment. 

Use in LTPP empirical 

analyses where a singlevalue traffic loading 

statistic is desired, and 

also for quantifying and 

comparing traffic loads 

between sites. 

1, 5, 6, 7 Yes Computed using 

available axle load 

spectra, AADTT in the 

test lane, and truck 

growth trends.

ATL Based on measured or 

estimated GVW data in 

the LTPP Lane for 

FHWA Classes 4–13 

combined. This value is 

computed for each year 

the pavement site was in 

service up to the end of 

the site’s participation 

in the LTPP experiment. 

Use for LTPP empirical 

analyses where a singlevalue traffic loading 

statistic is desired and 

for quantifying and 

comparing traffic loads 

between sites. 

1, 5, 6, 7 Yes Computed using 

available GVW data 

and truck growth trends. 
74

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

Monthly NALS Normalized monthly 

axle load distribution 

and total number of 

axles for each vehicle 

class (FHWA Classes

4–13) and each axle 

type (i.e., single, 

tandem, tridem, and 

quad) for LTPP sites 

with WIM systems 

performing within 

ASTM E1318-09 

accuracy requirements 

for Type I WIM 

sensors. Provided for 

each month with data 

satisfying ASTM 

E1318-09 accuracy 

requirements for Type I 

WIM sensors. 

Use for computing 

traffic loading defaults 

and

mechanistic–empirical 

studies of load-related 

pavement distresses 

when seasonal changes 

in pavement response 

and performance are 

considered. 

1, 5, 6, 8 Yes Similar parameters are 

provided in the 

MM_AX table by DOW 

but require further 

summarization to obtain 

normalized monthly 

axle load distributions 

for each month. 

Traffic 

data–usability 

indicators*

An ordinal scale 

indicator summarizing 

the quantity, quality,

and recommended 

applicability of WIM 

data and computed 

traffic load estimates. 

Use as an aid to help 

users make informed 

decisions about traffic 

data or parameter 

applicability for 

specific, detailed 

analyses. 

1, 5, 6, 7, 8 Yes These indicators 

identify LTPP sites with 

loading data acceptable 

as Level 1 (i.e., sitespecific) MEPDG 

inputs and sites that 

may be better served by 

default values. 
75

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

AADTT by vehicle 

class 

AADTT by vehicle 

class for FHWA Classes 

4–13. Based on 

measured or estimated 

data for each year a 

pavement site was in 

service up to the end of 

the site’s participation 

in the LTPP experiment. 

Use in LTPP empirical 

analyses where truck 

volume for specific 

truck classes is 

important, analyses 

where truck volume 

needs to be aggregated 

to simpler truck classes, 

and analyses where 

truck volume is used as 

a secondary parameter. 

1, 5, 6 No LTPP data are available 

for parameter 

computation.

Percent trucks in LTPP 

Lane 

Percentage of vehicles 

in the LTPP lane that 

are trucks 

(FHWA Classes 4–13). 

Use in combination 

with AADT to compute 

AADTT for the LTPP 

lane. Used in high-level 

analyses to identify 

LTPP sites with similar 

levels of truck usage. 

1, 5, 6, 8 No Information to compute 

this parameter is 

available in LTAS table 

the YY_CT.

Cumulative truck traffic 

volume 

Traffic summary 

statistic used to measure 

total road use by 

vehicles in FHWA 

Classes 4–13, from the 

day the road opened to 

traffic to the end of 

service life or the end of 

the site’s participation 

in the LTPP experiment. 

Should be accumulated 

and reported annually. 

Use in empirical 

analyses of effects of 

traffic on nonstructural 

pavement distresses. 

Use as a supplemental 

input in analyses of 

effects of traffic on 

structural pavement 

response and 

performance. 

1, 5, 6 No Computed using 

available truck volume 

data and truck growth 

trends for vehicles in 

FHWA Classes 4–13. 
76

Parameter Description LTPP Analysis Use

LTPP Strategic 

Analysis 

Objective 

Core 

Parameter 

Identifier Comment 

Cumulative volume of 

FHWA Class 9 trucks 

Traffic summary 

statistic used to measure 

road use by FHWA 

Class 9 vehicles from 

the day the road opened

to traffic to the end of 

service life or the end 

the site’s participation 

in the LTPP experiment. 

Should be accumulated 

and reported annually. 

Use as a heavy truck 

traffic indicator in 

empirical analyses of 

pavement performance 

for LTPP sites with 

dominant 

FHWA Class 9 trucks. 

1, 5, 6 No Computed using 

available truck volume 

and truck growth trends 

for vehicles in FHWA 

Class 9. 

Cumulative volume of 

heavy trucks (FHWA 

Classes 4 and 6–13) 

Traffic summary 

statistic used to measure 

road use by vehicles in 

FHWA Classes 4 and

6–13 from the day the 

road opened to traffic to 

the end of service life or 

the end of the site’s 

participation in the 

LTPP experiment. 

Should be accumulated 

and reported annually. 

Can be used as an input 

in empirical analyses of 

pavement performance 

when traffic loading 

data are not available. 

1, 5, 6 No Computed using truck 

volume data and truck 

growth trends for 

vehicles in FHWA 

Classes 4 and 6–13.

*Metadata 

DOW = day of week.
77

CHAPTER 4. ALTERNATIVE TRAFFIC DATA SOURCES 

PURPOSE OF ALTERNATIVE DATA SOURCES 

In this study, the research team evaluated the availability and applicability of alternative traffic 

data sources to address the limitations in LTPP traffic data quality or availability. This chapter 

includes assessment of available alternative traffic data sources and recommendations for using 

data from non-LTPP sources. 

Because pavement performance is site-specific, using available, valid, site-specific data is the 

analysts’ first choice for creating summary traffic statistics. Unfortunately, there are times when 

no data are present in the LTPP database for a specific LTPP site or data present are unreliable 

and need replacing by other data sources. This chapter discusses the availability of other data 

sources that can be used to help supply data for key traffic data items or insight that can be used 

to identify the best defaults to use when site-specific data are not available. Basic traffic data 

items for which alternative sources of data are explored in this chapter include the following: 

• AADT. 

• AADTT. 

• Truck volumes by FHWA vehicle classification. 

• Hourly truck-volume distributions. 

• Truck-weight (i.e., axle load) distributions. 

Each of these basic traffic data items are discussed in the following sections. In some cases, one 

dataset may serve as an alternative data source for more than one of these basic traffic data items.

Key aspects of each of these sources are discussed the first time they are recommended. This 

discussion is referenced, but not repeated, each time that source is considered as an alternative 

data source. 

AADT

AADT is the most commonly used measure of traffic on a roadway. It is not especially 

significant for most pavement analyses, but engineers often use it to get a quick idea of the size 

or importance of the roadway with which they are working. AADT is also used for many 

traditional pavement analyses as a way of quickly computing approximate ESAL loading values. 

In these cases, AADT is multiplied by the percentage of trucks and then the State/Province’s 

ESAL/truck statistic to estimate daily pavement loads. Having the AADT statistic in the database 

is of low to moderate importance. 

Most LTPP sites have at least a few years of AADT statistics either supplied by the 

State/Provincial highway agency or computed from traffic monitoring data collected specifically 

for the LTPP program. These values can be compared against each other as an initial QA test. 

Best Alternative Data Source 

If additional AADT values are needed, such as to fill gaps in temporal data coverage, the best 

source for obtaining these data is the HPMS. The HPMS dataset is submitted annually to FHWA 
78

by each State for computing statewide vehicle-miles traveled, which is a statistic used in the 

allocation of the Federal Highway Trust Fund. Because of their importance, data used to 

compute AADT in the HPMS undergo a robust QA process examining the methodology in 

which AADT values are computed and ensures unusual volume trends are supported by valid 

data. 

Unfortunately, obtaining annual HPMS data files takes considerable effort and coordination, and 

identifying the appropriate HPMS traffic section from which to obtain AADT values is also 

complex.

The process developed under this study to link LTPP test sites to HPMS sections uses 

geographic information system (GIS) functionality, which matches the latitude and longitude of 

the LTPP test site to the GIS road network for HPMS.(18)

This process is as follows: 

The LTPP test location was linked to the HPMS segment, using the ArcGIS “Near” 

command to calculate which specific segment of the HPMS national coverage was 

associated with a specific LTPP site (represented as a point). The Near command gives 

the distance and the ArcGIS internal unique identifier (Object ID) to the nearest feature 

of interest (i.e., roadway segment). The search can be improved by also including the 

Route Number obtained from the LTPP INV_ID table.

The table of the HPMS dataset was then “Joined” to the LTPP points using this common 

unique identifier (Object ID). 

LTPP sites within 50 ft of an HPMS line segment for the correct Route ID were assigned 

to that segment.

Beginning and ending mileposts for that HPMS segment were then determined from the 

HPMS database along with the route name, as these end points identify the HPMS 

segment and are used to extract data from the HPMS dataset and identify the same HPMS 

road segment from earlier years of HPMS data. 

If no segment was identified, the dataset was queried for sites farther than 50 ft from the 

closest HPMS line segment because these LTPP sites do not directly associate with the 

HPMS GIS “road geometry” due to differences in the GIS line work for HPMS and the 

latitude/longitude values associated with the LTPP test section. 

One issue with the above process is that the current HPMS GIS shape file is nondirectional. 

Thus, when a roadway has separate alignments for opposite directions of travel, the HPMS shape 

file only shows segments in one direction. In many cases, the LTPP site is located on the 

alignment not used in the HPMS shape file (i.e., in the opposite direction of travel). 

The initial automated process failed to locate 330 sites because the LTPP location point was 

farther than 50 ft from the HPMS segment GIS line. These 330 sites were visually inspected on a 

GIS map, as illustrated in figure 4, and assigned to specific HPMS roadway sections. Visual 

inspection allows for easy detection of when the HPMS line segment follows one side of a 

divided roadway, but the LTPP study site is on the other side of that divided roadway. 
79

© 1995-2020 Esri. 

Screen capture performed by University of Washington from ArcGIS software produced by ESRI and used 

under license. Photomap layer source credited within that product to: Esri, DigitalGlobe, GeoEye, Earthstar 

Geographics, CNES/Airbus, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopt, and the 

GIS User Community. Additional HPMS and LTPP layer data obtained from FHWA and incorporated 

using ArcGIS. 

Figure 4. Photo. Example of an LTPP test section located on a divided roadway where that 

side of the roadway is not used in the HPMS shape file. 

LTPP sites were flagged if they appeared not to be associated with an HPMS segment. 

Nine sites were flagged. These all had LTPP identifiers of “0800” or “A800” and were 

not found in the InfoPave database. These nine sites were SPS-8 LTPP environmental test 

sections not subject to traffic loads that only experience deterioration due to 

environmental effects. Therefore, they were not likely to be located on roads included in 

the HPMS road network. An example of one of these SPS-8 locations is shown in 

figure 5. 
80

© 1995-2020 Esri. 

Screen capture performed by University of Washington from ArcGIS software produced by ESRI and 

used under license. Photomap layer source credited within that product to: Esri, DigitalGlobe, GeoEye, 

Earthstar Geographics, CNES/Airbus, USDA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopt, 

and the GIS User Community. Additional HPMS and LTPP layer data obtained from FHWA and 

incorporated using ArcGIS. 

Figure 5. Photo. Example of an SPS-8 site location relative to the 

nearest HPMS roadway segment.

Sites were also flagged if visual inspection showed the LTPP site was associated with an 

incorrect HPMS segment due to the LTPP point feature being closer to an HPMS segment on 

which it is not located. This was done by cross-checking the route name with the InfoPave 

database. Seven sites fell into this category. These seven sites were joined by their unique 

identifier to the correct HPMS segment, as determined through visual inspection. 

Once these steps were performed, it was possible to extract AADT values from the HPMS 

database for all sites. If additional years of HPMS data were needed to examine how the State 

reported changes in traffic volume levels over time on a given HPMS segment, additional years 

of HPMS data need to be obtained from FHWA HPMS databases created for these years. These 

data are available dating back to the 1970s. Data were retrieved from those additional HPMS 

files based on the HPMS segment end points and route name taken from the GIS database. 

Other Options 

The other option for obtaining information on AADT is to contact each State agency and request 

access to their archived AADT data. Although this is possible, it is time-consuming and places 
81

considerable burden on State highway agencies. In addition, many States maintain only a limited 

number of years of traffic data online. Obtaining AADT estimates from State agencies for years 

prior to the 1990s could easily entail retrieving and transcribing data from hardcopies of old 

reports. This option is not recommended. 

AADTT 

AADTT is used to categorize the level of truck traffic on a roadway, which is one way of quickly 

determining the type of pavement needed for that roadway. AADTT is also used for many 

traditional pavement analyses as a way to quickly compute approximate ESAL loading values. 

AADTT is a key truck volume input used by AASHTOWare Pavement ME Design software to 

estimate the number of axle loads. Due to its multiple uses, AADTT is of high importance to the 

LTPP program. Fortunately, the majority of LTPP sites have multiple years of AADTT values 

from traffic monitoring performed specifically for the LTPP program or supplied by State 

highway agencies. Only about 3 percent of site-years of AADTT values are missing from the 

LTPP database for those years during which the LTPP sites are part of the LTPP experiment. 

Best Alternative Data Source 

If a review of AADTT data in the LTPP database indicates that the data present are of 

questionable accuracy, or if additional data are required to determine AADTT for missing years 

for some sites at LTPP test sections, the best data source for this statistic is the HPMS dataset. 

The HPMS dataset includes both AADT and a percent truck variable. When combined, these two 

variables compute AADTT for two-way traffic. 

By following the steps described in the AADT section for linking LTPP test site locations to 

specific HPMS roadway sections, and by retrieving annual HPMS samples from FHWA, it is 

possible to compute AADTT for any year required. To compute the AADTT value for the LTPP 

lane, the roadway AADTT needs to be adjusted based on direction and lane distribution of truck 

traffic. 

Other Options 

The other option for obtaining information on AADTT is to contact each State agency and 

request access to their archived AADTT data. Unlike AADT, the majority of AADTT values are 

present in the LTPP database. Consequently, it may be easier and faster to use existing data to 

estimate most of the missing AADTT values. 

Roughly 85 LTPP traffic sites are missing AADTT values, but only 20 of those sites are missing 

more than 3 yr of data, and only 12 sites are missing more than 5 yr. Several of the sites with 

multiple missing years of data are from the same State. Many States have no LTPP sites missing 

years of AADTT data, and those States would not need to be contacted. This would limit the 

time required to obtain data in this manner. 

It may be easier (for those few sites requiring data from outside of the LTPP database to estimate 

missing AADTT data) to contact State highway agencies instead of obtaining and working 

through HPMS datasets. However, if HPMS datasets were already downloaded and processed,
82

the HPMS process is the best approach, as the difficult and time-consuming tasks would already 

be completed. 

TRUCK VOLUMES BY FHWA VEHICLE CLASSIFICATION 

Annual truck volumes by vehicle classification are used with axle weight distribution factors to 

compute pavement loads for traffic design. Most LTPP sites have some vehicle classification 

data collected as part of the LTPP traffic data collection effort. These data are the best source of 

truck volume distributions. However, if QA and data consistency reviews indicate that LTPP 

data are of suspect quality, it will be necessary to examine alternative data sources to confirm the 

applicability of those data and/or to develop reliable default values. 

Annual truck volumes by vehicle classification data are needed for computing a number of 

different truck volume parameters, including average annual daily truck volume, total truck 

volume by class for each year for the life of a pavement, monthly truck volume needed to 

measure seasonal traffic loads, and time of day (hourly) distributions of truck volume. 

Best Alternative Data Source 

There are two important alternative sources of data describing truck volume by vehicle 

classification on specific roadway sections. Both alternative datasets are necessary if missing 

data are estimated because both datasets have different characteristics meeting different LTPP 

needs. 

The HPMS is the best available alternative data source for examining the total number of trucks 

using roadways included in the LTPP program. However, the HPMS does not include sufficient 

detail on travel by time of day or on the distribution of travel between different classes of trucks. 

For that information, the FHWA Travel Monitoring Analysis System (TMAS) database is the 

best alternative data source. 

HPMS Applicability 

The HPMS dataset includes the State highway agency’s best estimate of total traffic volume 

(AADT) and the percentage of that traffic consisting of trucks on the road segment containing 

the LTPP test section. (Estimates of truck use are of limited accuracy prior to the 1990s, as many 

States did relatively little truck counting prior to that time; HPMS truck volume estimates 

become increasingly more reliable around 2000.) 

The current HPMS dataset structure also divides truck percentage into single-unit and 

combination trucks. Having estimates of both single-unit and combination trucks gives further 

insight into the expected mix of trucks, as measured by the State highway agency. The HPMS 

does not provide truck volume for each FHWA vehicle class, nor does it describe the time of day 

(hourly) distribution of truck volumes.

TMAS Applicability 

To gain better insight into the distribution of trucks between different FHWA truck vehicle 

classes, it is necessary to look at more detailed data than those submitted to the HPMS. The best 
83

national data source for providing additional detailed truck volume insight is the TMAS. FHWA 

encourages States to routinely upload permanent count data for volume, vehicle classification, 

and truck weight to FHWA’s TMAS database, which provides a mechanism for QA testing as 

well as analyzing and sharing data. 

From TMAS, it is relatively easy to obtain vehicle class distributions for all sites submitted by a 

State. These distributions can then be analyzed for statewide trends in classification that can be 

used to evaluate the reasonableness of data submitted to the LTPP database. For example, TMAS 

data can be used to determine whether major changes in classification distributions are occurring 

at other sites in the State or if such changes are observed only at the LTPP site, which would 

likely be caused by sensor or software issues at that test site.

If necessary, TMAS data can also be used to develop default vehicle classifications. Although 

TMAS is not a preferred source of data for these defaults, it may be needed for States that have 

submitted relatively little volume by classification data as part of their routine monitoring 

program. TMAS can also examine classification data and patterns across State lines.

MONTHLY TRUCK VOLUMES BY FHWA VEHICLE CLASS 

Monthly truck volumes by vehicle classification are used with axle weight distributions to 

compute differences in seasonal pavement loading for pavement performance analysis and 

design. Given its narrow analysis application, this parameter is moderately important. 

As part of the LTPP program, each State placed a permanent, continuously operating vehicle 

classifier near the LTPP test site. Data from these classifiers can be used to directly measure 

monthly truck volumes by class.

Where continuous data are not available, it is necessary to compute monthly adjustment factors 

for each class of trucks and multiply those factors by the AADTT values available for each year. 

The best source of monthly adjustment factors is from a site-specific counter at the LTPP site. It 

is possible to use 1 yr (or more) of data to compute a constant monthly adjustment factor, which 

can be applied against AADTT when actual monthly truck volume data are not available. This 

process is used within AASHTOWare Pavement ME Design software to estimate monthly truck 

volume by vehicle classification. Where site-specific data are not available in the LTPP database 

(the DD_CL_CT table), the recommended approach is to use data from other LTPP sites within 

that State—or data from a nearby counter on that same road but in a different State—to 

determine the likely monthly truck volume pattern by vehicle class. If a nearby source of 

monthly truck volume data is not available and other LTPP data for that State and region exhibit 

strong truck travel patterns, an average statewide monthly pattern is computed and made 

available for use at the site without site-specific data with appropriate metadata describing the 

limitations of those adjustments.

Best Alternative Data Source 

The only national data source that can support the computation of monthly truck volume factors 

is FHWA’s TMAS. TMAS has a reporting function that produces truck volume reports by month 

and vehicle class, from which monthly truck volume factors can be computed. Because TMAS 

contains multiple years of data, it is also possible to use TMAS to analyze changes in monthly 
84

factors over time, and thus compute composite multiyear monthly factors (i.e., one set of 

monthly adjustment factors that can be entered into AASHTOWare Pavement ME Design 

software). 

The difficulties of using TMAS are gaining access to the system and determining which TMAS 

reporting site(s) should be associated with a given LTPP test site. The latter difficulty is a 

function of where geographically TMAS data are available versus where the LTPP site needing 

the application of monthly adjustment factors is located. TMAS data may be available from a 

data collection point on the same road that is too far away to be considered a site-specific or siterelated to the LTPP data collection site (i.e., located on the same road but away from the test 

site). Where such a site-related location is not present in TMAS, data from multiple TMAS sites 

can be averaged and used to estimate monthly patterns for the LTPP site. 

A significant part of the work of developing monthly factors is correctly grouping sites that can

be averaged to create a monthly pattern to apply at an LTPP site not directly associated to the 

sites in that group (i.e., the LTPP site does not carry the same traffic stream). The best group of 

TMAS sites predicting monthly truck volume patterns is likely to be created differently for 

different LTPP sites. Groups can be formed using sites from the same route or geographic 

region, or for sites in the same geographic region but only for the same functional roadway 

classification. When grouping sites, the research team looked at both the variation in the monthly 

adjustment factor between sites within a defined group and the ability to affectively assign an 

LTPP site to that group. 

Secondary Data Source 

If the number of sites for which TMAS monthly volume data are needed is small, it is

worthwhile to contact individual State highway agencies for those sites and ask either their 

traffic monitoring group or their freight planning group to describe the types of truck patterns 

using the LTPP route. This information (e.g., agricultural, mineral extraction, or long-haul 

intercity trucking movement) can be used to design a specific set of TMAS sites that carry 

similar trucking movements. Monthly truck volume factors for those sites can be averaged to 

create desired default monthly distribution tables. 

HOURLY TRUCK VOLUME DISTRIBUTIONS 

The hourly distribution of truck volumes is needed for some concrete pavement analyses 

investigating the combined effect of truck loads and slab curling or warping due to time-of-day 

temperature changes. Because most LTPP sites have traffic monitoring data that include truck 

volumes by vehicle class for at least several years, the best data source for developing an hourly 

truck volume distribution is the LTPP HH_CL_CT database table. 

The HH_CL_CT table can be used to directly compute hourly truck volume patterns. Even when 

only a few days of truck volume by class data are present, the HH_CL_CT table provides the 

data needed to compute site-specific, time-of-day (e.g., hourly) truck volume patterns. 

Where no data are available for a given LTPP site, the second-best data source is LTPP 

monitoring data for other LTPP sites within the same State located on the roads serving similar 

trucking needs as the LTPP site in question. 
85

Best Alternative Data Source

For sites where available LTPP data are not sufficient to compute an average hourly truck 

distribution, it is possible to obtain similar data from FHWA’s TMAS database. TMAS has an 

existing report function that produces traffic volume by class and by hour statistics.

The only limitation with TMAS hourly distribution data is that they do not apply directly to the 

LTPP test section. However, TMAS offers an additional set of sites that might contain a counter 

producing a more accurate time-of-day distribution value than simply using the average of such 

estimates from the other LTPP test sites.

ANNUAL TRUCK (AXLE) WEIGHT DISTRIBUTIONS

Normalized truck axle weight distribution or NALS are one of the primary loading statistics used 

for the majority of load-related pavement analyses. Historically, NALS were used to estimate an 

ESAL-per-truck statistic, which was multiplied by truck volume to estimate total pavement 

loading. In more modern and sophisticated pavement analyses, truck volumes by vehicle class 

are multiplied directly by the NALS to estimate the number of axle loads, by load range, applied 

to a pavement study section.

Best Alternative Data Source

Where site-specific NALS data are not present for a given LTPP test section, either because no 

WIM data were collected at a test site or because WIM data collected are unreliable, it is 

necessary to obtain NALS from alternative sources. The best of these alternative sources are SPS 

TPF-5(004) site-specific data, which are already in the LTPP database, and axle loading default 

NALS developed based on SPS TPF-5(004) data. The default NALS are summarized in LTPP 

PLUG report and available for use in the PLUG database.(10)

The only difficulty in using LTPP PLUG data is that a user must still select a specific default 

NALS to use in an analysis. For example, PLUG defaults are specifically designed to allow a 

user to select “heavy” NALS distributions if the user knows a site contains heavy vehicle loads 

or “light” NALS distributions if the user knows a site serves a truck population that carries light 

cargo (e.g., computer parts) or where a significant percentage of trucks are unloaded and 

returning to their loading docks.

Similarly, some States have unique loading characteristics for some types of vehicles. This 

occurs when State regulations encourage the use of specific types of truck configurations to 

efficiently haul loads that the State legislature has decided are particularly important to the 

State’s economy. In these cases, a pavement analyst would want to select State-specific NALS 

for those vehicle types. But, to select these State default distributions or compute State-specific 

distributions not observed at SPS TPF-5(004) sites, data from outside the current LTPP SDR 

database are needed.

There are two nationally available sources of truck axle weight data that help identify the proper 

default NALS to select for a given LTPP site or compute new State-specific loading patterns not 

observed in the SPS TPF-5(004) study. The first source is data submitted to the LTPP program 

that failed the LTPP QA or data rationality tests. The second source is truck weight data stored in 
86

TMAS. Analysts have reservations about using both data sources. LTPP data that did not pass 

quality or rationality tests came from poorly calibrated and/or poorly functioning WIM 

equipment. TMAS axle weight data came from WIM equipment with unknown calibration and 

are therefore of unknown quality. 

First Option: LTPP Data 

This is the recommended initial step for obtaining site-specific NALS.

It is possible to extract previously submitted but unused axle weight data from the LTPP LTAS 

database. Typically, these data would have been identified by LTPP data processing and QC 

software as levels A through D(292)and not released to public or used in computation of traffic 

loading summary statistics. The data needed are in LTAS YY_AX, MM_AX, and DD_AX tables 

and can be identified by their record status (A to D) as not having passed all data quality steps. 

These can be data from the TRF* series of tables that did not pass data rationality checks 

described in this report. On a case-by-case basis, it is often possible to determine the basic shape 

of the loading curve from these data. When the problem that kept WIM data at less than record 

status E is a simple calibration issue, it is possible to use the shape of the NALS curves, even 

though they are poorly calibrated, to identify the best default load spectra to select. Thus, the 

actual NALS come from the PLUG database and take advantage of high-quality calibrated WIM 

data from the SPS TPF-5(004) study, but the NALS selection is aided by the NALS shapes 

determined using data from the poorly calibrated but site-specific WIM site. Basically, poorly 

calibrated data can provide a good estimate of the fraction of loaded versus empty trucks within 

each vehicle class (FHWA Classes 4–13). This allows an analyst to select the appropriate PLUG 

default. 

This process is slow and requires manual review, but it takes advantage of data collected at the 

LTPP site, provides site-specific insight into the loading patterns at sites for which WIM data 

were collected, even when data came from poorly calibrated equipment, and significantly 

improves the accuracy of assigning default loading estimates at each LTPP site.

Second Option: Use of TMAS Data 

There are LTPP sites for which no loading data were collected or where WIM equipment 

operated so poorly due to poor pavement conditions that no usable data exist in the LTPP

database. In these cases, the best option for assigning default NALS to an LTPP site is to 

examine available data for that State and/or roadway (when valid WIM data exist for the same 

route at a different location).

Some States have insufficient valid WIM data in the LTPP database. In these cases, WIM data 

from the TMAS database can provide insight into the truck-loading patterns found with that State 

or geographic region. 

Relying on TMAS data is not as good as having site-specific insight because truck-loading 

conditions can differ substantially from site to site within a State, as trucking characteristics 

often change from one road to another. For example, one interstate freeway can carry a large 

amount of long-haul truck traffic, while another interstate can carry almost exclusively local 
87

truck traffic. The former has a much heavier weight distribution than the latter. Thus, data from 

TMAS are helpful, but they are the secondary choice to having site-specific data. 

Another major limitation of using TMAS data is that they are stored in a format different from 

the LTPP or MEPDG formats. Future versions of TMAS will eliminate this problem, but until 

MEPDG-compatible data become available in TMAS, its usability may be limited.

Third Option: TMAS Subset Data Used in Other Studies 

There is a third option, which is a subset of the TMAS option. FHWA previously shared TMAS 

data with outside researchers who built visualization tools with TMAS WIM data. The research 

team did not explore the availability of these data summaries. 

One potential benefit from analyzing TMAS data is that TMAS may be able to produce new 

default NALS for use within PLUG. NALS from these sources need to be evaluated using the 

LTPP NALS rationality procedure described in chapter 8 of this report for the location of loaded 

and unloaded peaks in NALS. Where these data pass rationality tests and do not fit an existing 

PLUG loading pattern, they can be added as additional default loading patterns to the PLUG 

database, which allows them to be selected in conjunction with the first option (LTPP data) (i.e., 

TMAS data may provide a valid load spectrum not observed in SPS TPF-5(004) data but that 

appear to match load spectra patterns seen in poorly calibrated LTPP data). This would be 

particularly useful for vehicle classes other than Class 9 where traffic loading patterns might vary 

more substantially from one State to another. 

MONTHLY TRUCK (AXLE) WEIGHT DISTRIBUTIONS 

The options described for the annual NALS are applicable for monthly NALS. Most LTPP test 

sites do not have WIM data of sufficient quality and quantity to estimate monthly changes in 

NALS. It is expected that only significant shifts in loads will be captured using non-site-specific 

or poorly calibrated WIM data. For example, it may be possible to observe shifts in loading 

patterns due to major seasonal load restrictions, but it is unlikely that more modest shifts in 

loading patterns can be observed. Consequently, LTPP sites with limited site-specific data that 

miss axle load distributions for most of the month will be assigned a constant NALS for each 

month without site-specific data because there is no data source of sufficient quality and quantity 

to estimate monthly changes for the majority of LTPP test sites.

89

CHAPTER 5. ANALYSIS-READY TRAFFIC PARAMETERS SELECTED FOR THIS 

STUDYAND GENERAL APPROACH FOR PARAMETER COMPUTATION AND 

DELIVERY TO USERS 

ANALYSIS-READY TRAFFIC PARAMETERS SELECTED FOR THIS STUDY 

At the conclusion of Phase I of this study, a list of analysis-ready traffic parameters was 

approved by FHWA for development in the subsequent phases. The parameters can be 

summarized into three categories based on their use in pavement analyses, as follows: 

• Parameters supporting analyses using a complete time history of changes in a selected 

traffic parameter. 

• Parameters supporting analyses using a representative or an average value of a selected 

traffic parameter (i.e., summary statistics). 

• Parameters supporting analyses based on the MEPDG method and AASHTOWare 

Pavement ME Design software (See appendix A of this report for the AASHTOWare 

Pavement ME Design software graphic user interface (GUI) used to enter traffic inputs). 

Table 17 contains a list of analysis-ready traffic parameters approved by FHWA for development 

in subsequent phases of this project. These parameters are expected to be used by researchers for 

analyses supporting multiple LTPP strategic data analysis objectives. These parameters represent 

a subset of parameters recommended earlier in table 16. 

Table 17. Analysis-ready traffic parameters approved by FHWA.

Parameter Description LTPP Analysis Use

LTPP 

Strategic 

Analysis 

Objective 

MEPDG truck traffic 

growth function by 

vehicle class 

(AASHTOWare 

Pavement ME Design 

software input) 

Either a linear or 

compound growth function 

that best describes changes 

in truck volume by vehicle 

class over the pavement’s 

service life from the year 

the pavement was first 

opened to traffic to the end 

of the experiment or to the 

last year with data (if still 

in the experiment) or, 

where applicable, from 

each year where major 

structural pavement 

rehabilitation took place to 

the end of that experiment 

or to the last year with data 

(if still in the experiment). 

Use in all LTPP pavement 

performance analyses 

based on the MEPDG 

method and analyses for 

enhancing, developing, and 

calibrating MEPDG 

models. 

Use in all LTPP analyses 

requiring estimates of truck 

volumes by year or CTVs 

over an analysis period. 

1, 5, 6, 7, 8
90

Parameter Description LTPP Analysis Use

LTPP 

Strategic 

Analysis 

Objective 

MEPDG truck traffic 

growth rate by vehicle 

class (AASHTOWare 

Pavement ME Design 

software input) 

Annual growth rate 

(percent) for each truck 

class (FHWA Classes 4–

13) from the year the 

pavement was first opened

to traffic to the end of the 

experiment or to the last 

year with data (if still in the 

experiment) and, where 

applicable, from each year 

where major structural 

pavement rehabilitation 

took place to the end of 

that experiment or to the 

last year with data (if still 

in the experiment). 

Use in all LTPP pavement 

performance analyses 

based on the MEPDG 

method and for enhancing, 

developing, and calibrating 

of MEPDG models. 

Use in all LTPP analyses 

requiring estimates of truck 

volumes by year or CTVs 

over an analysis period. 

1, 5, 6, 7, 8

MEPDG base (first) 

year AADTT LTPP 

Lane (AASHTOWare 

Pavement ME Design 

software input) 

AADTT for the first full 

year the pavement was 

open to traffic and for each 

year where major structural 

pavement rehabilitation 

events took place. 

Use in all LTPP pavement 

performance analyses using 

the MEPDG method and

analyses for enhancing, 

developing, and calibrating 

MEPDG models. 

Use in all LTPP analyses 

requiring time history of 

truck volumes or CTV 

values. 

1, 5, 6, 7, 8

MEPDG vehicle class 

volume distribution 

(AASHTOWare 

Pavement ME Design 

software input) 

One normalized 

distribution of vehicles in 

FHWA Classes 

4–13 is used to represent 

the expected vehicle class 

distribution for the base 

design/analysis year. The 

sum for all truck classes is

100. Data used for 

computation are screened 

to remove outliers and 

discrepancies before 

computing the 

representative distribution. 

Use in all LTPP pavement 

performance analyses 

based on the MEPDG 

method and analyses for 

enhancing, developing, and 

calibrating MEPDG 

models. 

Use in LTPP analyses 

where truck type and 

configuration effect 

pavement response and 

performance. 

1, 5, 6, 7, 8
91

Parameter Description LTPP Analysis Use

LTPP 

Strategic 

Analysis 

Objective 

MEPDG ALDF 

(AASHTOWare 

Pavement ME Design 

software input) 

One representative 

normalized axle loading 

distribution (i.e., axle load 

spectrum) for each vehicle 

class (FHWA Classes 4–

13), axle group type 

(i.e., single, tandem, 

tridem, quad), and calendar 

month of year (January -

December) to represent 

loads for a typical day of 

each calendar month for a 

base design/analysis year 

for each LTPP site. 

Use in all LTPP pavement 

performance analyses 

based on the MEPDG 

method and in analyses for 

enhancing, developing, and 

calibrating MEPDG 

models. 

Use to compute ESAL and 

other summary loading 

statistics. 

Use to assess how heavy 

truck loads are for a given 

LTPP sites. Use for loadrelated pavement response 

and performance analysis. 

1, 5, 6, 7, 8

MEPDG number of 

axles per truck 

(AASHTOWare 

Pavement ME Design 

software input) 

One set of representative 

values describing the 

number of axles (i.e., 

single, tandem, tridem, 

quad) per truck for each of 

the 10 vehicle classes 

(FHWA Classes 4–13) 

typical for each LTPP site. 

Use in all LTPP pavement 

performance analyses 

based on the MEPDG 

method and in analyses for 

enhancing, developing, and 

calibrating MEPDG 

models. 

1, 5, 6, 7, 8

AADTT for each truck 

class (FHWA Classes 

and 4–13) for each year 

Estimate of vehicle classspecific AADTT for the 

LTPP test lane for each inservice year, up to the end 

of a site’s participation in 

the LTPP experiment, with 

missing years estimated, 

and any data discrepancies 

resolved. 

Use in LTPP empirical 

analyses where truck 

volume for specific truck 

classes is important, 

analyses where truck 

volume needs to be 

aggregated to simpler truck 

classes (e.g., single-unit, 

combination unit, 

multiunit), and analyses 

where truck volume is used 

as a secondary traffic input 

parameter. 

1, 5, 6
92

Parameter Description LTPP Analysis Use

LTPP 

Strategic 

Analysis 

Objective 

AADTT (vehicles in 

FHWA Classes 4–13 

combined), estimated 

annually 

Estimate of AADTT for the 

LTPP test lane since 

opening to traffic, reported 

for each in-service year up 

to the end of the site’s 

participation in the LTPP 

experiment. 

Use in empirical analyses 

of effects of traffic on 

nonstructural pavement 

distresses. 

Use as a supplemental 

input in analyses of effects 

of traffic on structural 

pavement response and 

performance. 

1, 5, 6

Annual total truck 

volume (vehicles in 

FHWA Classes 4–13 

combined), estimated 

annually 

Estimate of total annual 

truck volume for the LTPP 

test lane since opening to 

traffic, reported for each 

in-service year up to the 

end of site’s participation 

in the LTPP experiment. 

Use in empirical analyses 

of effects of traffic on 

nonstructural pavement 

distresses. 

Use as a supplemental 

input in analyses of effects 

of traffic on structural 

pavement response and 

performance. 

1, 5, 6

Cumulative volume of 

heavy trucks (vehicles in 

FHWA Classes 4 and 6–

13 combined), estimated 

annually 

Estimate of cumulative 

heavy truck volume for 

vehicles in FHWA Classes 

4 and 6–13 combined, for 

the LTPP test lane since 

opening to traffic, reported 

for each in-service year up 

to the end of site’s 

participation in the LTPP 

experiment. 

Use in empirical analyses 

of effects of traffic on 

nonstructural pavement 

distresses. 

Use as a supplemental 

input in analyses of effects 

of traffic on structural 

pavement response and 

performance. 

1, 5, 6

Cumulative volume of 

FHWA Class 9 trucks, 

estimated annually 

Estimate of cumulative 

volume of FHWA Class 9 

trucks for the LTPP test 

lane since opening to 

traffic, reported for each 

in-service year up to the 

end of site’s participation 

in the LTPP experiment. 

Use in empirical analyses 

of effects of traffic on 

nonstructural pavement 

distresses. 

Use as a supplemental 

input in analyses of effects 

of traffic on structural 

pavement response and 

performance. 

1, 5, 6
93

Parameter Description LTPP Analysis Use

LTPP 

Strategic 

Analysis 

Objective 

Annual ESAL, 

estimated annually 

Based on measured or 

estimated axle loading data 

in the LTPP lane for 

FHWA Classes 4–13 

combined and information 

about pavement structure 

and pavement 

serviceability, this value is 

computed for each year the 

pavement site was in 

service up to the end of the 

site’s participation in the 

LTPP experiment. 

Use in LTPP empirical 

analyses where a singlevalue traffic loading 

statistic is desired and for 

quantifying and comparing 

traffic loads between sites. 

Use to compute cumulative 

ESAL for the pavement 

design method described in 

the 1993 version of the 

AASHTO Guide for Design 

of Pavement Structures. 

1, 5, 6, 7

Annual GESAL, 

estimated annually 

Based on measured or 

estimated axle loading data 

in the LTPP lane for 

FHWA Classes 4–13 

combined, this value is 

computed for each year the 

pavement site was in 

service up to the end of the 

site’s participation in the 

LTPP experiment. 

Use in LTPP empirical 

analyses where a singlevalue traffic loading 

statistic is desired and for 

quantifying and comparing 

traffic loads between sites. 

1, 5, 6, 7

Annual total GVW of all 

trucks (FHWA Classes 

4–13), estimated 

annually 

Based on measured or 

estimated GVW data in the 

LTPP Lane for FHWA 

Classes 4–13 combined, 

this value is computed for 

each year the pavement site 

was in service up to the end 

of the site’s participation in 

the LTPP experiment. 

Use in LTPP empirical 

analyses where a singlevalue traffic loading 

statistic is desired and for 

quantifying and comparing 

traffic loads between sites. 

1, 5, 6, 7

Codes describing traffic 

data and method used 

for parameter estimation 

Codes to be included in 

CPTs providing 

information about the 

quantity and quality of 

traffic data or alternative 

data sources or default 

values used to develop 

specific analysis-ready 

traffic parameters. 

Use to help users make 

informed decisions about 

the applicability of each 

computed parameter for 

specific analysis 

applications. 

1, 5, 6, 7, 8
94

GENERAL APPROACH FOR PARAMETER COMPUTATION

Challenges Using Available Traffic Data

Estimating the parameters presented in table 17 posed several challenges that were addressed in 

the computational methodology. First, it was important to define approaches for how to handle 

missing data. Data might have been missing for several years of the pavement service life or for 

the entire pavement service life (i.e., limited availability of traffic loading data). Second, it was 

important to develop approaches for how to address low or unknown accuracy of traffic loading

data. Third, it was important to address situations where available data showed a high degree of 

variability over time. High variability can occur because of variations in local economic activity 

but can also occur because of data collection errors. Procedures were required to deal with these 

conditions when supporting information did not indicate whether the high range of values or 

atypical/unexpected values should have been included when developing computed parameters.

Addressing Missing or Questionable Traffic Data

Different procedures were developed to account for missing or questionable traffic data for 

different traffic parameters, as described in the ensuing chapters. The majority of the effort was

spent accounting for unreliable data and identifying trusted data against other comparable 

site-specific data. Outlier values (i.e., values significantly different from other observations of 

the same kind for the same site) were identified, and ancillary data, when available, were used to 

identify possible reasons for observed outliers (e.g., small data samples used in parameter 

computation, data collected by equipment with unknown calibration, or historical data reported 

for a segment rather than an LTPP lane). Based on information gathered, case-by-case decisions 

were made about whether to keep or remove outliers from representative or annual value

computations.

For missing data, accessible datasets that identified truck volumes and/or loads on the same road 

near the LTPP test site were used as the basis for LTPP CPT values. Data sources, such as the 

HPMS and FHWA’s TMAS, were considered, but in the majority of cases, available LTPP truck 

traffic volume data were sufficient for these purposes.

For situations where no alternative data sources were available, sites were assigned default 

values. Traffic loading defaults developed by the LTPP program based on trusted data collected 

as part of TPF-5(004) were recommended.

Estimating Traffic Changes over Time

Significant effort was on developing methodologies to characterize changes in traffic over time, 

such as growth rates or annual estimates of truck traffic volume for years where no data were 

available or data from different sources gathered over the years were not in agreement.

Because different factors contribute to differences in truck volume, vehicle class distribution, and 

axle weight, varying approaches for estimating traffic data were established to best suit the type 

of data being estimated. Estimating techniques depended on the types of errors observed during 

data assessment. For errors occurring only sporadically, simple manual data review procedures 

were developed. For errors occurring commonly (e.g., poorly calibrated WIM scales), the
95

research team developed heuristic rules that could be automated. Detailed descriptions of 

procedures and algorithms for estimating analysis-ready traffic parameters are provided in 

chapters 6 through 11 of this report. 

For sites where some site-specific traffic data, such as truck volume, were available for some but 

not all years the site was in service, a traffic-projection methodology was developed and applied 

to estimate traffic volume changes over time. One of the approaches used to accomplish this task 

was to identify traffic and/or truck volume growth rates producing the smallest errors when 

AADT and AADTT computed based on these growth rates were compared with available 

site-specific AADT and AADTT estimates. Additional constraints were developed to limit 

minimum and maximum expected traffic and/or truck volumes. As part of this work, the research 

team also referenced existing LTPP metadata describing State responses to QA/QC checks 

performed by the LTPP program’s regional support contractors. 

Development of Representative Traffic Values 

The parameters representing typical truck traffic volume, vehicle classification, and axle weight 

characteristics were developed for each LTPP site with site-specific traffic data. Based on the 

availability and quality of supporting data, different approaches were employed to develop these 

parameters, including the following: 

• Data for LTPP sites with site-specific data were reviewed for accuracy when information 

quantifying data accuracy was available or reasonableness when supporting information 

to infer data accuracy was limited or not available. 

• All site-specific values collected as part of the LTPP traffic monitoring data collection 

program were identified as accurate or reasonable and incorporated in the analysis to 

compute representative values for each site. 

• There were cases when specific, valid discontinuities in traffic trends were observed, 

such as when a major change in truck volume or composition occurred. In these cases, 

discontinuities were noted in the CPT and the computation of representative values 

accounted for whether these discontinuities were due to temporary or permanent changes 

in traffic patterns. 

• Site-specific traffic loading data identified through review as being of limited accuracy 

were used, to the extent feasible, to assign qualitative characteristics to LTPP sites 

describing loading patterns for dominant heavy truck classes. These characteristics can be 

used to assign default values. 

• LTPP sites with no usable site-specific vehicle classification data (due to quality issues 

identified through data review) had representative values developed using values from 

nearby similar sites, where possible. The method used for assignment is noted for each 

LTPP site and each parameter in the CPT. 

• LTPP sites with no usable site-specific detailed axle loading characterization data (due to 

quality issues or lack of data collection) were identified and reported to the LTPP 

program. These sites required default assignments outside of this study. 
96

Development of Input Values for AASHTOWare Pavement ME Design software 

Several key traffic parameters necessary to support LTPP analyses using AASHTOWare 

Pavement ME Design software were developed in this study, including the following: 

• MEPDG base (first) year AADTT. 

• MEPDG vehicle class distribution factors.

• MEPDG vehicle class growth rate and growth type (e.g., linear or compound). 

• MEPDG ALDF. 

• MEPDG number of axles per vehicle (i.e., truck) class. 

All parameter definitions and formats were based on descriptions provided in AASHTOWare 

Pavement ME Design software help files. Computational procedures and tools were developed to 

store computed parameters in database tables in tabular format providing maximum 

compatibility with the software’s traffic input requirements (i.e., to allow a cut-and-paste 

operation between the LTPP table and the GUI of AASHTOWare Pavement ME Design 

software). In addition, a tool based on the LTPP PLUG database application was developed to 

create ALDF files in XML format for direct upload to AASHTOWare Pavement ME Design 

software, including ALDF based on unique monthly loading data.

DELIVERY OF COMPUTED PARAMETERS TO LTPP USERS 

CPTs 

LTPP uses the term “computed parameter” to identify certain parameters developed or computed 

based on data available in the LTPP database. Computed parameters are designed to drastically 

reduce the time required to prepare datasets for analysis. Computed parameters are stored in 

CPTs in the LTPP database.

Use InfoPave to Access New CPTs 

The LTPP program’s preferred way for users to access or download CPTs is the online through 

the InfoPave website.(19)

User Guide for Selecting and Using LTPP Traffic Data 

The Guide was developed in this study and provided as a standalone publication to help LTPP 

users identify traffic data and parameters necessary for their analysis needs, including analysisready traffic tables and traffic data–usability indices.
97

CHAPTER 6. ESTIMATING PARAMETERS DESCRIBING TRUCK VOLUMES AND 

TRUCK VOLUME CHANGES OVER TIME 

Truck volume parameters are used as primary or supplemental inputs in virtually all pavement 

performance and management analyses. Parameters described in this chapter include the 

following: 

• AADTT for the test lane for each in-service year, all truck classes (FHWA Classes 4–13) 

combined.

• AADTT for the test lane for each in-service year for each truck class (FHWA Classes 4–

13). 

• Total annual truck volume for the test lane for each in-service year, all truck classes 

(FHWA Classes 4–13) combined. 

• Total annual truck volume for the test lane for each in-service year for each truck class 

(FHWA Classes 4–13). 

• CTV for the test lane for vehicles in FHWA Classes 4–13 combined, estimated and 

compounded annually. 

• CTV for the test lane for each truck in FHWA Classes 4–13, estimated and compounded 

annually.

• MEPDG truck traffic growth for the test lane for each truck in FHWA Classes 4–13, 

including growth rate and type of growth function (e.g., linear or compound). 

• MEPDG base year AADTT for the LTPP lane for all truck classes (FHWA Classes 4–13) 

combined (first in-service year). 

• MEPDG percentile vehicle class distribution for the LTPP lane for all truck classes 

(FHWA Classes 4–13) (for use with the first in-service year MEPDG base year AADTT 

parameter). 

AADTT FOR EACH IN-SERVICE YEAR, ALL TRUCKS COMBINED 

Computed Parameter Description 

AADTT in the LTPP test lane describes the total number of trucks (FHWA Classes 4–13) 

traveling across the LTPP test pavement on a typical day of the year. This statistic averages out 

seasonal and day of week (DOW) variation present at the site as well as any growth occurring at 

the site during the year. As a traffic statistic, AADTT is the most common value chosen to 

represent truck traffic at a site, and it is a common choice for a simple data input for pavement 

performance models and analysis.

AADTT for each in-service year, all trucks combined, is developed for each year a given LTPP 

test section is open to traffic until the end of the test section’s participation in the LTPP 

experiment. AADTT for each year may be computed based on monitoring data, estimated by a 

State agency, or estimated by an LTPP contractor based on available monitored and 

State-provided data. 
98

Data Sources Used for Parameter Computation

AADTT values are available for some years, in some form, for almost all LTPP test sites within 

the LTPP database. The majority of LTPP test sites have data on AADTT for the LTPP lane in 

one of two pavement performance database tables available through the LTPP annual SDR. The 

first is the traffic monitoring table (TRF_MONITOR_LTPP_LN), which presents the AADTT 

value computed from traffic monitoring data submitted by State/Provincial highway agencies to 

LTPP program. If data pass LTPP QA tests, an AADTT value is present in the variable AADTT 

within that table. These records can come from an automatic classifier (TRF_DATA_TYPE is

4), a WIM system (TRF_DATA_TYPE is 7), or have a record type (TRF_DATA_TYPE is 0) 

indicating that both sources were used. Record status “E” results, regardless of record type, are 

selected over record type “D” results. Given the initial outcome, TRF_DATA_TYPE is 4 results 

are used to estimate LTPP test site truck volumes.

The second source for AADTT for each in-service year, all trucks combined, is a custom table 

provided by the LTPP CSSC which contains estimates of truck and total volume submitted by 

State highway agencies. These annual estimates may be based on counts near the LTPP test site.

This table includes a value for AADTT in the LTPP test lane (AADT_TRUCK_COMBO_EST). 

It also includes AADT for the total roadway for all lanes in both directions 

(AADT_ALL_VEHIC_EST) as well as a value for the percentage of AADT that are trucks in the 

LTPP lane (PERCENT_TRUCKS_LTPP_LN_EST). The AADT_VALUES table was computed 

by combining data from SDR tables TRF_MON_EST_ESAL for traffic estimates from 1990 and 

TRF_HIST_EST_ESAL for traffic volume estimates prior to 1990.

If additional information is desired, several other tables in the LTPP database contain data that

can be analyzed to see if details on truck volumes were collected as part of the traffic monitoring 

effort or were submitted by State highway agencies. SDR table TRF_HST_CLASS_DATA 

contains truck volumes by vehicle classification (not always FHWA Class) submitted by State

highway agencies. These class-specific volumes can be summed to provide an estimate of total 

truck volume for specific sites and specific time periods. Variables of interest are stored in the 

NO_VEH_LTPP_LN field, where the specific class of trucks for that record is identified in the 

VEHICLE_CLASS_DESCRIPTION field. Summing the volumes across vehicle classes 4 to 13

produces an ADTT value for the LTPP lane. A summary of these data is available in the 

TRF_HST_CLASS_MASTER table. The fields CLASS_COUNT_BEGIN_TIME, 

CLASS_COUNT_DURATION_HOUR, and CLASS_NO_TRUCKS_LTPP_LN can be used to 

compute daily truck volume in the LTPP test lane. Finally, the AADT_TRUCK_COMBO field

in the TRF_HIST_EST_ESAL table provides a truck volume estimate for the LTPP lane.

If many years are missing from the LTPP database, or if an independent check of reported 

volumes in the LTPP database is desired, there are two useful data sources that can be examined. 

The first source is data at nearby LTPP sites on the same roadway for which data were collected. 

The second source, if no data supplied by a State highway agency are present, is the State’s 

annual HPMS submittal. These data are not uniformly available, and they are only stored for 

some road sections back to 1982. (Data prior to that are not readily available.) In addition, the 

HPMS submittal contains truck volume estimates primarily using a simplified classification 

scheme of single-unit trucks, combination trucks, and multitrailer trucks. Note that truck volume 

data may be unavailable for early service years for some old pavements in the LTPP experiment. 
99

Pavements built prior to the mid to late 1980s were built during a time when little truck volume 

data was collected. In these cases, it is necessary to mathematically estimate volumes using the 

historical growth rates computed from data that are present. 

Methodology for Parameter Computing or Estimating 

The purpose of AADTT for each in-service year, all trucks combined, is to give LTPP users a 

means of estimating total truck traffic volume each LTPP experiment site was exposed to from 

opening to traffic through the end of the site’s participation in the LTPP experiment. To do that, 

any gaps in AADTT estimates available to LTPP data users need filling. To construct a 

continuous set of AADTT values, monitoring data collected as part of the LTPP experiment and 

State-supplied AADTT estimates are combined and used to develop mathematical projections 

using linear or compound growth functions to estimate years for which no AADTT estimate 

exists. The projection providing the best fit with available AADTT estimates is used to compute 

AADTT for the missing years. These computed estimates are combined with available AADTT 

estimates to develop the complete time history of AADTT changes over each LTPP site’s life. 

To differentiate between sources of data used to develop estimates, codes are assigned to each 

annual estimate: “M” indicates an estimate based on monitoring data, “S” indicates a 

State-provided estimate, and “E” indicates an estimate based on a mathematical projection using 

available monitored and State-provided data. A quality or rationality check of data is performed 

to determine if the computed parameters need revising to account for unusual conditions.

Computational Procedure or Algorithm 

The initial step in the AADTT computational procedure (for each in-service year, vehicles in 

FHWA Classes 4-13 combined) is to set up the basic working table allowing for the creation of 

the CPT. The basic working table structure consists of a row for all calendar years for an LTPP 

traffic site, with secondary tables computed for all LTPP experimental sections using that traffic 

site’s data. This table structure is then filled with available monitoring and State-supplied traffic 

estimates. Summary statistics are computed and used as necessary, and missing years of data are 

estimated and incorporated into the table. The specific steps for this procedure are as follows: 

For each LTPP traffic site, determine the first year for which traffic data are available. 

This value is extracted from the Microsoft Excel® spreadsheet 

Traffic_site_experiment_links.xlsx which was provided by the LTPP CSSC. The 

SHRP_INFO tab within the spreadsheet is used. The first year for which data are 

available is found in the column START_DATE. The end date for that traffic site is then 

extracted from the column END_DATE from that same Excel file and tab. The date 31-

Dec-50 denotes that a traffic site is still collecting data. 

The table for that site is sized for one row for each year from the start date to the end 

date. The table has the following column headers (and the same associated variable 

names: CalendarYear, Age, AADTT, Source, AADTT4, AADTT5, AADTT6, AADTT7, 

AADTT8, AADTT9, AADTT10, AADTT11, AADTT12, AADTT13, Per4, Per5, Per6, Per7, Per8, 

Per9, Per10, Per11, Per12, Per13, ClassCountDays, HistClassCountHours, QAflag, 

CountType.
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A second working table is constructed for every LTPP pavement site. For each site, the 

year the site opened to traffic is obtained from the LTPP Inventory database file in the 

TRAFFIC_OPEN_DATE table. For SPS sites, the SPS_ID table is extracted from LTPP 

database Standard Data Release (SDR 30 was used at the time of this study) data set. The 

date the site opened to traffic is taken from the DATE_OPEN_TRAFFIC column. This 

date is kept as a value to be used later. The year from that date is placed in the YEAR 

field in the first row of this working table. The number of days into the year represented 

by that date (e.g., January 10 would be 10 d into the year) is computed and retained. 

The end date for the site is extracted from the DEASSIGN_DATE column in the 

EXPERIMENT_SECTION table of the SDR 30 data set. The year extracted from the 

DEASSIGN_DATE column (assigned as 2016 if the column is blank, meaning the site is 

still active) is used to size this working table. The table is sized to include one row for 

each year from the year the site opened to traffic to the year identified in the 

deassignment date. 

For GPS sites, a similar process is followed, but different tables are used to build the 

second working table. The INV_AGE column is used from within the INVENTORY 

table to select the date the site opened to traffic. For SPS sites that transition to GPS sites, 

the open to traffic date for the new experiment is understood to be the ASSIGN_DATE. 

The end date for the site is extracted from the DEASSIGN_DATE column in the 

EXPERIMENT_SECTION table of the SDR 30 data set. The year extracted from the 

DEASSIGN_DATE column (assigned as 2016 if the column is blank, meaning the site is 

still active) is used to size this working table. The table is sized to include one row for 

each year from the year the site opened to traffic date to the year identified in the 

deassignment date. 

The second working table has the same structure as the initial working table. The table 

has the following column headers (and the same associated variable names): 

CalendarYear, Age, AADTT, Source, AADTT4, AADTT5, AADTT6, AADTT7, AADTT8, 

AADTT9, AADTT10, AADTT11, AADTT12, AADTT13, Per4, Per5, Per6, Per7, Per8, Per9, 

Per10, Per11, Per12, Per13, ClassCountDays, HistClassCountHours, QAflag, CountType. 

a. The first row of the table is given the CalendarYear equal to the year the section 

opened to traffic. 

b. CalendarYear in the last row of the table is set equal to the value in the 

DEASSIGN_DATE column. 

c. The CalendarYear value is incremented by 1 yr for each row in the table, ending with 

the last row where CalendarYear should equal the year in the DEASSIGN_DATE 

column.

d. The value for Age for the first row of the table is set to “0” (i.e., there is no age of the 

pavement in the first year). 
101

e. Age is computed for each succeeding row by adding “1.” If the site opened to traffic 

in 1990, Age for the row containing CalendarYear of 1990 is “0.” Age for the row 

containing CalendarYear of 2000 is “10.” 

For the LTPP traffic site, use the TRF_MONITOR_LTPP_LN table in SDR 30 to extract 

the AADTT estimate (from the TRUCKS_LTPP_LN field) for each year and place that 

value in the appropriate row of the new table. Then, set the Source value of “M” for that 

row as containing monitoring data. 

Note: if “extra” years of monitoring data are present in the TRF_MONITOR_LTPP_LN 

table after the traffic site leaves the LTPP experiment, those years will be added to the 

traffic working table. Those years may be included in future analyses when manual 

review of data at that site results in a decision that adding data from those years will 

result in better computed parameters. For these additional rows, the variable QAflag will 

be set to “2” to indicate that extra years of data are present and should be reviewed for 

inclusion in the analysis. The criteria behind the decision to use or ignore these extra 

years is flexible, as it is not clear without examination whether those data should be used. 

In general, it is good to have more data. However, trends may have changed in the last 

few years due to economic changes or local events, making new data less beneficial for 

use in predicting historical AADTT values. In general, external years of data are used 

when little data internal to the LTPP study exist and are excluded from the equation 

fitting when the majority of years in the LTPP life of the pavement have monitoring or 

State-supplied AADT values. 

If an AADTT value is not present for a given year in the TRF_MONITOR_LTPP_LN 

table, search the AADT_TRUCK_COMBO_EST column in the AADT_VALUES table,

extract any annual values for that site for years where no monitoring data are present, and 

fill the AADTT column with this data and set the Source value on the same row as “S” 

(“S” means containing State-submitted estimates). 

If an AADTT value is not in either TRF_MONITOR_LTPP_LN or 

AADT_TRUCK_COMBO_EST tables, search the AADT_TRUCK_COMBO column in 

the TRF_HIST_EST_ESAL table for values for that site and year. The 

AADT_TRUCK_COMBO column in the TRF_HIST_EST_ESAL table contains 

historical estimates (pre 1990) of LTPP lane truck travel provided by State highway 

agencies and set the Source value on the same row as “S” for containing State-submitted 

estimates. 

If no data are available for a site for any year from the TRF_MONITOR_LTPP_LN or 

TRF_HIST_EST_ESAL tables, set the Source value to “D” for default required. This will 

be used to stop processing for this site. A default set of AADTT values is supplied 

manually by the analyst using the process described in steps 13 through 20.

A quality or rationality check of the data is performed based on year-to-year differences 

in truck volume. The data rationality check determines if either AADTT values change by 

more than 25 percent from year to year and if the change in volume is larger than 

50 trucks. If a site has such a change, it is flagged for further review with QAFlag is “1.” 
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Note that a 25 percent change in truck volume at a low-volume LTPP test site is a 

reasonable change in traffic loading and accepted as valid because traffic data in SDR 30 

were already reviewed by the LTPP program. It is also possible that the site will be 

flagged to indicate that two or more distinct traffic patterns are present due to changes in 

traffic characteristics caused by changes in the economy. The manual review process may 

determine that, given other available information, some submitted AADTT estimates are 

not reasonable for use in the CPT. 

If there are still years for which there are no AADTT estimates, the remaining AADTT

estimates are estimated using a linear or compound growth function. 

Assuming only one growth equation is developed, available AADTT values are used to 

compute two regression equations, one using a linear growth rate and the other using a 

compound growth rate. Regression is computed using the AADTT and Age columns of the 

traffic site working table and only uses rows for which a valid AADTT statistic is present. 

Unless one of the growth equations predicts a negative AADTT value for a year in which 

the LTPP site was open for traffic or for the year 2030, the formula with the best Rsquared value is used to estimate any missing AADTT values. If a negative value occurs, 

the other growth equation is selected, even if it has a lower R-squared value. (i.e., if the 

negative value appears in the linear equation, use the compound equation and vise versa.) 

If a negative value is determined for the second growth equation as well, the first growth 

equation is used until the year when a negative AADTT is first computed. For this (first 

negative AADTT year) and all the following years, AADTT value from the last year with 

positive AADTT value is used. If this occurs, set the QAflag to “6.” 

For any years for which AADTT values are estimated with the regression equation, set the 

Source value for that row as “E” for containing a computed estimate. 

If the manual review shows major discontinuities exist in truck trends (e.g., one LTPP 

site is significantly affected by a construction road closure changing truck travel patterns 

for several years), a different forecast process is used for the duration of the construction 

event versus those years when no closure was present. The same process noted in 

steps 6–8 is followed for developing the regression equations, but the years of data used 

are restricted to those years when a given pattern is present. This process is done 

manually, with data manually inserted into the CPT table being created. 

When manual adjustments are made to the automated process, such as described in 

step 17, the QAflag variable for all years included in the manually adjusted years of data 

is set to “9.” 

Upon completion of traffic site statistics, LTPP experimental sites are constructed. A 

crosswalk between individual LTPP experiment sites and traffic sites is found in the 

Excel spreadsheet Traffic_sites_experiment_links.xlsx. In general, the experimental site 

is listed in table fields with the headings STATE_CODE and SHRP_ID. The traffic count 

site used for classification data is found in the CLASS_SITE column. The one exception 

to this is that all SPS experimental sections are automatically assigned to their parent 
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traffic site. For example, SPS sites 040113, 040115, 040122, and 040163 are all assigned 

to LTPP traffic site 040100. Some manual revision of these rules was required where 

some SPS sections were on different sides of the road from other test sections for that 

SPS site. These were hand-coded to be exceptions to the assignment of the specific 

experimental sites to the traffic data collection location for the parent site. (That is, the 

sections on the north side of the road have been manually assigned to a traffic location on 

the north side, while the sections on the south side of that road are assigned to the parent 

site, which is also on the south side of the road.) 

The experimental site working tables are filled with the AADTT for those years in the 

table from the appropriate traffic site. 

Examples of Parameter Estimating 

AADTT Estimation for LTPP Traffic Site 040100, Case of Complete Dataset with Large Truck 

Volume Variations 

The first example of the AADTT estimation process is for LTPP traffic site 040100. This traffic 

site is used for many SPS test sections (040113–040124 and 040160–040163.) The site opened

to traffic on June 1, 1993 and left the experiment June 1, 2006. 

The site has traffic monitoring data for two distinct periods. From 1994 to 2001, monitoring data 

are present. After a brief loss of data, starting in 2003, traffic monitoring data were again 

available; the site continues to provide data even though the site left the LTPP experiment in 

2006. The site also has State-submitted AADTT data for 1993–1995, 2001, and 2002 (table 18). 

State-submitted estimates are only used for 1993 and 2002. Data for the years beyond 2006 are 

shown in table 18 because this allows continuing trends to be observed. Data from 2006 onward 

can be used in the computation of the CPT but would not be included in the table, which covers 

only the years during which the test site was part of the LTPP experiment. 

Table 18. AADTT estimates for LTPP traffic site 040100.

Year AADTT 

Monitoring (M) or 

State Estimate (S) 

1993 400 S 

1994 629 M 

1995 657 M 

1996 714 M 

1997 770 M 

1998 835 M 

1999 875 M 

2000 950 M 

2001 1,006 M 

2002 1,085 S 

2003 214 M 

2004 221 M 

2005 220 M 

2006 219 M 
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Year AADTT 

Monitoring (M) or 

State Estimate (S) 

2007 413 M 

2008 237 M 

2009 286 M 

2010 379 M 

2011 942 M 

2012 1,031 M 

2013 1,186 M 

2014 1,334 M 

As can be seen in the table, truck volumes dropped by approximately a factor of four from 2003–

2010. Discussions with the LTPP program’s regional support contractor indicated that traffic 

volumes using this road changed due to a major road construction project altering traffic patterns 

during this period. (There were truck restrictions imposed on this route after September 11, 2001, 

that were in place for several years prior to the construction project.) This creates a difficult 

problem for developing computed parameters for this site, in that truck volumes experience a 

major discontinuity during the period of the LTPP experiment. There are really two truck volume 

patterns, the pattern from 1993–2003, which resumes part way through 2011 when the 

construction project ends and traffic is restored to its previous patterns, and from 2003–2010 

when much lower truck volumes cross this test site. 

Another observation is that the truck volume reported in 1993 is only about two-thirds of what 

might be expected in a trend line formed from 1994–2001. This is doubly surprising when a 

casual comparison of State-supplied and monitoring truck volumes for 1994 and 1995 shows 

State-supplied volumes for those 2 yr significantly overestimate truck volumes collected as part 

of the LTPP monitoring program. However, because it is not possible to guess whether traffic 

ramped up on the pavement at this LTPP traffic site, State-supplied values for AADTT are used 

for 1993. 

Lower truck volumes observed in the LTPP database starting in 2003 fail the consistency test 

used for QA purposes—that is, they demonstrate greater-than-30-percent changes in AADTT 

from one year to the next, and the percent of truck travel by FHWA Class 9 trucks also changes 

by more than 20 percent. These data were manually reviewed and ultimately left in the dataset as 

valid because they were accurate representations of actual traffic loads experienced by the LTPP 

traffic site. They are simply a symptom of the impacts of construction traffic changes on the 

truck traffic volumes experienced by the LTPP site.

The effects of this 9.5-yr discontinuity are discussed in later examples in this chapter. 

AADTT Estimation for LTPP Traffic Site 320100, Case of Partial Dataset with Moderate 

Truck Volume Variations and No Growth 

The second example of the AADTT estimation process is for LTPP traffic site 320100. This 

traffic site is used for 12 different SPS test sections (320101–320112). The site was assigned to 

the LTPP experiment in 1993, opened to traffic on September 1, 1995, and left the experiment 

July 1, 2009. Data collection continued at the site after leaving the LTPP experiment. AADTT 
105

estimates based on monitored data at the site are present in the LTPP database through 2015 and 

are shown in table 19. 

Table 19. AADTT estimates for LTPP traffic site 320100.

Year AADTT 

Monitoring (M), 

State Estimate (S), or 

Regression Estimate (E) 

1995 1,261 E 

1996 1,050 M 

1997 1,212 M 

1998 1,287 M 

1999 1,396 M 

2000 1,369 M 

2001 1,316 M 

2002 1,285 M 

2003 1,404 M 

2004 1,479 M 

2005 1,193 M 

2006 1,285 M 

2007 1,244 E 

2008 961 M 

2009 926 M 

2010 1,190 M 

2011 1,191 M 

2012 1,376 M 

2013 1,298 M 

2014 1,294 M 

2015 1,334 M 

At this site, traffic monitoring data are present for all years except 1995 and 2007. No 

State-submitted data are available. To develop estimates for missing years, it was necessary to 

compute best fit regression curves to available data and use the best of these two curves to 

estimate AADTT for 1995 and 2007. 

Linear and compound regression equations were fit through the traffic monitoring data, with the 

year the site opened to traffic as the independent variable. Neither curve provides an especially 

good fit because AADTT values do not closely follow either a linear or compound growth 

pattern. Figure 6 is a graph of monitored AADTT values from 1996 to 2015, which shows a 

significant drop in trucking activity at this site in 2008 and 2009, during the heart of the 

Great Recession. However, even outside of the recession, truck volumes at this site were volatile, 

showing no clear growth trend, often increasing for several years and then decreasing for several 

years. 
106

Source: FHWA. 

Figure 6. Graph. Monitored AADTT by year at LTPP traffic site 320100. 

Even without the drop in truck volumes during heart of the Great Recession, there is no clear 

growth pattern at this site. The best fit of the two growth equations is the compound equation, 

but its R-squared value is 0.0029 when using all years through 2015. The linear regression 

equation’s R-squared value is 0.0028. Actual AADTT values computed using the two equations 

are not significantly different from each other. Both equations place predicted AADTT values for 

missing years in the middle of the range of AADTT values illustrated in figure 6. 

If instead of using all years for which data are present, only data from 1996–2009 are used 

(the period when the site was part of the LTPP experiment), and the R-squared value goes up 

considerably for both the linear and compound equations. If only data through 2009 are used, the 

R-squared values for the two regression curves are 0.138 for the compound equation and 0.117 

for the linear equation. Both the linear and compound regression equations predict slightly 

decreasing truck volumes at this site regardless of whether data from 1996–2006 (prior to the 

recession), through 2009, or through 2015 are used in equation development.
107

Given the fluctuation in truck volumes over time, equations that use only the data through 2009 

are considered better for predicting missing AADTT values during the LTPP experiment, as it is 

not possible to predict fluctuating volumes with a high degree of precision. However, if AADTT 

values for later years are predicted, equations based on the fully available traffic monitoring 

dataset (i.e., through 2015) should be used despite the lower R-squared value, as additional years 

of traffic monitoring data reflect changes occurring at the site (i.e., the continuing rebound in 

truck traffic after the Great Recession). 

AADTT Estimation for LTPP Traffic Site 321020, Case of Partial Dataset with Consistent 

Truck Volume Growth Trend 

The third example of the AADTT estimation process is for LTPP traffic site 321020. This traffic 

site was initially part of the GPS-1 experiment. The pavement was opened to traffic on June 1,

1984, and left the LTPP experiment in September 2000. At that time, the LTPP test site became 

part of the GPS-6 experiment, and it continues to be part of that experiment today.

Monitoring traffic data exist for parts of 8 different years, beginning in 1991 and ending in 2003. 

In addition, the Nevada DOT supplied estimates of AADTT for 1984–2000. These data are 

summarized in table 20. Years 2001–2015 are covered by the ongoing GPS-6 experiment. 

Table 20. AADTT estimates for LTPP traffic site 321020. 

Year AADTT 

Monitoring (M), 

State Estimate (S), or 

Regression Estimate (E) 

1984 117 S 

1985 109 S 

1986 109 S 

1987 144 S 

1988 183 S 

1989 183 S 

1990 168 S 

1991 165 M 

1992 176 M 

1993 194 M 

1994 230 S 

1995 293 S 

1996 271 M 

1997 270 M 

1998 240 S 

1999 390 S 

2000 329 M 

2001 334 E 

2002 342 M 

2003 276 M 

2004 402 E 

2005 427 E 
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Year AADTT 

Monitoring (M), 

State Estimate (S), or 

Regression Estimate (E) 

2006 454 E 

2007 483 E 

2008 513 E 

2009 546 E 

2010 580 E 

2011 617 E 

2012 656 E 

2013 697 E 

2014 742 E 

2015 788 E 

To supply missing AADTT values for which there are neither traffic monitoring data nor 

State-supplied estimates, the regression technique was used. Both linear and compound 

regression equations were tested against the combined State-supplied and traffic monitoring data. 

The R-squared value for the compound equation was slightly better than the linear equation (0.85 

versus 0.81, respectively). Consequently, the compound equation was used to estimate missing 

AADTT values. 

AADTT FOR EACH TRUCK CLASS AND EACH IN-SERVICE YEAR 

Computed Parameter Description 

This computed parameter describes the number of trucks that travel across the LTPP test 

pavement on a typical day of the year, with values provided for each in-service year since the 

LTPP site opened to traffic until the end of LTPP site’s participation in the experiment. This 

computed parameter disaggregates total AADTT volumes into AADTT volumes for individual 

FHWA vehicle Classes 4 through 13 so changes in the mix of trucks occurring over time can be 

incorporated into pavement loading calculations. 

AADTT for each truck class and each in-service year averages out seasonal and DOW variations 

present at the site as well as any growth occurring at the site during the year. It is the best simple 

traffic volume statistic for characterizing annual changes in the truck traffic mix. 

Data Sources used for Parameter Computation 

The primary source for truck volumes by vehicle classification are traffic-monitoring traffic 

monitoring data submitted by State highway agencies stored in the TRF_MONITOR_LTPP_LN 

table in the LTPP database. This table includes the LTPP program’s best estimate of annual truck 

volume by vehicle class (FHWA Classes 4–13). Where counts have been made across multiple 

years of data, trend lines can be developed with these data. Variability of the percentage of travel 

across various truck classes (FHWA Classes 4–13) can also be examined. 

If additional data are required as part of the manual review of available data, LTAS monthly 

traffic tables can be accessed. These tables are found in SDR releases and start with “MM_CT.” 

LTAS monthly tables provide more detailed truck volume data collected by State highway 
109

agencies specifically for the LTPP program by class of truck, year, month, lane, and DOW.

Should they be needed as part of a manual data quality review, these data allow the direct 

computation of truck volumes by FHWA vehicle class in the LTPP test lane as well as 

comparison of LTPP lane volumes versus truck volumes counted in other lanes on that roadway. 

Where no monitoring data were collected by a State highway agency at a specific LTPP test site, 

there are three useful data sources that can be examined. The first is estimates of annual volume 

traffic data for the site supplied by a State highway agency. These data are stored in the 

TRF_HIST_CLASS_DATA table of SDR 30. The data include estimates of vehicle volumes by 

class of vehicle and typically include car volumes and truck volumes. The 

TRF_HIST_CLASS_MASTER table includes a description of these counts, including the 

duration of any counts submitted. In some cases, these data are submitted using a vehicle 

classification scheme that is somewhat different than FHWA’s 13-bin vehicle classification 

table. Using these data requires manual review because of the potential variability in data 

reported. 

Computed AADTT parameters (combined for all trucks) described in the previous section of this 

document provide annual control totals for truck volumes at a site. These can be used with data 

on the distribution of trucks within each class to estimate AADTT by truck class when traffic 

monitoring data are not available. 

If no data supplied by State highway agencies are present, then to gain insight into the likely 

percentage of travel by each FHWA vehicle class, it is possible to examine: data at other LTPP 

sites on the same roadway located nearby geographically; sites elsewhere in the State; sites on 

similar roads that are not physically close to the site being examined; and a State’s HPMS 

submittal for the roadway being examined to understand the State’s expectations for the size and 

basic composition (e.g., single versus combination units) of truck volumes at a given site. The 

HPMS submittal contains an estimate of truck volume by class. Most, but not all, roads used in 

the LTPP experiment have data available in the HPMS. There are two major limitations in the 

data available through the HPMS: limited or no truck volume data exist at most sites prior to the 

1990s because most State agencies did relatively little truck volume counting; and most truck 

volume data in the HPMS are limited to simplified truck categories (e.g., single and combination 

unit volumes) as opposed to more detailed FHWA vehicle classification categories. 

Consequently, wherever possible, the computed parameter development process relies on 

submitted LTPP traffic monitoring data and State highway agency submittals.

Methodology for Parameter Computing or Estimating 

The methodology for parameter computing or estimating begins with the completion of the 

previously computed base computational table used for developing an AADTT value for all 

truck classes combined for all years. At the conclusion of that task, an AADTT value is available 

for all years for which each test site is open to traffic. These values are used in combination with 

the parameters described in the following sections. 
110

Extraction of Annual Truck Percentage Data by Vehicle Classification 

The next step is to extract all detailed annual truck volume data by vehicle classification from the 

LTPP database that have passed at least through Level D of the LTPP QA checks. For traffic 

monitoring data, these come from the TRF_MONITOR_LTPP_LN table; for historical data, 

these come from the TRF_HIST_CLASS_DATA table. 

Both datasets are examined to determine the number of days of data present in each yearly 

estimate. Data come from the COUNT_DAYS variable in the TRF_MONITOR_LTPP_LN table 

and from the CLASS_COUNT_DURATION_HOUR variable in the 

TRF_HIST_CLASS_MASTER table for pre-LTPP traffic monitoring data collection counts 

submitted by State highway agencies. These data are used to make a manual reliability 

judgement for producing annual estimates of truck travel by FHWA vehicle classes 4 through 13. 

This is important only when percentages of truck travel by class of trucks from these counts are 

significantly different from other values obtained from monitoring counts, which include a larger 

number of days. 

Next, if historical volume data are not collected and submitted using FHWA truck classes, 

adjustments are manually made to convert data to FHWA classes, as needed. 

Available data are converted to estimates of annual average daily truck volumes for each of 

FHWA’s truck classes. In the case of data taken from the TRF_MON_LTPP_LN table, the 

annual truck volume estimate for each class is divided by the number of days in the year 

(365 or 366). Daily volume estimates by class of vehicles are also converted into a percentage of 

total truck volume. This is done for each year for which data are present. For historical data 

supplied by State highway agencies, this computation may be for a short duration count, not for 

an average annual condition.

Test Reasonableness of the Available Data 

Where data exist, the percentage distribution of truck volumes across FHWA truck classes is 

computed. Large changes in FHWA Class 5, 8, and 9 truck volumes indicate a manual QA 

review of the reliability of these counts is required. Otherwise, data are used as collected. 

This QA review is performed because, when errors in data collection occur in the field, major 

changes in the percentage of vehicles in specific truck classes can occur. FHWA Class 5 or 8 

trucks are particularly susceptible to classification errors. Shifts between FHWA Classes 5 and 8 

also occur when new classification equipment and/or algorithms are put in place. When these 

errors occur, total truck volumes also typically rise or fall significantly as pick-up trucks and 

light-duty vehicles pulling trailers are classified as heavy trucks in FHWA Classes 5 and 8, or are 

no longer classified in those categories, respectively. The difficulty with confirming errors in 

counts associated with these vehicle classes is that, without other information, it is hard to 

identify which class assignments are correct and which are incorrect or whether other factors 

actually caused observed changes in traffic characteristics, such as the traffic pattern shift 

described earlier for LTPP traffic site 040100. 

To look for these classification errors, a two-stage process was proposed. The process uses 

annual LTPP traffic monitoring data where at least 8 of the 10 FHWA vehicle classes have 
111 

annual traffic volume estimates with a record status of “E” resulting from the LTPP QA

process.1 The first stage of the QA review includes the following three steps: 

For each LTPP site, determine the maximum and minimum annual percentage of all 

trucks within each vehicle class for all years of data present in the database (e.g., Class 4 

volume/total truck volume). 

Where the percentage of Class 9 trucks changes by more than 20 percent (i.e., maximum 

percentage minus minimum percentage), a detailed manual review of that site’s vehicle

classification data is performed. 

Where the absolute percentage of the maximum percentage minus the minimum 

percentage changes by more than 15 percent for any other truck class, a detailed review 

of that site’s vehicle classification data is performed.

For sites that fail this initial review, the second review stage is performed. In this stage, detailed 

monthly and, if necessary, daily truck volumes by vehicle classification data are examined to 

determine if specific bad datasets are causing these fluctuations (i.e., determining if a small 

number of counts are causing these errors). In addition, review available metadata to determine if 

data collection equipment changes are implicated in some of the data variation observed in the 

truck volumes by vehicle classification. Where metadata exist, they are stored in the 

TRF_CALIBRATION_AVC table and, in some cases, the TRF_EQUIPMENT_MASTER table 

in SDR 30. 

After extracting detailed classification data and supporting metadata, a manual review is 

performed to determine if observed data are a result of a data collection error or poor device 

calibration. An example of such a finding might occur if data at a site were initially obtained 

with a portable counter and the computed percentage of trucks by class changed significantly 

when a permanent counter was put in place. In such cases, data from the permanent counter (if it 

exhibits consistent data quality) are accepted for use in computing vehicle classification 

percentages used in the CPT. A flag will also be placed in the database stating that a change in 

equipment caused a change in classification. Similarly, if the annual estimate of truck traffic is 

based on a short-duration count that is very different from the majority of other available counts, 

the accuracy of that annual estimate is discounted in the estimation of annual volumes. 

The second stage of the QA review includes the following three steps: 

Check if estimates from short portable counts are significantly different than data 

collected from permanently mounted sensors (e.g., check if year-to-year volumes of 

FHWA Classes 5, 8, or 9 change by more than 30 percent). Where those changes are 

larger than 50 vehicles per day, does the percentage of truck traffic falling within any of 

these three vehicle classes change by more than 10 percent? Permanent count data 

showing consistent patterns are accepted over portable counts. Where this occurs, set 

QAflag variable value to “7.” 

1

For some sites, data for most classes are at record status E, but one class may be at record status D. These data are 

assumed to be valid for computed parameter development.
112

Check if data are collected via calibrated WIM equipment2versus uncalibrated 

equipment and/or portable vehicle classifiers. If the shift in classification percentage 

occurs with a change in equipment, choose calibrated WIM data over other sources. The 

choice between other data sources is a function of the consistency of collected data, 

where consistency is computed as a change in monthly classification percentages of other 

data sources. The more consistent data source is selected. Where this occurs, set QAflag 

variable value to “8.” 

Compare the total volume of trucks counted per day to the change in percentage in each 

truck class. If the change in Class 9 percentage is primarily due to a significant change in 

the volume of Class 5 or 8 trucks, this is likely due to errors in the classification data 

collection process (e.g., overcounting Class 5 and 8 vehicles). Unusual traffic volume 

patterns are flagged and described in a note. Those findings are also used when selecting 

representative truck percentages, where the high percentage of Class 5 and 8 trucks is

generally discounted. When large changes in the percentage of Class 5 or 8 volumes 

occur, it is frequently (but not always) a sign of poor vehicle classification system 

performance. Where this occurs, set QAflag variable value to “8.” 

Note that equipment changes are not always reported to the LTPP program and are therefore not 

always noted in the LTPP database. However, the basic comparison of total truck volumes versus 

percentage of Class 5, 8, and 9 trucks can be used on a monthly or even weekly basis to detect 

changes in equipment performance, even when no specific notification of equipment change 

occurred. The change may be the result of site maintenance, equipment failure, or a change in a 

State highway agency’s vehicle classification algorithm. 

Finally, note that some major changes in both truck volumes and percentages are correct. An 

example of this is the LTPP traffic site 040100, which experienced major changes in traffic 

volume due to a construction project.

Approach for Parameter Computation 

After completing the QA review, available traffic monitoring data are used to compute AADTT 

for each vehicle class (FHWA Classes 4–13) for each year a pavement at an LTPP site was open 

to traffic and for which data are present using the following approach: 

From the TRUCKS_LTPP_LN column of the TRF_MONITOR_LTPP_LN table in 

SDR 30, extract the estimate of annual truck volume by vehicle by class, by year. These 

data are available (when submitted by States) from roughly 1990 through the present day. 

Compute the percentage of truck traffic in each class of trucks. 

If the traffic site was open to traffic prior to 1990, extract and summarize historical data 

from the TRF_HIST_CLASS_DATA table in SDR 30. Convert these estimates to a 

percentage of truck traffic in each class of trucks. 

2The LTAS database contains information on equipment types used to collect traffic monitoring data. These data, 

while not always present, may explain the reason for changes in vehicle class distribution data. They do not directly 

confirm which data are correct, but they can explain why changes occur. 
113

Where traffic monitoring data are not available for specific years, the 

classification-specific annual average daily truck volume is computed by multiplying the 

AADTT value in the CPT by either the historical percentage of truck traffic in each 

FHWA classification (where those data exist) or the mean percentage of trucks in each 

truck category for all years for which monitoring data passing quality checks are 

available. 

If a significant change in truck volume distributions occurs, and the resulting manual 

review determines from other metadata that different patterns apply to different years, the 

mean value for just the years that follow the correct pattern is applied to AADTT values 

in the CPT for missing years. 

Where no monitoring data are available for a site, a manual search for an alternative data 

source is performed. Data on the percentage of truck travel by vehicle classification 

(FHWA Classes 4–13) are obtained from one of three sources. The first source for which 

data are found is used. The search for an alternative data source is performed in the 

following order: 

a. Estimates of annual truck volume by vehicle classification (FHWA Classes 4–13) 

data for the site supplied by State highway agencies. 

b. Percentage of LTPP data existing at test sites that are geographically nearby 

(e.g., <50 mi) on the same roadway, provided those estimates are based on continuous 

count locations. 

c. An average of annual truck volume by vehicle classification (FHWA Classes 4–13) 

estimates for the roads in the same road functional classification and serving similar 

trucks or goods movement in that State. 

AADTT for each class of trucks is computed for each year for which a traffic monitoring 

estimate is not available by multiplying the AADTT value for all trucks combined from 

the CPT by the percentage of truck traffic in each class of vehicles. 

Computational Procedure or Algorithm 

The specific computational procedure for the development of AADTT by FHWA vehicle 

classification (FHWA Classes 4–13) parameters is performed in the following three steps: 

Use the working table created to develop the computed parameter called AADTT by year 

(presented earlier in this report), which already contains AADTT values from monitoring, 

State-supplied, and estimated sources for each year of pavement service life. 

Where data exist for a traffic site, extract the estimated annual truck volume (by class and 

by year) from the TRUCKS_LTPP_LN column of the TRF_MONITOR_LTPP_LN table 

from SDR 30. Divide that value by the number of days in that calendar year and store the 

outcome in the appropriate variable for that class (e.g., AADTT4 for FHWA Class 4). Also 

extract the highest value for that year (across all vehicle classes) from the column and 

place it in the variable ClassCountDays in the working table. 
114

Compute the percentage trucks in each classification from monitoring counts using the 

following formula: 

(1) 

Where: 

Perij = percent of annual truck traffic occurring in FHWA vehicle class j during year 

i.

AADTTij = AADTT in truck class j during year i. 

j = the specific class of trucks for which the percentage is being computed (FHWA 

Classes 4–13) 

Extract historical truck volume data from the TRF_HIST_CLASS_DATA table in 

SDR 30 with the following tasks: 

a. Create a new working table including one row for each historical data collection 

session for a given site and columns for each of the 10 FHWA truck classes (FHWA 

Classes–13), plus columns for the year of the count, count duration, and whether the 

count is for just the LTPP test lane or multiple lanes and/or directions. 

b. Create a row in the table for each unique date for that site, found in the 

CLASS_COUNT_BEGIN_DATE column. 

c. Extract the year for that count from the date value in the 

CLASS_COUNT_BEGIN_DATE column and place that value in the year column of 

the working table. 

d. Fill the columns in the working table with the count data from the 

NO_VEH_LTPP_LN column where data exist in that column. Also set the type of 

count to “9,” meaning the count is just for the LTPP lane. 

e. If no data exist in the NO_VEH_LTPP_LN column, but data do exist in the 

NO_VEH_LTPP_DIR column, use those data to fill the count columns in the 

working table, but set the count type to “8,” meaning directional count. 

f. If no data exist in the NO_VEH_LTPP_DIR column, but data do exist in the 

NO_VEH_2WAY column, use those data to fill the count columns in the working 

table, but set the count type to “7,” meaning traffic data was submitted for both 

directions.

g. Extract the value from the CLASS_COUNT_DURATION_HOUR column of the 

TRF_HST_CLASS_MASTER table in SDR 30 and place it in the working table in 

the variable HistClassCountHours.

Compute the percentage trucks in each classification from historical counts with the 

following tasks: 
115

a. For each year of historical data present in the working table, select all rows for that 

year. If only one row of data is present for each year, compute the percentage of truck 

traffic in each vehicle class from that row’s values and enter those data in the CPT 

working table in the appropriate column (e.g., Per4 holds the percentage of truck 

traffic occurring in FHWA Class 4). Use the following equation: 

 (2) 

Where: 

Volume_Classij = annual volume of vehicles in FHWA vehicle class j during year

i.

j = the specific class of trucks for which the volume is being computed (FHWA 

vehicle Classes 4 to 13) 

Also retrieve from the historical data working table and store the hourly duration 

value in the HistClassCountHours column of the CPT working table. Finally, 

store the CountType variable in the CountType column of the CPT working 

table.

b. If more than one row of data exists in the historical count working table for a given 

year, and all rows in a given year are for the same type of count (i.e., they are all for 

the LTPP lane, or all for both directions combined), add the classification volumes in 

each column together, and then compute the percentage of truck volume for the 

combined volumes that falls within each FHWA vehicle class 4 through 13. Place this 

percentage in the CPT working table. Sum the count duration hours in the multiple 

records for that year, and place that total in the CPT working table. Finally, place the 

count type value in the CountType column. 

c. If more than one type of count is performed in any given year for a site (that is, at 

least two different counts were made, and they have different start dates and were of 

different count types), choose only those records which are the most associated with 

the LTPP lane (so choose data with a CountType of “9” over data with a CountType 

of “8” or “7,” but choose a CountType of “8” over a CountType of “7”). Compute the 

appropriate statistics as defined in step 5a or 5b depending on the number of rows of 

data available for that year or that count type. The only difference is that CountType 

should be set equal to 6, to indicate that multiple types of data were present. This will 

be used as a manual review flag to determine if manual intervention is needed to take 

better advantage of the available historical classification count data. 

The CPT working table already has AADTT values (all truck classes combined) for all 

years the traffic site was part of the LTPP experiment. It also has truck percentages for all 

years for which data were submitted, either traffic monitoring data collected as part of the 

LTPP experiment or State-submitted historical counts. These data are used in the data 

QA/ reasonableness review to find shifts and/or outliers in vehicle classification 
116

percentages using data from multiple years. The following steps are used to perform that 

review: 

a. Process the CPT working table to see if, during any year-to-year comparison, the 

percentage of truck volume assigned to any given vehicle class changes by more than 

15 percent (e.g., if in 2006, Class 9 trucks account for 50 percent of truck travel, and 

in 2007, Class 9 trucks account for 65.2 percent of truck travel, this site’s vehicle 

classification data would be flagged for manual review). Where this occurs, set 

QAflag variable value to “3.” 

b. If these large changes are found, perform a manual review of data. The review will 

look at whether volumes at the site are very small (so small that minor changes in 

truck volumes result in large percentage changes); whether there were changes in data 

collection equipment (and if one set of data should be considered less accurate); and 

if some data should be ignored because their collection represents a small sample size 

(e.g., only a few hours of data were collected) and are therefore subject to potential 

bias as an annual estimate of conditions relative to other data available at the site. 

Based on the review, the reviewer will select which years to use to compute the mean 

condition for the site. This may mean one set of years is used to predict use for all years 

for which previously submitted data are not present. Alternatively, the manual review 

might indicate the need to use more than one vehicle pattern to represent traffic 

characteristics over the life of the LTPP test site. For example, in the case of the LTPP 

traffic site 040100, two different periods are identified in the manual review. One vehicle 

classification pattern was used for the period when construction affected the site, and a 

second was used for the period when construction did not affect the site. Historical data 

were included in these computations if data passed the data QA/reasonableness review 

performed in step 6. 

The mean value of the percentage of truck traffic occurring in each truck class j

(MeanPercentClassj) is computed using all years for which data are available and of 

acceptable quality. This is computed for each class using the following formula: 

(3) 

Where: 

Perj = percent of annual truck traffic occurring in FHWA vehicle class j.

n = total number of years (not necessarily consecutive) for which valid traffic 

monitoring data are available at this site. 

i = in-service year. 

N = number of years for which valid traffic monitoring data exist at this site. 

These values will be assigned to all years for which traffic monitoring data are not 

present and any years for which historical data are considered unreliable by the manual 

review process. 
117

If more than one (confirmed) vehicle classification pattern is observed in the data, the 

calculation in step 9 is repeated for each verified classification pattern. Specific years of 

data included within each pattern will be determined as part of the manual review. 

In this step, it must be confirmed that the sum of the mean percentage values computed in 

steps 9 and 10 equals 100 percent. If the sum of these values across all 10 truck classes is 

not equal to 100 percent, normalize the values so that they equal 100 percent. 

Compute AADTT for each FHWA vehicle class j (j= 4 to 13) for all years for which 

traffic monitoring data are not available by multiplying the mean truck percentage 

MeanPercentClassj by the total AADTT (FHWA Classes 4–13 combined) for that year i.

This is expressed in the following equation: 

 AADTij = MeanPercentClassj x AADTTi_total (4) 

Where: 

AADTTi_total = AADTT in year i for all truck classes combined. 

Although other methods for estimating volumes for individual truck classes are possible, 

lower volume truck classes are often volatile. For low-volume classes, changes can be 

modest in absolute size but represent significant percentage changes (e.g., a change from 

a count of two to three represents a 50 percent increase in truck volume). A review of 

collected data shows that many of these low-count values follow relatively little pattern. 

It was determined that a consistent replacement—the mean of all valid data—provided a 

more transparent and potentially more reliable replacement value than trying to directly 

interpolate between available data points, particularly given multiple gap sizes and no 

knowledge of underlying factors affecting truck traffic at the LTPP test sites.

The LTPP traffic site now has a complete set of AADTTij values. Populate the CPT with 

AADTTij values from the CPT working table, along with other required ancillary data, 

such as the data source values in the “Source” column. The data source value will be set 

as “M,” meaning data are from monitored data; “S,” meaning data are from historically 

collected count data; “E,” meaning data are estimated from a combination of Statesupplied volume estimates and average vehicle distributions; or “D,” meaning data 

external to the LTPP program or borrowed from other LTPP test sites must be used to 

estimate the distribution of trucks across the 10 FHWA vehicle classes. 

Examples of Parameter Estimating 

AADTT by Vehicle Class Estimation for LTPP Traffic Site 040100, Case of Significant 

Change in Vehicle Classification Due to a Construction Event 

The first example of the process for computing AADTTij for each vehicle classification (FHWA 

Classes 4–13) is for LTPP traffic site 040100. As described above for the development of the 

computed parameter for the AADTT value for each year (AADTTi) for all truck classes 

combined, this site has traffic monitoring data for 1994–2001, and from 2003 to the current year. 

It has State-submitted AADTT estimates for 1993–1995, and 2001. The CPT for total AADTTi
118

was shown in table 18. Table 21 shows the AADTTij by vehicle classification (FHWA Classes 4–

13) for each year for which monitoring data were submitted for this site. 

Table 21. AADTTij for each vehicle class for LTPP traffic site 040100. 

Year 

FHWA 

Class 4 

FHWA 

Class 5 

FHWA 

Class 6 

FHWA 

Class 7 

FHWA 

Class 8 

FHWA 

Class 9 

FHWA 

Class 10 

FHWA 

Class 11 

FHWA 

Class 12 

FHWA 

Class 13 

1994 9 109 18 0 53 384 3 43 9 0

1995 9 113 16 1 55 413 3 37 9 1

1996 9 112 18 0 58 459 4 42 8 1

1997 11 159 24 0 43 477 5 41 8 1

1998 10 182 22 0 47 518 4 43 8 1

1999 14 182 22 0 44 549 4 49 9 1

2000 20 221 25 1 49 571 5 46 9 1

2001 20 223 24 1 57 617 6 49 9 1

2002 — — — — — — — — — —

2003 6 131 9 0 22 45 0 0 0 0

2004 6 119 8 0 27 58 0 1 0 0

2005 9 118 9 0 24 58 0 0 0 1

2006 5 175 5 0 7 25 0 0 0 1

— = No data. 

Table 22 shows the percentile distribution of trucks in each FHWA vehicle class 4 through 13 for 

each year for which traffic monitoring data are available. Table 22 also shows the mean 

percentile values at the bottom of the table for all years for which classification counts were 

submitted as part of the traffic monitoring process.

Table 22. Percentage of trucks in each vehicle class for LTPP traffic site 040100. 

Year 

FHWA 

Class 4 

(Percent) 

FHWA 

Class 5 

(Percent) 

FHWA 

Class 6 

(Percent) 

FHWA 

Class 7 

(Percent) 

FHWA 

Class 8 

(Percent) 

FHWA 

Class 9 

(Percent) 

FHWA 

Class 10 

(Percent) 

FHWA 

Class 11 

(Percent) 

FHWA 

Class 12 

(Percent) 

FHWA 

Class 13 

(Percent) 

1993 — — — — — — — — — —

1994 1.4 17.4 2.9 0.0 8.4 61.1 0.5 6.8 1.5 0.0

1995 1.4 17.2 2.5 0.1 8.4 62.9 0.5 5.6 1.3 0.1

1996 1.3 15.7 2.6 0.0 8.1 64.4 0.6 5.9 1.2 0.1

1997 1.4 20.7 3.2 0.0 5.6 62.0 0.6 5.3 1.0 0.1

1998 1.2 21.8 2.6 0.0 5.6 62.1 0.5 5.2 1.0 0.1

1999 1.6 20.9 2.5 0.1 5.0 62.8 0.5 5.6 1.1 0.1

2000 2.1 23.3 2.6 0.1 5.2 60.3 0.5 4.9 1.0 0.1

2001 2.0 22.1 2.4 0.1 5.7 61.4 0.6 4.8 0.9 0.1

2002 — — — — — — — — — —

2003 3.0 61.5 4.1 0.1 10.1 20.9 0.2 0.1 0.0 0.1

2004 2.9 53.9 3.8 0.1 12.2 26.3 0.2 0.4 0.0 0.1

2005 4.0 53.7 4.1 0.2 11.0 26.5 0.2 0.1 0.0 0.2

2006 2.2 79.9 2.3 0.0 3.4 11.6 0.2 0.0 0.0 0.3

Mean 2.0 34.0 3.0 0.1 7.4 48.5 0.4 3.7 0.7 0.1

— = No data. 
119

As can be seen in Table 22, years 1993 and 2002 don’t have traffic monitoring data. Table 22

shows that truck percentages changed dramatically in 2003 when construction events changed 

the nature of truck traffic using this road. Thus, the shift observable in the data is valid. What is 

unclear is exactly when in 2002 the traffic pattern changes. In cases where no additional 

information is available, the mean percentage of truck traffic by class of vehicles is used to 

estimate these percentages. At LTPP traffic site 040100, the dramatic change in traffic volume 

between 2002 and 2003 signals a major change in the truck traffic composition. Because the 

State highway agency understands when this change occurred and submitted an AADTT value 

(for all truck classes combined) that mimics the preconstruction traffic pattern, it is assumed that 

the preconstruction pattern applies to 2002. Thus, the average of only pre-2002 yr are used to 

estimate the truck traffic distribution pattern. Table 23 shows both the percentage values used for 

estimation and the estimated 1993–2002 AADTT values for each class of trucks, which are 

computed by multiplying the 1993 or 2002 AADTT value for all truck classes combined by the 

percentage of traffic by class. 

Table 23. Computed truck volumes and percentages by truck class for LTPP traffic site 

040100 in 2002 and 1993.

Year 

FHWA 

Class 4 

FHWA 

Class 5 

FHWA 

Class 6 

FHWA 

Class 7 

FHWA 

Class 8 

FHWA 

Class 9 

FHWA 

Class 10 

FHWA 

Class 11 

FHWA 

Class 12 

FHWA 

Class 13 

Mean, 

pre-2002 

percent 

by class 

1.5

percent 

19.9

percent 

2.7

percent 

0.0

percent 

6.5

percent 

62.1 

percent 

0.5

percent 

5.5

percent 

1.1

percent 

0.1

percent 

2002 

AADTT 

by class 

17 216 29 1 71 674 6 60 12 1

1993 

AADTT 

by class 

6 80 11 0 26 248 2 22 4 0

The estimated 1993 and 2002 AADTT values by FHWA vehicle classification are then used to 

fill in missing 1993–2002 values in the CPT.

AADTT by Vehicle Class Estimation for LTPP Traffic Site 321020, a Case of a Dataset with 

Gap Years and a Short-Duration Change in Vehicle Classification 

The second example of the AADTTij estimation process is for LTPP traffic site 321020. The 

section was opened to traffic on June 1, 1984, and left the LTPP GPS-1 experiment in September 

2000. At that time, the LTPP test site entered the GPS-6 experiment, and it continues to be part 

of that experiment today. As shown in table 20, traffic monitoring data exist for parts of 

8 different years beginning in 1991 and ending in 2003. Counts for these years provided data 

from between 16 and 295 d of the year. In addition, the Nevada DOT supplied estimates of 

AADTT (total for all truck classes) for 1984–2000. AADTT values were displayed in table 20.

In addition, two short-duration vehicle classification counts, both lasting less than 24 h, were 

performed in 1985 and 1988. Table 24 shows AADTTij values for the years during which traffic 

monitoring data were submitted. Table 25 shows these values converted to a percentage of 
120

average annual truck volume. Also, table 25 contains the historical percentages from the 1985 

and 1988 short-duration vehicle classification counts, shown on the first two rows. 

Table 24. AADTTij by FHWA truck class at LTPP traffic site 321020. 

Year 

FHWA 

Class 4 

FHWA 

Class 5 

FHWA 

Class 6 

FHWA 

Class 7 

FHWA 

Class 8 

FHWA 

Class 9 

FHWA 

Class 10 

FHWA 

Class 11 

FHWA 

Class 12 

FHWA 

Class 13 

Total 

Truck 

Volume 

1991 2 25 3 1 6 102 3 9 4 9 165

1992 2 24 3 2 5 110 2 9 5 14 176

1993 2 22 4 1 8 122 2 15 5 12 194

1996 2 36 10 0 5 148 8 11 7 45 271

1997 3 17 7 2 7 157 14 18 10 35 270

2000 2 13 4 1 8 135 23 37 26 81 329

2002 5 24 3 0 4 260 3 15 6 21 342

2003 3 24 6 1 10 204 3 8 6 14 276

Table 25. Percent of AADTTij by FHWA truck class by year at LTPP traffic site 321020. 

Year 

FHWA 

Class 4 

(Percent) 

FHWA 

Class 5 

(Percent) 

FHWA 

Class 6 

(Percent) 

FHWA 

Class 7 

(Percent) 

FHWA 

Class 8 

(Percent) 

FHWA 

Class 9 

(Percent) 

FHWA 

Class 10 

(Percent) 

FHWA 

Class 11 

(Percent) 

FHWA 

Class 12 

(Percent) 

FHWA 

Class 13 

(Percent) 

1985 1.1 21.1 7.4 3.2 0.0 54.7 0.0 4.2 1.1 7.4

1988 0.6 14.3 2.5 0.0 3.1 55.9 0.0 1.9 6.2 15.5

1991 1.1 15.2 1.9 0.8 3.6 62.2 1.8 5.6 2.4 5.4

1992 1.1 13.4 1.7 0.9 2.9 62.6 1.1 5.4 2.6 8.2

1993 1.2 11.6 2.0 0.6 4.3 62.7 1.1 7.8 2.6 6.2

1996 0.8 13.2 3.6 0.0 1.7 54.4 2.8 4.0 2.7 16.6

1997 1.0 6.4 2.7 0.6 2.6 58.1 5.2 6.5 3.8 13.1

2000 0.6 4.0 1.1 0.4 2.5 41.1 6.8 11.1 7.8 24.6

2002 1.4 7.0 0.9 0.0 1.1 76.1 1.0 4.5 1.8 6.3

2003 1.2 8.6 2.0 0.2 3.4 73.6 1.1 2.8 2.1 5.0

The data QA/reasonableness review found two periods when large changes in vehicle 

percentages and volumes occurred. This resulted in a manual review of available LTPP data. A 

decrease of greater than 15 percent occurs in FHWA Class 9 trucks in 2000 when compared to 

the previous record (1997); this is followed by an increase in 2002 (the next year of data). In 

2000, there is also a significant increase in the volume of FHWA Class 13 vehicles, a lesser 

increase in other combination truck classes, and a decrease in FHWA Class 9 vehicles. These 

increased volumes go away in 2002 when volumes return to a more typical pattern, although 

FHWA Class 9 volume continues to be higher than in previous years. 

An examination of equipment replacement and calibration records in the LTPP database provides 

no information on the equipment. An examination of the data in the LTAS database shows 

changes in traffic volume patterns. It is possible that some vehicle misclassification occurred in 

2000. However, it is also possible that changes in local traffic patterns due to non-LTPP-related 

road construction activity caused these volume changes. 
121

The analysis of data in tables 25 and 26 indicate that truck volumes and percentages tend to vary 

substantially over time at this site, yet the basic pattern of the percentage of travel by each truck 

class is reasonably consistent. That is, FHWA Class 9 trucks are by far the largest category of 

trucks. Only in 2000 does FHWA Class 9 drop below 50 percent of the truck traffic, and during 

2000, the major reason for the decline in FHWA Class 9 percentage was a unique growth in 

FHWA Class 11 and 13 truck volumes rather than a significant decline in FHWA Class 9 

volumes. Such growth—without a commensurate increase in FHWA Class 5 or 8 truck 

volumes—is not a common classification error; it is likely the result of temporary construction 

activity placing heavy truck loads on this road section. 

The manual review of historical records available in LTPP database concluded nothing in these 

historical data suggest that taking the mean value of the percentage of travel by vehicle 

classification from traffic monitoring data would lead to a poor estimate of truck travel by 

classification. This was done, and these percentages were applied to AADTTi values found in the 

previously developed CPT whenever actual traffic monitoring data were not available. (Note that 

these mean values were also used to create the CPT describing the representative percentages of 

travel by vehicle classification.) Table 26 is the resulting new CPT. The exception to this was 

that historical short-count data were used for the percentage of truck travel in the two years 

where those data were collected. 

Table 26 shows the final table of AADTTij for LTPP traffic site 321020 from the year it first 

opened to traffic until the end of 2015. Table 26 was used to extract the two CPTs for LTPP 

traffic site 321020, one for the GPS-1 experiment (July 1984–September 2000) and the other for 

the GPS-6 experiment (September 2000 to present).
122

Table 26. CPT for AADTTij by FHWA vehicle classification for FHWA Classes 4–13 at LTPP traffic site 321020. 

Year 

FHWA 

Class 4 

FHWA 

Class 5 

FHWA 

Class 6 

FHWA 

Class 7 

FHWA 

Class 8 

FHWA 

Class 9 

FHWA 

Class 10 

FHWA 

Class 11 

FHWA 

Class 12 

FHWA 

Class 13 Source 

1984 1 12 2 1 3 72 3 7 4 13 S 

1985 1 23 8 3 0 60 0 5 1 8 S 

1986 1 11 2 0 3 67 3 6 3 12 S 

1987 1 14 3 1 4 88 4 9 5 15 S 

1988 1 26 5 0 6 102 0 3 11 28 S 

1989 2 18 4 1 5 112 5 11 6 20 S 

1990 2 17 3 1 5 103 4 10 5 18 S 

1991 2 25 3 1 6 102 3 9 4 9 M 

1992 2 24 3 2 5 110 2 9 5 14 M 

1993 2 22 4 1 8 122 2 15 5 12 M 

1994 2 23 5 1 6 141 6 14 7 25 S 

1995 3 29 6 1 8 180 8 17 9 31 S 

1996 2 36 10 0 5 148 8 11 7 45 M 

1997 3 17 7 2 7 157 14 18 10 35 M 

1998 2 24 5 1 7 147 6 14 8 26 S 

1999 4 39 8 2 11 239 10 23 13 42 S 

2000 2 13 4 1 8 135 23 37 26 81 M 

2001 3 33 7 1 9 205 9 20 11 36 E 

2002 5 24 3 0 4 260 3 15 6 21 M 

2003 3 24 6 1 10 204 3 8 6 14 M 

2004 4 40 8 2 11 246 11 24 13 43 E 

2005 4 42 9 2 12 262 11 25 14 46 E 

2006 5 45 9 2 13 279 12 27 15 49 E 

2007 5 48 10 2 13 296 13 29 15 52 E 

2008 5 51 10 2 14 315 13 31 16 55 E 

2009 6 54 11 2 15 335 14 33 18 58 E 

2010 6 58 12 3 16 356 15 35 19 62 E 

2011 6 61 12 3 17 379 16 37 20 66 E 
123

Year 

FHWA 

Class 4 

FHWA 

Class 5 

FHWA 

Class 6 

FHWA 

Class 7 

FHWA 

Class 8 

FHWA 

Class 9 

FHWA 

Class 10 

FHWA 

Class 11 

FHWA 

Class 12 

FHWA 

Class 13 Source 

2012 7 65 13 3 18 403 17 39 21 70 E 

2013 7 69 14 3 19 428 18 42 22 75 E 

2014 8 73 15 3 21 455 19 44 24 79 E 

2015 8 78 16 3 22 484 21 47 25 84 E 

AADTT by Vehicle Class Estimation for LTPP Traffic Site 320100, Case of Dataset with Gap Years and No Significant Changes in 

Vehicle Classification over the Years 

The third example of the AADTTij estimation process is for LTPP traffic site 32-0100. This site joined the LTPP experiment in 1993, 

opened to traffic on September 1, 1995, and left the experiment July 1, 2009. Table 27 shows the AADTTi for each of the 10 FHWA 

truck classes for each year for which traffic monitoring data are available through 2009, when the site left the LTPP experiment.
124

Table 27. AADTTij for each vehicle class for LTPP traffic site 320100. 

Year 

FHWA 

Class 4 

FHWA 

Class 5 

FHWA 

Class 6 

FHWA 

Class 7 

FHWA 

Class 8 

FHWA 

Class 9 

FHWA 

Class 10 

FHWA 

Class 11 

FHWA 

Class 12 

FHWA 

Class 13 

1996 15 64 15 0 17 798 22 18 18 82

1997 17 165 15 0 17 828 25 18 14 113

1998 17 154 17 0 17 880 27 19 12 143

1999 16 175 14 0 19 980 23 22 10 137

2000 16 154 13 0 19 934 20 23 10 181

2001 16 152 14 0 17 922 22 25 12 136

2002 16 153 12 0 17 914 19 19 11 123

2003 19 182 13 0 19 993 17 19 11 132

2004 21 215 16 0 21 1013 18 22 13 141

2005 8 86 14 1 22 959 24 22 14 61

2006 25 25 28 0 82 898 38 24 16 147

2007 — — — — — — — — — —

2008 23 23 23 1 34 705 22 26 16 84

2009 23 19 19 1 25 702 20 22 17 80

— = No data. 

These values were used in the CPT. However, annual statistics were also needed for 1995 and 

2007. These values were computed by taking the mean percentage of truck traffic across all years 

and multiplying that by AADTT for all truck classes combined (See computation results in 

Table 29). 

Table 28 shows the percentage of trucks in each FHWA vehicle classes 4 through 13 for each 

year for which traffic monitoring data are available. Analysis of these data shows no major 

changes in classification percentages during the LTPP experiment. Thus, the mean value of these 

conditions is assumed to be the best estimator of truck percentages during the years when no 

traffic monitoring data are available. The mean value of these annual statistics is shown at the 

base of the table. 

Table 28. Percent of AADTT by FHWA truck class at LTPP traffic site 320100. 

Year 

FHWA 

Class 4 

(Percent) 

FHWA 

Class 5 

(Percent) 

FHWA 

Class 6 

(Percent) 

FHWA 

Class 7 

(Percent) 

FHWA 

Class 8 

(Percent) 

FHWA 

Class 9 

(Percent) 

FHWA 

Class 10 

(Percent) 

FHWA 

Class 11 

(Percent) 

FHWA 

Class 12 

(Percent) 

FHWA 

Class 13 

(Percent) 

1996 1.5 6.1 1.4 0.0 1.6 76.0 2.1 1.7 1.7 7.8

1997 1.4 13.6 1.2 0.0 1.4 68.3 2.0 1.4 1.2 9.3

1998 1.3 12.0 1.4 0.0 1.4 68.3 2.1 1.5 0.9 11.1

1999 1.1 12.5 1.0 0.0 1.4 70.2 1.6 1.6 0.7 9.8

2000 1.1 11.2 1.0 0.0 1.4 68.2 1.4 1.7 0.8 13.2

2001 1.2 11.5 1.0 0.0 1.3 70.1 1.7 1.9 0.9 10.4

2002 1.3 11.9 1.0 0.0 1.3 71.1 1.5 1.5 0.8 9.6

2003 1.4 12.9 0.9 0.0 1.3 70.7 1.2 1.3 0.8 9.4

2004 1.4 14.5 1.1 0.0 1.4 68.5 1.2 1.5 0.9 9.5
125

Year 

FHWA 

Class 4 

(Percent) 

FHWA 

Class 5 

(Percent) 

FHWA 

Class 6 

(Percent) 

FHWA 

Class 7 

(Percent) 

FHWA 

Class 8 

(Percent) 

FHWA 

Class 9 

(Percent) 

FHWA 

Class 10 

(Percent) 

FHWA 

Class 11 

(Percent) 

FHWA 

Class 12 

(Percent) 

FHWA 

Class 13 

(Percent) 

2005 0.7 7.1 1.1 0.1 1.9 79.2 2.0 1.8 1.2 5.1

2006 2.0 2.0 2.2 0.0 6.4 69.9 3.0 1.9 1.2 11.5

2007 — — — — — — — — — —

2008 2.4 2.4 2.4 0.1 3.6 73.6 2.3 2.8 1.7 8.8

2009 2.5 2.1 2.1 0.1 2.7 75.6 2.1 2.4 1.8 8.6

Mean 

value 

2.1 8.5 1.5 0.0 2.0 71.7 1.8 1.6 1.2 9.6

— = No data. 

Using these percentages and the AADTT values from table 19, the computed parameters for the 

2 yr during which no traffic monitoring data were collected are computed and shown in 

table 29. 

Table 29. Computed AADTTij values by FHWA truck class for missing years at LTPP 

traffic site 320100. 

Year 

FHWA 

Class 4 

FHWA 

Class 5 

FHWA 

Class 6 

FHWA 

Class 7 

FHWA 

Class 8 

FHWA 

Class 9 

FHWA 

Class 10 

FHWA 

Class 11 

FHWA 

Class 12 

FHWA 

Class 13 

1995 26 107 19 0 25 904 23 20 16 121

2007 26 106 19 0 25 891 23 19 15 119

These values were included with traffic monitoring data shown previously in table 27 to produce 

the required CPT. The mean percentage values are also used to create the CPT for representative 

percentage of travel by each vehicle class (FHWA Classes 4–13). 

TOTAL ANNUAL TRUCK VOLUME 

Computed Parameter Description 

Total annual truck volume (Total Annual Truck Volumei) is a parameter derived from AADTT. 

Total Annual Truck Volumei is the sum of all truck traffic for each day, while AADTT is the 

average daily value. Total Annual Truck Volumei is computed for each in-service year i and 

characterizes total annual volume of vehicles in FHWA Classes 4–13. 

Data Sources used for Parameter Computation 

Total annual truck volume is based on the AADTTi computed parameter statistic described in 

previous sections. It relies on no additional data other than the dates when the LTPP test lane was 

open to traffic for each year, which are used to determine the number of days included in years 

where the LTPP site was open for only a portion of the year. 

Methodology and Computational Procedure for Parameter Computing or Estimating 

Total Annual Truck Volumei is computed for each in-service year the pavement was part of the 

LTPP experiment using a simple computational formula shown in the following equation: 
126

 (5) 

Where: 

number of days open to traffic in year i = number of days the roadway was open to traffic in 

year i. 

Examples of Parameter Estimating 

Because this computation is simple, only one example is given. Table 30 shows how total annual 

truck volume is computed for LTPP traffic site 040100. This road opened to traffic on June 1, 

1993, and left the LTPP experiment on June 1, 2006.1 Thus, the first year of traffic is a partial 

year. In addition, the computation needs to account for the extra day in leap years (1996, 2000, 

2004). Note that, if the LTPP test section leaves the experiment partway through the last calendar 

year, the last year of traffic can also be expressed as a partial year. 

Table 30. Total annual truck volume estimates for LTPP traffic site 040100. 

Year AADTT 

Number of Days 

Open to Traffic 

Total Annual Truck 

Volume 

1993 400 184 73,600

1994 629 365 229,585

1995 657 365 239,805

1996 714 366 261,324

1997 770 365 281,050

1998 835 365 304,775

1999 875 365 319,375

2000 950 366 347,700

2001 1,006 365 367,190

2002 1,085 365 396,025

2003 214 365 78,110

2004 221 366 80,886

2005 220 365 80,300

2006 219 151 33,069

TOTAL ANNUAL TRUCK VOLUME FOR EACH TRUCK CLASS (FHWA VEHICLE 

CLASSES 4–13) 

Computed Parameter Description 

Total annual truck volume for each truck class (Total Annual Volume Truck Classij) is related to 

the computed parameter AADTTij described previously. Total Annual Volume Truck Classij is the 

product of AADTTij by the number of days in a given year the LTPP site was open to traffic. This 

1The annual total volume for 2006 is low because it is assumed that the last day of traffic for the LTPP experiment 

was May 31, 2006. 

Total Annual Truck Volumei = AADTTi × (number of days open to traffic in year i)
127

statistic is computed for each in-service year (i) and each truck class (j) (i.e., FHWA 

Classes 4–13). 

Data Sources used for Parameter Computation 

Total Annual Volume Truck Classij is based on the computed parameter AADTT by FHWA 

vehicle class. It relies on no additional data other than the dates the LTPP test lane was open to 

traffic for each year. 

Methodology and Computational Procedure for Parameter Computing 

The procedure for computing Total Annual Volume Truck Classij for each FHWA vehicle class j

(j changes from 4 to 13) and year i is shown in the following equation: 

 (6) 

If the LTPP test section leaves the experiment partway through the last calendar year, the number 

of days in the last year of traffic will be less than a full year. 

Examples of Parameter Estimating 

Because this computation is simple, only one example is given. Table 31 shows how Total 

Annual Truck Volumei is computed for FHWA Class 9 vehicles for LTPP traffic site 040100. 

This same computation is performed for each of the 10 FHWA vehicle classes, and the multiplier 

is the same for each vehicle class. This road opened to traffic on June 1,1993 and left the LTPP 

experiment on June 1, 2006. Thus, both the first and last year of traffic are partial years. In 

addition, the computation needs to account for the extra day in leap years (1996, 2000, 2004). 

Table 31. Total annual truck volume estimates for FHWA Class 9 for LTPP traffic site 

040100.

Year 

AADTT for 

FHWA 

Class 9 

Number of Days 

Open to Traffic 

Total Annual Volume 

for FHWA Class 9 

1993 248 184 45,632

1994 384 365 140,160

1995 413 365 150,745

1996 459 366 167,994

1997 477 365 174,105

1998 518 365 189,070

1999 549 365 200,385

2000 571 366 208,986

2001 617 365 225,205

2002 674 365 246,010

2003 45 365 16,425

2004 58 366 21,228

2005 58 365 21,170

2006 25 151 3,775
128

CUMULATIVE TOTAL TRUCK VOLUME FOR VEHICLES IN FHWA CLASSES 4–13 

COMBINED, ESTIMATED AND COMPOUNDED ANNUALLY 

Computed Parameter Description 

Cumulative total truck volume for vehicles in FHWA Classes 4–13 combined (Cumulative Total 

Truck Volume) is defined as the total number of trucks that passed over the LTPP test section at a 

defined point in time, typically the end of a given calendar year. A separate value is computed 

for each year an LTPP site is open to traffic, considering traffic from all previous years. This 

process continues until the site leaves the LTPP experiment. For LTPP sites at which traffic data 

are being collected and where those traffic volumes apply to more than one LTPP experiment, 

these computations are made for each experiment and are performed for each traffic site until the 

last LTPP experiment using that site’s traffic data leaves the LTPP experiment. 

Data Sources used for Parameter Computation 

Cumulative Total Truck Volume is based on the computed parameter Total Annual Truck 

Volumei. It relies on no additional data other than the dates the LTPP traffic site was open to 

traffic for each year and decommissioned from LTPP experiment. 

Methodology and Computational Procedure for Parameter Computing 

Cumulative Total Truck Volumei is the sum of all Total Annual Truck Volumeistatistics for each 

year from the date an LTPP site opened to traffic to the end of the year (i) for which cumulative 

truck traffic was computed. Thus, if a roadway opened to traffic on July 1, 1985, and the year for 

which cumulative traffic was provided is 1990, the reported value would be the sum of the Total 

Annual Truck Volumei for 1985–1990, and where the value of Total Annual Truck Volumei for 

1985 represents only 6 mo of traffic. The basic procedure is represented mathematically in the 

following equation: 

 (7) 

Where: 

Opening Date = date when pavement was first opened to traffic. 

Date of Interest = last date in the accumulating period or the date when the site was 

deassigned from the LTPP experiment. 

Note that, in both the first and last years of this computation, Total Annual Truck Volumei is

based on the total period during the year the site was part of the LTPP experiment. If the LTPP 

traffic site leaves the experiment partway through the last calendar year, the Total Annual Truck 

Volumei for the last year of traffic will represent a partial year. 

Examples of Parameter Estimating 

Because this computation is simple, only two examples are given. 
129

Cumulative Total Truck Volume Estimation for LTPP Traffic Site 040100 

Table 32 shows how Cumulative Total Truck Volumei is computed at LTPP traffic site 040100. 

This road was opened to traffic on June 1,1993, and left the LTPP experiment on June 1, 2006. 

Thus, the first and last years of traffic are partial years. 

Table 32. Cumulative total truck volume for LTPP traffic site 040100. 

Year AADTT 

Number of Days 

Open to Traffic 

Total Annual Truck 

Volume 

Cumulative Total 

Truck Volume 

1993 400 184 73,600 73,600

1994 629 365 229,585 303,185

1995 657 365 239,805 542,990

1996 714 366 261,324 804,314

1997 770 365 281,050 1,085,364

1998 835 365 304,775 1,390,139

1999 875 365 319,375 1,709,514

2000 950 366 347,700 2,057,214

2001 1,006 365 367,190 2,424,404

2002 1,085 365 396,025 2,820,429

2003 214 365 78,110 2,898,539

2004 221 366 80,886 2,979,425

2005 220 365 80,300 3,059,725

2006 219 151 33,069 3,092,794

Cumulative Total Truck Volume Estimation for LTPP Traffic Site 321020, Case of Two LTPP 

Experiments Using a Single Data Source 

The second example uses LTPP traffic site 321020 because it serves two different LTPP 

experiments. The first experiment, a GPS-1 project, experienced traffic from June 1, 1984, until 

September 11, 2000. These cumulative totals are shown in table 33. After de-assignment from 

GPS-1 project, this site joined the GPS-6 experiment and continues to be part of that experiment 

today. CTVs restart at “0” when a site enters a new experiment. Computed parameters are 

developed through 2015 for the GPS-6 experiment. These values are shown in table 34.

Table 33. Cumulative total truck volume for LTPP traffic site 321020: GPS-1 experiment. 

Year AADTT 

Number of Days 

Open to Traffic 

Total Annual Truck 

Volume 

Cumulative Total 

Truck Volume 

1984 117 151 17,667 17,667

1985 109 365 39,785 57,452

1986 109 365 39,785 97,237

1987 144 365 52,560 149,797

1988 183 366 66,978 216,775

1989 183 365 66,795 283,570

1990 168 365 61,320 344,890

1991 165 366 60,390 405,280

1992 176 365 64,240 469,520
130

Year AADTT 

Number of Days 

Open to Traffic 

Total Annual Truck 

Volume 

Cumulative Total 

Truck Volume 

1993 194 365 70,810 540,330

1994 230 365 83,950 624,280

1995 293 366 107,238 731,518

1996 271 365 98,915 830,433

1997 270 365 98,550 928,983

1998 240 365 87,600 1,016,583

1999 390 366 142,740 1,159,323

2000 329 254 83,566 1,242,889

Table 34. Cumulative total truck volume for LTPP traffic site 321020: GPS-6 experiment. 

Year AADTT 

Number of Days 

Open to Traffic 

Total Annual Truck 

Volume 

Cumulative Total 

Truck Volume 

2000 329 112 36,848 36,848

2001 334 365 121,910 158,758

2002 342 365 124,830 283,588

2003 276 365 100,740 384,328

2004 402 366 147,132 531,460

2005 427 365 155,855 687,315

2006 454 365 165,710 853,025

2007 483 365 176,295 1,029,320

2008 513 366 187,758 1,217,078

2009 546 365 199,290 1,416,368

2010 580 365 211,700 1,628,068

2011 617 365 225,205 1,853,273

2012 656 366 240,096 2,093,369

2013 697 365 254,405 2,347,774

2014 742 365 270,830 2,618,604

2015 788 365 287,620 2,906,224

CUMULATIVE TRUCK VOLUME FOR EACH VEHICLE CLASS (FHWA CLASSES 

4–13), ESTIMATED AND COMPOUNDED ANNUALLY 

Computed Parameter Description 

Cumulative truck volume by vehicle class (Cumulative Volume Truck Classij) is defined as the 

total number of trucks passing over the LTPP test section at a defined point in time. A separate 

value is computed for each class of trucks (j) for each year (i) an LTPP site is open to traffic. 

This process continues until the site leaves the LTPP experiment. For LTPP sites at which traffic 

data are collected and where those traffic volumes apply to more than one LTPP experiment, 

these computations are made for each experiment and performed for each traffic site until the last 

LTPP experiment using that site’s data leaves the LTPP experiment. 
131

Data Sources used for Parameter Computation 

Cumulative Volume Truck Classij is based on the Total Annual Volume Truck Classij parameter 

described previously in this chapter. It relies on no additional data other than the dates the LTPP 

test lane was open to traffic for each year and when the LTPP site left the experiment. 

Methodology and Computational Procedure for Parameter Computing 

Cumulative Volume Truck Classij value is the sum of all Total Annual Volume Truck Classij

statistics for each year from the date a site opened to traffic to the last year (i) for which 

cumulative traffic was computed. Thus, if a roadway opened to traffic July 1, 1985, and the year 

for which cumulative traffic was provided is the end of 1990, the reported value would be the 

sum of the Total Annual Volume Truck Classij for 1985–1990, where Total Annual Volume Truck 

Classij for 1985 represents only 6 mo of traffic. This is represented mathematically in the 

following equation: 

(8)

From these cumulative statistics, one additional traffic parameter is computed: Cumulative 

Heavy Truck Volumei for each year (i), which is the sum of all Cumulative Volume Truck Classij

values across all FHWA Classes 4–13, except FHWA Class 5 for that year. 

Examples of Parameter Estimating 

Because this computation is simple, only one example is given. Table 35 shows how Cumulative 

Volume Truck Classij is computed for FHWA Class 9 for LTPP traffic site 040100. The only 

difference between this process and what is shown in table 32 is that the computation is 

performed for each vehicle class j (j=9 in this example) starting from the CPT Total Annual 

Volume Truck Classij rather than starting from Total Annual Truck Volumei (for all truck classes 

combined). This same computation is performed for each of the 10 vehicle classes. LTPP traffic 

site 040100 opened to traffic June 1, 1993, and left the LTPP experiment June 1, 2006. Thus, 

both the first and last year of traffic are partial years. 

Table 35. Cumulative total truck volume for FHWA Class 9 trucks for LTPP traffic site 

040100.

Year 

AADTT 

for 

FHWA 

Class 9 

Number of Days 

Open to Traffic 

Total Annual Truck 

Volume for FHWA 

Class 9 

Cumulative Total 

Truck Volume for 

FHWA Class 9 

1993 248 184 45,632 45,632

1994 384 365 140,160 185,792

1995 413 365 150,745 336,537

1996 459 366 167,994 504,531

1997 477 365 174,105 678,636

1998 518 365 189,070 867,706
132

Year 

AADTT 

for 

FHWA 

Class 9 

Number of Days 

Open to Traffic 

Total Annual Truck 

Volume for FHWA 

Class 9 

Cumulative Total 

Truck Volume for 

FHWA Class 9 

1999 549 365 200,385 1,068,091

2000 571 366 208,986 1,277,077

2001 617 365 225,205 1,502,282

2002 674 365 246,010 1,748,292

2003 45 365 16,425 1,764,717

2004 58 366 21,228 1,785,945

2005 58 365 21,170 1,807,115

2006 25 151 3,775 1,810,890

Producing the TRF_TREND CPT 

The TRF_TREND CPT, was developed by the research team to include traffic parameters 

described in the previous sections of this chapter for each LTPP traffic site. The methodologies 

described in the previous sections of this chapter were used for parameter extraction from the 

available LTPP traffic data sources or for parameter estimation. For each LTPP traffic site, one 

row in the TRF_TREND table is present for each calendar year a site was open to traffic until the 

site leaves the LTPP experiment. 

MEPDG TRUCK VOLUME GROWTH PARAMETERS 

Computed Parameter Description 

For analyses using AASHTOWare Pavement ME Design software, there is no option for direct 

entry of annual truck volume statistics for each year of pavement analysis. Instead, the software 

requires the user to enter truck volume growth parameters: an initial (i.e., base year) AADTT 

value, percentile vehicle class distribution for FHWA Classes 4–13, growth function, and growth 

rate. These parameters are used within AASHTOWare Pavement ME Design software to provide 

truck volume estimates over the course of the pavement analysis. These parameters include the 

following: 

• AADTT_FIRST_YEAR_LTPP_LANE, which represents base year AADTT used in 

pavement analysis with AASHTOWare Pavement ME Design software. 

• VEH_CLASS_DISTRIBUTION_PERCENTi, which is the percent of the base year 

AADTT related to a specific FHWA vehicle class (i) provided for each of the 10 truck 

classes (FHWA Classes 4–13). 

• VEH_CLASS_GROWTH_FUNCTIONi, which defines whether the MEPDG will use a 

linear or compound growth function to estimate volumes for vehicle class (i) for years 

after the base year. 

• VEH_CLASS_GROWTH_RATEi, which is the rate of growth applied to vehicle class (i) 

in the equation type specified using VEH_CLASS_GROWTH_FUNCTIONi. 
133

A separate growth function and growth rate is provided for each FHWA vehicle Class 4 to 13. 

Only one usability rating, describing how well computed parameters represent traffic at the site, 

is provided for the site.

Data Sources Used for Parameter Computation

Data for developing MEPDG truck volume growth parameters come from the computed 

parameters tables described in previous sections. Growth parameter computations are based on 

the values of the parameter AADTTij provided for each in-service year for each truck class. 

Because they are computed parameters, these data are present for all years an LTPP site was

open to traffic. In a few cases, additional data are present at the traffic count site for years after 

the site left the LTPP experiment. These data are available for computing AADTT values and 

developing growth rates.

Methodology and Computational Procedure for Parameter Computing

An important aspect of developing the MEPDG truck volume growth parameters is that the 

AADTTij estimates produced using these parameters may not be identical to the the AADTTij

estimates provided in the TRF_TREND table. This is because AASHTOWare Pavement ME 

Design software allows input of only one growth function and growth rate per vehicle class for 

the entire analysis period and one base year AADTT value (for FHWA Classes 4–13 combined). 

Base year AADTT is converted into base year AADTTi within the software using the 

VEH_CLASS_DISTRIBUTION_PERCENTi values. The purpose of the MEPDG truck growth 

parameters (MEPDG base year AADTT, percent truck distribution by vehicle class, growth 

function, and growth rate) is to allow AASHTOWare Pavement ME Design software to generate 

annual truck volumes over the analysis period that provide the best estimates of cumulative truck 

traffic at a site given the limitation of input options in AASHTOWare Pavement ME Design 

software (i.e., allowing only one base year AADTT value, one growth function, and one growth 

rate per each vehicle class).

Truck volumes can be volatile from year to year, and changes in truck volumes year over year do

not always follow consistent linear or compound trends. The percentage difference between 

computed truck volumes based on these growth functions and reported truck volumes based on 

actual traffic monitoring data may be high for some years, especially for low-volume roads or 

truck classes where year-to-year variation is high. The methodology for developing computed 

parameters is intended to select values that minimize differences in the total truck volume by 

class accumulated over the total analysis period; however, the percentage difference observed 

during any given year may be substantial.

To compute the MEPDG truck volume growth parameters, AADTTij values are extracted from 

the CPT TRF_TREND table for each in-service year for each truck class. These values are used 

in two different regression models, one linear and one compound in formulation. The equation 

with the better (i.e., larger) R-squared value is selected as the model for each vehicle class.

Growth coefficients and the formulation of that model are extracted and used to populate the 

CPT housing the MEPDG truck volume growth parameter. The CPT also includes an indicator 

of which formula is used for that vehicle class’s growth curve.
134

The linear growth function formulation is shown in the following equation: 

(9) 

Where: 

MEPDG_AADTTij = AADTT value for FHWA Class j in year i estimated by MEPDG 

growth function. 

MEPDG_AADTTBase j = base year AADTT for FHWA Class j, which is computed by 

multiplying the LTPP lane AADTT value for the first year a site opened to traffic 

(AADTT_FIRST_YEAR_LTPP_LANE) by the percentage of vehicles in class j

(VEH_CLASS_DISTRIBUTION_PERCENTj). 

Agei = age of the traffic site in year i where the first year of traffic operating over the site is 

equal to year “0.” 

Growthpercent = linear growth rate, expressed as a percent. 

The compound growth function formulation is shown in the following equation: 

(10) 

Note that all AADTT values are reported rounded to the nearest integer. Mathematically, for 

low-volume vehicle classes, MEPDG_AADTTBase-j can be less than 1. In all cases where this 

occurs and vehicles of class j are present, the MEPDG_AADTTBase-j value is rounded up to 1.

The AADTT_FIRST_YEAR_LTPP_LANE value in the CPT is computed as shown in the 

following equation: 

(11) 

Where AADTT_FIRST_YEAR_LTPP_LANE is the LTPP lane AADTT value for the first year a 

site opened to traffic. 

The values for the percentage of base year AADTT in each of the vehicle classes 

(VEH_CLASS_DISTRIBUTION_PERCENTj) are computed by dividing MEPDG_AADTTBase j by 

AADTT_FIRST_YEAR_LTPP_LANE and multiplying the result by 100. 

VEH_CLASS_GROWTH_USE_RATING index was developed by the research team to describe 

how effectively growth functions computed from available data for the site represent estimates of 

truck traffic volume growth occurring over the life of the pavement. The index is determined by 

examining a combination of the sources of the AADTTi and AADTTij traffic parameters presented 

in previous sections and the consistency (i.e., uniformity) of truck volume trends at a site. 

Generally, the more traffic monitoring data present for each LTPP site, the better the rating. But,

since the MEPDG requires using a consistent linear or compound growth trend, significant 

discontinuities or abrupt truck volume changes in those trends detract from the reliability of such 

a formulation when predicting annual loading values. Thus, even if good, site-specific data are 

present for creating the growth function, if external factors caused major discontinuities or 
135

changes in truck volume trends, the usability rating is lowered to reflect that the trend function 

may not produce a close approximation of actual traffic loading conditions experienced by the 

best fit growth function. 

Details of the usability rating for the VEH_CLASS_GROWTH_USE_RATING parameter are 

provided in chapter 11 and summarized as follows: 

• A “Best” (code 1) rating is given when more than 75 percent of years of data used in 

computing the growth equation have vehicle class data from the traffic monitoring 

program and during no year of traffic monitoring data does the year-to-year percentage of 

truck traffic within one class of trucks change by more than 15 percent (e.g., FHWA 

Class 9 trucks does not grow from 50 percent to more than 65 percent of total truck 

traffic). 

• A “Better” rating (code 2) is given when more than 50 percent of years of data used in 

computing the growth equation have vehicle class data from the traffic monitoring 

program and during no year of monitoring data does the year-to-year percentage of truck 

traffic within one class of trucks change by more than 15 percent. 

• A “Good” (code 3) rating is given when more than 50 percent, but less than 75 percent, 

of years of data used in computing the growth equation have vehicle class data from the 

traffic monitoring program and during no year of traffic monitoring data does the year-toyear percentage of truck traffic within one class of trucks change by more than 

15 percent. 

• A “Fair” (code 4) rating is given if no code has been assigned above, and more than 

25 percent of years of data used in computing the growth equation have vehicle class data 

from the traffic monitoring program. 

• A “Poor” (code 5) rating is given if less than 25 percent of years of data used in 

computing the growth equation have data from the traffic monitoring program, but at 

least 1 yr of traffic monitoring vehicle classification data is collected during the 

experiment. 

• A rating of “Bad” (code 6) is given if no traffic monitoring data are present for a site. 

Examples of Parameter Estimating 

Estimation of Truck Volume Growth Parameters for LTPP Traffic Site 040100 

The first example describes the computation of growth parameters for LTPP traffic site 040100. 

Table 36 shows values for the computed parameter AADTTij extracted from the TRF_TREND 

table for each in-service year for each truck class for this site. Table 37 shows the R-squared 

statistics for each growth model formulation. The best fit formulations, as judged by the 

R-squared value, are identified by “*.” The R-squared value is based on comparison between 

computed parameter AADTTij values and MEPDG_AADTTij values predicted by the growth 

model. Growth rate and AADTT base value are given in Table 37 for the “best fit” equation 
136

formulation for each vehicle class (FHWA Classes 4–13), along with an indicator of which 

formulation was selected for that class. 

Table 36. AADTTij for each vehicle class for LTPP traffic site 040100. 

Year 

FHWA 

Class 4 

FHWA 

Class 5 

FHWA 

Class 6 

FHWA 

Class 7 

FHWA 

Class 8 

FHWA 

Class 9 

FHWA 

Class 10 

FHWA 

Class 11 

FHWA 

Class 12 

FHWA 

Class 13 

1993 6 80 11 0 26 248 2 22 4 0

1994 9 109 18 0 53 384 3 43 9 0

1995 9 113 16 1 55 413 3 37 9 1

1996 9 112 18 0 58 459 4 42 8 1

1997 11 159 24 0 43 477 5 41 8 1

1998 10 182 22 0 47 518 4 43 8 1

1999 14 182 22 0 44 549 4 49 9 1

2000 20 221 25 1 49 571 5 46 9 1

2001 20 223 24 1 57 617 6 49 9 1

2002 17 216 29 1 71 674 6 60 12 1

2003 6 131 9 0 22 45 0 0 0 0

2004 6 119 8 0 27 58 0 1 0 0

2005 9 118 9 0 24 58 0 0 0 1

Table 37. Growth function attributes for each vehicle class for LTPP traffic site 040100. 

Parameter 

FHWA 

Class 4 

FHWA 

Class 5 

FHWA 

Class 6 

FHWA 

Class 7 

FHWA 

Class 8 

FHWA 

Class 9 

FHWA 

Class 10 

FHWA 

Class 11 

FHWA 

Class 12 

FHWA 

Class 13 

Linear 

R-squared 

0.000 0.146 0.114 0.002 0.213 0.199 0.170 0.289 0.332 0.015*

Compound 

R-squared 

0.006

*

0.185

*

0.221

*

0.015

*

0.296

*

0.428

*

0.404* 0.548* 0.556* 0.014

Growth rate, 

percent 

-0.8 3.3 -5.8 -1.7 -7.4 -16.5 -15.6 -35.4 -37.1 -0.8 

MEPDG 

AADTTBase j 

10 118 23 1 61 779 6 165 35 1

Vehicle 

class 

distribution, 

percent 

0.83 9.84 1.92 0.08 5.09 64.97 0.50 13.76 2.92 0.08 

Equation 

type 

C C C C C C C C C L 

*Best fit model. 

C = compound growth; L = linear growth. 

AADTT_FIRST_YEAR_LTPP_LANE is the sum of base year AADTTij values in Table 37, which 

is 1,199. 

Because LTPP traffic site 040100 experiences a major discontinuity in truck travel in 2003, 

table 36 illustrates the limitations of using a single growth formula for estimating truck travel on 
137

roads that do not experience consistent traffic patterns. The resulting R-squared value illustrates 

how poorly the linear and compound growth functions replicate traffic monitoring volume 

patterns. But, since these are the only formulas allowed by AASHTOWare Pavement ME Design 

software, the best of these allowable formulations are computed, as shown in Table 37, and are 

placed in the computed parameter for MEPDG truck volume growth parameters. 

Discontinuity caused the VEH_CLASS_GROWTH_USE_RATING for LTPP traffic site 040100 to 

be set to 4 (Fair). If not for the discontinuity, this site would be rated as 1 (Best). 

Estimation of Truck Volume Growth Parameters for LTPP Traffic Site 320100 

The second example describes the computation of MEPDG truck volume growth parameters for 

LTPP traffic site 320100. Table 38 shows the computed parameter AADTTij values extracted for 

each in-service year for each truck class for this site. Table 39 shows the R-squared statistics for 

each model formulation. The best fit formulations, as judged by the R-squared value, are 

identified by “*.” The R-squared value is based on comparison between computed parameter 

AADTTij values and MEPDG_AADTTij values predicted by the growth model. Growth rate and 

AADTT base value are given in Table 39 for the “best fit” equation formulation for each vehicle 

class (FHWA Classes 4–13), along with an indicator of which formulation was selected for 

that class. 

Table 38. AADTTij for each vehicle class for LTPP traffic site 320100. 

Year 

FHWA 

Class 4 

FHWA 

Class 5 

FHWA 

Class 6 

FHWA 

Class 7 

FHWA 

Class 8 

FHWA 

Class 9 

FHWA 

Class 10 

FHWA 

Class 11 

FHWA 

Class 12 

FHWA 

Class 13 

1995 26 107 19 0 25 904 23 20 16 121

1996 15 64 15 0 17 798 22 18 18 82

1997 17 165 15 0 17 828 25 18 14 113

1998 17 154 17 0 17 880 27 19 12 143

1999 16 175 14 0 19 980 23 22 10 137

2000 16 154 13 0 19 934 20 23 10 181

2001 16 152 14 0 17 922 22 25 12 136

2002 16 153 12 0 17 914 19 19 11 123

2003 19 182 13 0 19 993 17 19 11 132

2004 21 215 16 0 21 1013 18 22 13 141

2005 8 86 14 1 22 959 24 22 14 61

2006 25 25 28 0 82 898 38 24 16 147

2007 26 106 19 0 25 891 23 19 15 119

2008 23 23 23 1 34 705 22 26 16 84

2009 23 19 19 1 25 702 20 22 17 80

Table 39. Growth function characteristics for each vehicle class for LTPP traffic site 

320100.

Parameter 

FHWA 

Class 4 

FHWA 

Class 5 

FHWA 

Class 6 

FHWA 

Class 7 

FHWA 

Class 8 

FHWA 

Class 9 

FHWA 

Class 10 

FHWA 

Class 11 

FHWA 

Class 12 

FHWA 

Class 13 

Linear 

R-squared 

0.002 0.116 0.051* 0.300* 0.113 0.054 0.000 0.3029* 0.0019 0.054
138 

Parameter

FHWA 

Class 4

FHWA 

Class 5

FHWA 

Class 6

FHWA 

Class 7

FHWA 

Class 8

FHWA 

Class 9

FHWA 

Class 10

FHWA 

Class 11

FHWA 

Class 12

FHWA 

Class 13

Compound 

R-squared

0.005* 0.253* 0.034 0.239 0.136* 0.070* 0.003* 0.3029 0.0024* 0.074* 

Growth 

rate, 

percent

-0.4 -7.3 18.5 2.1 2.8 -0.5 -0.2 28.3 0.2 -1.5 

MEPDG

AADTTBase

j 

20 176 16 1 18 925 23 19 13 131 

Vehicle 

class 

distribution, 

percent

1.49 13.11 1.19 0.07 1.34 68.93 1.71 1.42 0.97 9.76 

Equation 

type 

C C L L C C C L C L 

*Best fit model.

C = compound growth; L = linear growth.

AADTT_FIRST_YEAR_LTPP_LANE is the sum of base year AADTTi values in table 39, which is 

1,342. 

The VEH_CLASS_GROWTH_USE_RATING for this site is 1 (Best), because more than 75 

percent of years (13 of 17) have data collected as part of the LTPP traffic monitoring program, 

and no major discontinuities are apparent in those data. This means that the majority of data used 

to produce these formulas are LTPP site-specific and underwent a variety of QC checks, making 

the estimates reasonable. However, the low R-squared values shown in table 39, indicate these 

equation formulations may not always provide a highly accurate replication of actual truck 

volume conditions. 

Estimation of Truck Volume Growth Parameters for LTPP Traffic Site 321020 

The third example describes the computation of growth equations for LTPP traffic site 321020. 

Two separate equations are developed for this traffic site because it is used for two separate 

LTPP experiments. Initially, from June 1984 to September 2000, this site was part of the GPS-1 

experiment. Then, after a major maintenance treatment was performed, it became part of the 

GPS-6 experiment, and it continues to be part of that experiment today. Separate growth 

equations were produced for both experiments. Table 40 shows computed parameter AADTTij

values extracted for each in-service year for each truck class while in the GPS-1 experiment. 

Table 42 shows computed parameter AADTTij values extracted for each in-service year for each 

truck class while part of the ongoing GPS-6 experiment. 

Similar to the two previous examples, table 41 and table 43 show the R-squared statistics for 

each model formulation, and the resulting values for MEPDG growth rate and base year AADTT 

values for the “best fit” equations. 
139 

Table 40. AADTTij for each FHWA truck class for LTPP traffic site 321020, GPS-1 

experiment. 

Year

FHWA 

Class 4

FHWA 

Class 5

FHWA 

Class 6

FHWA 

Class 7

FHWA 

Class 8

FHWA 

Class 9

FHWA 

Class 10

FHWA 

Class 11

FHWA 

Class 12

FHWA 

Class 13

1984 1 12 2 1 3 72 3 7 4 13 

1985 1 23 8 3 0 60 0 5 1 8 

1986 1 11 2 0 3 67 3 6 3 12 

1987 1 14 3 1 4 88 4 9 5 15 

1988 2 18 4 1 5 112 5 11 6 20 

1989 2 18 4 1 5 112 5 11 6 20 

1990 2 17 3 1 5 103 4 10 5 18 

1991 2 25 3 1 6 102 3 9 4 9 

1992 2 24 3 2 5 110 2 9 5 14 

1993 2 22 4 1 8 122 2 15 5 12 

1994 2 23 5 1 6 141 6 14 7 25 

1995 3 29 6 1 8 180 8 17 9 31 

1996 2 36 10 0 5 148 8 11 7 45 

1997 3 17 7 2 7 157 14 18 10 35 

1998 2 24 5 1 7 147 6 14 8 26 

1999 4 39 8 2 11 239 10 23 13 42 

2000 2 13 4 1 8 135 23 37 26 81 

Table 41. Growth function characteristics for each FHWA truck class for 

LTPP traffic site 321020, GPS-1 experiment.

Parameter

FHWA 

Class 4

FHWA 

Class 5

FHWA 

Class 6

FHWA 

Class 7

FHWA 

Class 8

FHWA 

Class 9

FHWA 

Class 10

FHWA 

Class 11

FHWA 

Class 12

FHWA 

Class 13

Linear 

R-squared 

0.639 0.220* 0.190 0.004* 0.655 0.726 0.571* 0.664 0.470 0.547 

Compoun

d 

R-squared

0.723* 0.212 0.264* 0.200 0.227* 0.811* 0.303 0.740* 0.580* 0.614* 

Growth 

rate, 

percent

0.064 0.735 0.046 0.266 0.401 0.067 0.841 0.104 0.107 0.103 

MEPDG

AADTTBa

se j 

1 16 3 1 2 69 1 5 3 10 

Vehicle 

class 

distributio

n, percent

0.90 14.41 2.70 0.90 1.80 62.16 0.90 4.50 2.70 9.01 

Equation 

type 

C L C C L C L C C C 

*Best fit model.

C = compound growth; L = linear growth.
140 

For the GPS-1 experiment, the value for AADTT_FIRST_YEAR_LTPP_LANE is the sum of base 

year AADTTi values in table 40, which is 111. 

Based on R-squared values provided in table 41 for the GPS-1 experiment, the compound growth 

formulation generally performs better than the linear formulation. The 

VEH_CLASS_GROWTH_USE_RATING is 4 (Fair) because only 6 of 17 yr have traffic 

monitoring data collected by vehicle class. 

Table 42. AADTTij for each FHWA truck class for GPS site 321020. 

Year

FHWA 

Class 4

FHWA 

Class 5

FHWA 

Class 6

FHWA 

Class 7

FHWA 

Class 8

FHWA 

Class 9

FHWA 

Class 10

FHWA 

Class 11

FHWA 

Class 12

FHWA 

Class 13

2000 2 13 4 1 8 135 23 37 26 81 

2001 3 33 7 1 9 205 9 20 11 36 

2002 5 24 3 0 4 260 3 15 6 21 

2003 3 24 6 1 10 204 3 8 6 14 

2004 4 40 8 2 11 246 11 24 13 43 

2005 4 42 9 2 12 262 11 25 14 46 

2006 5 45 9 2 13 279 12 27 15 49 

2007 5 48 10 2 13 296 13 29 15 52 

2008 5 51 10 2 14 315 13 31 16 55 

2009 6 54 11 2 15 335 14 33 18 58 

2010 6 58 12 3 16 356 15 35 19 62 

2011 6 61 12 3 17 379 16 37 20 66 

2012 7 65 13 3 18 403 17 39 21 70 

2013 7 69 14 3 19 428 18 42 22 75 

2014 8 73 15 3 21 455 19 44 24 79 

2015 8 78 16 3 22 484 21 47 25 84 
141

Table 43. Growth Function Characteristics for Each FHWA Truck Class for 

GPS Site 321020. 

Parameter 

FHWA 

Class 4 

FHWA 

Class 5 

FHWA 

Class 6 

FHWA 

Class 7 

FHWA 

Class 8 

FHWA 

Class 9 

FHWA 

Class 10 

FHWA 

Class 11 

FHWA 

Class 12 

FHWA 

Class 13 

Linear 

R-squared 

0.934

*

0.954* 0.945* 0.836* 0.921* 0.960* 0.348* 0.619* 0.413* 0.452*

Compound 

R-squared 

0.839 0.833 0.824 0.439 0.748 0.912 0.341 0.469 0.409 0.397

Growth rate, 

percent 

0.349 3.861 0.770 0.182 0.989 20.144 0.694 1.760 0.831 2.912

MEPDG 

AADTTBase j

3 20 4 1 6 165 8 17 11 34

Vehicle 

class 

distribution, 

percent 

1.12 

percen

t 

7.43 

percent 

1.49 

percent 

0.37 

percent 

2.23 

percent 

61.34 

percent 

2.97 

percent 

6.32 

percent 

4.09 

percent 

12.64 

percent 

Equation 

type 

L L L L L L L L L L 

*Best fit model. 

L = linear growth. 

For the GPS-6 experiment, the value for AADTT_FIRST_YEAR_LTPP_LANE is the sum of 

AADTTi values in Table 42, which is 269. Note that this is a different base year value than used 

for the GPS-1 site at the same physical location. The growth equations are also different because 

the base year for the GPS-6 site is computed from the time the site became a GPS-6 site, which is 

different from when the site became a GPS-1 site. 

For the GPS-6 experiment, unlike the GPS-1 experiment, the linear formulation does a better job 

of estimating AADTT for all truck classes than the compound formulation. For this experiment, 

the VEH_CLASS_GROWTH_USE_RATING is 5 (Poor) because the majority (13 of 16 yr) of 

traffic estimates for this experiment are extrapolations of earlier data collection efforts. As a 

result, the R-squared values are high, only because the growth equation is based on already 

modeled numbers. In reality the values may not be accurate estimates of actual truck traffic 

volumes.

143

CHAPTER 7. ESTIMATING REPRESENTATIVE TRUCK VOLUMES AND TRUCK 

VOLUME DISTRIBUTIONS BY VEHICLE CLASS

Representative truck volume or vehicle class distributions are used in analyses when one value or 

one set of values (as in the case of a distribution by vehicle class) is used to characterize truck 

traffic for an LTPP site. For example, representative AADTT values are used to differentiate 

between LTPP sites that have high, moderate, or low truck volumes. Representative vehicle class 

distributions are used to identify LTPP sites exposed predominantly to freight trucks (i.e., a high 

volume or percentage of FHWA Class 9 trucks compared to other heavy vehicle classes) versus 

those exposed predominantly to local service trucks (i.e., a high volume or percentage of FHWA 

Class 5 trucks compared to other heavy vehicle classes).

REPRESENTATIVE AADTT FOR LTPP LANE, FHWA VEHICLE CLASSES 4–13 

COMBINED

Computed Parameter Description

Representative AADTT describes the total number of trucks (all vehicles in FHWA Classes

4–13) expected to pass over an LTPP test section in a typical day of a given experiment. It serves

as an easily selected statistic when identifying which LTPP test sections should be included in an 

analysis.

For example, a researcher might want to include only sites with more than 5,000 trucks per day 

in their analysis. Since truck volumes change over time, the representative AADTT value 

provides a single, simple representative statistic.

Data Sources used for Parameter Computation

Representative AADTT is computed from the previously developed computed parameter 

AADTT for each in-service year, all trucks combined. Data sources underlying those statistics 

are described in chapter 6.

A CPT TRF_TREND was selected as a data source over limited traffic monitoring data because 

it better accounts for changes in traffic conditions over the life of an experiment and allows for 

the incorporation of those changes in the representative value. This approach also provides

consistency between CPT datasets.

Methodology for Parameter Computing or Estimating

Representative AADTT is computed as the mean of AADTT values for each year from the CPT

TRF_TREND containing AADTTi values for each in-service year, all trucks combined. The mean 

of AADTT values is selected because some sites show highly variable AADTT values, and thus 

an alternative approach of taking a specific year might not provide an accurate estimate of truck 

volume over the duration of that site’s involvement in the LTPP experiment. High variability is 

often found at LTPP sites with lower truck volumes, as changes in economic activity in nearby 

locations (or at geographic locations generating truck travel on that specific road) can cause large 

percentage changes in truck volumes. In other cases, construction projects on nearby or parallel 
144

routes (or the opening of newly constructed roads) can shift truck traffic onto, or off, the road on 

which the LTPP site is located. By choosing the mean value rather than the median value, 

representative AADTT accounts for both high- and low-volume traffic periods in proportion to 

the time those periods are present. 

Note that for roads experiencing significant changes in truck volumes during the LTPP 

experiment, a single representative traffic statistic is not necessarily a good measure of traffic 

loading during any specific year. 

Computational Procedure or Algorithm 

Representative AADTT is computed as the mean value taken from all years of the computed 

parameter statistic AADTTi for each in-service year, all trucks combined. 

To help LTPP users understand the applicability of this parameter for their studies, a usability 

rating (REP_AADTT_USE_RATING) is assigned to this value in the TRF_REP CPT table. The 

usability rating parameter describes how effectively a single number, computed from available 

data for the site, represents traffic loading typical daily truck volume at an LTPP site. It is 

determined by examining a combination of the source of individual AADTT statistics and the 

consistency of travel trends at the site. Generally, the more traffic monitoring data present for 

each LTPP site, the better the rating. Also, the more consistent the traffic trends, the better the 

rating. Significant discontinuities in truck volume trends reduce the ability of a single number to 

represent the traffic loading condition at a site. Even if large amounts of good, site-specific data 

are present, the usability rating is lowered if external factors caused major discontinuities in 

truck volume trends, reflecting that one number may not be capable of representing actual traffic 

loading truck traffic volumes at a site. 

The following criteria were developed to define usability ratings: 

• A “Best” (code 1) rating is given when more than 75 percent of years of data come from 

the LTPP traffic monitoring program and no major changes in truck volume trends 

occurred at the site. A major discontinuity is defined as a year-to-year change in AADTT 

of more than 25 percent that is also larger than 50 vehicles. 

• A “Better” rating (code 2) is given when more than 75 percent of annual AADTT 

estimates available from a site come from either the LTPP traffic monitoring program or 

State-supplied estimates and there are no major discontinuities in the truck volume trend 

line.

• A “Good” (code 3) rating is given when more than 50 percent, but less than 75 percent,

of years of data come from the LTPP traffic monitoring program or State-supplied AADT 

statistics, and the site’s truck volume trend does not contain major year-to-year 

discontinuity. 

• A “Fair” (code 4) rating is given if less than 50 percent, but more than 25 percent, of the 

years of data come from the LTPP traffic monitoring program or State-supplied AADTT 
145

values or major discontinuity in truck volumes occurred during the life of the LTPP 

experiment. 

• A “Poor” (code 5) rating is given if data do not meet the “Fair” standard. 

Examples of Parameter Estimating 

Estimation of Representative AADTT for LTPP Traffic Site 040100 (LTPP Lane Only) 

The first example illustrated is for LTPP traffic site 040100, which is the single traffic data 

collection site for several SPS-1 sections. The LTPP site opened on June 1, 1993, and left the 

study in June 2006. As discussed in chapter 6, this site experienced two major shifts in truck 

volume during its life in the LTPP experiment. Early in the monitored life of the test pavement, 

the site carried 600 to 1,000 trucks per day, with an upward trend in truck volumes each year. In 

2003, AADTT dropped significantly due to a major construction project shifting truck travel 

patterns in the area. AADTT fell to around 220 trucks per day, with a couple higher volume 

years of up to about 400 trucks per day. After 2011, truck volumes returned to previous levels, 

basically following the growth trend line existing prior to the construction activity. However, the 

site’s participation in the LTPP experiment ended by that time. Table 44 shows the values for a 

computed parameter AADTTi for this site. 

Table 44. Computed parameter AADTTi values for LTPP traffic site 040100. 

Calendar year AADTTi

1993 400

1994 629

1995 657

1996 714

1997 770

1998 835

1999 875

2000 950

2001 1,006 

2002 1,085 

2003 214

2004 221

2005 220

2006 219

Using the adopted definition, representative AADTT value for LTPP traffic site 040100 is 

computed as a mean of AADTTi values presented in Table 44, which is 628. 

Although this value understates the volume of trucks experienced early in the experiment, it 

accounts for lower volumes during the later portion of the LTPP experiment, when the site was 

experiencing low truck traffic volumes. Thus, the value of 628 represents AADTT experienced 

on average during the experiment.
146

REP_AADTT_USE_RATING is set to 4 (Fair) because of the large discontinuity in truck 

volumes occurring because of construction traffic rerouting. 

Estimation of Representative AADTT for LTPP Traffic Site 320100 (LTPP Lane Only) 

The second example of computing representative AADTT is for LTPP traffic site 320100. This 

traffic site is used for 12 SPS-1 experimental sections (320101–320112). The site opened to 

traffic on September 1, 1995, and left the experiment July 1, 2009. These data are shown in 

table 45. 

Table 45. Computed parameter AADTTi values for LTPP traffic site 320100. 

Year AADTT 

Monitoring (M), 

State Estimate (S), or 

Regression Estimate (E) 

1995 1,261 E 

1996 1,050 M 

1997 1,212 M 

1998 1,287 M 

1999 1,396 M 

2000 1,369 M 

2001 1,316 M 

2002 1,285 M 

2003 1,404 M 

2004 1,479 M 

2005 1,193 M 

2006 1,285 M 

2007 1,244 E 

2008 961 M 

2009 926 M 

As with LTPP traffic site 040100, LTPP traffic site 320100 experienced changes in truck volume 

from year to year, although these shifts were not as dramatic as those from LTPP traffic site 

040100. Most noticeable was the drop in truck volume during the Great Recession in 2008 and 

2009. The mean value used for the representative AADTT of 1,247 is a compromise between 

steady traffic volumes experienced in the 1990s, higher volumes in the early 2000s, and the 

decline in truck traffic starting in 2005. 

REP_AADTT_USE_RATING for this site is set to 1 (Best) because more than 75 percent of 

years (13 of 17 yr) have data collected as part of the LTPP traffic monitoring program and no 

major discontinuities are apparent. 

Estimation of Representative AADTT for LTPP Traffic Site 321020 (LTPP Lane Only) 

The third example of the AADTT estimation process is for LTPP traffic site 321020. This site 

was initially part of the GPS-1 experiment. The site opened to traffic in June 1984 and left the 
147

LTPP experiment in September 2000. At that time, the LTPP test site entered the GPS-6 

experiment, and it continues to be part of that experiment today. 

Because this site served two different LTPP experiments, two different representative AADTT 

values were computed. The first was for the GPS-1 experiment from 1984 to 2000. The second 

was for the GPS-6 experiment from 2000 to 2015. Table 46 shows the computed parameter 

AADTTi values for this site. 

Table 46. Computed parameter AADTTi values for LTPP traffic site 321020. 

Year AADTT 

Monitoring (M), 

State-Supplied (S), or 

Estimated (E) 

1984 117 S 

1985 109 S 

1986 109 S 

1987 144 S 

1988 183 S 

1989 183 S 

1990 168 M 

1991 165 M 

1992 176 M 

1993 194 S 

1994 230 S 

1995 293 M 

1996 271 M 

1997 270 S 

1998 240 S 

1999 390 M 

2000 329 E 

2001 334 M 

2002 342 M 

2003 276 E 

2004 402 E 

2005 427 E 

2006 454 E 

2007 483 E 

2008 513 E 

2009 546 E 

2010 580 E 

2011 617 E 

2012 656 E 

2013 697 E 

2014 742 E 

2015 788 S 
148

During the GPS-1 experiment, the pavement experienced consistently growing truck volumes.

The representative AADTT value of 210 trucks per day was computed as a mean of AADTTi

values for 1984–2000).

The growth pattern observed during the GPS-1 experiment continued after the site transitioned

into the GPS-6 experiment. Consequently, the GPS-6 pavement experienced considerably more 

truck traffic per day than did the GPS-1 pavement. The lowest year of traffic the GPS-6 

pavement experienced was higher than the representative value computed for the GPS-1 

experiment. The representative AADTT value for the GPS-6 experiment (i.e., mean value 

computed using AADTTi values for 2001–2015) was 524 trucks.

REP_AADTT_USE_RATING for the GPS-1 site (1984–2000) was set to 2 (Better) because 

more than 75 percent of the years had data either collected under the LTPP traffic monitoring 

program or as State-supplied estimates. However, because much of the data came from 

State-supplied estimates and were not collected at the site and did not undergo a QC as part of 

the LTPP traffic monitoring program, the index is not set to 1. There were no major 

discontinuities during the life of the GPS-1 experiment.

REP_AADTT_USE_RATING for the GPS-6 site (2001–2015) was set to 5 (Poor) because the 

majority (13 of 16 yr) of traffic estimates for this experiment were extrapolations of earlier data 

collection efforts.

REPRESENTATIVE PERCENTILE VEHICLE CLASS DISTRIBUTION FOR FHWA 

CLASSES 4–13 IN LTPP LANE

Computed Parameter Description

Representative percentile vehicle class distribution for FHWA Classes 4–13 in LTPP lane

provides information that can be used with AASHTOWare Pavement ME Design software. The 

software requires the user to enter a set of 10 values representing a percentile distribution of 

heavy vehicles by FHWA classification (for vehicles in FHWA Classes 4–13). This set of 

computed parameters provides the best available set of these values for each LTPP test site.

Data Sources used for Parameter Computation

The dataset used to develop these values is the same one used for developing AADTTij for each 

truck class and each in-service year described in chapter 6. Representative percentile vehicle 

class distribution for FHWA Classes 4–13 in the LTPP lane has the same value as 

MeanPercentClassj used to compute AADTTij. To compute the representative percentile vehicle 

class distribution for FHWA Classes 4–13 in the LTPP lane, it is necessary to compute the mean 

percentage of trucks in each FHWA Class 4 through 13, which is done using all years of vehicle 

classification monitoring data, except when data fail the QA review process.

Methodology for Parameter Computation or Estimation

The methodology for computing the representative percentile vehicle class distribution for 

FHWA Classes 4–13 in the LTPP lane is similar to computing the MeanPercentClassj values 

described in chapter 6.
149

Computational Procedure or Algorithm 

The following steps describe the process for computing representative percentile vehicle 

class distributions for FHWA Classes 4–13 in the LTPP lane. Compute AADTTij by 

FHWA class (j) and year (i) and AADTTi for all truck classes combined using the annual 

total truck volume estimates reported in the TRF_MONITOR_LTPP_LN table. These 

estimates are based on monitored vehicle classification data submitted by each State for 

each year of traffic monitoring. Methodology for AADTTij and AADTTi computation was 

presented in chapter 6. 

Use the AADTTij estimates by FHWA class and AADTTi for all truck classes combined to 

compute the fraction of that truck traffic occurring in each truck class (j) during the year 

(i): AADTTij / AADTTi. 

For years traffic monitoring data are present in the TRF_MONITOR_LTPP_LN table and 

pass basic QA tests, compute the mean percentage of truck traffic occurring in each class 

across by averaging the results of the previous step over all years of valid data. 

Normalize these percentage values to ensure the total of the mean percentages equals 

100 percent. 

If no vehicle classification data from the LTPP traffic monitoring program are available 

for a site, use any historical traffic monitoring data submitted for that site and follow 

Steps 1-4. Do not use these data to estimate representative values if traffic monitoring 

data are present in the LTPP database. 

If neither traffic monitoring data nor detailed historical data are present at the site, first 

attempt to use data at other LTPP sites nearby on the same roadway. If no such data exist, 

average the representative vehicle classification data from the other LTPP sites located on 

similar roadways (i.e., same road functional classification) in the same State to estimate 

the representative percentage distribution of truck travel within each of the 10 vehicle 

classes (i.e., FHWA Classes 4–13). 

The usability rating index REP_VEH_CLASS_USE_RATING was developed to help LTPP 

users in selecting the representative percentile vehicle class distributions for FHWA Classes 4–

13 in the LTPP lane. This index describes data used in parameter computation and how well the 

computed parameter represents the percentile distribution of truck volume by vehicle class. 

Details of the usability rating are provided in chapter 11. 

The following criteria were developed to define usability ratings: 

• A “Best” (code 1) rating is given when more than 75 percent of the years of data come 

from the LTPP traffic monitoring program, no major changes in truck volume trends 

occurred at the site, and no major discontinuities in truck mix are reported. A major 

discontinuity is defined as a change in truck class percentage of more than 15 percent. 

• A “Better” (code 2) rating is given when more than 50 percent of years contain traffic 

monitoring data. 
150

• A “Good” (code 3) rating is given when more than 50 percent of years contain traffic 

monitoring data, but the site’s traffic trend contains a moderately large year-to-year 

discontinuity in AADTT. 

• A “Fair” (code 4) rating is given if less than 50 percent, but more than 25 percent, of 

years have traffic monitoring data or a major change in truck mix (e.g., a shift of more 

than 15 percent of truck traffic occurring within a single vehicle class) occurs at a site. 

• A “Poor” (code 5) rating is given if less than 25 percent of years have traffic monitoring 

data. 

Examples of Parameter Estimating 

Example: Develop Representative Percentile Vehicle Class Distribution for LTPP Traffic Site 

040100 

The first example of estimating representative percentile vehicle class distribution for FHWA 

Classes 4–13 for LTPP traffic site 040100. This site has traffic monitoring data for 1994–2001 

and 2003–present. The percentages of truck traffic in each FHWA truck class (Perij) from these 

counts were shown previously in table 22. Table 47 repeats these values and shows the mean 

value of these statistics. The row containing the mean value was used to report representative 

percentile vehicle class distribution for FHWA Classes 4–13 in LTPP lane. 

Table 47. Percentage of trucks in each vehicle class for LTPP traffic site 040100. 

Year 

FHWA 

Class 4 

(Percent) 

FHWA 

Class 5 

(Percent) 

FHWA 

Class 6 

(Percent) 

FHWA 

Class 7 

(Percent) 

FHWA 

Class 8 

(Percent) 

FHWA 

Class 9 

(Percent) 

FHWA 

Class 10 

(Percent) 

FHWA 

Class 11 

(Percent) 

FHWA 

Class 12 

(Percent) 

FHWA 

Class 13 

(Percent) 

1994 1.4 17.4 2.9 0.0 8.4 61.1 0.5 6.8 1.5 0.0

1995 1.4 17.2 2.5 0.1 8.4 62.9 0.5 5.6 1.3 0.1

1996 1.3 15.7 2.6 0.0 8.1 64.4 0.6 5.9 1.2 0.1

1997 1.4 20.7 3.2 0.0 5.6 62.0 0.6 5.3 1.0 0.1

1998 1.2 21.8 2.6 0.0 5.6 62.1 0.5 5.2 1.0 0.1

1999 1.6 20.9 2.5 0.1 5.0 62.8 0.5 5.6 1.1 0.1

2000 2.1 23.3 2.6 0.1 5.2 60.3 0.5 4.9 1.0 0.1

2001 2.0 22.1 2.4 0.1 5.7 61.4 0.6 4.8 0.9 0.1

2002 — — — — — — — — — —

2003 3.0 61.5 4.1 0.1 10.1 20.9 0.2 0.1 0.0 0.1

2004 2.9 53.9 3.8 0.1 12.2 26.3 0.2 0.4 0.0 0.1

2005 4.0 53.7 4.1 0.2 11.0 26.5 0.2 0.1 0.0 0.2

2006 2.2 79.9 2.3 0.0 3.4 11.6 0.2 0.0 0.0 0.3

Mean 

value 

2.0 34.0 3.0 0.1 7.4 48.5 0.4 3.7 0.7 0.1

— = No data. 

As noted previously, the difficulty with this statistic is that LTPP traffic site 040100 experiences 

two entirely different truck traffic patterns during its time in the LTPP experiment. One pattern is 

present from 1993, when it opened to traffic, until 2001, when construction events elsewhere 
151

caused a change in traffic patterns. This pattern reasserts itself in 2006, when the construction 

project ends. During the construction event (2003–2005) a very different pattern exists in both 

total truck volume and the fractional distribution of truck types within that total volume.

Because the construction event was identified and reported, the research team concluded that the 

observed shift was valid. Consequently, using the mean value of all years the site was open to 

traffic produces a reasonable estimate of overall traffic conditions, even if that value may not 

represent traffic during any specific year of the road’s operation (i.e., because some years 

experienced a low percentage of heavy truck traffic, while others experienced a high percentage, 

a representative value for the entire period must be a mix of these two patterns).

Because this site experienced major discontinuity in truck travel in 2003, the 

REP_VEH_CLASS_USE_RATING index for this site is set to 4 (Fair). If not for the 

discontinuity, this site would be rated as 1 (Best) due to the sufficient monitoring data available. 

Example: Develop Representative Percentile Vehicle Class Distribution for LTPP Traffic Site 

321020 

Table 48 shows the percentage of annual average daily truck travel observed in LTPP traffic 

monitoring data collected at site 321020. This site is part of two different LTPP experiments. 

The pavement opened to traffic in June 1984 as part of the GPS-1 experiment and left that 

experiment in September 2000. At that time, the LTPP test site entered the GPS-6 experiment, 

and it continues to be part of that experiment today. Consequently, two separate mean values 

were computed for the representative percentile vehicle class distribution, as shown in Table 49.

The first used traffic monitoring data through 2000. The second used traffic monitoring data 

collected after 2000. If no traffic monitoring data were collected during one of these periods, the 

mean value from the other period was used. 

Table 48. Percentage of trucks in each vehicle class for LTPP traffic site 321020. 

Year 

FHWA 

Class 4 

(Percent) 

FHWA 

Class 5 

(Percent) 

FHWA 

Class 6 

(Percent) 

FHWA 

Class 7 

(Percent) 

FHWA 

Class 8 

(Percent) 

FHWA 

Class 9 

(Percent) 

FHWA 

Class 10 

(Percent) 

FHWA 

Class 11 

(Percent) 

FHWA 

Class 12 

(Percent) 

FHWA 

Class 13 

(Percent) 

1991 1.1 15.2 1.9 0.8 3.6 62.2 1.8 5.6 2.4 5.4

1992 1.1 13.4 1.7 0.9 2.9 62.6 1.1 5.4 2.6 8.2

1993 1.2 11.6 2.0 0.6 4.3 62.7 1.1 7.8 2.6 6.2

1996 0.8 13.2 3.6 0.0 1.7 54.4 2.8 4.0 2.7 16.6

1997 1.0 6.4 2.7 0.6 2.6 58.1 5.2 6.5 3.8 13.1

2000 0.6 4.0 1.1 0.4 2.5 41.1 6.8 11.1 7.8 24.6

2002 1.4 7.0 0.9 0.0 1.1 76.1 1.0 4.5 1.8 6.3

2003 1.2 8.6 2.0 0.2 3.4 73.6 1.1 2.8 2.1 5.0
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Table 49. Mean percentage of trucks in each vehicle class for 

LTPP traffic site 321020. 

GPS or 

SPS 

and 

Years 

FHWA 

Class 4 

(Percent) 

FHWA 

Class 5 

(Percent) 

FHWA 

Class 6 

(Percent) 

FHWA 

Class 7 

(Percent) 

FHWA 

Class 8 

(Percent) 

FHWA 

Class 9 

(Percent) 

FHWA 

Class 10 

(Percent) 

FHWA 

Class 11 

(Percent) 

FHWA 

Class 12 

(Percent) 

FHWA 

Class 13 

(Percent) 

GPS-1 

prior 

to 2000 

0.97 10.63 2.17 0.55 2.93 56.85 3.13 6.73 3.65 12.35

GPS-6 

post 

2000

1.30 7.80 1.45 0.10 2.25 74.85 1.05 3.65 1.95 5.65

For the GPS-1 experiment, REP_VEH_CLASS_USE_RATING index is set to 4 (Fair) because 

only 6 of 17 yr have traffic monitoring data collected by vehicle class. 

For the GPS-6 experiment, REP_VEH_CLASS_USE_RATING index is 5 (Poor) because the 

majority (13 of 16 yr) of traffic estimates are extrapolations of earlier data collection efforts and 

are not based on actual data collected in the field. 
153

CHAPTER 8. ESTIMATING REPRESENTATIVE AXLE LOADING DISTRIBUTION 

FACTORS BY VEHICLE CLASS AND AXLE GROUP FOR MEPDG USE 

COMPUTED PARAMETER DESCRIPTION 

ALDF are used to characterize traffic loading for pavement analysis and design based on the 

MEPDG method. The format and meaning of this parameter is specific to AASHTOWare 

Pavement ME Design software. ALDF provide the percentile distribution of axle counts by load 

range (or load bin) for each heavy vehicle class (FHWA Classes 4–13) and axle group (i.e., 

single, tandem, tridem, and quad+) for a typical year of pavement service life. Each ALDF 

provides an estimate of the percentage of loads within a specified load range applied to a 

pavement during a typical day of each of the 12 calendar months of a year (January to 

December). In the current AASHTOWare Pavement ME Design software, ALDF are defined for 

each calendar month of the first (base) analysis or design year and remain constant throughout 

the analysis or design period. Thus, to be used as an input in AASHTOWare Pavement ME 

Design software, each LTPP site needs one set of ALDF representing an axle loading 

distribution for a typical year in the pavement’s life.

The ALDF parameter has the same meaning as the traffic loading parameter NALS. The latter 

parameter is generic and not exclusive to AASHTOWare Pavement ME Design software. 

DATA SOURCES USED FOR PARAMETER COMPUTATION 

Data sources containing information for computing the ALDF parameter include several LTPP 

tables. Names and descriptions of the LTPP traffic loading tables are as follows: 

• DD_AX—contains the number of axles measured in each weight range summarized by 

site, lane, direction, year, month, day, vehicle class, and axle group. The DD_AX table is 

created by accumulating axle distributions over all hours by vehicle class in a day. All 

vehicle classes, including passenger vehicles, may be in this table. Data are in a 

State/Provincial agency-specified classification method and may not follow the FHWA 

TMG 13-bin classification table. Data are obtained from WIM equipment installed at or 

near the LTPP test section. 

• MM_AX—contains information about the number of axles measured in each weight 

range summarized by site, lane, direction, year, month, DOW, vehicle class, and axle 

group. The MM_AX table is created by summing up the number of days and axle 

distributions by axle group over the days in a week for a month from the DD_AX table. 

Only vehicles reported in FHWA TMG 13-bin classification (FHWA Classes 4–13) are 

included. Any data in an agency-specified classification method are converted into TMG 

13-bin classes. 

• TRF_MONITOR_AXLE_DISTRIB—contains the number of axles measured in each 

weight range summarized by site, year, vehicle class, and axle group for the LTPP lane. 

Data are obtained from WIM equipment installed at or near the LTPP test section. The 
154

WEIGHT_BIN_SIZE field contains sizes of weight bins used to describe weight 

distribution by axle type. This distribution is for the LTPP lane only. 

At the time of this study, two new LTPP traffic loading tables became available: monthly NALS 

and annual NALS.

Monthly and annual NALS tables were selected as a primary traffic loading data source for 

developing site-specific ALDF. Both tables were developed by the LTPP program using SDR 30 

data for LTPP traffic sites with LTPP WIM data at Levels D and E. 

In addition, information about the type and operational characteristics of WIM equipment used to 

collect axle loading data was used to assess the accuracy of collected loading data and make 

determinations about whether data are accurate enough to compute site-specific axle loading 

inputs for pavement analysis and design. To be adequate, data must come from a calibrated WIM 

device capable of collecting WIM data satisfying the performance parameters of Type I WIM 

systems per ASTM E1318-09. The following LTPP tables contain information to make this 

determination: 

• TRF_EQUIPMENT_MASTER—contains information about WIM equipment in place 

during a calibration event and specifies WIM calibration protocol used at some WIM 

sites.

• TRF_CALIBRATION_WIM—contains statistics quantifying weight measurement 

accuracy for heavy vehicles and axle groups for each calibration period. This information 

is available for some, but not all, LTPP WIM sites. 

NUMBER OF LTPP SITES REQUIRING PARAMETER ESTIMATION 

The number of LTPP sites requiring ALDF information was assessed in collaboration with 

another LTPP traffic data analysis study. This was done to avoid duplicating the effort of the 

other LTPP analysis study estimating traffic loading for LTPP sites with no site-specific WIM 

data. Based on this assessment, 647 LTPP traffic sites were identified as candidates for ALDF 

development under this study. These sites are listed in appendix B. After reviewing axle loading 

data, some of these sites were identified as candidates for assigning the default ALDF due to 

issues with the quality and/or availability of loading data. ALDF for these sites were assigned as 

part of the outcome from the LTPP traffic data analysis study, Predicting Truck and Axle 

Loading Patterns. As a result, all LTPP sites have ALDF information for use in 

MEPDG analyses. 

METHODOLOGY FOR PARAMETER COMPUTATION OR ESTIMATION 

Two approaches were developed for ALDF computation depending on the quality and/or 

availability of WIM data: 

• Computing ALDF for LTPP sites with site-specific WIM data of sufficient quantity and 

quality.

• Estimating ALDF for LTPP sites with insufficient or poor-quality WIM data. 
155

For the LTPP sites with multiple WIM data collection periods, the methodology for ALDF 

computation focused on how to select the appropriate dataset for computation. For LTPP sites 

with insufficient site-specific axle loading data, the methodology focused on selecting ALDF 

factors from alternative data sources or assigning default values to best represent expected traffic 

loading patterns. 

Hierarchical Approach for WIM Data Selection and Usability Code Assignment Based on 

Data Quality Considerations 

To be successfully used in pavement analysis, ALDF must be computed using accurate axle 

loading data, especially accurate estimates of heavy axle loads. Ideally, WIM data for ALDF 

computation should be from a calibrated WIM system satisfying the performance parameters of 

Type I WIM systems per ASTM E1318-09 during the data collection period (table 1). Reviewing 

data in TRF_EQUIPMENT_MASTER and TRF_CALIBRATION_WIM tables revealed that 

only a limited number of the WIM sites contain the information necessary to quantify WIM data 

quality. The limited available information showed that some LTPP WIM sites satisfied ASTM 

E1318-09 Type I or Type II WIM system performance requirements. 

To overcome this limitation, a hierarchical data assessment methodology and series of data 

reasonableness checks were developed to evaluate and qualify reasonableness and usability of 

available axle loading data for ALDF computation. The following hierarchical approach was 

applied to identify and qualify datasets for computing site-specific ALDF: 

Axle loading distributions used for ALDF computations (monthly or annual) must be 

identified as LTPP data QC Level D or E in the LTPP database. 

If supporting information is available to determine that axle loading data were collected 

by a WIM system satisfying the performance parameters of Type I WIM systems per 

ASTM E1318-09 during some data collection periods, use data from these data collection 

periods to compute the ALDF. 

a. If data from Type I WIM systems are available for at least one instance of each 

calendar month and are free from error due to WIM sensor temperature sensitivity, 

use these data to compute a unique ALDF for each calendar month. Computed 

parameters based on these data have a data–usability code labeled “Best.” 

b. If monthly data are insufficient to compute a unique ALDF for each calendar month,

or if a WIM sensor is susceptible to temperature sensitivity (i.e., additional errors due 

to temperature fluctuation), use annual data summaries for ALDF computation. 

Computed parameters based on these data have a data–usability code labeled 

“Better.” 

c. If data collection periods satisfying the performance parameters of Type I WIM 

systems per ASTM E1318-09 are too short to provide representative values (less than 

1 yr of data), data from other data collection periods can be used to compute 

site-specific ALDF provided these data pass data reasonableness checks (described in 

the ensuing sections) and show similar loading trends as data from the periods 
156

corresponding to Type I WIM performance characteristics. Computed parameters 

based on these data have a data–usability code labeled “Good.” 

If no information is available about WIM system performance parameters at the time of 

data collection or data were collected by a Type II WIM system, apply data 

reasonableness checks described in the ensuing sections to axle loading distributions. 

a. Identify data periods that do not show signs of low WIM data precision or bias in axle 

loading distributions. Use data from these periods to compute site-specific ALDF. 

Computed parameters based on these data have a data–usability code labeled “Good.” 

b. If data assessment shows data are likely affected by low precision or low-to-moderate 

measurement bias, evaluate if the measurement bias or low precision is likely to have 

less than 10 percent difference in the percentage of heavy loads for FHWA Class 9 

vehicles (estimated based on the axle load-distribution shape). 

i. If the difference is under 10 percent, use these data to compute site-specific ALDF. 

Computed parameters based on these data have a data–usability code labeled 

“Fair.” 

ii. If the difference is over 10 percent in the percentage of heavy loads for FHWA 

Class 9 vehicles. Check if the shape of the axle loading distribution is similar to 

typical shapes identified for given vehicle classes and axle types. Typical shapes 

are described in the LTPP PLUG defaults. 

1. If the loading pattern can be identified, assign loading patterns based on 

similarities of the loading pattern shapes with the LTPP PLUG defaults or other 

available data sources. Use assigned loading patterns to select site-related or 

default ALDF. Consider WIM data for this site as “Poor.” 

2. If the loading pattern does not follow any known pattern, assign ALDF default 

values based on truck volume, vehicle class distribution, and road functional 

classification. Consider WIM data for this site as “Bad.” 

WIM Data Rationality and Data Reasonableness Checks 

The majority of axle weight data collected did not have information about WIM equipment 

accuracy, and if issues with the accuracy of WIM measurements were known, data 

reasonableness checks were developed to screen and differentiate between WIM records that 

likely had or had not been affected by WIM equipment accuracy limitations. The purpose of 

these checks is to identify and classify WIM datasets into the following three categories: 

• Suitable to compute site-specific axle loading values based on monthly or annual NALS. 

• Not suitable to compute site-specific axle loading values but useful for identifying 

loading patterns for high-volume heavy truck classes. 

• Not suitable to compute site-specific axle loading values or for identifying loading 

patterns due to poor quality and/or low truck volume. 
157

WIM data reasonableness checks were designed to identify axle loading data with strong 

measurement bias (due to lack of calibration or calibration drift) and/or very low precision of 

heavy-weight measurements or possibly a vehicle misclassification. Data reasonableness checks 

described in this section were applied to monthly and annual NALS for FHWA Class 9 vehicles. 

FHWA Class 9 NALS were selected because of well-known axle weight-distribution attributes 

for this category of vehicles. The data reasonableness assessment analyzes tandem NALS for 

FHWA Class 9 vehicles followed by single NALS for FHWA Class 9 vehicles. Changes in these 

distributions over time were used to identify data with suspected quality issues. 

These checks were applied in a three-step process. In step one of the data reasonableness 

assessment, the LTPP program applied “tail checks” to identify monthly and annual NALS 

datasets likely to have been collected by equipment with measurement accuracy issues. These 

checks were designed to identify high percentages of unusually light or unusually heavy axle 

loads for FHWA Class 9 vehicles. High percentages of very light loads indicate a likely vehicle 

misclassification. Very high percentages of loads exceeding the legal load limit on tandem axles 

indicate a likely calibration drift. NALS with high percentages of both very light and very heavy 

loads indicate low precision of weight measurements (typically due to temperature sensitivity of 

the ceramic or polymer piezo sensors). The following criteria were used for the “tail checks” to 

identify atypical distributions.

“Tail checks” for FHWA Class 9 single axle NALS checks include the following criteria (applies 

to both U.S. and Canadian LTPP WIM sites): 

• 10 percent or more under 5,000 lb. 

• 10 percent or more greater than or equal to 21,000 lb.

“Tail checks” for FHWA Class 9 tandem axle NALS checks include the following criteria: 

• U.S. sites—10 percent or more under 8,000 lb. 

• U.S. sites—20 percent or more greater than or equal to 34,000 lb.

• Canadian sites—10 percent or more under 8,000 lb.

• Canadian sites—20 percent or more greater than or equal to 38,000 lb.

In step two of the data reasonableness assessment, the research team applied additional 

automated checks to flag monthly and annual NALS for FHWA Class 9 axles with several key 

axle weight-distribution attributes outside the expected range for FHWA Class 9 vehicles. The 

following additional checks were applied to monthly and annual NALS. 

Checks to identify atypical patterns in FHWA Class 9 single axle NALS include the following: 

• Average single axle weight less than 9,000 lb. 

• Average single axle weight greater than 12,500 lb. 

• Single axles weighing 20,000 lb or more is above 3 percent. 
158

Checks to identify atypical patterns in FHWA Class 9 tandem axle NALS include the following: 

• Average weight of loaded tandem axles (computed as average weight of FHWA Class 9 

tandem axles weighing 26,000 lb or more) less than 29,000 lb. 

• Average weight of loaded tandem axles (computed as average weight of FHWA Class 9 

tandem axles weighing 26,000 lb or more) greater than or equal to 34,000 lb. 

• Tandem axles weighing 34,000 lb or more above 20 percent. 

o WIM sites located on a road with predominantly empty trucks have overloaded 

tandems greater than 7 percent. 

• Loaded peak location checks (calibration drift checks) meeting the following criteria: 

o When the percent of axles between 30,000 and 35,999 lb is less than the percent of 

axles between 36,000 and 41,999 lb (overestimation of loads). 

o When the percent of axles between 26,000 and 33,999 lb is less than the percent of 

axles between 20,000 and 25,999 lb, which applies to sites with less than 30 percent 

of axles between 10,000 and 15,999 lb (underestimation of loads). 

• First loading peak (tandem axles on empty trucks) is not between 10,000 and 15,999 lb. 

In step three of the data reasonableness assessment, FHWA Class 9 NALS flagged in the 

previous two steps were manually reviewed and compared to historical NALS for the same site 

to identify whether a flagged record represents an expected distribution for a given site or 

indicates a calibration drift or other event resulting in atypical distribution. The review process 

used the following guidance: 

1. Check the shape of the NALS distribution for consistency between years. If more than 

one type of WIM sensor was used over the years, put more trust in years with a higher 

quality sensor (e.g., choose data from quartz sensors over polymer piezo sensors).

2. Trust NALS with the following attributes (typically observed in NALS based on data 

from calibrated WIM systems):

a. Repeating shape of distribution.

b. Loaded tandem peak between 30,000 and 34,000 lb.

c. Single axle peak between 10,000 and 12,000 lb.

d. Short axle distribution tail for overloaded tandem axles (typically less than 10 

percent of overloads for roads with a balanced loading pattern of FHWA Class 9 

trucks or less than 7 percent for roads with mostly empty FHWA Class 9 trucks). 

3. Check for calibration drift and favor years with NALS that have the location for loaded 

tandem peaks and percentages of overloads close to typically expected values for FHWA 

Class 9 vehicles.

4. Identify annual NALS affected by likely WIM measurement bias through analysis of 

shifts in peaks of axle loading distributions 
159

5. Identify annual NALS affected by likely low WIM measurement precision through 

presence of fat, long tail for overloads; high percentage of very light vehicles; and low 

percentages associated with axle load distribution peak values. 

Based on the results of manual review, a decision was made whether to keep or remove atypical 

monthly or annual NALS from further computations using site-specific WIM data. If FHWA 

Class 9 monthly or annual NALS were labeled for removal, NALS for all vehicle classes 

corresponding to the same month or year were removed from the ALDF computation.

WIM Data Availability Criteria 

In addition to being accurate, the quantity of WIM data must be sufficient to develop 

representative ALDF describing average monthly or annual axle loading conditions at a given 

LTPP site. Therefore, once all monthly or annual axle loading data are considered acceptable for 

computation, and site-specific ALDF have been identified based on data quality or 

reasonableness checks, the quantity of data are checked using the following data availability 

criteria: 

• For computing ALDF representing a typical day of each calendar month, the following 

criteria must be met: 

o Data must be collected by equipment satisfying the performance parameters of Type I 

WIM systems per ASTM E1318-09 for the entire length of data collection. Due to 

temperature sensitivity, data collected by piezo-polymer or piezo-ceramic sensors are 

excluded from this computation because data accuracy cannot be guaranteed over 

data collection periods lasting many months. 

o Daily axle loading distributions for a given vehicle class and axle group combination 

(referred as class–axle combination in ALDF discussions) must be available for at 

least one instance of each DOW for each of the 12 calendar months (e.g., January 

WIM data should be available for at least one Monday, one Tuesday, one 

Wednesday) (i.e., at least 7 d, each representing a different DOW, per each of the 12 

calendar months). 

o At least 200 axle load applications must be available for each calendar month for a 

given class–axle combination to compute monthly ALDF. If fewer than 200 axle load

applications are available in a month for a given class–axle combination, then the 

ALDF representing a typical day of the year should be computed for that class–axle 

combination.

o ALDF results developed based on data satisfying the preceding criteria are labeled 

“Best.” 

• For computing ALDF representing a typical day of the year, all available data, 

aggregated to the annual level that passed data QC and reasonableness checks discussed 

in this chapter, are used. Depending on data availability, different letters are assigned to 

the usability codes as discussed previously. 
160

o No additional letters are assigned at the end of the usability codes, as discussed 

previously, if one of the following WIM data availability criteria for a given 

class–axle combination (considering all data collection periods) are satisfied: 

• At least 180 d. 

• At least 6 mo with 7 DOW in each month. 

o If the above data availability criteria are not satisfied for a given class–axle 

combination, “ALS” is assigned at the end of the usability codes, as discussed above, 

to indicate that particular ALDF may be an atypical axle distribution due to a small 

data sample size used to compute the ALDF. Using default values may be a better 

option for cases with small sample sizes. ALDF computed based on limited data 

should not be used to develop defaults for other LTPP sites. 

o If one of the above criteria is satisfied but the count for a particular class–axle 

combination is very low, atypically low count (ALC) is assigned at the end of the 

usability codes to indicate that particular ALDF may be atypical due to a low axle 

count. Using default values may be a better option for cases with low axle counts. 

ALDF computations based on limited data should not be used to develop defaults for 

other LTPP sites. A very low count is defined as a total number of axles, considering 

all data collection periods, of less than 200. 

Handling Atypical Axle Groupings 

During the data review, the research team discovered that many LTPP WIM sites have a very 

small percentage of axle loads reported for axle types not typical for assigned vehicle class based 

on the FHWA vehicle classification scheme. Likely causes are vehicle misclassification, a Statespecific vehicle classification algorithm deviating from the FHWA vehicle classification scheme, 

or an incorrect axle grouping assignment during data processing. For over 95 percent of LTPP 

WIM sites, the number of misclassified axles was less than 0.5 percent of the total number of 

axles, and for 98 percent of sites, the number of misclassified axles was less than 1 percent. 

Axle counts are used to develop NALS and axles per truck (APT) numbers. APT is computed for 

each site and vehicle class as a ratio of axle counts reported for each axle grouping to the total 

number of vehicles in that vehicle class. Because APT is reported in the AASHTOWare 

Pavement ME Design software to the nearest one-hundredth (X.XX), any ratio less than 0.005 is 

rounded to 0. Therefore, when the ratio is less than 0.005, ALDF for that axle grouping and 

vehicle class is not used in MEPDG computations. To avoid confusion with misclassified or 

atypical ALDF, a decision was made not to report ALDF for atypical axle groupings if the ratio 

of the number of the atypical axles to the total number of trucks was less than 0.005 for any site, 

vehicle class, and axle group. 

If the ratio of the number of atypical axles to the total number of trucks was greater than 0.005, 

the research team decided to keep these unexpected ALDF to ensure these axles are accounted 

for in pavement analyses, including misclassified axle groups. If NALS, APT, and ALDF were 

computed for atypical axle grouping, an “AA” code was added to the usability codes discussed 
161

previously to indicate that data are atypical (most likely due to misclassification) and should not 

be used for other LTPP sites or defaults. 

Method 1—Develop ALDF Based on Site-Specific WIM Data 

In AASHTOWare Pavement ME Design software, ALDF are entered for each axle group (i.e., 

single, tandem, tridem, and quad+), for each heavy vehicle class (FHWA Classes 4–13), and for 

each calendar month. ALDF represent distribution of traffic loads for a typical analysis or design 

year. Therefore, to prepare this input using site-specific axle loading data, site-specific axle 

loading distributions available in the LTPP database from multiple data collection periods must 

be summarized in a way that provides one representative estimate of axle loading frequency 

distribution for a typical year of pavement service life.

All ALDF computations are made on the LTPP site, vehicle class, and axle group level. The 

high-level algorithm for computing site-specific ALDF includes the following operations: 

Obtain axle loading information and supporting metadata from LTPP database tables: 

a. Monthly and annual NALS from LTPP traffic data tables. 

b. Number of days with WIM data per month and year used in monthly and annual 

NALS computations from LTPP axle count tables. 

c. Information about WIM sensor types and calibration efforts from LTPP ancillary data 

tables. 

Review NALS data availability and identify LTPP sites with data sufficient for ALDF 

computation. 

Subject NALS identified in the previous step to automatic data quality/reasonableness 

checks. 

Manually review records flagged during automatic checks. If flagged NALS indicate low 

precision or bias in WIM measurement or vehicle misclassification and/or the extent of 

the data anomaly is likely to significantly effect MEPDG predictions, remove them from 

further computations. 

Select site-specific NALS that passed data availability and data quality/reasonableness 

criteria for computing site-specific representative ALDF. 

Select the ALDF computational procedure based on data availability and quality and

compute the ALDF. 

Store the ALDF in the database table in the format compatible with AASHTOWare 

Pavement ME Design software. 
162

Assign a usability code in the ALDF database table specifying the source of the data and 

the method used for the ALDF computation for each LTPP site, vehicle class, and axle 

group included in ALDF database table. 

Method 2—Estimate ALDF Using Alternative Data Sources or Defaults 

For LTPP sites with insufficient WIM data or data with limited accuracy, several estimation 

approaches were developed based on available information.

All ALDF estimations are made on the LTPP site, vehicle class, and axle group level. The 

high-level algorithm for estimating ALDF for sites with insufficient site-specific data includes 

the following operations: 

If limited or low-quality site-specific NALS are available to draw conclusions about the 

general loading pattern, assign a loading pattern for each axle group for each dominant 

heavy vehicle class. Dominant heavy vehicle class is expected to contribute 20 percent or 

more to the total traffic load at a given LTPP site. When assigning a loading pattern, 

account for low-precision or high-bias WIM data, if applicable. 

Use the assigned loading pattern to identify the ALDF or NALS from alternative sources 

best describing that pattern through the following approaches, listed in the order of 

preference: 

a. Assign an ALDF developed for a different LTPP site located on the same roadway

but at a different location if that location had a similar loading pattern for the selected 

dominant vehicle class and axle group. Assign ALDF from the same LTPP site for all 

other nondominant vehicle classes. 

b. Assign an average ALDF computed for roadways in the same State and the same 

functional classification that had a similar loading pattern for the selected dominant 

vehicle class and axle group. Assign an average ALDF from the same roadways for 

all other nondominant vehicle classes. 

c. Assign the LTPP PLUG NALS default best describing available site-specific 

information about loading patterns for the selected vehicle class and axle group. 

Assign the LTPP PLUG NALS default for all other nondominant vehicle classes. 

If available WIM data are insufficient or inconclusive to characterize the axle loading 

distribution or general loading pattern at an LTPP site, use the following estimation 

approaches, listed in the order of preference: 

a. Assign the ALDF available for the same roadway but from a different location 

provided vehicle classification and truck volume is similar between the two locations.

b. Assign an average ALDF computed for roadways in the same State and the same 

functional classification with similar vehicle class distribution and truck volume. 
163

c. Assign the LTPP PLUG default based on available site-specific information about 

dominant truck types, road functional classification, vehicle class distribution, 

AADTT, and location. 

Storing and Using ALDF 

ALDF developed for LTPP sites are stored in the MEPDG_AXLE_LOAD_DIST_FACTOR 

table in a format compatible with AASHTOWare Pavement ME Design software. The ALDF 

database table was designed in a format that allows copying and pasting information directly into 

the AASHTOWare Pavement ME Design software GUI screen for axle loading input. A code 

was provided for each site, vehicle class, and axle group specifying the method used to compute 

ALDF or assign ALDF from alternative sources. 

The research team developed ALDF for all LTPP WIM sites with sufficient data to support 

ALDF computation Method 1 described above. In addition, ALDF .xml tables compatible with 

AASHTOWare Pavement ME Design software were developed for each LTPP site that had data 

to support ALDF computed using Method 1. LTPP sites that required use of the ALDF 

computation Method 2 were addressed in another LTPP traffic data analysis study.(22)

 

PROCEDURES FOR ALDF COMPUTATION 

This section contains a description of the detailed procedures to compute ALDF, which include 

the following: 

• Review axle loading data availability, accuracy, and rationality and assign the ALDF 

computation method. 

• Identify the loading pattern using annual NALS of limited accuracy. 

• Compute ALDF based on site-specific WIM data using Method 1. 

• Estimate ALDF for sites with insufficient or no site-specific WIM data using Method 2. 

Procedure to Review Axle Loading Data Availability, Accuracy, and Rationality and 

Assign the ALDF Computation Method 

The following procedure describes steps for selecting the ALDF computational method (e.g., 

Method 1 or Method 2) based on site-specific WIM data quality and availability for each LTPP 

WIM site. 

Step 1. Prepare intermediate database tables to store site-level information about ALDF methods. 

1.1. Obtain MM_AX, NALS_MONTHLY_DISTRIB and NALS_ANNUAL_DISTRIB tables 

from the LTPP database. These tables contain normalized axle load distributions for LTPP 

traffic sites with WIM equipment. Add these tables to an empty analysis database. Obtain 

the following additional tables from LTPP database and add them to the analysis database: 

MM_AX, TRF_CALIBRATION_WIM, TRF_EQUIPMENT_MASTER, and 

TOTAL_AXLES_YY_TSSC 

1.2. Create a new database table called ALDF_ASSIGNMENT_METHOD with fields for 

STATE_CODE, SHRP_ID, VEHICLE_CLASS, AXLE_GROUP, and ALDF_METHOD. 
164

1.3. Populate the ALDF_ASSIGNMENT_METHOD table with the LTPP traffic site IDs, 

vehicle classes, and axle group entries included in the NALS_ANNUAL_DISTRIB table. 

The list of WIM site IDs is included in appendix B of this report. The 

ALDF_ASSIGNMENT_METHOD table is used to keep track of the assigned ALDF 

computation or estimation method for each LTPP traffic site ID, vehicle class, and axle 

group. 

1.4. Create a new database table called ALDF_ANNUAL_CANDIDATES with fields for 

STATE_CODE, SHRP_ID, YEAR, and WIM_TYPE_BY_YEAR. Use the 

NALS_ANNUAL_DISTRIB table to populate the ALDF_ANNUAL_CANDIDATES table 

with corresponding WIM site IDs and years. 

1.5. Create a new empty database table called ALDF_MONTHLY_CANDIDATES with fields 

for STATE_CODE, SHRP_ID, MONTH, YEAR, and WIM_TYPE_BY_MONTH. 

Step 2. Identify records in monthly NALS tables satisfying minimum data availability criteria. 

2.1. Use the NALS_MONTHLY_DISTRIB and MM_AX tables to identify WIM sites 

(identified by STATE_CODE and SHRP_ID fields), vehicle classes, and axle groups 

satisfying the following conditions: 

• There is at least one occurrence for each of the 12 calendar months at Level E in the 

NALS_MONTHLY_DISTRIB table for a given WIM site ID. 

• Each calendar month has at least one occurrence for each of the 7 DOW (i.e., Sunday–

Saturday). 

• The total number of axles per month at Level E is 200 or more for a selected vehicle 

class and axle group. 

2.2. Populate the ALDF_MONTHLY_CANDIDATES table with WIM site IDs and all months 

and years satisfying the data availability criteria described in step 2.1. 

Step 3. Identify WIM equipment type and measurement accuracy for each LTPP traffic site. 

Perform the following steps for each LTPP WIM site included in 

ALDF_MONTHLY_CANDIDATES or ALDF_ANNUAL_CANDIDATES tables: 

3.1. Match WIM site IDs with the LTPP section IDs in TRF_CALIBRATION_WIM and 

TRF_EQUIPMENT_MASTER tables and extract information from the WIM_SENSOR 

field in the TRF_EQUIPMENT_MASTER table. 

3.2. Use the TRF_CALIBRATION_WIM table to quantify weight measurement accuracy or bias 

for each calibration period by using *_DIFF fields and precision by using *DIFF_SD fields 

and computing the statistical 95 percent confidence interval. Compute the total error as bias 

± precision. 
165

3.2.1. If no records are found in the TRF_CALIBRATION_WIM table for a given LTPP 

WIM site ID or values in DYNAMIC_STAT_SINGLE_AXLE_DIFF, 

DYNAMIC_STAT_TANDEM_AXLE_DIFF, 

DYNAMIC_STAT_SINGLE_DIFF_SD, or 

DYNAMIC_STAT_TANDEM_DIFF_SD fields are missing, identify WIM site ID as

“unknown accuracy.” Proceed to step 4. 

3.2.2. Use values in DYNAMIC_STAT_SINGLE_AXLE_DIFF and 

DYNAMIC_STAT_TANDEM_AXLE_DIFF fields to assess measurement bias for 

each WIM site. 

3.2.2.1. If bias is over 5 percent in either of these fields, identify the corresponding 

month and year as “poor or unknown” in the WIM_TYPE_BY_MONTH 

field. Proceed to step 4. 

3.2.2.2. If bias is less than 5 percent for both fields, obtain values from 

DYNAMIC_STAT_SINGLE_DIFF_SD and 

DYNAMIC_STAT_TANDEM_DIFF_SD fields, multiply these values by 2,

and add to the corresponding bias value from 

DYNAMIC_STAT_SINGLE_AXLE_DIFF or 

DYNAMIC_STAT_TANDEM_AXLE_DIFF fields. This is the total 

measurement error value. 

3.2.3. If the total error value is less than 20 percent for single axles and less than 15 percent 

for tandem axles, identify the corresponding month and year and enter “Type I” in the 

WIM_TYPE_BY_MONTH field. Proceed to step 4. 

3.2.4. If the total error value for single axles is less than 30 percent and less than 20 percent 

for tandem axles, identify the corresponding month and year and enter “Type II” in 

the WIM_TYPE_BY_MONTH field. Proceed to step 4.

3.2.5. Identify the traffic site ID and corresponding month and year as “poor or unknown” 

in the WIM_TYPE_BY_MONTH field. Proceed to step 4.

Step 4. Assign WIM type and select data accuracy assessment option for each LTPP WIM site.

4.1 Using results of step 3, label candidate months and years that likely have data collected by 

WIM sensors satisfying the performance parameters of Type I WIM systems per 

ASTM E1318-09 for each LTPP WIM site included in the 

ALDF_MONTHLY_CANDIDATES table. 

4.1.1. If the WIM sensor type is bending plate or load cell and Type I is identified for a given 

calibration month and year, assign a Type I value for all months following the 

calibration month for a period of 2 yr after calibration or until any other value is

assigned in the WIM_TYPE_BY_MONTH field, whichever comes first. 

4.1.2. If the WIM sensor type is piezo quartz and Type I is identified for a given calibration 

month and year, assign this value for 12 mo following the calibration month or until 
166

any other value is assigned in the WIM_TYPE_BY_MONTH field, whichever comes 

first. 

4.1.3. If the WIM sensor type is not bending plate, load cell, or piezo quartz, exclude from 

WIM Type I monthly assignments (weight changes due to high seasonal temperature 

sensitivity and lower precision of heavy axle weight measurements between 

calibrations were observed for these sensors). 

4.2. Using results of step 3, label candidate years that likely have data collected by WIM sensors 

satisfying the performance parameters of Type I or II WIM systems per ASTM E1318-09 

for each LTPP WIM site included in the ALDF_ANNUAL_CANDIDATES table.

4.2.1. If Type I is identified in the WIM_TYPE_BY_MONTH field for a given calibration 

month and year, assign a Type I value for the next year after calibration for all types 

of piezo sensors and 2 yr for bending plate and load cell sensors in the 

WIM_TYPE_BY_YEAR field unless any other value is already assigned in the 

WIM_TYPE_BY_YEAR field for this year(s). 

4.2.2. If Type II is identified in the WIM_TYPE_BY_MONTH field for a given calibration 

month and year, assign a Type II value in WIM_TYPE_BY_YEAR field for the next 

year after calibration unless any other value is already assigned in the 

WIM_TYPE_BY_YEAR field for this year(s). 

4.2.3. If “poor or unknown” is found in the WIM_TYPE_BY_MONTH field, assign this 

value in the WIM_TYPE_BY_YEAR field for all following years unless another 

value is already assigned in the WIM_TYPE_BY_YEAR field. 

4.2.4. Assign “unknown accuracy” values in the WIM_TYPE_BY_YEAR field for all 

remaining months and years with no other values in WIM_TYPE_BY_YEAR fields 

of ALDF_ANNUAL_CANDIDATES tables. 

4.3. Using results of step 4.2, assess WIM data availability based on the values assigned in 

WIM_TYPE_BY_MONTH and WIM_TYPE_BY_YEAR fields and identify data analysis 

option, as described in the proceeding steps. 

4.3.1. For WIM site IDs with at least one instance for each of the 12 calendar months in the 

ALDF_MONTHLY_CANDIDATES table with a Type I value in the 

WIM_TYPE_BY_MONTH field, assign “OPTION 1: WIM Type I accuracy, 

monthly.” Proceed to step 5. 

4.3.2. For WIM site IDs with at least 1 yr in the ALDF_ANNUAL_CANDIDATES table 

with a Type I value in the WIM_TYPE_BY_YEAR field, assign “OPTION 2: WIM 

Type I accuracy, annual.” Proceed to step 5. 

4.3.3. For WIM site IDs without at least 1 yr in the ALDF_ANNUAL_CANDIDATES table 

with a Type I value in the WIM_TYPE_BY_YEAR field, but with Type II or 

“unknown” values, assign “OPTION 3 and 4: WIM Type II or unknown accuracy.” 

Proceed to step 5. 
167

Step 5. Subject monthly and annual NALS to data quality and reasonableness review and 

identify NALS for ALDF computation. 

Use the following procedure to review quality and rationality of WIM data and identify NALS 

datasets for ALDF computation: 

5.1. For LTPP traffic site ID, months, and years identified as “OPTION 1: WIM Type I 

accuracy, monthly,” perform the following: 

5.1.1. Extract monthly NALS for FHWA Class 9 satisfying minimum monthly data 

availability criteria of at least 7 DOW and 200 axles per month for 12 calendar 

months.

5.1.2. Subject the monthly NALS for FHWA Class 9 vehicles to the automated 

reasonableness checks described in section WIM Data Rationality and Data 

Reasonableness Checks. Flag monthly NALS records failing the checks. 

5.1.3. For the sites with flagged FHWA Class 9 NALS, manually review all available 

FHWA Class 9 NALS for that site (using procedure provided in section WIM Data 

Rationality and Data Reasonableness Checks) and conclude if the flagged NALS 

follow the historical loading trend for the site. If the flagged average axle weight for a 

single axle or for a loaded tandem axle weight deviates from the historical trend 

(based on reporting periods satisfying the performance parameters of Type I WIM 

systems per ASTM E1318-09) and this deviation does not represent a repeatable 

seasonal pattern, consider the flagged monthly NALS a true outlier. 

5.1.4. Compile a list of LTPP sites and dates (i.e., month and year) with outlier monthly 

NALS confirmed during the manual review. Remove records with matching LTPP 

site IDs, months, and years from the ALDF_MONTHLY_CANDIDATES table. 

5.1.5. Check the availability of monthly NALS in ALDF_MONTHLY_CANDIDATES 

table. For each set of records defined by LTPP traffic site ID, vehicle class, and axle 

group, check if available NALS have at least one occurrence of each of the 

12 calendar months (January to December) with at least 200 axles per month.

5.1.5.1. If the above data availability criteria are not met for a given LTPP site, 

vehicle class, and axle group, delete the site, vehicle class, and axle group 

from the ALDF_MONTHLY_CANDIDATES table, then proceed to step 5.2 

and check usability of the annual NALS for ALDF computation for that 

LTPP site, vehicle class, and axle group. 

5.1.5.2. If the above data availability criteria are met, populate the ALDF_METHOD 

field in the ALDF_ASSIGNMENT_METHOD table with “M1MR” values 

for the corresponding traffic site ID, vehicle class, and axle group. 

Assignment is completed. Proceed to step 5.4 to check for atypical axle 

groups and then to the Compute ALDF with Unique Values for Each 

Calendar Month (Method 1—Monthly) section. 
168

5.2. For annual NALS identified as “OPTION 2: WIM Type I accuracy, annual” in step 4 and 

LTPP sites initially identified as monthly NALS option 1 that did not pass data 

reasonableness and/or data availability checks in step 5.1, perform the following: 

5.2.1. Extract annual NALS for FHWA Class 9 and apply the automated reasonableness 

checks described in section WIM Data Rationality and Data Reasonableness Checks. 

Flag annual NALS records failing the checks. 

5.2.2. For sites with FHWA Class 9 NALS flagged during the automated checks, manually 

review all available FHWA Class 9 NALS (using procedure provided in section WIM 

Data Rationality and Data Reasonableness Checks) and conclude whether the flagged 

NALS follow the historical loading trend for the site or shows possible calibration or 

low precision issues. If the flagged NALS do not follow a pattern typical for FHWA 

Class 9 NALS and is not consistent with other NALS for a given WIM site, vehicle 

class, and axle group, consider the flagged NALS an outlier. 

5.2.3. Compile the list of LTPP site IDs and years that passed automated and manual QC 

checks for Type 1 annual NALS. Save the table as 

ALDF_ANNUAL_OPTION2_FINALISTS. 

5.2.4. For each LTPP site ID, vehicle class, and axle group included in the 

ALDF_ANNUAL_OPTION2_FINALISTS table, check if the corresponding record 

in the ALDF_METHOD field in the ALDF_ASSIGNMENT_METHOD table is 

empty. 

5.2.4.1. If not empty, do not overwrite the previous assignment. Remove site ID, 

vehicle class, and axle group records from the 

ALDF_ANNUAL_OPTION2_FINALISTS table. 

5.2.5. Check the total number of axles per year in the TOTAL_AXLES_YY_TSSC table for 

all years reported in the ANNUAL_NALS_OPTION2_FINALISTS table. 

5.2.5.1. If 200 or more for each year, add NALS with more than 200 axles per year to 

the new table called 

ANNUAL_NALS_OPTION2_FOR_BY_YEAR_COMPUTATION. These 

records are used to compute ALDF values based on annual averages for sites 

with Type I WIM data accuracy. Add “M1” values to the ALDF_METHOD 

field in the ALDF_ASSIGNMENT_METHOD table. Assignment is 

complete. 

5.2.5.2. If fewer than 200 for each year, add NALS to the new table called 

ANNUAL_NALS_OPTION2_FOR_YEARS_COMBINED_COMPUTATION. 

These records are used to compute ALDF values based on combined annual axle 

counts from multiple years marked as option 2 yr. Add “M1YC” values to the 

ALDF_METHOD field in the ALDF_ASSIGNMENT_METHOD table. 

5.2.5.3. For each LTPP traffic site, vehicle class, and axle group included in 

ANNUAL_NALS_OPTION2_FOR_YEARS_COMBINED_COMPUTATION 
169

table, compute the total number of axles for all years combined and save as a 

new TOTAL_AXLES_OPTION2_FOR_YEARS_COMBINED_ 

COMPUTATION table. If the total number of axles for all years combined is 

fewer than 200 but more than 1, add “AL” values to the “M1YC” values in 

ALDF_METHOD field in the ALDF_ASSIGNMENT_METHOD table for 

identified LTPP traffic sites, vehicle classes, and axle groups. Assignment is 

complete. 

5.2.6. Proceed to step 5.4 to check for atypical axle groups and then to the Procedure to 

Compute ALDF Based on Site-Specific WIM Data Using Method 1 section.

5.3. Proceed with this step after ALDF are computed for option 1 and 2 sites. 

5.3.1. Use the ALDF_ASSIGNMENT_METHOD table to create a list of records defined by 

the LTPP traffic site ID that do not have ALDF_METHOD assignments. These are 

sites initially identified as “OPTION 3 and 4: WIM Type II or unknown accuracy” 

and sites initially identified as option 1 or 2 but did not pass the data reasonableness 

assessment.

5.3.2. Use the list compiled in step 5.3.1 to extract annual NALS for LTPP sites missing 

ALDF_METHOD assignments. The accuracy of WIM data for these sites is either 

unknown or of a lesser quality than for Type I sites. The goal of the data 

reasonableness review for these sites is to identify annual NALS falling into one of 

the following three categories: 

5.3.2.1 Data satisfy data availability and reasonableness criteria for computing 

site-specific ALDF values.

5.3.2.2 Data quality and/or quantity prevent accurate quantification of loading but 

are descriptive enough to define a loading pattern for dominant heavy trucks 

(typically FHWA Class 9 trucks). 

5.3.2.3 Data quality and/or quantity is too low to infer meaningful loading 

information. 

5.3.3. Subject annual NALS for FHWA Class 9 vehicles to the automated reasonableness 

checks described in section WIM Data Rationality and Data Reasonableness Checks. 

Flag annual NALS records failing the checks. 

5.3.4. Manually review LTPP sites with FHWA Class 9 NALS flagged during the 

automated checks and conclude whether flagged NALS follow the historical loading 

trend for the site or show possible calibration or low precision issues. For additional 

guidance, refer to the Procedure to Assess Rationality and Identify Loading Pattern 

Using Annual NALS of Limited Accuracy section.

5.3.5. Use the results of the manual review to categorize LTPP sites and years using the 

following criteria: 
170

5.3.5.1 If at least 1 yr with NALS passed automated and manual QC checks, identify 

these NALS records and label the resulting ALDF for the site as “Good.” 

Add LTPP sites and year(s) identified “Good” to a new table called 

OPTION34_S_SITES_YEARS and proceed to step 5.3.6.

5.3.5.2 If all years were flagged, check if any of these years had errors not likely to 

significantly effect pavement analysis outcomes using the MEPDG method 

(e.g., FHWA Class 9 NALS with less than 10 percent difference in the 

expected percentages of heavy loads typical for a given type of road). 

Identify NALS records for these years and label the resulting ALDF for the 

site as “Fair.” Add LTPP sites and years identified “Fair” to the 

OPTION34_S_SITES_YEARS table and proceed to step 5.3.6. 

5.3.5.3 If no years passed automated and manual QC checks but typical axle loading 

pattern can be clearly identified in annual axle load distributions for FHWA 

Class 9 trucks, add “M2P” values to the ALDF_METHOD field in the 

ALDF_ASSIGNMENT_METHOD table. 

5.3.5.4 If no years passed automated and manual QC checks and no expected loading 

pattern can be identified in annual axle load distributions for FHWA Class 9 

trucks, add “M2NoP” values to the ALDF_METHOD field in the 

ALDF_ASSIGNMENT_METHOD table. 

5.3.6. For each LTPP site ID, vehicle class, axle group, and year included in the 

OPTION34_S_SITES_YEARS table, check if the total number of axles per year 

reported in the TOTAL_AXLES_YY_TSSC table at Level E is 200 or more. 

5.3.6.1. If 200 axles or more per year, perform the following actions: 

5.3.6.1.1. Add NALS for class-axles with more than 200 axles per year to a 

new table called 

ANNUAL_NALS_OPTION34_FOR_BY_YEAR_ 

COMPUTATION. These records are used to compute ALDF 

values based on annual averages. 

5.3.6.1.2. Using the results of the manual review in step 5.3.3, add 

“M1U-Good” and “M1U-Fair” values to the ALDF_METHOD 

field in the ALDF_ASSIGNMENT_METHOD table. Assignment 

is complete. 

5.3.6.2. If fewer than 200 axles per year, perform the following actions: 

5.3.6.2.1. Add NALS to a new table called 

ANNUAL_NALS_OPTION34_FOR_YEARS_COMBINED_ 

COMPUTATION. These records are used to compute ALDF 

values based on combined annual axle counts from multiple years. 
171

5.3.6.2.2. Using the results of the manual review in step 5.3.5, add 

“M1UYC,” “M1UYC-Good,” and “M1UYC-Fair” values to the 

ALDF_METHOD field in the ALDF_ASSIGNMENT_METHOD 

table. Assignment is complete. 

5.3.6.2.3. For each LTPP site, class, and axle included in the 

OPTION34_S_SITES_YEARS table, compute the total number of 

axles for all years. If the total number of axles for all years is fewer 

than 200 but more than 1 for a given class and axle, identify the 

record for a given class and axle as “ALC” (for “atypically low 

count”) in the ALDF_ASSIGNMENT_METHOD table. 

5.3.7. Proceed to step 5.4 to check for atypical axle groups and then to the Procedure to 

Compute ALDF Based on Site-Specific WIM Data Using Method 1 section.

5.4. Check for atypical axle groups 

5.4.1 Review axle group assignments for each site and vehicle class in the 

ALDF_ASSIGNMENT_METHOD table and identify records defined by LTPP 

traffic site ID, vehicle class, and axle group with axle groups extremely unlikely or 

impossible based on FHWA TMG 13-bin vehicle class definitions (e.g., tridem axle 

group reported for 2-axle Class 5 vehicle). 

5.4.2 For the identified records with atypical axle groups, add the letters “AA” for “atypical 

axle” to the assigned code in the ALDF_METHOD field. Assignment is complete.

Procedure to Assess Rationality and Identify Loading Pattern Using Annual NALS of 

Limited Accuracy 

Data from Type II WIM systems, systems with unknown accuracy, or data from limited data 

samples may or may not be applicable for direct use as site-specific traffic loading inputs due to 

low precision, bias, or a small, nonrepresentative sample size. NALS developed based on 

calibrated Type II or lower accuracy WIM systems tend to have a shape of loading distribution 

similar to NALS developed based on Type I WIM data but with wider spread of axle loading 

distribution, characterized by heavier tails of distribution and lower peaks, as a result of low 

precision. 

These NALS may be used with some caution for developing site-specific ALDF if the effect of 

the lower precision or bias does not significantly affect the percentage of heavy and overloaded 

axles (e.g., less than 10 percent difference in the percentage of heavy loads) compared to 

similarly shaped NALS developed based on research-quality data. If the bias or low precision 

observed in NALS results in a significant deviation from the expected percentage of heavy and 

overloaded axles for a given loading pattern, these NALS should not be used for direct 

computation of ALDF. However, if the general shape of the NALS follows that typically 

observed for FHWA Class 9 vehicles, the NALS can be used as an aid for selecting the default 

axle loading distribution developed from research-quality WIM data with a general NALS shape 

resembling the axle load distribution based on lower quality WIM data. 
172

The following procedures can be used to identify and evaluate axle loading patterns using WIM 

data of lower quality and aid in selecting default axle loading distribution values: 

For each WIM site, plot the annual NALS for FHWA Class 9 vehicles on an x–y plot 

(separately for single and tandem axles). Conduct an initial visual and quantitative 

assessment of axle load distributions using the criteria listed in step 2 below. Note that 

sites exhibiting axle weights that deviate from the following criteria may, in fact, 

represent actual axle weights. However, not knowing specific details about the 

commodities being transported, it is important to identify and flag the sites with atypical 

distributions. 

Flag NALS distributions that do not satisfy the following attributes that identify typical 

or expected axle load distributions for FHWA Class 9 vehicles: 

For single axles, the following criteria apply: 

• A bell-shaped distribution with a single peak load bin. A secondary heavy loaded 

peak between 16,000 and 22,000 lb is acceptable (possible when split-tandem axle 

groups are reported as two single axles). 

• The peak load bin is within the 9,000–11,999 lb range. 

• Less than 3 percent of single axle loads exceed 18,000 lb, and less than 1 percent of 

single axle loads exceed 20,000 lb. 

For tandem axles, the following criteria apply: 

• A camel-back distribution showing two peaks: one for loaded axles and one for 

unloaded axles. It is possible, but uncommon, to see only one peak (either loaded or 

unloaded) at the location of either the first or second peak. 

• First peak load bin is between 10,000 and 15,999 lb (optional check for lightly loaded 

or empty trucks).

• Second peak load bin is between 28,000 and 35,999 lb 

• Less than 20 percent of loads exceed the Federal legal limit of 34,000 lb. The 

majority of sites are expected to have less than 10 percent of axles over the Federal 

legal limit. 

• Less than 3 percent of loads exceed 40,000 lb. 

• No loads over 60,000 lb are expected. Loads over 60,000 lb should be less than 

0.1 percent of all tandem loads for FHWA Class 9 vehicles. 

If the initial assessment does not indicate anomalies in axle load distribution shape (i.e., 

the expected shape of distribution with a well-defined bell-shaped distribution of loaded 

and unloaded axles, values of unloaded and loaded peak loads are within reasonable 

range, and typical overload values) and no automated checks (described in the WIM Data 
173

Rationality and Data Reasonableness Checks section) are flagged, assume that WIM 

equipment collects data without significant bias and has adequate precision. NALS 

passing this assessment can be used to construct site-specific ALDF. ALDF computed 

based on these NALS should have an ALDF usability code labeled as “Good.” Add the 

corresponding site IDs and years to the OPTION34_S_SITES_YEARS table and proceed 

to step 5.3.6 to assess the total number of axles per year and select the ALDF 

computation procedure. 

If the initial assessment does not indicate anomalies in axle load distribution shape 

(i.e., the expected shape of distribution with a well-defined bell-shaped distribution of 

loaded and unloaded axles, values of unloaded and loaded peak loads are within 

reasonable range, and typical overload values), but some automated checks described in 

the WIM Data Rationality and Data Reasonableness Checks section resulted in flagged 

NALS, WIM equipment likely collected data with some bias or lower precision. Check if 

these NALS can be used for ALDF computation using the following assessments: 

4.1 Assess the precision of the estimates by comparing the peaks and tails of the 

distribution and the percentages of overloaded axles with the corresponding values 

in the default distribution built based on research-quality data with a similar loading 

shape. Lower precision will result in lower values of both unloaded and loaded peak 

loads and a wider spread of bell-shaped axle distributions along the x-axis. If the 

lower precision does not increase the percentage of overloads by an additional 

10 percent (e.g., increase in overloads from 7 percent to 17 percent) compared to the 

expected percentages for a given loading pattern, these values can be used to 

construct site-specific ALDF. Identify these NALS as SLP (site-specific low 

precision). 

4.2 Assess the likely value of the bias by reviewing the locations of the loading peaks 

along the x-axis in comparison with the location of the loading peaks in the default 

NALS distribution built based on research-quality data with a similar loading shape. 

If some bias is evidenced by the shifted peak load location for the loaded axles, 

assess if this shift is likely to result in less than a 10 percent addition or subtraction 

to/from the percentage of overloads expected for a given loading pattern. If less than 

a 10-percent change is likely, these NALS can be used to construct site-specific 

ALDF. Identify these NALS as SNB (site-specific negative bias), or SPB (sitespecific positive bias). 

4.3 For NALS identified as SLP, SNB, or SPB, check if a combination of bias and poor 

precision attributes is likely to increase or decrease the percentage of overloads by 

over 10 percent. If less than a 10 percent change is likely, these NALS can be used 

to construct site-specific ALDF. ALDF computed based on these NALS should 

have an ALDF usability code labeled as “Fair.” Add these corresponding WIM site 

IDs and years to the OPTION34_S_SITES_YEARS table and proceed to step 5.3.6 

to assess the total number of axles per year and select the ALD computation 

procedure. 
174

If the initial assessment does not indicate anomalies in the axle load distribution shape 

(i.e., the expected shape of distribution with a reasonable peak load location on the 

x-axis), but the precision is too low to be used as a site-specific loading input value, use 

table 50 to assign the default axle loading category based on the percentage of heavy 

loads computed for the FHWA Class 9 tandem NALS. Label FHWA Class 9 tandem 

NALS using loading category codes shown in table 50. Identify these NALS as “M2P” in 

the ALDF_ METHOD field in the ALDF_ASSIGNMENT_METHOD table. Note that 

the labels used to describe loading categories in table 50 are consistent with the labels 

used by the LTPP PLUG. However, to account for a wider spread of load distributions 

due to the lower precision of Type II WIM systems compared to distributions based on 

the LTPP research-quality WIM data, the percentages of heavy load values in table 50

were increased by 5 to 10 percent compared to the percentages used in the LTPP PLUG. 

Table 50. Loading category assignment for Type II or low-precision WIM data.

Loading Category 

Percent Heavy Loads 

(>26,000 lb) 

Light—L <20

Moderate—M 20–40

Heavy 1—H1 40–50

Heavy 2—H2 50–60

Very heavy—VH >60

If the initial assessment indicates that NALS under review do not have some or all 

expected attributes, the following two outcomes are possible: 

• The site location represents unusual loading conditions due to local or regional 

commodities. If additional information to confirm the observed loading is available

and weights are representative of freight being moved, these data may be used as 

site-specific inputs. In this case, identify the NALS as “M1Special.” 

• If no additional information is available or information does not support the observed 

axle weight distribution, assume the WIM equipment setup, sampling duration, and/or 

site conditions resulted in an axle load spectrum of limited quality/usability. In this 

case, check if the tandem axle load spectrum at least has the expected camel-back 

shape with two peaks corresponding to light and heavy loads. After this, one of the 

following two outcomes is possible. 

a. If the tandem axle distribution has the expected shape but location of unloaded 

and loaded peaks is shifted from expected values, proceed with identifying the 

loading category based on analyzing the ratio between unloaded and loaded peak 

axle loads of tandem axle distribution. To do this, find the highest percentage of 

axle loads corresponding to the first (unloaded) peak (for loads less than 

26,000 lb) and the second (loaded) peak (for loads greater than 26,000 lb) and 

compute the peak load ratio between those peaks (i.e., the percentage of axles 

corresponding to the unloaded peak load bin divided by the percentage of axles 

corresponding to the loaded peak load bin). Use table 51 to find the loading 

category based on the computed peak load ratio. Label FHWA Class 9 tandem 
175

NALS using loading category codes shown in table 51. Identify these NALS as 

“M2P” in the ALDF_METHOD field in the ALDF_ASSIGNMENT_METHOD 

table. Add LTPP site ID to a new table called OPTION34_P_SITES_YEARS. 

Table 51. Unloaded to loaded peak ratios in tandem axle load distributions for different 

loading categories.

Loading Category 

Peak Loads Ratio 

(Unloaded Peak/Loaded Peak) 

Light—L >6

Moderate—M ≤6 and >2.33 

Heavy—H ≤2.33 and >1 

Very heavy—VH ≤1 

b. If the shape of the distribution is unexpected, stop further analysis. This WIM dataset 

is unusable for determining the LTPP site’s loading condition. These data should not 

be used as site-specific inputs or to select the defaults. Identify these NALS as 

“M2NoP” in the ALDF_METHOD field in the ALDF_ASSIGNMENT_METHOD 

table.

Procedure to Compute ALDF Based on Site-Specific WIM Data Using Method 1 

Based on available axle loading data, ALDF are computed from either monthly or annual NALS 

using the ensuing procedures. 

Compute ALDF with Unique Values for Each Calendar Month (Method 1—Monthly) 

The following procedure describes ALDF computation using monthly NALS records identified 

as ALDF “M1MR” in the ALDF_METHOD field in the ALDF_ASSIGNMENT_METHOD 

table: 

For each traffic site ID, vehicle class (FHWA Classes 4–13), axle group, and calendar 

month, compute average monthly NALS for each calendar month (January to December) 

by averaging monthly NALS over multiple years included in the 

ALDF_MONTHLY_CANDIDATES table. 

Renormalize (i.e., express in percentile form) average monthly NALS so the sum of the 

percentages across all load bins equals 100 percent.

Populate fields of the TRF_MEPDG_ALDF_Method1_Monthly_Results table with the 

computed percentages for each traffic site ID, vehicle class (FHWA Classes 4–13), axle 

group, and calendar month.

Review the TRF_MEPDG_ALDF_Method1_Monthly_Results table for completeness 

(i.e., presence of 12 calendar months for each vehicle class [FHWA Classes 4–13] and 

axle group for each site). Identify any missing records defined by a combination of site 

ID, vehicle class, axle group, and month. Use the procedure described in the Compute 
176

ALDF Representing a Typical Day of a Year (Method 1–Annual) section to compute 

missing values. 

Compute ALDF Representing a Typical Day of a Year (Method 1—Annual) 

The following procedure describes ALDF computation using records identified with “M*” codes 

(except for the “M1MR” code) in the in the ALDF_METHOD field in the 

ALDF_ASSIGNMENT_METHOD table: 

1. Develop representative annual NALS for each LTPP site with codes M1, M1U*, M1_AA, or 

M1U_AA in the ALDF_METHOD field in the ALDF_ASSIGNMENT_METHOD table 

using the following steps: 

1.1. Average corresponding NALS in 

ANNUAL_NALS_OPTION2_FOR_BY_YEAR_COMPUTATION or 

ANNUAL_NALS_OPTION3_4_ FOR_BY_YEAR_COMPUTATION tables over all 

available years. 

1.2. Renormalize (i.e., express in percentile form) average NALS to ensure the sum of 

percentages across all load bins equals 100.00 when rounded to the nearest hundredth. 

1.3. Save the results in a new table called ANNUAL_NALS_FOR_ALDF. 

2. Develop representative annual NALS for each LTPP site with codes M1YC, M1UYC*, 

M1YC_AA, or M1UYC_AA in the ALDF_METHOD field in the 

ALDF_ASSIGNMENT_METHOD table using the following steps: 

2.1. Use NALS in the ANNUAL_NALS_OPTION2_YEARS_COMBINED table and the 

total number of axles per year computed by the LTPP CSSC and reported in the 

TOTAL_AXLES_YY_TSSC table to compute axle load distributions for years included 

in the ANNUAL_NALS_OPTION2_YEARS_COMBINED table. 

2.2. Sum axle load distributions for years included in the 

ANNUAL_NALS_OPTION2_YEARS_COMBINED table. 

2.3. Normalize the resulting axle load distribution (i.e., express in percentile form by 

dividing the number of axle counts in each load bin by the total number of axle counts in 

all load bins then multiplying by 100 percent) and save results in the 

ANNUAL_NALS_FOR_ALDF table. 

3. For each traffic site ID, vehicle class (FHWA Classes 4–13), and axle group included in the 

ANNUAL_NALS_FOR_ALDF table, repeat the same NALS for each of 12 calendar months 

and save results in a new table called TRF_MEPDG_ALDF_Method1_All_Annual_Results. 

4. Identify NALS based either on a small sample due to short count duration (code ALS, 

meaning fewer than 6 calendar months and fewer than 180 d) or low count of an 

underrepresented vehicle class or axle type (code ALC, meaning long count duration but low 

axle volume, fewer than 200 axles total; code APD0, meaning very low number of axles per 
177

day; or code APT0, meaning very low average axle number or no axle group present for a 

vehicle class). 

5. Add identifiers (i.e., flags) of ALS, meaning low sample, or ALC, meaning low axle count, 

in ALDF_ASSIGNMENT_METHOD tables. These NALS may not be representative of the 

loading condition due to sample size limitations. 

Assemble the TRF_MEPDG_ALDF Table 

The TRF_MEPDG_ALDF table is designed to facilitate copy–paste operations between tables in 

AASHTOWare Pavement ME Design software. See chapter 11 for table design information. 

Assemble the TRF_MEPDG_ALDF table using the following steps: 

1. Add previously computed site-specific ALDF values stored in 

TRF_MEPDG_ALDF_Method1_Monthly_Results and 

TRF_MEPDG_ALDF_Method1_All_Annual_Results tables to a new table called 

TRF_MEPDG_ALDF_Method1_All_Sites. 

2. For LTPP sites with monthly and/or annual MEPDG ALDF in 

TRF_MEPDG_ALDF_Method1_Monthly_Results and 

TRF_MEPDG_ALDF_Method1_All_Annual_Results tables, identify those vehicle classes 

and axle types not having site-specific WIM data to compute ALDF (i.e., a particular vehicle 

class or axle configuration was not observed in data selected for NALS computation) but 

vehicle classification data were available in the 

TRF_MEDPG_TRUCK_VOLUME_PARAMETERS table for some other years. For these 

sites, add missing vehicle classes, axle types, and “0” ALDF values to the 

TRF_MEPDG_ALDF_Method1_All_Sites table. Add code APT0, meaning no axle group or 

vehicle class present, to the APD0_FLAG field in the ALDF_ASSIGNMENT_METHOD 

table. 

3. Format site-specific ALDF for tridem- and quad-axle groups to match the 31-bins used in the 

AASHTOWare Pavement ME Design software by combining percentages reported in load 

bins 32 and higher and reporting the results in load bin 31. 

4. Use entries in the ALDF_METHOD field in the ALDF_ASSIGNMENT_METHOD table to 

assign ALDF usability ratings to the TRF_MEPDG_ALDF_Method1_All_Sites table.

Procedure to Estimate ALDF for Sites with Insufficient or no Site-Specific WIM Data 

Using Method 2 

The task of assigning ALDF for sites with insufficient or no site-specific WIM data was 

performed outside of this study by a different contractor using a different procedure. 
178

Method 2.1 Estimate ALDF Using Limited Site-Specific Axle Weight Information (SiteSpecific Loading Pattern) 

1. Apply the following hierarchical process for each LTPP traffic site identified as M2P in the 

ALDF_ASSIGNMENT_METHOD table, meaning it has a site-specific loading pattern but 

low-quality axle loading data: 

1.1 Check if another WIM site is available in the LTPP database with the following 

attributes: 

• Located on the same roadway with the same road functional classification as the site 

for which ALDF are being estimated.

• Has similar loading pattern for the dominant heavy vehicle classes as the site for 

which ALDF are being estimated. 

• Has ALDF computed based on Method 1. 

1.2 If such a Method 1 site is found, assign ALDF from that site to the “M2P” site being 

estimated. 

1.3 If no suitable Method 1 site is found, check if Method 1 sites are available for the same 

State and road functional classification with similar loading patterns as the site being 

evaluated. 

1.3.1 If such sites are available, compute average ALDF for these sites and assign them 

to the “M2P” site being estimated. 

1.4 If no suitable Method 1 sites are available, assign the LTPP PLUG default that best 

describes the loading category for the “M2P” site, vehicle class, and axle group being 

estimated using the ensuing procedure. 

1.4.1 Review available axle load distributions and assign an axle loading category using 

the following criteria: 

1.4.1.1 If the distribution has a well-defined shape, corresponding to the shape of 

one of the LTPP PLUG default distributions for a given vehicle class and 

axle group, assign the loading category using one of the LTPP PLUG 

default names. Focus on matching the shape of the distribution representing 

heavy loads. The distribution can be shifted, stretched, or compressed 

along the x-axis due to calibration issues, but the general shape should be 

the same. 

1.4.1.2 If the shape is not well-defined (i.e., axle load counts are low and highly 

variable), loads are atypically high or low (as compared to LTPP defaults), 

or distribution does not have the expected shape for a given vehicle class 

and axle group, proceed to step 2. 
179

Method 2.2 Estimate ALDF for Sites with No Usable Site-Specific Axle Weight Information 

(No Loading Pattern) 

The following ALDF estimation procedure is for LTPP traffic sites with no weight data or 

weight data insufficient or inconclusive to characterize axle loading pattern. Perform the 

following steps for each LTPP traffic site, vehicle class, and axle group identified as “M2NoP” 

in the ALDF_ASSIGNMENT_METHOD table: 

1. Check if another WIM site is available in the LTPP database with the following attributes: 

• Located on the same roadway as the site for which ALDF are being estimated, no more 

than 200 mi away. 

• Has the same road functional classification. 

• Has a similar AADTT. 

• Has a similar vehicle class distribution. 

• Has an ALDF computed using Method 1. 

2. If such a Method 1 site is found, assign the ALDF from that site to the Method 2 site, vehicle 

class, and axle group being estimated. 

3. If no suitable Method 1 site is found on the same roadway, check if any Method 1 sites are 

available for the same State and road functional classification with similar AADTT and 

vehicle class distributions as the site being evaluated.

3.1 If such sites are available, compute average ALDF and assign them to the “M2NoP” site 

being estimated. 

3.2 If no suitable Method 1 sites are available, assign the LTPP PLUG default recommended 

for sites in the same road functional classification that have similar AADTT and vehicle 

class distribution. For FHWA Class 9 vehicles, assign loading defaults based on road 

functional use, truck volume, and vehicle class distribution information using table 52.

Table 52. Recommended LTPP default axle loading categories for 

FHWA Class 9 vehicles based on road functional use, truck volume, and vehicle class 

distribution information. 

LTPP PLUG 

Default Loading 

Category 

AADTT 

(LTPP Lane) 

Percent 

FHWA Class 9 

(LTPP Lane) 

Road Functional 

Classification Road Use and Freight Route 

Moderate <1,000 (at 

least 100 

for FHWA 

Class 9) 

<50 Noninterstate 

roads: urban, 

rural, other 

principal and 

minor arterials. 

Local movement of goods 

away from industrial and 

multimodal facilities. Not a 

freight route. 
180

LTPP PLUG 

Default Loading 

Category 

AADTT 

(LTPP Lane) 

Percent 

FHWA Class 9 

(LTPP Lane) 

Road Functional 

Classification Road Use and Freight Route 

Heavy 1 1,000 to 

3,000

50 to 80 Rural and urban 

interstates and 

principal 

arterials. 

Combined local distribution 

and State-to-State freight 

movements. Mostly 

noninterstate rural primary 

arterials, but also urban 

interstates designated as 

freight routes. 

Heavy 2 >2,000 >80 Rural and urban 

interstates or 

rural principal 

arterials. 

Heavy State-to-State freight 

movements. Also routes 

serving major industrial and 

multimodal transportation 

facilities and warehouses. 

Main freight routes. 

EXAMPLES OF ALDF ESTIMATION 

The following examples show ALDF estimation based on different scenarios of NALS quality 

and availability. 

ALDF with Unique Values for Each Calendar Month Based on Site-Specific WIM Data 

(Method 1—Monthly) 

This example demonstrates the ALDF computation for tandem axle group for vehicles in FHWA 

Class 9 for a site with at least 200 FHWA Class 9 tandem axle counts per month for at least 1 yr. 

For this example, LTPP traffic site 060200 was selected from a list of sites with sufficient 

monthly NALS. The same procedure should be used for all other vehicle classes and axle groups 

that satisfy the aforementioned monthly data availability criterion. ALDF were computed using 

the following procedure: 

For this site, available calibration and validation data indicate WIM data collected from 

April 2008 to December 2015 satisfying the performance parameters of Type I WIM 

systems per ASTM E1318-09. These data were used to compute monthly NALS, and

figure 7 shows a plot of the available monthly NALS for tandem axle group for FHWA 

Class 9 vehicles. Each line on the plot corresponds to a specific month–year combination. 

In addition, dashed lines connect all minimum and maximum values for each load bin 

observed over the analysis period. This plot shows that, while all months show similar 

camel-back distribution, some variability between different monthly NALS exist. 

Specifically, the location of the heavy peak loads ranges between 28,000 and 35,999 lb.
181

Source: FHWA. 

Figure 7. Chart. Monthly NALS for LTPP traffic site 060200 FHWA Class 9 tandem axles 

from 2008 to 2015. 

Using the available monthly NALS, average monthly NALS were computed for each 

calendar month (January to December) by averaging each monthly NALS over multiple 

years. Figure 8 shows a plot of average monthly NALS for tandem axle group for FHWA 

Class 9 vehicles. Average monthly NALS indicate the percentage and distribution of 

heavily loaded trucks (those loaded to 75 percent or more of the Federal legal load limit 

of 34,000 lb) remains approximately the same during different calendar months. Axle 

load distributions between different months are almost identical; thus, the variability 

observed in figure 7 was due to year-to-year (most likely due to calibration adjustment),

rather than month-to-month, changes in loading distribution. Figure 9 further 

demonstrates the distribution of the percentages of heavily loaded FHWA Class 9 tandem 

axles by month. As can be seen from Figure 8 and Figure 9, this site does not have 

seasonal changes in loading pattern for FHWA Class 9 tandem loads. For LTPP traffic 

site 060200, ALDF representing an average day of the month or an average day of the 

year results in similar pavement performance prediction. 
182

Source: FHWA. 

Figure 8. Chart. Average monthly NALS for LTPP traffic site 060200 FHWA Class 9 

tandem axles. 
183

Source: FHWA. 

Figure 9. Chart. Percentages of heavily loaded FHWA Class 9 tandem axles by month for 

LTPP traffic site 060200. 

Computed monthly averages were renormalized to account for rounding errors (i.e., 

expressed in percentile form) so the sum of percentages across all load bins equals 100 

percent. Table 53 shows a sample of computed ALDF factors for tandem axle group for 

FHWA Class 9 vehicles for LTPP traffic site 060200. 

The TRF_MEPDG_ALDF table was populated with the computed percentages of axles 

for each load bin for the selected LTPP traffic site ID (060200), vehicle class (FHWA 

Class 9), axle group (axle group 2 for tandem), and each calendar month. 

Table 53. ALDF table populated with records for FHWA Class 9 tandem axles for LTPP 

traffic site 060200. 

Month Class Total 

6,000

lb 

8,000

lb 

10,000 

lb 

12,000 

lb 

14,000 

lb

16,000 

lb * 

82,000 

lb 

January 9 100 0.051250

0

1.446250

0

4.392500

0

10.12125

00 

11.30500

00

7.391250

0

* 0.00000

February 9 100 0.057142

9

1.564285

7

4.502857

1

10.28857

14 

10.95857

14

7.068571

4

* 0.00000
184

Month Class Total 

6,000

lb 

8,000

lb 

10,000 

lb 

12,000 

lb 

14,000 

lb

16,000 

lb * 

82,000 

lb 

March 9 100 0.060000

0

1.670000

0

4.642857

1

10.71285

71 

10.50428

57

6.640000

0

* 0.00000

April 9 100 0.061428

6

1.671428

6

4.704285

7

10.81428

57 

10.42000

00

6.522857

1

* 0.00000

May 9 100 0.056666

7

1.656666

7

4.740000

0

10.71000

00 

9.951666

7

6.416666

7

* 0.00000

June 9 100 0.068333

3

1.808333

3

4.945000

0

10.88500

00 

9.913333

3

6.333333

3

* 0.00000

July 9 100 0.058333

3

1.793333

3

5.065000

0

11.20500

00 

10.04500

00

6.593333

3

* 0.00000

August 9 100 0.070000

0

1.880000

0

5.055000

0

11.44750

00 

10.40750

00

6.845000

0

* 0.00000

Septemb

er

9 100 0.078750

0

1.825000

0

5.203750

0

11.59750

00 

10.60250

00

6.981250

0

* 0.00000

October 9 100 0.067500

0

1.577500

0

4.978750

0

11.13250

00 

10.94250

00

7.221250

0

* 0.00000

Novemb

er

9 100 0.056250

0

1.475000

0

4.682500

0

10.37375

00 

10.95000

00

7.297500

0

* 0.00000

Decembe

r 

9 100 0.053750

0

1.402500

0

4.40000 10.16500

00 

11.34750

00

7.616250

0

* 0.00000

*Load bins between 16,000 and 82,000 lb are not shown for space saving. 

ALDF Representing a Typical Day of a Year Based on Site-Specific WIM Data 

(Method 1—Annual) 

This example demonstrates ALDF computation for tandem axle group for FHWA Class 9 

vehicles for a site with good quality WIM data, but available data do not satisfy minimum data 

availability criteria to compute unique monthly ALDF (i.e., at least 200 axle counts for a selected 

vehicle class and axle group per month for each of the 12 calendar months for at least 1 yr). The 

same procedure should be used for all other vehicle classes and axle groups satisfying this annual 

data availability criterion. 

ALDF values computed in this example represent axle loading for a typical day of the year. In 

MEPDG applications, this input is repeated for each calendar month (January to December) for a 

selected vehicle class and axle group. Tandem axle group for FHWA Class 9 vehicles for LTPP 

traffic site 090960 was selected for this example. ALDF were computed using the following 

procedure: 

WIM calibration records show that LTPP traffic site 090960 has 3 yr of WIM data 

satisfying the performance parameters of Type I WIM systems per ASTM E1318-09. 

However, the available data do not have over 200 axles for each of 12 calendar month 

(January to December). This site has quartz piezo WIM sensors. 

Available annual NALS for FHWA Class 9 tandem axle group vehicles for LTPP traffic 

site 090960 were extracted and reviewed using the plot shown in figure 10. All years 
185

show a consistent camel-back distribution with a higher percentage of unloaded axles 

than loaded axles. There is virtually no variability in the distribution between the 3 yr. 

The location and shape of the loaded peak does not indicate any issues with equipment 

accuracy or precision. 

Source: FHWA. 

Figure 10. Chart. Annual NALS for LTPP traffic site 090960 FHWA Class 9 tandem axles 

from 2002 to 2004. 

Average annual NALS were computed by averaging annual NALS over the 3 yr. 

Average annual NALS were renormalized (i.e., expressed in percentile form) so the sum 

of percentages across all load bins equals 100 percent.

Fields in the TRF_MEPDG_ALDF table were populated with the computed percentages 

of axles for each load bin for the selected LTPP traffic site ID (090960), vehicle class 

(FHWA Class 9), and axle group (axle group 2 for tandems). The same distribution was 

repeated for each calendar month. Table 54 shows computed ALDF factors for FHWA 

Class 9 tandem axle group vehicles for LTPP traffic site 090960.

Table 54. ALDF table populated with records for FHWA Class 9 tandem axles for LTPP 

traffic site 090960. 

Month Class Total 

6,000

lb 

8,000

lb 

10,000 

lb 

12,000 

lb 

14,000 

lb 

16,000 

lb * 

82,000 

lb

January 9 100 0.20667 3.88000 12.28000 14.21667 10.02333 8.02000 * 0.00000
186

Month Class Total 

6,000

lb 

8,000

lb 

10,000 

lb 

12,000 

lb 

14,000 

lb 

16,000 

lb * 

82,000 

lb

February 9 100 0.20667 3.88000 12.28000 14.21667 10.02333 8.02000 * 0.00000

March 9 100 0.20667 3.88000 12.28000 14.21667 10.02333 8.02000 * 0.00000

April 9 100 0.20667 3.88000 12.28000 14.21667 10.02333 8.02000 * 0.00000

May 9 100 0.20667 3.88000 12.28000 14.21667 10.02333 8.02000 * 0.00000

June 9 100 0.20667 3.88000 12.28000 14.21667 10.02333 8.02000 * 0.00000

July 9 100 0.20667 3.88000 12.28000 14.21667 10.02333 8.02000 * 0.00000

August 9 100 0.20667 3.88000 12.28000 14.21667 10.02333 8.02000 * 0.00000

September 9 100 0.20667 3.88000 12.28000 14.21667 10.02333 8.02000 * 0.00000

October 9 100 0.20667 3.88000 12.28000 14.21667 10.02333 8.02000 * 0.00000

November 9 100 0.20667 3.88000 12.28000 14.21667 10.02333 8.02000 * 0.00000

December 9 100 0.20667 3.88000 12.28000 14.21667 10.02333 8.02000 * 0.00000

*Load bins between 16,000 and 82,000 lb are not shown for space saving. 

ALDF Representing a Typical Day of a Year Based on Site-Specific WIM Data 

(Method 1—Annual Combined) 

This example demonstrates ALDF computation for tridem-axle group for FHWA Class 13 

vehicles for a site with good quality WIM data but a low volume of FHWA Class 13 tridem 

axles. The low occurrence poses some challenges for computing representative values. The 

method used in this example is for computing ALDF for LTPP sites with fewer than 200 axle 

counts per year for a selected vehicle class and axle group. The same procedure should be used 

for all other vehicle classes and axle groups falling under this data availability criterion. ALDF 

computed in this example represent axle loading for a typical day of the year. In MEPDG 

applications, this input is repeated for each calendar month for a selected vehicle class and axle 

group. 

Tridem-axle group for FHWA Class 13 vehicles for LTPP traffic site 060200 were selected for 

this example. ALDF were computed using the following procedure: 

For LTPP traffic site 060200, available calibration and validation data indicate WIM data 

collected from 2008 to 2015 satisfying the performance parameters of Type I WIM 

systems per ASTM E1318-09. Figure 11 shows a plot of available tridem axle group 

annual NALS for FHWA Class 13 vehicles for LTPP traffic site 060200.

Annual axle counts were too low (between 25 and 69 counts per year) and too variable to 

identify a dominant shape of the distribution. Counts observed in the load bins range 

from 0 to 71,999 lb. 
187

Source: FHWA. 

Figure 11. Chart. Annual NALS for LTPP traffic site 060200 FHWA Class 13 tridem axles 

from 2008 to 2015. 

Due to low values, NALS based on individual years were not used to compute average 

NALS, as in the previous example. Instead, axle counts in each load bin were summed 

over the years to compute the combined axle load distribution. The combined distribution 

was normalized (i.e., expressed in percentile form) to develop one representative NALS. 

This was accomplished by dividing the number of counts in each load bin by the total 

number of axle counts. 

Figure 12 shows NALS computed based on axle counts combined for 2008–2015. The 

resulting distribution has a more defined shape with a major peak corresponding to 

loaded axles and two minor peaks corresponding to unloaded axles (15,000–26,9999 lb). 

The highest percentage of loads is between 39,000 and 50,999 lb. 
188

Source: FHWA. 

Figure 12. Chart. Axle load distribution for LTPP traffic site 060500 FHWA Class 13 

tridem axles based on data from 2008 to 2015 combined. 

Fields in the TRF_MEPDG_ALDF table were populated with the computed percentages 

of axles for each load bin for the selected LTPP traffic site ID (060500), vehicle class 

(FHWA Class 13), and axle group (axle group 3 for tridem). The same values are 

repeated for each calendar month. Table 55 shows computed ALDF for tridem-axle 

group for FHWA Class 13 vehicles for LTPP traffic site 060500. 

Table 55. ALDF table populated with records for FHWA Class 13 tridem axles for LTPP 

traffic site 060200. 

Month Class Total 

12,000 

lb 

15,000 

lb 

18,000 

lb 

21,000 

lb 

24,000 

lb 

27,000 

lb * 

102,000 

lb 

January 13 100 0.66375 1.35750 3.27750 6.92125 3.15750 5.04250 * 0.00000

February 13 100 0.66375 1.35750 3.27750 6.92125 3.15750 5.04250 * 0.00000

March 13 100 0.66375 1.35750 3.27750 6.92125 3.15750 5.04250 * 0.00000

April 13 100 0.66375 1.35750 3.27750 6.92125 3.15750 5.04250 * 0.00000

May 13 100 0.66375 1.35750 3.27750 6.92125 3.15750 5.04250 * 0.00000

June 13 100 0.66375 1.35750 3.27750 6.92125 3.15750 5.04250 * 0.00000

July 13 100 0.66375 1.35750 3.27750 6.92125 3.15750 5.04250 * 0.00000

August 13 100 0.66375 1.35750 3.27750 6.92125 3.15750 5.04250 * 0.00000

September 13 100 0.66375 1.35750 3.27750 6.92125 3.15750 5.04250 * 0.00000
189 

Month Class Total

12,000

lb 

15,000

lb 

18,000

lb 

21,000

lb 

24,000

lb 

27,000

lb * 

102,000

lb 

October 13 100 0.66375 1.35750 3.27750 6.92125 3.15750 5.04250 * 0.00000 

November 13 100 0.66375 1.35750 3.27750 6.92125 3.15750 5.04250 * 0.00000 

December 13 100 0.66375 1.35750 3.27750 6.92125 3.15750 5.04250 * 0.00000 

*Load bins between 27,000 and 102,000 lb are not shown for space saving. 

ALDF Estimated for a Site with Insufficient Site-Specific WIM Data (Method 2—Limited 

Data) 

This example demonstrates the ALDF computation for tandem axle group for FHWA Class 9 

vehicles for LTPP traffic site 135023, which has limited WIM data of questionable quality. This 

site is located on I–95 in a rural area. I–95 is a major North–South interstate in the eastern United 

States and has a high volume of heavy loaded FHWA Class 9 trucks. There is only one annual 

NALS at Level E. This NALS is reported for 2014. The NALS for FHWA Class 9 tandem axle 

loads was developed based on site-specific WIM data shown in figure 13. This NALS has the 

expected shape for FHWA Class 9 tandem axle distribution with loaded and unloaded peaks 

clearly defined, but load bin values corresponding to both unloaded and loaded peaks are 

atypically low. This distribution shows no loaded FHWA Class 9 trucks during 2014 on I–95 and 

atypically light tandem loads even for empty trucks. Such low values are typically a sign of a 

significant negative measurement bias due to poor WIM calibration or calibration drift. If this 

distribution is used directly in pavement design or analysis, it would likely cause a significant 

underprediction of pavement distresses. Therefore, an alternative approach to characterize traffic 

loading at this site is needed. In this case, the site-specific distribution of tandem axle loads is 

used to describe a general loading pattern in terms of the relation between loaded and unloaded 

axles. Unloaded axles are assumed to be represented by the first peak in the site-specific NALS, 

and loaded axles are represented by the second peak. As shown in figure 13, the percentage of 

loaded axles exceed the percentage of unloaded axles. The ratio between the unloaded and 

loaded peak is about 0.77. Using table 51, the loading category based on the computed load ratio 

can be defined as “very heavy.” 

The available LTPP PLUG default best representing this shape of a distribution is labeled as 

“LTPP Heavy 2 Default,” which is illustrated in figure 13. This type of default distribution is 

recommended for rural interstates subjected to heavy volumes of FHWA Class 9 trucks, which is 

consistent with truck traffic observed at LTPP traffic site 135023. Therefore, LTPP Heavy 2 

Default is selected to represent tandem axle loading for FHWA Class 9 trucks for LTPP traffic 

site 135023 based on the limited site-specific WIM data and information about the location of 

this site.
190

Source: FHWA. 

Figure 13. Chart. Axle load distribution for LTPP traffic site 135023 FHWA Class 9 

tandem axles based on data from 2014 and LTPP Heavy 2 default distribution. 

Default ALDF values selected in this example represent axle loading for a typical day of the 

year. In MEPDG applications, this input is repeated for each calendar month for the selected 

vehicle class and axle group. More information about LTPP defaults can be found in the LTPP 

PLUG.(10)

ALDF Estimated for Site with No Usable Site-Specific WIM Data (Method 2—No Usable 

Data) 

For LTPP sites with insufficient data describing the general loading pattern or that contain no 

loading data, default values are selected using the approach presented in the following example. 

For this example, LTPP traffic site 131001 was selected. This site is located on SR 10 in Georgia 

and has 2 yr of WIM data. However, available WIM data are highly questionable. These data 

were collected over a 2-d sampling period using a portable WIM that was not calibrated in 

accordance with ASTM E1318-09. ALDF were computed using the following procedure: 

Available axle loading data were used to develop an axle loading distribution for tandem 

axle loads for FHWA Class 9 vehicles. The resulting NALS plot is shown in figure 14. 

Weights of the tandem axles (less than 6,000 lb) were much less than expected, even 

assuming all axles belong to empty FHWA Class 9 trucks (tandem axle weights between 

12,000 and 18,000 lb are expected for FHWA Class 9 trucks with empty trailers). 
191

Analysis of available loading data led to the conclusion that these data should not be used 

for pavement analysis; alternative data sources should be identified. 

Source: FHWA. 

Figure 14. Chart. Axle load distribution for LTPP traffic site 131003 FHWA Class 9 

tandem axles based on data from 1994 and 1996. 

Analysis of available truck volume and vehicle classification data for 1994 and 1996 

revealed that LTPP traffic site 131003 had about 134 FHWA Class 9 vehicles per day in 

1994 and only 13 in 1996. This dramatic change in volume further suggests data 

collection errors at this site. The percent of FHWA Class 9 trucks in the AADTT estimate 

is about 30. 

Using truck volume and classification information, the LTPP database was searched to 

see if any other sites in Georgia with similar truck traffic characteristics have usable 

weight data. The results of the search led to the following conclusions: 

• No Method 1 WIM site located on the same route is available in the LTPP database. 

• No Method 1 WIM site located in the same State with the same road functional 

classification (Rural Principal Arterial – Other) and similar AADTT and vehicle class 

distribution is available in LTPP database. 

Since no suitable Method 1 site was identified, the LTPP PLUG default recommended for 

sites in the same road functional classification with similar AADTT and vehicle class 

distribution was selected. For FHWA Class 9 vehicles, the recommended LTPP default 
192

axle loading categories were presented in table 52. Using table 52, the default NALS 

called “Moderate” was assigned to represent FHWA Class 9 tandem axle loads for LTPP 

traffic site 131001. This default NALS for tandem axle loads is graphically shown in 

figure 15. 

Source: FHWA. 

Figure 15. Chart. Axle load distribution selected for LTPP traffic site 131003 FHWA 

Class 9 tandem axles based on the LTPP PLUG “Moderate” default. 
193

CHAPTER 9. ESTIMATING REPRESENTATIVE NUMBER OF AXLES PER TRUCK 

BY VEHICLE CLASS 

COMPUTED PARAMETER DESCRIPTION 

In AASHTOWare Pavement ME Design software, one set of representative values describing the 

number of axles per truck is used to estimate the number of axle loads for each of the 10 vehicle 

classes (FHWA Classes 4–13). Representative APTs are provided for heavy vehicles (i.e., those 

in FHWA Classes 4–13) for each axle group (i.e., single, tandem, tridem, and quad). The quad 

group includes axle groups with four or more axles. Without these values, the number of axle 

loads associated with individual vehicle classes cannot be estimated in AASHTOWare Pavement 

ME Design software. 

DATA SOURCES USED FOR PARAMETER COMPUTATION 

Data for computing representative APT values are available in the 

TRF_MEPDG_AX_PER_TRUCK table. In addition, the LTPP program developed a new table 

called VEHICLE_CLASS_AVG_AX_ANL containing similar data. These data represent annual 

APT values computed for LTPP sites for each year with WIM data reported in the LTPP 

database. 

APT parameters are computed using site-specific total truck and axle count data. For LTPP sites, 

axle count data are only available for sites with individual vehicle weight records collected by 

WIM equipment. Therefore, site-specific APT can be developed only for LTPP sites with WIM 

data. For LTPP sites with no site-specific values, site-related values or default values, such as 

LTPP PLUG defaults, can be used. 

METHODOLOGY FOR PARAMETER COMPUTING OR ESTIMATING 

APT values are dependent on the truck fleet observed at the site and the vehicle classification 

algorithm used to collect vehicle classification data. Experience shows that truck configuration 

(i.e., number and spacing of axles) for a given truck type typically remains stable over the years 

with little to no change. Truck body and axle types within vehicle classes using a particular 

roadway also typically do not change significantly over time. Therefore, the methodology for 

computing representative site-specific APT values focuses on reviewing annual APT values in 

the VEHICLE_CLASS_AVG_AX_ANL table, identifying atypical values and potential vehicle 

classification issues, and averaging selected annual APT values to compute representative 

values. 

The two approaches used to develop representative APT values, depending on WIM data 

availability, are as follows: 

• Computing APT for LTPP sites with WIM data. This approach focuses on computing 

typical site-specific APT values. 

• Estimating APT for LTPP sites without WIM data. This approach focuses on identifying 

site-related or default values using information about site location, road functional 
194

classification, vehicle class distribution, and ADTT. Default values are selected using a 

hierarchical approach and may include values for the same roadway, same State, or 

national defaults. 

COMPUTATIONAL PROCEDURE 

Procedure to Compute Representative APT Based on Site-Specific WIM Data 

The following procedure was used to compute representative APT for LTPP sites included in the 

VEHICLE_CLASS_AVG_AX_ANL table: 

1. Obtain annual average APT values from the VEHICLE_CLASS_AVG_AX_ANL table 

for LTPP sites for the following years: 

1.1. For sites with site-specific ALDF entries in the TRF_MEPDG_ALDF table, use only 

years used to compute ALDF. 

1.2. For sites with no site-specific ALDF entries in the TRF_MEPDG_ALDF table, use 

all years in the VEHICLE_CLASS_AVG_AX_ANL table. 

2. Compute average APT values for each LTPP site and vehicle class by averaging annual 

APT values selected in step 1 and save information in an intermediate table.

3. Compute the total average number of axles per truck per vehicle class for each LTPP site 

using values reported for each axle group in the intermediate table created in step 2. 

4. Use the TRF_MONITOR_LTPP_LN table to identify the number of days with data and 

the number of trucks used to compute annual APT values for years selected in step 3. 

This is to identify years where APTs were computed based on small samples. 

5. For each LTPP site, analyze average APT values for each vehicle class and axle group 

and the total number of axles per truck for each vehicle class. Note any significant 

deviations from typical ranges of APT values for each vehicle class and axle type. 

Typical ranges of APT values are provided in table 56. These values are based on the 

FHWA 13-bin vehicle classification scheme, the LTPP classification rules table, and 

observations based on data from TPF-5(004) WIM sites. A description of the FHWA 

TMG 13-bin vehicle classification scheme is provided in table 57.

6. Use data availability information and the results of annual APT values analysis to assign 

APT usability codes (table 98). 

7. Save the computed average APT and the assigned APT usability codes to a new table 

called TRF_MEPDG_APT. 
195

Table 56. Typical APT ranges for vehicles in FHWA Classes 4–13. 

Vehicle 

Class 

Number of Axles per Truck 

Single Tandem Tridem Quad Total 

4 Two Zero or one Zero Zero Two or three 

5 Two or more* Zero or one* Zero Zero Two or more* 

6 One One Zero Zero Three 

7 One Zero or one Zero or one Zero or one Four or more 

8 One, two, or 

three 

Zero or one Zero Zero Three or four 

9 One, two, or 

three 

Zero, one, or 

two

Zero or one Zero Five 

10 One One Zero or one Zero or one Six or more 

11 Five Zero Zero Zero Five 

12 One, two, three, 

or four 

One Zero Zero Six 

13 One, two, or 

three 

Two, three, or 

four 

Zero, one, or 

two

Zero, one, or 

two

Seven or more 

*Allows lightweight trailer attached to two-axle unit per LTPP classification table. 

Table 57. FHWA vehicle classification definitions. 

Class 

Group Class Definition Class Includes 

Number of 

Axles 

1 Motorcycles Motorcycles Two

2 Passenger cars All cars 

Cars with one-axle trailers 

Cars with two-axle trailers

Two, 

three, or 

four 

3 Other two-axle, four-tire, singleunit vehicles Pick-ups and vans 

Pick-ups and vans with one- and twoaxle trailers 

Two, 

three, or 

four 

4 Buses Two- and three-axle buses Two or 

three 

5 Two-axle, six-tire, single-unit 

trucks 

Two-axle trucks Two

6 Three-axle, single-unit trucks Three-axle trucks 

Three-axle tractors without trailers 

Three 

7 Four-or-more-axle, single-unit 

trucks 

Four-, five-, six- and seven-axle, singleunit trucks Four or 

more 

8 Four-or-fewer-axle, single-trailer 

trucks 

Two-axle trucks pulling one- and twoaxle trailers 

Two-axle tractors pulling one- and twoaxle trailers 

Three-axle tractors pulling one-axle 

trailers 

Three or 

four 
196

Class 

Group Class Definition Class Includes 

Number of 

Axles 

9 Five-axle, single-trailer trucks Two-axle tractors pulling three-axle 

trailers 

Three-axle tractors pulling two-axle 

trailers 

Five 

10 Six-or-more-axle, single-trailer 

trucks 

Multiple configurations Six or 

more 

11 Five-or-fewer-axle multitrailer 

trucks 

Multiple configurations Four or 

five 

12 Six-axle multitrailer trucks Multiple configurations Six 

13 Seven-or-more-axle multitrailer 

trucks 

Multiple configurations Seven or 

more 

Procedure to Estimate Representative APT for Sites with No Site-Specific WIM Data 

The following hierarchical procedure was developed to estimate representative APT for LTPP 

sites with no reliable site-specific WIM data to compute APTs. The actual APT estimations were 

performed outside of this study by the LTPP program. The procedure to estimate representative 

APT is as follows: 

1. Check if another LTPP WIM site is available in the TRF_MEPDG_APT table with the 

following attributes: 

• Site located on the same roadway as the site for which APTs are being estimated, no 

more than 200 mi away. 

• Has the same road functional classification. 

• Has similar AADTT. 

• Has similar vehicle class distribution. 

• Has representative APTs computed based on WIM data. 

1.1. If a site satisfying the above attributes is found, assign APTs from that site. If more 

than one site is found, use values from the closest site or average values from multiple 

sites. 

1.2. If no site is found, check if sites are available in the TRF_MEPDG_APT table in the 

same State and road functional classification with a similar vehicle class distribution as 

the site being evaluated (use only sites with site-specific APT values).

1.2.1. If such sites are available, compute average APTs and assign them to the site 

being evaluated. 

1.2.2. If no sites are available for the same road functional classification or with a 

similar vehicle class distribution, check if sites are available in the 

TRF_MEPDG_APT table in the same State (use only sites with site-specific APT 

values).
197

1.2.2.1. If such sites are available, compute average APTs using sites from the 

same State (use only sites with site-specific APT values). 

1.2.2.2. If no sites are available with APT values based on site-specific WIM data, 

and if none are available from the same State in the TRF_MEPDG_APT 

table, assign default values computed based on WIM data from 

TPF-5(004) study sites. These values are included in the LTPP PLUG. 

LTPP PLUG default APT values are provided in table 58. More 

information about LTPP defaults can be found in the LTPP PLUG.(10)

Table 58. LTPP PLUG default APT values. 

Vehicle 

Class Single Tandem Tridem Quad 

4 1.43 0.57 0.00 0.00

5 2.16 0.02 0.00 0.00

6 1.02 0.99 0.00 0.00

7 1.26 0.20 0.63 0.15

8 2.62 0.49 0.00 0.00

9 1.27 1.86 0.00 0.00

10 1.09 1.15 0.79 0.05

11 4.99 0.00 0.00 0.00

12 3.99 1.00 0.00 0.00

13 1.59 1.26 0.69 0.31

EXAMPLES OF ESTIMATING REPRESENTATIVE APTs 

Estimating Representative APTs Based on Site-Specific WIM Data 

LTPP traffic site 245807 was selected for this example. The procedure to estimate representative 

APTs is as follows: 

Assessment of data in the TRF_MEPDG_ALDF table showed that this site has 

site-specific ALDF values. The years used for ALDF computation were 2002–2006. 

Annual APT values for these years were downloaded from the 

VEHICLE_CLASS_AVG_AX_ANL table and saved to the TRF_MONITOR_LTPP_LN 

table.

The TRF_MONITOR_LTPP_LN table was used to identify the number of days with data 

and the number of trucks used to compute annual APT values. Columns showing number 

of days with data and number of trucks were added to table 59 to facilitate further data 

assessment.
198

Table 59. Annual APT values for LTPP traffic site 245807 for 

years used in ALDF computation. 

State 

Code 

SHRP 

ID Year 

Vehicle 

Class Single Tandem Tridem Quad+ 

Trucks 

Weighed 

Weight 

Days 

24 5807 2002 4 1.82 0.18 0 0 2,199 51

24 5807 2002 5 2 0 0 0 8,199 51

24 5807 2002 6 1.05 0.98 0 0 2,291 51

24 5807 2002 7 1 0.13 0.87 0 715 43

24 5807 2002 8 2.52 0.45 0.01 0 1,327 51

24 5807 2002 9 1.07 1.96 0 0 4,458 51

24 5807 2002 10 1 1.45 0.55 0 47 34

24 5807 2002 11 5 0 0 0 5 5

24 5807 2002 12 4 1 0 0 1 1

24 5807 2003 4 1.8 0.2 0 0 13,905 268

24 5807 2003 5 2 0 0 0 49,268 268

24 5807 2003 6 1.05 0.97 0 0 10,678 268

24 5807 2003 7 1.02 0.15 0.86 0 1,086 212

24 5807 2003 8 2.63 0.35 0 0 7,226 268

24 5807 2003 9 1.08 1.96 0 0 23,074 268

24 5807 2003 10 1.06 1.47 0.48 0.07 321 199

24 5807 2003 11 5 0 0 0 71 68

24 5807 2003 12 4 1 0 0 13 13

24 5807 2003 13 1.11 1.5 1.61 0.67 13 13

24 5807 2004 4 1.84 0.16 0 0 19,886 308

24 5807 2004 5 2 0 0 0 59,409 308

24 5807 2004 6 1.05 0.98 0 0 10,833 308

24 5807 2004 7 1.02 0.2 0.8 0 840 250

24 5807 2004 8 2.61 0.37 0 0 10,270 308

24 5807 2004 9 1.07 1.96 0 0 24,110 308

24 5807 2004 10 1.05 1.4 0.56 0.05 322 220

24 5807 2004 11 5 0 0 0 71 69

24 5807 2004 12 4 1 0 0 23 23

24 5807 2004 13 1.33 2 1.75 0 6 6

24 5807 2005 4 1.83 0.17 0 0 20,290 313

24 5807 2005 5 2 0 0 0 58,355 313

24 5807 2005 6 1 1 0 0 10,773 313

24 5807 2005 7 1.01 0.11 0.9 0 1,123 262

24 5807 2005 8 2.35 0.64 0 0 17,414 313

24 5807 2005 9 1.07 1.96 0 0 22,425 313

24 5807 2005 10 1.02 1.34 0.6 0.07 243 183

24 5807 2005 11 5 0 0 0 187 169

24 5807 2005 12 3.95 1 0 0 26 26

24 5807 2005 13 1.33 1 1.5 1 3 3

24 5807 2006 4 1.82 0.18 0 0 17,082 255

24 5807 2006 5 2 0 0 0 47,743 255
199

State 

Code 

SHRP 

ID Year 

Vehicle 

Class Single Tandem Tridem Quad+ 

Trucks 

Weighed 

Weight 

Days 

24 5807 2006 6 1 1 0 0 9,211 255

24 5807 2006 7 1.04 0.19 0.8 0 959 220

24 5807 2006 8 2.36 0.63 0 0 15,082 255

24 5807 2006 9 1.07 1.96 0 0 17,678 255

24 5807 2006 10 1.03 1.3 0.59 0.11 171 142

24 5807 2006 11 5 0 0 0 73 72

24 5807 2006 12 4 1 0 0 5 5

24 5807 2006 13 1.33 1 1.33 0 4 4

Using annual APTs for 2002–2006, average APT values were computed for FHWA 

Classes 4–13 by averaging the annual values. Results are shown in table 60. Computed 

representative values for individual axle groups were used to compute the average values 

for the total axles per truck column of table 60 using the following formula: 

Total Axles per Truck = Single + 2*Tandem + 3*Tridem + 4*Quad 

Where: 

Total Axles per Truck = total number of axles per truck 

Single = number of single axles per truck 

Tandem = number of tandem axles per truck 

Tridem = number of tridem axles per truck 

Quad = number of quad+ axles per truck 

Table 60. AASHTOWare Pavement ME Design software APT input and total axles per 

truck values for LTPP traffic site 245807. 

Vehicle 

Class Single Tandem Tridem Quad 

Total 

Axles per 

Truck 

4 1.82 0.18 0 0 2.18

5 2 0 0 0 2

6 1.03 0.99 0 0 3.01

7 1.02 0.16 0.85 0 3.89

8 2.49 0.49 0 0 3.47

9 1.07 1.96 0 0 4.99

10 1.03 1.39 0.56 0.06 5.73

11 5 0 0 0 5

12 3.99 1 0 0 5.99

13 1.28 1.38 1.55 0.42 10.37

Computed average APT values were compared to expected values for each vehicle class 

provided in table 56. Minor deviations in the expected number of the total axles were 

noted for FHWA Classes 7 and 10, as shown in table 61. 
200

Table 61. Flagged atypical values for average total axles for the LTPP site 245807. 

STATE_ 

CODE 

SHRP_ 

ID

VEHICLE_ 

CLASS 

TOTAL_ 

AXLES ATYPICAL_TOTAL_AXLES_FLAG 

24 5807 7 3.89 Atypical total axles, four is expected 

24 5807 10 5.73 Atypical total axles, six is expected 

APT usability codes (table 98) were assigned using data availability information and 

results of analysis of annual APT values, as presented in table 62.

Results of APT computation and assigned APT usability codes were saved to the 

TRF_MEPDG_APT table. Records corresponding to LTPP traffic site 245807 are shown 

in table 62. Values shown in the Single, Tandem, Tridem, and Quad columns can be 

copied and pasted directly into the AASHTOWare Pavement ME Design software GUI 

for traffic inputs. 

Table 62. APT records from the TRF_MEPDG_APT table for LTPP traffic site 245807. 

STATE_COD

E 

SHRP_I

D 

VEHICLE_CLA

SS Single Tandem Tridem Quad APT_USE_CODE 

24 5807 4 1.82 0.18 0 0 1

24 5807 5 2 0 0 0 1

24 5807 6 1.03 0.99 0 0 1

24 5807 7 1.02 0.16 0.85 0 1, A0 

24 5807 8 2.49 0.49 0 0 1

24 5807 9 1.07 1.96 0 0 1

24 5807 10 1.03 1.39 0.56 0.06 1, A0 

24 5807 11 5 0 0 0 1

24 5807 12 3.99 1 0 0 5

24 5807 13 1.28 1.38 1.55 0.42 5

APT_USE_CODE 1 = APT is based on a sample of more than 200 trucks collected over more than 365 d in 

total: recommended for site-specific analyses; APT_USE_CODE 5 = APT is based on a sample of fewer than 

100 trucks: use with caution in site-specific analyses or consider using defaults; APT_USE_CODE A0 = total 

number of axles per truck does not follow FHWA 13-bin vehicle classification rules for a given vehicle class. 

Estimating Representative APT for Sites with No Site-Specific WIM Data 

LTPP traffic site 011021 was selected for this example. The procedure to estimate representative 

APTs is as follows: 

A review of records in the VEHICLE_CLASS_AVG_AX_ANL table indicated that 

LTPP traffic site 0101021 has no site-specific APT values in the LTPP database. 

The TRF_MEPDG_AX_PER_TRUCK table was searched to see if other similar LTPP 

sites located in the same State had site-specific APT values. Only one, LTPP traffic site 

011011 with similar AADTT values (low volume, AADTT <200) and the same road 

functional classification (rural minor arterial, State route) was found. LTPP traffic site 

011011 had site-specific APT values based on WIM data. 
201

APT values from LTPP traffic site 011011 were used to assign APT values to LTPP 

traffic site 011021. These values are shown in table 63 and can be used as an input to 

AASHTOWare Pavement ME Design software. 

Table 63. APT values assigned for LTPP traffic site 011021. 

VEH_CLASS Single Tandem Tridem Quad 

4 2 0 0 0

5 2 0 0 0

6 1 1 0 0

7 0 0 0 0

8 2.1 0.9 0 0

9 1.12 1.94 0 0

10 1 2 0 0

11 0 0 0 0

12 0 0 0 0

13 0 0 0 0

203

CHAPTER 10. ESTIMATING PARAMETERS DESCRIBING TRUCK LOADING AND 

TRUCK LOADING CHANGES OVER TIME 

This chapter describes the procedures for estimating parameters describing the changes in truck 

loading over time. 

ESAL FOR EACH IN-SERVICE YEAR, ALL TRUCKS COMBINED 

Computed Parameter Description 

Annual ESAL trend values provide traffic loading estimates in a form of ESAL for each 

in-service year. These values are frequently used in pavement performance analyses based on 

empirical methods. These values are also used as a source to compute cumulative ESAL for 

analyses based on the AASHTO Guide for Design of Pavement Structures.

(5)

Data Sources Used for Parameter Computation 

Annual ESAL values come from multiple LTPP data sources, including the following: 

• Annual ESAL values provided by local highway agencies for years designated by the 

LTPP program as “historical” and “monitored” (TRF_HIST_EST_ESAL and 

TRF_MON_EST_ESAL tables). The TRF_MON_EST_ESAL table contains ESAL 

estimates provided by local highway agencies for years when no WIM data were 

collected. 

• LTPP-computed ESAL values for years with acceptable WIM data submitted to the 

LTPP program (TRF_ESAL_COMPUTED table). 

• Estimated annual ESAL based on representative ESAL included in 

REP_ESAL_PER_VEH_CLASS* and REP_ESAL_PER_TRUCK fields in the LTPP 

table TRF_REP. To compute annual ESAL, these values are multiplied by site-specific 

vehicle classification and truck volume values from the LTPP table TRF_TREND. 

Methodology for Parameter Computation or Estimation 

The following methodology was used to develop the ANNUAL_ESAL_TREND parameter: 

Assess the extent of ESAL information currently available in TRF_HIST_EST_ESAL, 

TRF_MON_EST_ESAL, and TRF_ESAL_COMPUTED tables and identify data gaps 

(i.e., years without annual ESAL estimates). 

For all LTPP sites and years with ESAL and truck volume data, compute annual 

ESAL-per-truck values. 

For all LTPP sites, obtain representative ESAL-per-truck values for the TRF_REP table. 
204

Assess the rationality of computed ESAL-per-truck values by identifying and flagging 

values with the following characteristics: 

a. Differ significantly from representative ESAL-per-truck values for the site. 

b. Outside of the reasonable range for a given road functional classification and vehicle 

mix observed at the site. Table 64 and table 65 show average, minimum, and 

maximum ESAL-per-truck values for different road functional classifications and 

different percentages of heavy trucks in vehicle class distribution. These values were 

computed using 1,198 LTPP sections (each SPS site had multiple sections with 

different structural number (SN) or depth (D) values, resulting in a different ESAL 

estimate) with annual ESAL values available in the LTPP TRF_ESAL_COMPUTED 

table and site- and year-specific NALS values passing data consistency and 

rationality assessments.

Table 64. ESAL-per-truck statistics for design lanes with different percentages of trucks in 

FHWA Classes 7 and 9–13. 

Percentage of Vehicles in 

FHWA Classes 7 and 9–13 

Combined Average Minimum Maximum 

0 0.51 0.19 0.54

10 0.50 0.20 0.86

20 0.52 0.20 1.45

30 0.66 0.28 1.71

40 0.74 0.30 1.60

50 0.82 0.33 2.49

60 0.88 0.39 2.04

70 1.12 0.32 2.28

80 1.30 0.49 2.53

Table 65. ESAL-per-truck statistics for design lane for different road functional 

classification.

LTPP Road Functional Classification 

Description 

LTPP Road 

Functional 

Classification 

No. Average Minimum Maximum 

Rural principal arterial interstate 1 1.07 0.20 2.53

Rural principal arterial noninterstate 2 0.82 0.30 2.42 

Rural minor arterial 6 0.74 0.19 1.71

Rural major collector 7 0.68 0.20 1.17 

Rural local collector 9 0.68 0.68 0.68 

Urban principal arterial interstate 11 1.01 0.42 2.02 

Urban principal arterial 

freeway/expressway 

12 1.04 0.37 1.48 

Urban principal arterial other 14 0.91 0.28 1.39 

Urban minor arterial 16 0.78 0.59 1.03 
205

Use the following approach to populate the TRF_TREND table with 

ANNUAL_ESAL_TREND values: 

a. Review available annual ESAL values from LTPP sources. 

b. Check if data were flagged during the average ESAL-per-truck review and determine 

if data are typical for a given site.

c. When multiple sources of annual ESAL are available for the same year, select the 

source with the most consistent and rational ESAL-per-truck values for a given site. 

Give preference to ESALs computed using site-specific WIM data overestimates 

provided by local highway agencies. Select the value closest to the computed 

representative ESAL-per-truck value. 

d. For years with no or questionable ESAL values reported in the LTPP database, 

compute estimated annual ESAL values using site-specific vehicle classification and 

truck volume data from the TFR_TREND table and representative ESAL-per-truck 

values.

Assign data source code to provide information about the source of annual ESAL values. 

Include the LOAD_SOURCE field in the TRF_TREND table to help users identify the 

source of data and infer confidence in ESAL estimates. In the LOAD_SOURCE field, 

enter one of the following values, as applicable: 

• 0—no annual ESAL provided for the virtual SPS site (*00) or any site with no 

pavement structure information. 

• 1—annual ESAL computed by LTPP program based on site- and year-specific WIM 

data. 

• 2—annual ESAL computed based on average ESAL-per-truck values computed using 

selected years of site-specific WIM data and site- and year-specific annual truck 

volume from the TRF_TREND table. 

• 3—annual ESAL supplied by a highway agency for years during the LTPP traffic 

monitoring program. 

• 4—annual ESAL supplied by a highway agency for years prior to the LTPP traffic 

monitoring program. 

• 5—annual ESAL estimated based on the representative ESAL-per-truck value from 

the TRF_REP table and site- and year-specific annual truck volume by vehicle class 

from the TRF_TREND table. 

• 6—annual ESAL is 0 due to no truck volume. 

Procedure for Parameter Computation 

The following procedure was used to develop values for the ANNUAL_ESAL_TREND field in

the TRF_TREND table for each LTPP traffic site: 
206

Obtain available annual ESAL values from the LTPP tables TRF_HIST_EST_ESAL, 

TRF_MON_EST_ESAL, and TRF_ESAL_COMPUTED and categorize them as 

historical, monitored, or computed.

For each LTPP traffic site, year, and available ESAL data source, compute 

ESAL-per-truck values by dividing available annual ESAL values from each LTPP 

ESAL table by the annual truck volume value (ANNUAL_TRUCK_VOLUME_TREND 

field from TRF_TREND table) for the same year. This will result in computed 

parameters “COMPUTED_ESAL_PER_TRUCK,” “MON_ESAL_PER_TRUCK,” and 

HIST_ESAL_PER_TRUCK.” 

Use the results of the research team’s annual NALS rationality and consistency 

assessment to identify years with WIM data quality acceptable to compute year- and 

site-specific axle load distributions. Consider LTPP-computed annual ESAL values from 

the TRF_ESAL_COMPUTED table for these years as the most accurate and call them 

“VALIDATED” annual ESALs. Use “VALIDATED” annual ESALs to compute 

“VALIDATED_ESAL_PER_TRUCK” by dividing “VALIDATED” annual ESAL by 

the annual truck volume value (ANNUAL_TRUCK_VOLUME_TREND field from the 

TRF_TREND table) for the same year. 

For each LTPP traffic site with “VALIDATED” ESAL-per-truck values, compute an 

average of the “VALIDATED” ESAL per truck values. Call this average value 

“AVG_VAL_ESAL_PER_TRUCK.” 

For each LTPP traffic site, obtain “REP_ESAL_PER_TRUCK” from the TRF_REP 

table.

Assess rationality of ESAL-per-truck values computed in steps 2-5 using the following: 

6.1 For each LTPP traffic site and year, identify ESAL-per-truck values outside the 

range of the expected ESAL-per-truck values for roadways with similar vehicle class 

distribution and/or the same road functional classification (table 64 and table 65).

6.2 Flag ESAL-per-truck values outside the minimum and maximum values as 

“FLAG MIN” and “FLAG MAX,” respectively. 

6.3 For each LTPP traffic site with AVG_VAL_ESAL_PER_TRUCK values, review 

available ESAL-per-truck values for each year computed based on data from 

TRF_HIST_EST_ESAL, TRF_MON_EST_ESAL, and TRF_ESAL_COMPUTED 

tables and identify years with ESAL-per-truck values significantly deviating 

(20 percent or more was selected based on analysis of ESAL-per-truck variability for 

sites with ALDF categories 1 or 2) (i.e., sites with high confidence of WIM data 

accuracy) from AVG_VAL_ESAL_PER_TRUCK values for the site. Flag these 

years and sources as “FLAG AV VAL.” 

6.4 For each LTPP traffic site without AVG_VAL_ESAL_PER_TRUCK values, 

review available ESAL-per-truck values for each year computed based on data from 

TRF_HIST_EST_ESAL, TRF_MON_EST_ESAL, and TRF_ESAL_COMPUTED 
207

tables and identify ESAL-per-truck values significantly deviating (20 percent or 

more) from REP_ESAL_PER_TRUCK values for the site. Flag these years and 

sources as “FLAG REP.” 

6.5 Review years with ESAL-per-truck values flagged in the aforementioned checks. 

If rationality of the reported values can be established, flags can be manually removed 

and ESAL-per-truck values accepted. 

For each LTPP traffic site and year, determine method for determining the 

ANNUAL_ESAL_TREND parameter and assign ESAL source code. Use results of the 

ESAL-per-truck assessment from step 6 to guide the selection of the ESAL value best 

representing the loading condition for each LTPP traffic site and in-service year with the 

following steps: 

7.1For the LTPP traffic sites with some years of accepted VALIDATED_ESAL_ 

PER_TRUCK values, identify the ANNUAL_ESAL_TREND source for each year as 

follows: 

7.1.1 If a year has a VALIDATED_ESAL_PER_TRUCK value that passed the 

checks, multiply the KESAL_YEAR value from the TRF_ESAL_COMPUTED 

table for that year by 1,000 to compute the ANNUAL_ESAL_TREND value. 

Assign code “1” to indicate the ANNUAL_ESAL_TREND value was assigned 

based on the site-specific ESAL value from the TRF_ESAL_COMPUTED table. 

7.1.2 If a year has a VALIDATED_ESAL_PER_TRUCK value that did not pass 

the checks and the manual review, multiply the AVG_VAL_ESAL_PER_TRUCK 

value by the ANNUAL_TRUCK_VOLUME_TREND for a given year to compute 

the ANNUAL_ESAL_TREND value. Assign code “2” to indicate the 

ANNUAL_ESAL_TREND value was computed based on average ESAL-per-truck 

values computed using selected site-specific WIM data and site- and year-specific 

annual truck volume from the TRF_TREND table. 

7.1.3 If a year has no VALIDATED_ESAL_PER_TRUCK value associated with 

it, check if ESAL-per-truck values from other sources are available that were not 

flagged during the ESAL-per-truck assessment by performing one of the following 

tasks: 

7.1.3.1 If an annual ESAL value is available in one or more tables 

(e.g., TRF_ESAL_COMPUTED, TRF_MON_EST_ESAL, or 

TRF_HIST_EST_ESAL) and ESAL-per-truck values computed based on these 

sources passed the review described in step 6, select the kilo ESAL (KESAL) 

value in the following order of preference: 

TRF_ESAL_COMPUTED.KESAL_YEAR, 

TRF_MON_EST_ESAL.ANL_KESAL_LTPP_LN_YR, and 

TRF_HIST_EST_ESAL.ANL_KESAL_LTPP_LN_YR. Multiply the selected 

value by 1,000 to compute the ANNUAL_ESAL_TREND value. Assign 

code “1” if TRF_ESAL_COMPUTED was used as a source, indicating that 
208

ANNUAL_ESAL_TREND was computed based on site-specific WIM data. 

Assign code “3” if TRF_MON_EST_ESAL was used as a source, indicating 

that TREND ESAL was computed based on estimates from the 

TRF_MON_EST_ESAL table. Assign code “4” if TRF_HIST_EST_ESAL was 

used as a source, indicating that ANNUAL_ESAL_TREND was computed 

based on estimates from the TRF_HIST_EST_ESAL table. 

7.1.3.2 If no acceptable ESAL-per-truck values are available, multiply 

AVG_VAL_ESAL_PER_TRUCK value by 

ANNUAL_TRUCK_VOLUME_TREND for a given year to compute the 

ANNUAL_ESAL_TREND value. Assign code “2” to indicate the 

ANNUAL_ESAL_TREND value was computed based on the average 

ESAL-per-truck values using selected site-specific WIM data and year-specific 

annual truck volume from the TRF_TREND table. 

7.2 For LTPP traffic sites with no years of accepted 

VALIDATED_ESAL_PER_TRUCK values, check if ESAL-per-truck values from 

other sources are available that were not flagged during the ESAL-per-truck 

assessment by performing one of the following tasks: 

7.2.1 If an annual ESAL value is available in one or more tables 

(e.g., TRF_ESAL_COMPUTED, TRF_MON_EST_ESAL, or 

TRF_HIST_EST_ESAL) and ESAL-per-truck values computed based on these 

sources passed the review described in step 6, select a value in the following order 

of preference: TRF_ESAL_COMPUTED.KESAL_YEAR, 

TRF_MON_EST_ESAL.ANL_KESAL_LTPP_LN_YR, and 

TRF_HIST_EST_ESAL.ANL_KESAL_LTPP_LN_YR. Multiply the selected 

value by 1,000 to compute the ANNUAL_ESAL_TREND value. Assign code “1” 

if TRF_ESAL_COMPUTED was used as a source, indicating that TREND_ESAL 

was computed based on site-specific WIM data. Assign code “3” if 

TRF_MON_EST_ESAL was used as a source, indicating that TREND_ESAL was 

computed based on estimates from the TRF_MON_EST_ESAL table. Assign 

code “4” if TRF_HIST_EST_ESAL was used as a source, indicating that 

ANNUAL_ESAL_TREND was computed based on estimates from the 

TRF_HIST_EST_ESAL table. 

7.2.2 If no ESAL-per-truck values are available, use TRF_TREND and 

TRF_REP tables to compute annual ESAL with the following steps: 

7.2.2.1 For each vehicle class (*), multiply 

TRF_REP.REP_ESAL_PER_VEH_CLASS_* by 

TRF_TREND.AADTT_VEH_CLASS_*_TREND and sum the 

computed products across 10 vehicle classes (FHWA Classes 4–13).

7.2.2.2 Compute the number of days for each year included in the 

TRF_TREND table (the first and last year corresponding to each 

construction event in the TRF_TREND table are partial years) as 
209

ANNUAL_TRUCK_VOLUME_TREND/AADTT_ALL_TRUCKS

_TREND.

7.2.2.3 Multiply the results from the above two steps to come up with 

ANNUAL_ESAL_TREND value. 

7.2.2.4 Assign code “5” to indicate the ANNUAL_ESAL_TREND value 

was estimated based on representative ESAL-per-truck values from 

the TRF_REP table and year-specific annual truck volume by 

vehicle class from the TRF_TREND table. 

7.3 Store computed annual ESAL trend results in the ANNUAL_ESAL_TREND 

field in the TRF_TREND table 

7.4 For LTPP traffic sites with ANNUAL_TRUCK_VOLUME_TREND values from 

the TRF_TREND table equal to “0” for a given year, assign code “6” to indicate 

this ANNUAL_ESAL_TREND value is “0” due to no truck volume.

7.5 For LTPP traffic sites with no ESAL-per-truck values accepted during the review, 

review available data about the site and manually assign estimated 

ESAL-per-truck values in the EST_ESAL_PER_TRUCK field based on sitespecific traffic information and information about road use. Create a record 

describing the source or rationale of the EST_ESAL_PER_TRUCK value. 

Multiply EST_ESAL_PER_TRUCK by ANNUAL_TRUCK_VOLUME_TREND 

from the TRF_TREND table for a given year to compute the 

ANNUAL_ESAL_TREND value. Assign code “7” to indicate the 

ANNUAL_ESAL_TREND value was computed based on the estimated ESALper-truck value assigned by the analyst and year-specific annual truck volume 

from the TRF_TREND table. 

7.6 For LTPP SPS sites and years without LTPP pavement structure information, 

such as virtual SPS master sites with SHRP_ID field codes “0*00” or codes that 

start with a letter followed by “*00” or sites that are out of study, assign code “0” 

to indicate there is no pavement structure associated with a site or site-year. The 

ANNUAL_ESAL_TREND value is “NULL.” 

Example of Parameter Estimating 

This example demonstrates development of the annual ESAL trend for LTPP GPS-3 site 047613.

The GPS-3 experiment is for JPCP. Slab thickness (the parameter used in ESAL estimation) 

remained unchanged during the experiment. The site was constructed in 1979 and participated in 

the LTPP GPS-3 experiment from the inception of the LTPP (originally SHRP) program in 1987

through 2001. In 1990, a maintenance event for partial-depth slab patching occurred, which 

changed the construction event number from “1” to “2” in the LTPP database. 

Arizona DOT provided LTPP estimates of annual ESAL for LTPP GPS-3 site 047613 for years 

prior to the start of the GPS-3 experiment from 1979 to 1989. During the site’s participation in 

the LTPP experiment, Arizona DOT provided additional ESAL estimates for 1991, 1992, and 
210

2000. From 1993 to 1999 Arizona DOT collected and provided weight data to the LTPP 

database. The LTPP program used these weight data to estimate annual ESAL. However, due to 

low accuracy of early-generation WIM sensors, data quality was limited. 

The following steps show how annual ESAL trend estimates were developed for LTPP GPS-3 

site 047613 for the years since the site first opened to traffic through the end of its participation 

in the LTPP experiment: 

Available annualized KESAL data were extracted from TRF_HIST_EST_ESAL, 

TRF_MON_EST_ESAL, and TRF_ESAL_COMPUTED tables. KESAL values were 

multiplied by 1,000 to convert to annual ESAL. The results, along with 

ANNUAL_TRUCK_VOLUME_TREND values extracted from TRF_TREND table, are 

shown in table 66. 

Table 66. Annual ESAL values from multiple sources for LTPP GPS-3 site 047613. 

STATE_ 

CODE SHRP_ID 

CONSTR_ 

NO Year 

TRF_ESAL_ 

COMPUTED 

TRF_MON_ 

EST_ESAL 

TRF_HIST_ 

EST_ESAL 

ANNUAL_ 

TRUCK_ 

VOLUME_ 

TREND 

4 7613 1 1979 — — 2,475,000 25,300

4 7613 1 1980 — — 3,183,000 128,100

4 7613 1 1981 — — 3,448,000 146,000

4 7613 1 1982 — — 5,040,000 209,875

4 7613 1 1983 — — 5,658,000 237,250

4 7613 1 1984 — — 5,835,000 237,900

4 7613 1 1985 — — 6,789,000 255,500

4 7613 1 1986 — — 7,250,000 301,125

4 7613 1 1987 — — 8,311,000 337,625

4 7613 1 1988 — — 8,223,000 338,550

4 7613 1 1989 — — 8,930,000 365,000

4 7613 2 1990 — — — 298,205

4 7613 2 1991 — 190,000 — 309,155

4 7613 2 1992 — 200,000 — 320,616

4 7613 2 1993 187,000 — — 277,400

4 7613 2 1994 249,000 — — 385,440

4 7613 2 1995 275,000 — — 462,455

4 7613 2 1996 190,000 — — 466,284

4 7613 2 1997 228,000 — — 190,530

4 7613 2 1998 293,000 — — 343,465

4 7613 2 1999 223,000 — — 397,485

4 7613 2 2000 — 871,000 — 335,256

4 7613 2 2001 — — — 365,255

— = No data. 

Available data were accessed, and years without annual ESAL estimates were identified. 

Table 66 shows annual ESAL information is missing for 1990 and 2001. 
211

ESAL-per-truck values were computed for each year with annualized ESAL data by 

dividing annualized ESAL from available sources by annualized total truck volume 

(ANNUAL_TRUCK_VOLUME_TREND). The TRF_TREND table was used as a 

source for ANNUAL_TRUCK_VOLUME_TREND values. ESAL-per-truck values 

based on data from multiple sources are shown in table 67.

Representative ESAL-per-truck values for LTPP GPS-3 site 047613 were obtained from 

the TRF_REP table. Minimum and maximum ESAL-per-truck values were obtained from 

table 64 and table 65. These values were added to table 67. The year used to compute 

representative ALDF was 1999, and the corresponding ESAL-per-truck value based on 

site-specific WIM data was 0.56.

Table 67. ESAL-per-truck values by year from multiple sources for LTPP GPS-3 

site 047613.

STATE_ 

CODE SHRP_ID Year 

COMPUTED_ 

ESAL_PER_ 

TRUCK 

MON_ 

EST_ 

ESAL_ 

PER 

_TRUCK 

HIST_EST

_ESAL_ 

PER_ 

TRUCK 

REP_ESAL_ 

PER_TRUCK 

MIN_ 

RANGE 

MAX_ 

RANGE 

Selected 

Source 

4 7613 1979 — — 24.66 0.53 0.2 1.45 2

4 7613 1980 — — 24.92 0.53 0.2 1.45 2

4 7613 1981 — — 23.62 0.53 0.2 1.45 2

4 7613 1982 — — 24.01 0.53 0.2 1.45 2

4 7613 1983 — — 23.85 0.53 0.2 1.45 2

4 7613 1984 — — 24.59 0.53 0.2 1.45 2

4 7613 1985 — — 26.57 0.53 0.2 1.45 2

4 7613 1986 — — 24.08 0.53 0.2 1.45 2

4 7613 1987 — — 24.62 0.53 0.2 1.45 2

4 7613 1988 — — 24.36 0.53 0.2 1.45 2

4 7613 1989 — — 24.47 0.53 0.2 1.45 2

4 7613 1990 — — — 0.53 0.2 1.45 2

4 7613 1991 — 0.61 — 0.53 0.2 1.45 3

4 7613 1992 — 0.63 — 0.53 0.2 1.45 3

4 7613 1993 0.67 — — 0.53 0.2 1.45 1

4 7613 1994 0.65 — — 0.53 0.2 1.45 1

4 7613 1995 0.59 — — 0.53 0.2 1.45 1

4 7613 1996 0.41 — — 0.53 0.2 1.45 2

4 7613 1997 1.2 — — 0.53 0.2 1.45 2

4 7613 1998 0.85 — — 0.53 0.2 1.45 2

4 7613 1999 0.56 — — 0.53 0.2 1.45 1

4 7613 2000 — 2.61 — 0.53 0.2 1.45 2

4 7613 2001 — — — 0.53 0.2 1.45 2

— = No data. 

The rationality of computed ESAL-per-truck values shown in table 67 was assessed by 

identifying and flagging the values meeting the following criteria: 
212

a. Differ significantly from representative ESAL-per-truck values for the site 

(20 percent or more from value 0.53). 

b. Differ significantly from the value used to develop representative ALDF (20 percent 

or more from value 0.56). 

c. Outside of the reasonable range for a given road functional classification and vehicle 

mix observed at the site. 

Results of the rationality assessment show that historical ESAL-per-truck values are 

outside the reasonable range and not rational for any type of trucks. These historical 

ESAL values most likely were provided for the roadway and not for the LTPP lane, 

resulting in much higher than expected ESAL-per-truck values. Arizona DOT estimates 

for 1991 and 1992 were in agreement with both representative ESAL-per-truck and 

ESAL-per-truck values used to develop ALDF. Arizona DOT estimates for 2000 seem 

too high when compared to ranges observed at all other LTPP traffic sites with highquality WIM data. ESAL-per-truck values based on weight data collected in 1993–1995 

and 1999 were in agreement with representative values, while ESAL-per-truck values 

based on weight data collected in 1996–1998 differ significantly. Close examination of 

WIM data for FHWA Class 9 vehicles indicates no calibration records are available. 

Review of the axle load distribution indicates shifts in weights indicative of calibration 

drift between years. Load spectra data from 1994, 1995, and 1999 look the most rational 

and follow the expected shape and position for FHWA Class 9 tandem axle load 

distribution. 

Based on results of the rationality assessment of ESAL-per-truck values, annual ESAL 

trend sources were identified in the Selected Source column of table 67 using the 

following codes:

• 1—annual ESAL trend value was computed from the KESAL value from the 

TRF_ESAL_COMPUTED table. This value was based on site- and year-specific 

WIM data. 

• 2—annual ESAL trend value was computed based on site-specific ESAL-per-truck 

values for years used to compute representative ALDF (the ESAL-per-truck value of 

0.56 corresponds to the year used in ALDF computation) and site- and year-specific 

annual truck volume from the TRF_TREND table (shown in the 

ANNUAL_TRUCK_VOLUME_TREND column in table 66). To compute the annual 

ESAL trend value, the ESAL-per-truck value is multiplied by annual truck volume 

for a given year. 

• 3—annual ESAL trend value (from the TRF_MON_EST_ESAL table) was computed 

based on agency-supplied estimated annual KESAL for years during the LTPP 

program. 

Annualized ESAL values were checked for partial-year instances. Only one instance of a 

partial year was found (1979). Since the ANNUAL_TRUCK_VOLUME_TREND value 
213

used to compute the annual ESAL estimate for this year was already adjusted for partial 

year, no further adjustment was necessary. Final annual ESAL trend values and 

corresponding ESAL sources are shown in table 68.

Table 68. Annual ESAL trend estimates for LTPP GPS-3 site 047613. 

STATE_CODE 

SHRP_ 

ID

CONSTRUCTION_ 

NO Year 

ANNUAL_ESAL_ 

TREND 

TREND_ESAL_ 

SOURCE 

4 7613 1 1979 14,168 2

4 7613 1 1980 71,736 2

4 7613 1 1981 81,760 2

4 7613 1 1982 117,530 2

4 7613 1 1983 132,860 2

4 7613 1 1984 133,224 2

4 7613 1 1985 143,080 2

4 7613 1 1986 168,630 2

4 7613 1 1987 189,070 2

4 7613 1 1988 189,588 2

4 7613 1 1989 204,400 2

4 7613 2 1990 166,995 2

4 7613 2 1991 190,000 3

4 7613 2 1992 200,000 3

4 7613 2 1993 187,000 1

4 7613 2 1994 249,000 1

4 7613 2 1995 275,000 1

4 7613 2 1996 261,119 2

4 7613 2 1997 106,697 2

4 7613 2 1998 192,340 2

4 7613 2 1999 223,000 1

4 7613 2 2000 187,743 2

4 7613 2 2001 204,543 2

GESAL FOR EACH IN-SERVICE YEAR, ALL TRUCKS COMBINED 

Computed Parameter Description 

GESAL for each in-service year, all trucks combined takes advantage of the traditional ESAL 

formula to summarize traffic loading (with higher weight factors applied for heavier loads) but 

without a dependency on traditional nontraffic ESAL inputs like pavement structure or road 

serviceability. This statistic uses LEF values for flexible pavements with a structural number of 

“5” and a terminal serviceability index of “2.5.” 

The primary purpose of GESAL for each in-service year is to provide users with a single-value 

statistic to characterize and compare traffic loading between different pavement sites while 
214

emphasizing the importance of and similarity in heavy loads. Annual GESAL trend values are 

provided for all pavement in-service years. 

Data Sources Used for Parameter Computation 

The annual GESAL trend parameter is computed using the following computed parameters 

available in the LTPP database: 

• REP_GESAL_PER_TRUCK provided in the TRF_REP table.

• REP_GESAL_PER_VEH_CLASS_* provided in the TRF_REP table.

• ANNUAL_TRUCK_VOLUME_TREND provided in the TRF_TREND table. 

• ALDF_USE_RATING provided in the MEPDG_AXLE_LOAD_DIST_FACTOR table. 

In addition to this study, the LTPP program provided the 

GESAL_PER_VEH_CLASS_ALDF_YEARS_MONTHS_TSSC table that contained the 

VALIDATED_GESAL_PER_VEH_CLASS field with GESAL-per-vehicle-class values for each 

month and year used to compute site-specific ALDF. This table was used by the research team to 

compute average annual GESAL_VEH_CLASS_* by averaging 12 monthly 

VALIDATED_GESAL_PER_VEH_CLASS values. 

Methodology and Computational Procedure for Parameter Computing 

For each LTPP traffic site included in the TRF_TREND table, compute annual GESAL values 

for each in-service pavement year included in the TRF_TREND table using the following 

procedure: 

1. For LTPP sites and years used to compute site- and year-specific ALDF values (i.e., 

LTPP sites with an ALDF_USABILITY_RATING of “1–4” in the 

MEPDG_AXLE_LOAD_DIST_FACTOR table), perform the following tasks: 

1.1. Identify years used to compute ALDF for each vehicle class. 

1.2. Select the parameters to compute annual GESAL-per-truck for ALDF years using 

codes in the ALDF_METHOD field in the ALDF_ASSIGNMENT_METHOD table 

(chapter 8): 

1.2.1. If site-specific ALDF were computed based on data for all site-specific years 

combined for all vehicle classes (these sites contain the code “*YC*” in the 

ALDF_METHOD field in the ALDF_ASSIGNMENT_METHOD table), 

assign GESAL-per-truck values from the REP_GESAL_PER_TRUCK field 

in the TRF_REP table. Treat these years as non-ALDF years and proceed to 

step 2. 

1.2.2. If site-specific ALDF were computed based on data for individual years for 

some or all vehicle classes for a given LTPP site, select GESAL-per-truck 

values from the following table fields: 
215

• GESAL_VEH_CLASS_* values from the 

GESAL_PER_VEH_CLASS_ALDF_YEARS_MONTHS_TSSC table 

(custom table provided by the LTPP CSSC) for vehicles classes and years 

with ALDF values computed based on data from individual ALDF years. 

• REP_GESAL_PER_VEH_CLASS_* values from the TRF_REP table for 

vehicle classes with ALDF values computed based on data for all ALDF 

years combined. 

1.3. Create a new table called GESAL_PER_VEH_CLASS_ALDF_YEARS and 

populate it with annual GESAL-per-truck values by vehicle class for the ALDF 

years selected in the step 1.1.

1.4. To compute annual GESAL for ALDF years, perform the following tasks: 

1.4.1. For each vehicle class (*), multiply GESAL_VEH_CLASS_* by 

AADTT_VEH_CLASS_*_TREND and sum the computed products across 

10 vehicle classes (FHWA Classes 4–13). 

1.4.2. Compute the number of days for each year included in the TRF_TREND table 

(the first and last year corresponding to each construction event in the 

TRF_TREND table are partial years) as 

ANNUAL_TRUCK_VOLUME_TREND/AADTT_ALL_TRUCKS_TREND. 

1.4.3. Multiply the results from steps 1.4.1 and 1.4.2 to come up with 

ANNUAL_GESAL_ALDF_YEAR. 

1.5. Review computed ANNUAL_GESAL_ALDF_YEAR values. For LTPP test sites 

with an ALDF_USABILITY_RATING of “3” or “4” in the 

MEPDG_AXLE_LOAD_DIST_FACTOR table or an ESAL_TREND data source 

not equal to “1,” check if using individual years created atypically high or low 

annual GESAL values. Decide on a site-by-site basis if GESAL estimates based on 

representative GESAL-per-vehicle-class values (computed based on site-specific 

data) and year-specific truck volume by class would be more appropriate than using 

year-specific annual GESAL. Create a table documenting LTPP sites and years with 

exceptions called SITE_YEARS_FOR_GESAL_ATL_EXCEPTIONS. Treat these 

years as non-ALDF years and proceed to step 2. 

2. For all other LTPP sites and years (not used to compute site- and year-specific ALDF), 

perform the following tasks: 

2.1. For each vehicle class (*), multiply REP_GESAL_PER_VEH_CLASS_* by 

AADTT_VEH_CLASS_*_TREND and sum the computed products across 

10 vehicle classes (FHWA Classes 4–13) to compute total GESAL per day. 

2.2. Compute the number of days for each year included in the TRF_TREND table 

(the first and last year corresponding to each construction event in the TRF_TREND 
216

table are partial years) as 

ANNUAL_TRUCK_VOLUME_TREND/AADTT_ALL_TRUCKS_TREND. 

2.3. Multiply the results from steps 2.2 and 2.3 to come up with the ANNUAL_GESAL 

value based on representative GESAL per vehicle class and site-specific truck 

volume by class.

3. For each LTPP site, vehicle class, and in-service pavement year included in the 

TRF_TREND table, assign either ANNUAL_GESAL for non-ALDF years or 

ANNUAL_GESAL_ALDF_YEAR for ALDF years in the ANNUAL_GESAL_TREND 

field.

4. Assign an appropriate data source code for the annual GESAL trend parameter from the 

following options: 

• 1—annual GESAL was computed based on site- and year-specific WIM data. 

• 5—annual GESAL was estimated based on the representative 

GESAL-per-vehicle-class value from the TRF_REP table and site- and year-specific 

AADTT values by vehicle class from the TRF_TREND table. 

• 6—annual GESAL is “0” due to no truck volume. 

5. Populate the ANNUAL_GESAL_TREND field in the TRF_TREND table with the 

annual GESAL results for each LTPP site and each in-service year included in the 

TRF_TREND table. 

Example of Parameter Estimating 

This example demonstrates the development of the annual GESAL trend for LTPP site 271085. 

The site was constructed in 1984 and participated in the LTPP experiment from the inception of 

LTPP (originally SHRP) program in 1987 through 1999.

For LTPP site 271085, 1996, 1997, and 1999 were identified as the years used to 

compute ALDF. A review of the codes in the ALDF_METHOD field in the 

ALDF_ASSIGNMENT_METHOD table indicated that, for a majority of vehicle classes 

and axle groups, sample sizes used in ALDF computations were too low. These classes 

contained the code “*YC*” in the ALDF_METHOD field of the 

ALDF_ASSIGNMENT_METHOD table, as indicated in the “YC” Flag column in 

table 69. 

Table 69. ALDF_METHOD records from the ALDF_ASSIGNMENT_METHOD table for 

LTPP traffic site 271085. 

STATE_CODE SHRP_ID VEHICLE_CLASS AXLE_GROUP ALDF_METHOD “YC” Flag 

27 1085 4 1 M1UYC-Fair YC

27 1085 4 2 M1UYC-Fair YC

27 1085 5 1 M1U-Fair —
217

STATE_CODE SHRP_ID VEHICLE_CLASS AXLE_GROUP ALDF_METHOD “YC” Flag 

27 1085 5 2 M1U-Fair —

27 1085 6 1 M1U-Fair —

27 1085 6 2 M1U-Fair —

27 1085 7 1 M1UYC-Fair YC

27 1085 7 2 M1UYC-Fair YC

27 1085 7 3 M1UYC-Fair YC

27 1085 7 4 M1UYC-Fair YC

27 1085 8 1 M1U-Fair —

27 1085 8 2 M1UYC-Fair YC

27 1085 9 1 M1U-Fair —

27 1085 9 2 M1U-Fair —

27 1085 9 3 M1UYC-Fair YC

27 1085 10 1 M1UYC-Fair YC

27 1085 10 2 M1U-Fair —

27 1085 10 3 M1UYC-Fair YC

27 1085 10 4 M1UYC-Fair_AL YC

27 1085 11 1 M1UYC-Fair_AL YC

27 1085 11 2 M1UYCFair_AA_AL YC

27 1085 11 3 M1UYCFair_AA_AL YC

27 1085 12 1 M1UYC-Fair_AL YC

27 1085 12 2 M1UYC-Fair_AL YC

27 1085 12 3 M1UYCFair_AA_AL YC

27 1085 12 4 — —

27 1085 13 1 M1UYC-Fair_AL YC

27 1085 13 2 M1UYC-Fair_AL YC

27 1085 13 3 M1UYC-Fair_AL YC

27 1085 13 4 M1UYC-Fair_AL YC

— = No data. 

For vehicle classes without an entry in the “YC” Flag column in table 69, monthly 

GESAL_VEH_CLASS_* values from the 

GESAL_PER_VEH_CLASS_ALDF_YEARS_MONTHS_TSSC table were extracted 

and averaged by year for ALDF years. The results are shown in Table 70. 

Table 70. GESAL-per-vehicle class computed using data for individual ALDF years for 

LTPP site 271085. 

STATE_ 

CODE SHRP_ID Year 

GESAL_ 

PER_VEH_ 

CLASS_5 

GESAL_ 

PER_VEH_ 

CLASS_6 

GESAL_ 

PER_VEH_ 

CLASS_8 

GESAL_ 

PER_VEH_ 

CLASS_9 

GESAL_ 

PER_VEH_ 

CLASS_10 

27 1085 199

6

0.22182 0.42364 0.27545 0.84455 1.92091 
218

STATE_ 

CODE SHRP_ID Year 

GESAL_ 

PER_VEH_ 

CLASS_5 

GESAL_ 

PER_VEH_ 

CLASS_6 

GESAL_ 

PER_VEH_ 

CLASS_8 

GESAL_ 

PER_VEH_ 

CLASS_9 

GESAL_ 

PER_VEH_ 

CLASS_10 

27 1085 199

7

0.19333 0.40167 0.24167 0.65250 0.59917 

27 1085 199

9

0.33200 0.49400 0.28400 0.74400 1.04800 

For vehicle classes identified as “YC” in table 69, GESAL-per-truck values were 

extracted from REP_GESAL_PER_VEH_CLASS_* fields in the TRF_REP table. The 

results are shown in Table 71. 

Table 71. GESAL-per-vehicle class based on representative values for ALDF years for

LTPP site 271085. 

STATE_ 

CODE SHRP_ID Year 

REP_GESAL_ 

PER_VEH_ 

CLASS_4 

REP_GESAL_ 

PER_VEH_ 

CLASS_7 

REP_GESAL_ 

PER_VEH_ 

CLASS_11 

REP_GESAL_ 

PER_VEH_ 

CLASS_12 

REP_GESAL_ 

PER_VEH_ 

CLASS_13 

27 1085 1996 0.7011 1.2785 1.0033 0 0 

27 1085 1997 0.7011 1.2785 1.0033 0 0 

27 1085 1999 0.7011 1.2785 1.0033 0 0 

Extracted GESAL-per-vehicle-class values were saved to a table called 

GESAL_PER_VEH_CLASS_ALDF_YEARS. The results are shown in Table 72. 
219

Table 72. GESAL-per-vehicle-class values for ALDF years for LTPP site 271085. 

STATE

_CODE 

SHRP

_ID Year 

GESAL

_VEH_

CLASS_

4 

GESAL

_VEH_

CLASS_

5 

GESAL

_VEH_

CLASS_

6 

GESAL

_VEH_

CLASS_

7 

GESAL

_VEH_

CLASS_

8 

GESAL

_VEH_

CLASS_

9 

GESAL

_VEH_

CLASS_

10

GESAL

_VEH_

CLASS_

11

GESAL

_VEH_

CLASS_

12

GESAL

_VEH_

CLASS_

13

27 108

5

199

6

0.701

1

0.221

8

0.423

6

1.278

5

0.275

5

0.844

5

1.920

9

1.003

3

0.000

0

0.000

0

27 108

5

199

7

0.701

1

0.193

3

0.401

7

1.278

5

0.241

7

0.652

5

0.599

2

1.003

3

0.000

0

0.000

0

27 108

5

199

9

0.701

1

0.332

0

0.494

0

1.278

5

0.284

0

0.744

0

1.048

0

1.003

3

0.000

0

0.000

0

For years not used to compute ALDF, GESAL-per-truck values were extracted for each 

vehicle class from REP_GESAL_PER_VEH_CLASS_* fields in the TRF_REP table in 

the following sequence: 

Table 73. GESAL-per-vehicle-class values for non-ALDF years for 

LTPP site 271085. 

STATE

_CODE 

SHRP

_ID 

REP_ 

GESAL_

PER_ 

VEH_ 

CLASS_

4 

REP_ 

GESAL_

PER_ 

VEH_ 

CLASS_

5 

REP_ 

GESAL_

PER_ 

VEH_ 

CLASS_

6 

REP_ 

GESAL_

PER_ 

VEH_ 

CLASS_

7 

REP_ 

GESAL_

PER_ 

VEH_ 

CLASS_

8 

REP_ 

GESAL_

PER_ 

VEH_ 

CLASS_

9 

REP_ 

GESAL_

PER_ 

VEH_ 

CLASS_

10

REP_ 

GESAL_ 

PER 

_VEH_ 

CLASS_

11

REP_ 

GESAL_ 

PER_ 

VEH_ 

CLASS_

12

REP_ 

GESAL_ 

PER_ 

VEH_ 

CLASS_

13

27 108

5

0.7011 0.2558 0.4403 1.2785 0.2588 0.7411 1.1526 1.0033 0 0

a. A daily GESAL estimate was computed by multiplying 

REP_GESAL_PER_VEH_CLASS_* by AADTT_VEH_CLASS_*_TREND for each 

vehicle class (*) and then summing the products across 10 vehicle classes (FHWA 

Classes 4–13). 

b. To account for partial years of service, the number of days for each year was 

computed by dividing ANNUAL_TRUCK_VOLUME_TREND by 

AADTT_ALL_TRUCKS_TREND. 

c. Daily GESAL estimates were multiplied by the number of days in service per year to 

compute the annual GESAL trend estimate for each in-service year, as shown in table 

74.

Table 74. Truck volume, AADTT, and daily and annual GESAL trend values for LTPP site

271085.

STATE_CODE SHRP_ID Year 

ANNUAL_ 

TRUCK_ 

VOLUME_ 

TREND 

AADTT_ 

ALL_ 

TRUCKS_ 

TREND 

DAYS_IN_ 

SERVICE 

DAILY_ 

GESAL 

ANNUAL_ 

GESAL_ 

TREND 

SOURCE_ 

ANNUAL_ 

GESAL_ 

TREND 

27 1085 1984 32,940 90 366 51 18,554 5

27 1085 1985 32,850 90 365 51 18,503 5
220

STATE_CODE SHRP_ID Year 

ANNUAL_ 

TRUCK_ 

VOLUME_ 

TREND 

AADTT_ 

ALL_ 

TRUCKS_ 

TREND 

DAYS_IN_ 

SERVICE 

DAILY_ 

GESAL 

ANNUAL_ 

GESAL_ 

TREND 

SOURCE_ 

ANNUAL_ 

GESAL_ 

TREND 

27 1085 1986 32,850 90 365 51 18,503 5

27 1085 1987 32,850 90 365 51 18,503 5

27 1085 1988 32,940 90 366 51 18,554 5

27 1085 1989 34,675 95 365 54 19,569 5

27 1085 1990 27,375 75 365 42 15,467 5

27 1085 1991 23,360 64 365 35 12,820 5

27 1085 1992 33,672 92 366 54 19,724 5

27 1085 1993 27,740 76 365 44 15,914 5

27 1085 1994 28,470 78 365 42 15,503 5

27 1085 1995 31,025 85 365 49 17,764 5

27 1085 1996 30,378 83 366 51 18,817 1

27 1085 1997 31,390 86 365 41 14,809 1

27 1085 1998 29,200 80 365 46 16,793 5

27 1085 1999 10,150 70 145 42 6,101 1

Computed annual GESAL results were checked for atypically high or low annual GESAL 

values. For this suite, GESAL-per-year values typically range from 15,000 to 20,000. 

Observed exceptions to this range were 1991 and 1999. The low value in 1991 is 

explained by low AADTT observed for that year, and the low value in 1999 is explained 

by that year being the last partial year with low annual truck volumes. Because deviations 

from typical values had logical explanations, no changes to the computed values were 

necessary. 

Data source codes for annual GESAL trend parameter were assigned in the 

SOURCE_ANNUAL_GESAL_TREND column in table 74 as follows: 

• 1—annual GESAL was computed based on site- and year-specific WIM data. 

• 5—annual GESAL was estimated based on the representative 

GESAL-per-vehicle-class value from the TRF_REP table and site- and year-specific 

AADTT by vehicle class from the TRF_TREND table. 

Computed annual GESAL trend values and data source codes were added to the 

ANNUAL_GESAL_TREND field in the TRF_TREND table. 

Table 75. Annual GESAL trend and data source code values for 

LTPP site 271085. 

STATE_CODE SHRP_ID Year 

ANNUAL_ 

GESAL_TREND 

SOURCE_ANNUAL_ 

GESAL_TREND 

27 1085 1984 18,554 5

27 1085 1985 18,503 5

27 1085 1986 18,503 5

27 1085 1987 18,503 5
221

STATE_CODE SHRP_ID Year 

ANNUAL_ 

GESAL_TREND 

SOURCE_ANNUAL_ 

GESAL_TREND 

27 1085 1988 18,554 5

27 1085 1989 19,569 5

27 1085 1990 15,467 5

27 1085 1991 12,820 5

27 1085 1992 19,724 5

27 1085 1993 15,914 5

27 1085 1994 15,503 5

27 1085 1995 17,764 5

27 1085 1996 18,817 1

27 1085 1997 14,809 1

27 1085 1998 16,793 5

27 1085 1999 6,101 1

ANNUAL TOTAL GVW FOR EACH IN-SERVICE YEAR, ALL TRUCKS COMBINED 

Computed Parameter Description 

Annual total GVW for each in-service year, all trucks combined provides an estimate of total 

annual vehicular loading from vehicles in FHWA Classes 4–13. All vehicular loads are treated 

equally when computing this summary loading statistic. Thus, a site with a high truck volume 

and high percentage of light trucks may have the same annual total GVW statistic as a site with a 

low truck volume but high percentage of heavy trucks. This parameter is provided for all 

pavement in-service years. 

Data Sources used for Parameter Computation 

The annual total GVW trend parameter is computed using the following parameters available in 

the LTPP database: 

• REP_GVW_VEH_CLASS_* provided in the TRF_REP table. 

• AX_CT_* (annualized axle counts by vehicle class, axle group, load bin, and year) 

provided in the TRF_MONITOR_AXLE_DISTRIB table. 

• ANNUAL_TRUCK_VOLUME_TREND and AADTT_ALL_TRUCKS_TREND 

provided in the TRF_TREND table. 

In addition, data from the ALDF_METHOD field of the ALDF_ASSIGNMENT_METHOD 

table used to develop ALDF were also used to compute the annual total GVW trend parameter. 

Methodology and Computational Procedure for Parameter Computing 

Annual total GVW values are computed using the following procedure: 

1. For LTPP sites with site-specific ALDF values in the 

MEPDG_AXLE_LOAD_DIST_FACTOR table computed based on years with sufficient 

quantity of good-quality WIM data, do the following: 
222

1.1. Use values in the ALDF_METHOD field in the ALDF_ASSIGNMENT_METHOD 

table to check if all ALDF for the site were computed based on data from combined 

years (ALDF based on combined years were computed for sites with either lower quality 

WIM data for individual years or annual WIM data samples that were too small to be 

representative) or if the ANNUAL_ESAL_TREND data source code for ALDF years 

was not “1.” Treat these years as non-ALDF years and proceed to step 2. 

1.2. Compute the total annualized axle load for each LTPP site, vehicle class, axle group, and 

year by multiplying the annualized number of axle counts reported in each load bin in 

the TRF_MONITOR_AXLE_DISTRIB table by the corresponding midvalue of that load 

bin for a given axle group and summing the products over all load bins. 

1.3. Compute the total annualized GVW for each LTPP site and ALDF year by summing the 

total annual axle load computed in step 1.2 for each LTPP site, vehicle class, and axle 

group over all axle groups and vehicle classes. 

1.4. To account for partial years included in the TRF_TREND table, multiply the total 

annualized GVW computed in step 1.3 by a ratio of 

ANNUAL_TRUCK_VOLUME_TREND/(YEAR_DAYS*AADTT_ALL_TRUCKS_TRE

ND) using parameters found in the TRF_TREND table (where YEAR_DAYS is either 365 

or 366 for leap years). Save the results as the parameter 

ANNUAL_TOTAL_GVW_ALDF_YEARS. 

2. For all other LTPP sites and years included in the TRF_TREND table, do the following for 

each LTPP site and year: 

2.1. For each vehicle in FHWA Classes 4–13, select the representative GVW per vehicle 

class values from REP_GVW_VEH_CLASS_* fields in the TRF_REP table and the 

AADTT by vehicle class values from AADTT_VEH_CLASS_*_TREND fields in the 

TRF_TREND table. 

2.2. Multiply the representative GVW per vehicle class value by the AADTT value for the 

same vehicle class and sum products over all vehicle classes (FHWA Classes 4–13). This 

value represents the annual average daily load for a given LTPP site and year. 

2.3. Multiply the annual average daily load by the number of days corresponding to the year 

reported in the TRF_TREND table. The number of days for each year is estimated by 

dividing ANNUAL_TRUCK_VOLUME_TREND by 

AADTT_ALL_TRUCKS_TREND, which are found in the TRF_TREND table. Save the 

results as the parameter ANNUAL_TOTAL_GVW_NON_ALDF_YEARS. 

3. For each LTPP site, vehicle class, and in-service pavement year included in the 

TRF_TREND table, assign either an ANNUAL_TOTAL_GVW_NON_ALDF_YEARS 

value (for non-ALDF years) or an ANNUAL_TOTAL_GVW_ALDF_YEARS value 

(for ALDF years) in the ANNUAL_TOTAL_GVW_TREND field. 

4. Assign one of the following data source codes for annual total GVW parameter, as 

applicable: 
223

• 1—annual total load was computed based on site- and year-specific WIM data. 

• 5—annual total load was estimated based on the representative GVW-per-truck value 

from the TRF_REP table and the site- and year-specific annual truck volume from the 

TRF_TREND table. 

• 6—annual total load is “0” due to no truck volume. 

5. Store the annual total GVW results in the ANNUAL_TOTAL_GVW_TREND field in the 

TRF_TREND table for each LTPP site and each in-service year included in the 

TRF_TREND table. 

Example of Parameter Estimating 

This example demonstrates development of the total annual GVW trend for LTPP site 271085.

The site was constructed in 1984 and participated in LTPP experiment from the inception of the 

LTPP (originally SHRP) program in 1987 through 1999.

For LTPP site 271085, 1996, 1997, and 1999 were identified as the years used to 

compute ALDF. A review of codes in the ALDF_METHOD field in the 

ALDF_ASSIGNMENT_METHOD table presented in table 69 indicated that not all 

vehicle classes and axle groups have code “*YC*” in the ALDF_METHOD field. 

Therefore, the TRF_MONITOR_AXLE_DISTRIB table can be used to compute annual 

total GVW for ALDF years. 

To compute the annual total axle load (ATL_CLASS_AXLE) for each vehicle class, axle 

group and ALDF year, the annualized number of axle counts reported in each load bin in 

the TRF_MONITOR_AXLE_DISTRIB table was multiplied by the corresponding 

midvalue of the corresponding load bin for a given axle group and summed over all load 

bins. The resulting annual total axle loads by vehicle class and axle group are shown in 

table 76 in ATL_CLASS_AXLE column. 

Table 76. Annual total axle loads by vehicle class and axle group for LTPP site 271085. 

STATE_ 

CODE SHRP_ID Year 

VEHICLE_ 

CLASS 

AXLE_ 

GROUP 

ATL_CLASS_ 

AXLE (lb)

27 1085 1996 4 1 8,965,500

27 1085 1996 4 2 6,349,000

27 1085 1996 5 1 107,448,000

27 1085 1996 5 2 6,092,000

27 1085 1996 6 1 27,304,500

27 1085 1996 6 2 44,222,000

27 1085 1996 7 1 6,656,000

27 1085 1996 7 2 5,083,000

27 1085 1996 7 3 11,256,000

27 1085 1996 7 4 12,625,500

27 1085 1996 8 1 35,907,500

27 1085 1996 8 2 12,246,000

27 1085 1996 9 1 170,621,000
224

STATE_ 

CODE SHRP_ID Year 

VEHICLE_ 

CLASS 

AXLE_ 

GROUP 

ATL_CLASS_ 

AXLE (lb)

27 1085 1996 9 2 528,724,000

27 1085 1996 9 3 4,621,500

27 1085 1996 10 1 10,614,000

27 1085 1996 10 2 20,606,000

27 1085 1996 10 3 22,809,000

27 1085 1996 10 4 15,910,500

27 1085 1996 11 1 2,481,500

27 1085 1996 11 2 1,142,000

27 1085 1996 11 3 876,000

27 1085 1996 12 1 186,500

27 1085 1996 12 2 321,000

27 1085 1996 12 3 367,500

27 1085 1996 13 1 678,500

27 1085 1996 13 2 1,352,000

27 1085 1996 13 3 4,275,000

27 1085 1996 13 4 1,800,000

27 1085 1997 4 1 7,280,000

27 1085 1997 4 2 4,560,000

27 1085 1997 5 1 118,011,500

27 1085 1997 6 1 32,927,500

27 1085 1997 6 2 51,215,000

27 1085 1997 7 1 5,964,500

27 1085 1997 7 2 77,000

27 1085 1997 7 3 7,704,000

27 1085 1997 7 4 7,500,000

27 1085 1997 8 1 37,067,000

27 1085 1997 8 2 9,419,000

27 1085 1997 9 1 163,167,500

27 1085 1997 9 2 524,370,000

27 1085 1997 9 3 28,500

27 1085 1997 10 1 8,405,500

27 1085 1997 10 2 23,223,000

27 1085 1997 10 3 11,407,500

27 1085 1997 10 4 490,500

27 1085 1997 11 1 2,386,000

27 1085 1997 12 1 438,000

27 1085 1997 12 2 186,000

27 1085 1997 13 1 73,000

27 1085 1997 13 2 50,000

27 1085 1997 13 3 295,500

27 1085 1999 4 1 579,000

27 1085 1999 4 2 669,000

27 1085 1999 5 1 92,140,000
225

STATE_ 

CODE SHRP_ID Year 

VEHICLE_ 

CLASS 

AXLE_ 

GROUP 

ATL_CLASS_ 

AXLE (lb)

27 1085 1999 6 1 30,677,500

27 1085 1999 6 2 52,485,000

27 1085 1999 7 1 4,392,000

27 1085 1999 7 2 3,619,000

27 1085 1999 7 3 2,260,500

27 1085 1999 7 4 6,963,000

27 1085 1999 8 1 31,185,500

27 1085 1999 8 2 8,133,000

27 1085 1999 9 1 167,206,500

27 1085 1999 9 2 497,506,000

27 1085 1999 10 1 5,223,000

27 1085 1999 10 2 25,032,000

27 1085 1999 10 3 372,000

27 1085 1999 10 4 207,000

27 1085 1999 11 1 1,023,000

27 1085 1999 11 2 475,000

To compute the annualized total GVW for each ALDF year, ATL_CLASS_AXLE values 

computed in step 2 were summed over all vehicle classes and axle groups. The resulting 

annualized GVW values (ANNUALIZED_GVW_FOR_ALDF_YEARS) are shown in 

table 77.

Table 77. Total annualized GVW for ALDF years for LTPP site 271085. 

STATE_CODE SHRP_ID Year 

ANNUALIZED_ 

GVW_FOR_ALDF_YEARS 

(lb) 

27 1085 1996 1,071,541,000

27 1085 1997 1,016,246,500

27 1085 1999 930,148,000

To account for partial ALDF years included in the TRF_TREND table, the annualized total 

GVW computed in step 3 (ANNUALIZED_GVW_FOR_ALDF_YEARS) was multiplied 

by a year fraction. The year fraction was computed as a ratio of 

ANNUAL_TRUCK_VOLUME_TREND/(DAYS_IN_YEAR*AADTT_ALL_TRUCKS_

TREND) using parameters found in the TRF_TREND table (where DAYS_IN_YEAR 

value is either 365 or 366 for leap years). For LTPP site 271085, 1999 was a partial year 

with only 145 d included in the experiment. Table 78 shows annual total GVW for ALDF 

years, prorated for partial years. 
226

Table 78. Annual total GVW for ALDF years, prorated for partial years for LTPP site 

271085.

STATE_CODE SHRP_ID Year 

ANNUAL

_ 

TRUCK_ 

VOLUME

_ 

TREND 

AADTT_ 

ALL_ 

TRUCKS

_ 

TREND 

DAYS

_ 

IN_ 

YEAR 

YEAR_ 

FRACTIO

N 

ANNUALIZE

D_

GVW_FOR_ 

ALDF_YEAR

S 

ANNUAL_ 

TOTAL_GV

W_ 

ALDF_YEAR

S (lb) 

27 1085 1996 30,378 83 366 1 1,071,541,00

0

1,071,541,00

0

27 1085 1997 31,390 86 365 1 1,016,246,50

0

1,016,246,50

0

27 1085 1999 10,150 70 145 0.3973 930,148,000 369,510,849

For years not used to compute ALDF, representative GVW per vehicle class values were 

extracted from REP_GVW_VEH_CLASS_* fields in the TRF_REP table, and AADTT 

by vehicle class values were extracted from AADTT_VEH_CLASS_*_TREND fields in

the TRF_TREND table, as shown in table 79 and table 80, respectively.
227

Table 79. AADTT by vehicle class for LTPP site 271085.

STATE_ 

CODE 

SHRP_ 

ID Year 

AADTT_ 

VEH_ 

CLASS_4_ 

TREND 

AADTT_ 

VEH_ 

CLASS_5_ 

TREND 

AADTT_ 

VEH_ 

CLASS_6_ 

TREND 

AADTT_ 

VEH_ 

CLASS_7_

TREND 

AADTT_ 

VEH_ 

CLASS_8_ 

TREND 

AADTT_ 

VEH_ 

CLASS_9_ 

TREND 

AADTT_ 

VEH_ 

CLASS_10_ 

TREND 

AADTT_ 

VEH_ 

CLASS_11_ 

TREND 

AADTT_ 

VEH_ 

CLASS_12_ 

TREND 

AADTT_ 

VEH_ 

CLASS_13_ 

TREND 

27 1085 1984 1 26 9 1 5 45 3 0 0 0

27 1085 1985 1 26 9 1 5 45 3 0 0 0

27 1085 1986 1 26 9 1 5 45 3 0 0 0

27 1085 1987 1 26 9 1 5 45 3 0 0 0

27 1085 1988 1 26 9 1 5 45 3 0 0 0

27 1085 1989 1 27 10 1 5 48 3 0 0 0

27 1085 1990 1 21 8 1 4 38 2 0 0 0

27 1085 1991 0 19 7 1 4 32 1 0 0 0

27 1085 1992 1 24 9 1 4 49 3 1 0 0

27 1085 1993 1 23 7 1 4 38 3 0 0 0

27 1085 1994 1 26 7 1 4 37 2 0 0 0

27 1085 1995 1 25 8 1 4 42 4 0 0 0

27 1085 1998 0 19 10 1 5 43 2 0 0 0

Table 80. Representative GVW in pounds by vehicle class for LTPP site 271085. 

STATE_ 

CODE SHRP_ID 

REP_ 

GVW_ 

VEH_ 

CLASS_4

REP_ 

GVW_ 

VEH_ 

CLASS_5 

REP_ 

GVW_ 

VEH_ 

CLASS_6 

REP_ 

GVW_ 

VEH_ 

CLASS_7 

REP_ 

GVW_ 

VEH_ 

CLASS_8 

REP_ 

GVW_ 

VEH_ 

CLASS_9 

REP_ 

GVW_ 

VEH_ 

CLASS_10

REP_ 

GVW_ 

VEH_ 

CLASS_11

REP_ 

GVW_ 

VEH_ 

CLASS_12

REP_ 

GVW_ 

VEH_ 

CLASS_13

27 1085 31,162 12,948 27,270 62,908 24,285 47,419 62,554 58,757 0 0
228

To compute daily total GVW for each non-ALDF year, the representative GVW per 

vehicle class value for each vehicle class shown in table 80 was multiplied by the 

AADTT value for the corresponding vehicle class shown in table 79 and the results were 

summed across all vehicle classes. Results computed for each non-ALDF year are shown 

in table 81. 

Table 81. Daily total GVW estimate for each non-ALDF year for 

LTPP site 271085. 

STATE_CODE SHRP_ID Year 

DAILY_ 

TOTAL_GVW 

(lb) 

27 1085 1984 3,119,090

27 1085 1985 3,119,090

27 1085 1986 3,119,090

27 1085 1987 3,119,090

27 1085 1988 3,119,090

27 1085 1989 3,301,565

27 1085 1990 2,608,308

27 1085 1991 2,176,912

27 1085 1992 3,317,342

27 1085 1993 2,669,488

27 1085 1994 2,598,359

27 1085 1995 2,974,884

27 1085 1998 2,867,170

To compute annual total GVW for each non-ALDF year, total days in service per year 

were computed for each year and multiplied by the daily total GVW estimate. The 

number of in-service days for each year were computed by dividing the annual truck 

volume (ANNUAL_TRUCK_VOLUME_TREND) by the AADTT 

(AADTT_ALL_TRUCKS_TREND), found in the TRF_TREND table. Results of this 

computation are shown in table 82.

Table 82. Annual total GVW estimate for each non-ALDF year for 

LTPP site 271085. 

STATE_CODE SHRP_ID Year DAYS_IN_SERVICE 

DAILY_TOTAL_ 

GVW 

ANNUAL_TOTAL_GVW_ 

NON_ALDF (lb) 

27 1085 1984 366 3,119,090 1,141,586,940 

27 1085 1985 365 3,119,090 1,138,467,850 

27 1085 1986 365 3,119,090 1,138,467,850 

27 1085 1987 365 3,119,090 1,138,467,850 

27 1085 1988 366 3,119,090 1,141,586,940 

27 1085 1989 365 3,301,565 1,205,071,225 

27 1085 1990 365 2,608,308 952,032,420

27 1085 1991 365 2,176,912 794,572,880

27 1085 1992 366 3,317,342 1,214,147,172 

27 1085 1993 365 2,669,488 974,363,120
229

STATE_CODE SHRP_ID Year DAYS_IN_SERVICE 

DAILY_TOTAL_ 

GVW 

ANNUAL_TOTAL_GVW_ 

NON_ALDF (lb) 

27 1085 1994 365 2,598,359 948,401,035

27 1085 1995 365 2,974,884 1,085,832,660 

27 1085 1998 365 2,867,170 1,046,517,050 

For each in-service pavement year included in the TRF_TREND table for LTPP site 

271085, ANNUAL_TOTAL_GVW_NON_ALDF values were assigned for non-ALDF 

years and ANNUAL_TOTAL_GVW_ALDF_YEARS values were assigned for ALDF 

years in the ANNUAL_TOTAL_GVW_TREND field in the TRF_TREND table. In 

addition, the following data source codes were assigned, as applicable: 

• 1—annual total GVW was computed based on site- and year-specific WIM data. 

• 5—annual total GVW was estimated based on representative GVW per truck values 

from the TRF_REP table and site- and year-specific annual truck volumes from the 

TRF_TREND table. 

The results of this assignment are shown in table 83. 

Table 83. Annual total GVW trend for LTPP site 271085. 

STATE_CODE SHRP_ID 

CONSTRUCTION_ 

NO Year 

ANNUAL_TOTAL_ 

GVW_TREND (lb) 

SOURCE_ANNUAL_ 

TOTAL_GVW_TREND 

27 1085 1 1984 1,141,586,940 5

27 1085 1 1985 1,138,467,850 5

27 1085 1 1986 1,138,467,850 5

27 1085 1 1987 1,138,467,850 5

27 1085 1 1988 1,141,586,940 5

27 1085 1 1989 1,205,071,225 5

27 1085 1 1990 952,032,420 5

27 1085 1 1991 794,572,880 5

27 1085 1 1992 1,214,147,172 5

27 1085 1 1993 974,363,120 5

27 1085 1 1994 948,401,035 5

27 1085 1 1995 1,085,832,660 5

27 1085 1 1996 1,071,541,000 1

27 1085 1 1997 1,016,246,500 1

27 1085 1 1998 1,046,517,050 5

27 1085 1 1999 369,510,849 1

231

CHAPTER 11. DESIGN OF THE CPTS 

The new CPTs were designed to hold analysis-ready traffic input parameters frequently used in 

LTPP analyses and selected traffic inputs parameters for MEPDG-based pavement analyses 

using AASHTOWare Pavement ME Design software. Details about each CPT’s purpose and 

specific data fields (i.e., data dictionary) are provided in the ensuing sections. 

TRF_TREND 

The TRF_TREND table contains annual traffic statistics computed or estimated for each 

in-service and in-experiment year. The TRF_TREND table is useful when a complete time 

history of changes in traffic is a desired input for analyses. Traffic trend values are developed 

based on data consolidated from multiple sources with any discrepancies between multiple data 

sources resolved and missing values estimated. The data source or estimation method for each 

year is also provided in the TRF_TREND table. 

Table 84. Field names and descriptions for the TRF_TREND table. 

Field Name Field Type Description 

STATE_CODE NUMBER(2) Numerical code for State or Province. 

U.S. codes are consistent with Federal 

Information Processing Standards. 

SHRP_ID VARCHAR2(4) Test section identification number 

assigned by the LTPP program. Must 

be combined with STATE_CODE to 

be unique. 

CONSTRUCTION_NO NUMBER(2) Event number used to relate changes 

in pavement structure with other timedependent data elements. This field is 

set to “1” when a test section is 

initially accepted into the LTPP 

program and is incremented with each 

change to the layer structure. 

YEAR NUMBER(4) Year for which values apply. 

AADTT_ALL_TRUCKS_TREND NUMBER(6) AADTT for each year for vehicles in 

FHWA Classes 4–13 combined for the 

LTPP lane.

ANNUAL_TRUCK_VOLUME_TR

END 

NUMBER(8) Estimate of the total number of trucks 

(FHWA Classes 4–13) passing over a 

test section, during a calendar year, 

while a construction event was part of 

the LTPP experiment. 

AADTT_VEH_CLASS_4_TREND NUMBER(5) AADTT estimate for FHWA Class 4 

vehicles in the LTPP lane. 

AADTT_VEH_CLASS_5_TREND NUMBER(5) AADTT estimate for FHWA Class 5 

vehicles in the LTPP lane. 
232

Field Name Field Type Description 

AADTT_VEH_CLASS_6_TREND NUMBER(5) AADTT estimate for FHWA Class 6 

vehicles in the LTPP lane. 

AADTT_VEH_CLASS_7_TREND NUMBER(5) AADTT estimate for FHWA Class 7 

vehicles in the LTPP lane. 

AADTT_VEH_CLASS_8_TREND NUMBER(5) AADTT estimate for FHWA Class 8 

vehicles in the LTPP lane. 

AADTT_VEH_CLASS_9_TREND NUMBER(5) AADTT estimate for FHWA Class 9 

vehicles in the LTPP lane. 

AADTT_VEH_CLASS_10_TREN

D 

NUMBER(5) AADTT estimate for FHWA Class 10 

vehicles in the LTPP lane. 

AADTT_VEH_CLASS_11_TREN

D 

NUMBER(5) AADTT estimate for FHWA Class 11 

vehicles in the LTPP lane. 

AADTT_VEH_CLASS_12_TREN

D 

NUMBER(5) AADTT estimate for FHWA Class 12 

vehicles in the LTPP lane. 

AADTT_VEH_CLASS_13_TREN

D 

NUMBER(5) AADTT estimate for FHWA Class 13 

vehicles in the LTPP lane. 

CMLTV_VOL_VEH_CLASS_9_T

REND 

NUMBER(9) Estimate of the cumulative volume of 

FHWA Class 9 vehicles that passed 

over a test section since the site 

opened to traffic after assigned 

Construction Event. 

CMLTV_VOL_HEAVY_TRUCKS

_TREND 

NUMBER(9) Estimate of the cumulative volume of 

heavy trucks (FHWA Classes 4 and 6–

13) that passed over a test section 

since the site opened to traffic after 

assigned Construction Event. 

ANNUAL_ESAL_TREND NUMBER(8) Estimated annual ESAL for vehicles 

in FHWA Classes 4–13. 

ANNUAL_GESAL_TREND NUMBER(8) Estimated annual GESAL for vehicles 

in FHWA Classes 4–13. 

ANNUAL_TOTAL_GVW_TREND NUMBER(8) Estimated annual total truck load for 

vehicles in FHWA Classes 4–13.

AADTT_SOURCE VARCHAR2(10

) 

Code indicating source of truck 

volume data. 

VEH_CLASS_SOURCE VARCHAR2(10

) 

Code indicating source of vehicle 

classification data. 

ESAL_SOURCE VARCHAR2(10

) 

Code indicating source of data for 

ESAL trend estimate. 

GESAL_SOURCE VARCHAR2(10

) 

Code indicating source of data for 

GESAL trend estimate. 

GVW_SOURCE VARCHAR2(10

) 

Code indicating source of data for 

total annual GVW trend estimate. 
233

TRF_REP 

The TRF_REP table contains representative truck volume, vehicle classification, and loading 

summary parameters. For most LTPP sites, these values are developed based on available traffic 

monitoring data determined as representative of truck traffic characteristics at each LTPP site. 

For sites with insufficient monitoring data, these values were developed based on data from 

alternative sources or based on default values. The data source or estimation method is also 

provided in the TRF_REP table. 

Table 85. Field names and descriptions for the TRF_REP table.

Field Name Field Type Description 

STATE_CODE NUMBER(2) Numerical code for State or Province. U.S. 

codes are consistent with Federal 

Information Processing Standards. 

SHRP_ID VARCHAR2(4) Test section identification number assigned 

by the LTPP program. Must be combined 

with STATE_CODE to be unique. 

EXPERIMENT_NO VARCHAR2(3) Code indicating to which LTPP experiment 

a pavement section is assigned. 

GPS_SPS VARCHAR2(1) Code indicating if the section is a GPS or 

SPS section. 

ASSIGN_DATE DATE Date when a test section was assigned to the 

LTPP experiment. The experiment 

designation for a test section is the 

combination of EXPERIMENT_NO and 

GPS_SPS fields in the record. 

DEASSIGN_DATE DATE Date when a test section changed to another 

experiment or was placed in out-of-study 

status in the LTPP program. 

REP_AADTT NUMBER(6) Representative average annual daily truck 

volume since the roadway was opened to 

traffic. Computed as the mean value of 

AADTT values for all years from the site’s 

opening to traffic until it leaves the 

experiment. If the site is part of a continuing 

experiment, this value is based on the data 

available at the end of 2016. 

REP_PERCENT_VEH_CLASS

_4

NUMBER(5,2) Representative percent of trucks in FHWA 

Class 4 for the LTPP lane. 

REP_PERCENT_VEH_CLASS

_5

NUMBER(5,2) Representative percent of trucks in FHWA 

Class 5 for the LTPP lane. 

REP_PERCENT_VEH_CLASS

_6

NUMBER(5,2) Representative percent of trucks in FHWA 

Class 6 for the LTPP lane. 

REP_PERCENT_VEH_CLASS

_7

NUMBER(5,2) Representative percent of trucks in FHWA 

Class 7 for the LTPP lane. 
234

Field Name Field Type Description 

REP_PERCENT_VEH_CLASS

_8

NUMBER(5,2) Representative percent of trucks in FHWA 

Class 8 for the LTPP lane. 

REP_PERCENT_VEH_CLASS

_9

NUMBER(5,2) Representative percent of trucks in FHWA 

Class 9 for the LTPP lane. 

REP_PERCENT_VEH_CLASS

_10

NUMBER(5,2) Representative percent of trucks in FHWA 

Class 10 for the LTPP lane. 

REP_PERCENT_VEH_CLASS

_11

NUMBER(5,2) Representative percent of trucks in FHWA 

Class 11 for the LTPP lane. 

REP_PERCENT_VEH_CLASS

_12

NUMBER(5,2) Representative percent of trucks in FHWA 

Class 12 for the LTPP lane. 

REP_PERCENT_VEH_CLASS

_13

NUMBER(5,2) Representative percent of trucks in FHWA 

Class 13 for the LTPP lane. 

REP_GESAL_SINGLE_AXLE NUMBER(4,2) Representative GESAL per single axle, 

considering vehicles in FHWA Classes 4–

13 for the LTPP lane. 

REP_GESAL_TANDEM_AXL

E 

NUMBER(4,2) Representative GESAL per tandem axle, 

considering vehicles in FHWA Classes 4–

13 for the LTPP lane. 

REP_GESAL_TRIDEM_AXL

E 

NUMBER(4,2) Representative GESAL per tridem axle, 

considering vehicles in FHWA Classes 4–

13 for the LTPP lane. 

REP_GESAL_QUAD_AXLE NUMBER(4,2) Representative GESAL per quad axle, 

considering vehicles in FHWA Classes 4–

13 for the LTPP lane. 

REP_GESAL_PER_TRUCK NUMBER(5,2) Representative GESAL-per-truck, 

considering vehicles in FHWA Classes 4–

13 for the LTPP lane. 

REP_GESAL_PER_VEH_CLA

SS_4 

NUMBER(5,2) Representative GESAL per FHWA Class 4 

truck for the LTPP lane. 

REP_GESAL_PER_VEH_CLA

SS_5 

NUMBER(5,2) Representative GESAL per FHWA Class 5 

truck for the LTPP lane. 

REP_GESAL_PER_VEH_CLA

SS_6 

NUMBER(5,2) Representative GESAL per FHWA Class 6 

truck for the LTPP lane. 

REP_GESAL_PER_VEH_CLA

SS_7 

NUMBER(5,2) Representative GESAL per FHWA Class 7 

truck for the LTPP lane. 

REP_GESAL_PER_VEH_CLA

SS_8 

NUMBER(5,2) Representative GESAL per FHWA Class 8 

truck for the LTPP lane. 

REP_GESAL_PER_VEH_CLA

SS_9 

NUMBER(5,2) Representative GESAL per FHWA Class 9 

truck for the LTPP lane. 

REP_GESAL_PER_VEH_CLA

SS_10 

NUMBER(5,2) Representative GESAL per FHWA Class 10 

truck for the LTPP lane. 

REP_GESAL_PER_VEH_CLA

SS_11 

NUMBER(5,2) Representative GESAL per FHWA Class 11 

truck for the LTPP lane. 

REP_GESAL_PER_VEH_CLA

SS_12 

NUMBER(5,2) Representative GESAL per FHWA Class 12 

truck for the LTPP lane. 
235

Field Name Field Type Description 

REP_GESAL_PER_VEH_CLA

SS_13 

NUMBER(5,2) Representative GESAL per FHWA Class 13 

truck for the LTPP lane. 

REP_RPPIF_SINGLE_AXLE NUMBER(3,2) Representative RPPIF per single axle, 

considering vehicles in FHWA Classes 4–

13 for the LTPP lane. 

REP_RPPIF_TANDEM_AXLE NUMBER(3,2) Representative RPPIF per tandem axle, 

considering vehicles in FHWA Classes 4–

13 for the LTPP lane. 

REP_RPPIF_TRIDEM_AXLE NUMBER(3,2) Representative RPPIF per tridem axle, 

considering vehicles in FHWA Classes 4–

13 for the LTPP lane. 

REP_RPPIF_QUAD_AXLE NUMBER(3,2) Representative RPPIF per quad axle, 

considering vehicles in FHWA Classes 4–

13 for the LTPP lane. 

REP_RPPIF_PER_TRUCK NUMBER(4,2) Representative RPPIF per truck, considering 

vehicles in FHWA Classes 4–13 for the 

LTPP lane. 

REP_RPPIF_PER_VEH_CLAS

S_4 

NUMBER(5,2) Representative RPPIF per FHWA Class 4 

truck for the LTPP lane. 

REP_RPPIF_PER_VEH_CLAS

S_5 

NUMBER(5,2) Representative RPPIF per FHWA Class 5 

truck for the LTPP lane. 

REP_RPPIF_PER_VEH_CLAS

S_6 

NUMBER(5,2) Representative RPPIF per FHWA Class 6 

truck for LTPP lane. 

REP_RPPIF_PER_VEH_CLAS

S_7 

NUMBER(5,2) Representative RPPIF per FHWA Class 7 

truck for the LTPP lane. 

REP_RPPIF_PER_VEH_CLAS

S_8 

NUMBER(5,2) Representative RPPIF per FHWA Class 8 

truck for the LTPP lane. 

REP_RPPIF_PER_VEH_CLAS

S_9 

NUMBER(5,2) Representative RPPIF per FHWA Class 9 

truck for the LTPP lane. 

REP_RPPIF_PER_VEH_CLAS

S_10 

NUMBER(5,2) Representative RPPIF per FHWA Class 10 

truck for the LTPP lane. 

REP_RPPIF_PER_VEH_CLAS

S_11 

NUMBER(5,2) Representative RPPIF per FHWA Class 11 

truck for the LTPP lane. 

REP_RPPIF_PER_VEH_CLAS

S_12 

NUMBER(5,2) Representative RPPIF per FHWA Class 12 

truck for the LTPP lane. 

REP_RPPIF_PER_VEH_CLAS

S_13 

NUMBER(5,2) Representative RPPIF per FHWA Class 13 

truck for the LTPP lane. 

REP_GVW_TRUCK NUMBER(6) Representative GVW, considering vehicles 

in FHWA Classes 4–13 for the LTPP lane. 

REP_GVW_VEH_CLASS_4 NUMBER(6) Representative GVW for FHWA Class 4 

trucks for the LTPP lane. 

REP_GVW_VEH_CLASS_5 NUMBER(6) Representative GVW for FHWA Class 5 

trucks for the LTPP lane. 

REP_GVW_VEH_CLASS_6 NUMBER(6) Representative GVW for FHWA Class 6 

trucks for the LTPP lane. 
236

Field Name Field Type Description 

REP_GVW_VEH_CLASS_7 NUMBER(6) Representative GVW for FHWA Class 7 

trucks for the LTPP lane. 

REP_GVW_VEH_CLASS_8 NUMBER(6) Representative GVW for FHWA Class 8 

trucks for the LTPP lane. 

REP_GVW_VEH_CLASS_9 NUMBER(6) Representative GVW for FHWA Class 9 

trucks for the LTPP lane. 

REP_GVW_VEH_CLASS_10 NUMBER(6) Representative GVW for FHWA Class 10 

trucks for the LTPP lane. 

REP_GVW_VEH_CLASS_11 NUMBER(6) Representative GVW for FHWA Class 11 

trucks for the LTPP lane. 

REP_GVW_VEH_CLASS_12 NUMBER(6) Representative GVW for FHWA Class 12 

trucks for the LTPP lane. 

REP_GVW_VEH_CLASS_13 NUMBER(6) Representative GVW for FHWA Class 13 

trucks for the LTPP lane. 

REP_ESAL_SINGLE_AXLE NUMBER(4,2) Representative ESAL per single axle for 

vehicles in FHWA Classes 4–13 combined 

for the LTPP lane. 

REP_ESAL_TANDEM_AXLE NUMBER(4,2) Representative ESAL per tandem axle for 

FHWA Classes 4–13 combined for the 

LTPP lane. 

REP_ESAL_TRIDEM_AXLE NUMBER(4,2) Representative ESAL per tridem axle for 

vehicles in FHWA Classes 4–13 combined 

for the LTPP lane. 

REP_ESAL_QUAD_AXLE NUMBER(4,2) Representative ESAL per quad axle, 

considering vehicles in FHWA Classes 4–

13.

REP_ESAL_PER_TRUCK NUMBER(5,2) Representative ESAL per truck, considering 

vehicle Classes 4–13 for LTPP lane. 

REP_ESAL_PER_VEH_CLAS

S_4 

NUMBER(5,2) Representative ESAL per FHWA Class 4 

truck for the LTPP lane. 

REP_ESAL_PER_VEH_CLAS

S_5 

NUMBER(5,2) Representative ESAL per FHWA Class 5 

truck for the LTPP lane. 

REP_ESAL_PER_VEH_CLAS

S_6 

NUMBER(5,2) Representative ESAL per FHWA Class 6 

truck for the LTPP lane. 

REP_ESAL_PER_VEH_CLAS

S_7 

NUMBER(5,2) Representative ESAL per FHWA Class 7 

truck for the LTPP lane. 

REP_ESAL_PER_VEH_CLAS

S_8 

NUMBER(5,2) Representative ESAL per FHWA Class 8 

truck for the LTPP lane. 

REP_ESAL_PER_VEH_CLAS

S_9 

NUMBER(5,2) Representative ESAL per FHWA Class 9 

truck for the LTPP lane. 

REP_ESAL_PER_VEH_CLAS

S_10 

NUMBER(5,2) Representative ESAL per FHWA Class 10 

truck for the LTPP lane. 

REP_ESAL_PER_VEH_CLAS

S_11 

NUMBER(5,2) Representative ESAL per FHWA Class 11 

truck for the LTPP lane. 
237

Field Name Field Type Description 

REP_ESAL_PER_VEH_CLAS

S_12 

NUMBER(5,2) Representative ESAL per FHWA Class 12 

truck for the LTPP lane. 

REP_ESAL_PER_VEH_CLAS

S_13 

NUMBER(5,2) Representative ESAL per FHWA Class 13 

truck for the LTPP lane. 

REP_AADTT_USE_RATING VARCHAR2(1

0) 

Code providing an indication of how 

effectively the REP_AADTT value 

represents the lifetime of AADTT at a site. 

REP_VEH_CLASS_USE_RAT

ING 

VARCHAR2(1

0) 

Code indicating source of representative 

vehicle classification distribution data. 

REP_LOAD_USE_RATING VARCHAR2(1

0) 

Code indicating source of representative 

loading information. 

MEPDG_TRUCK_VOL_PARAMETERS 

The MEPDG_TRUCK_VOL_PARAMETERS table contains parameters used in AASHTOWare 

Pavement ME Design software to estimate traffic volume for FHWA Classes 4–13 (as defined in 

the FHWA TMG) over the analysis or design period, including the following for each LTPP site 

and experiment: LTPP lane AADTT for the first year the site was opened to traffic, normalized 

vehicle class volume distribution factors, and truck volume growth information.(14) Values 

included in the MEPDG_TRUCK_VOL_PARAMETERS table are derived from available data 

best representing changes in truck traffic over each pavement’s service life using functions 

integrated into AASHTOWare Pavement ME Design software. The LTPP program recommends 

researchers not use these values outside of AASHTOWare Pavement ME Design software; 

instead, values from the TRF_TREND table should be used. The 

MEPDG_TRUCK_VOL_PARAMETERS table is designed to resemble the AASHTOWare 

Pavement ME Design software input table to facilitate data entry using a copy–paste operation. 
238

Table 86. Field names and descriptions for the MEPDG_TRUCK_VOL_PARAMETERS 

table.

Field Name Field Type Description 

STATE_CODE NUMBER(2) Numerical code for State or 

Province. U.S. codes are 

consistent with Federal 

Information Processing 

Standards. 

SHRP_ID VARCHAR2(4) Test section identification 

number assigned by the LTPP 

program. Must be combined 

with STATE_CODE to be 

unique. 

EXPERIMENT_NO VARCHAR2(4) Code indicating to which 

LTPP experiment a pavement 

section is assigned. 

GPS_SPS VARCHAR2(1) Code indicating if a test

section is a GPS or SPS 

experiment. 

TRAFFIC_OPEN_DATE_EX

P_NO 

DATE Date when pavement first 

opened to traffic for a 

corresponding LTPP 

experiment. 

TRAFFIC_OPEN_YEAR_EX

P_NO 

NUMBER(4) First year a site opened to 

traffic for a corresponding 

LTPP experiment. 

END_YEAR NUMBER(4) Last year of site participation 

in the LTPP experiment or last 

year of estimate. 

AADTT_FIRST_YEAR_LTP

P_LANE 

NUMBER(8) AADTT (for FHWA Classes 

4–13 combined) for the first 

full year of service when 

pavement was first opened to 

traffic for a corresponding 

LTPP experiment. 

VEHICLE_CLASS NUMBER(2) Vehicle classification code 

using 13 bins as described in 

the FHWATMG. 

VEH_CLASS_DIST_PERCE

NT

NUMBER(5,2) Percent of trucks representing 

first year for each vehicle in 

FHWA Classes 4–13 in the 

LTPP lane for a period 

corresponding to the LTPP 

experiment. 
239

Field Name Field Type Description 

VEH_CLASS_GROWTH_FU

NCTION 

VARCHAR2(10) Type of truck volume growth 

function associated with each 

vehicle in FHWA Classes 4–

13 for a period corresponding 

to the LTPP experiment. There 

are two options available in 

AASHTOWare Pavement ME 

Design software: linear or 

compound. 

VEH_CLASS_GROWTH_RA

TE

NUMBER(3,2) Annual truck volume growth 

rate associated with each 

vehicle in FHWA Classes 4–

13 corresponding to the truck 

growth function type entered 

in the 

VEH_CLASS_GROWTH_FU

NCTION field for a period 

corresponding to the LTPP 

experiment. 

VEH_CLASS_GROWTH_US

E_RATING 

VARCHAR2(10) Code indicating the source of 

the truck growth function and 

base year AADTT and vehicle 

class distribution. 

MEPDG_AXLE_LOAD_DIST_FACTOR 

The MEPDG_AXLE_LOAD_DIST_FACTOR table contains normalized ALDF for use with 

AASHTOWare Pavement ME Design software. One set of ALDF values is provided for each 

LTPP site. In addition, information about the data or method used to develop ALDF is included. 

The MEPDG_AXLE_LOAD_DIST_FACTOR table is designed to resemble the AASHTOWare 

Pavement ME Design software input table to facilitate data entry using a copy–paste operation.

Table 87. Field names and descriptions for the MEPDG_AXLE_LOAD_DIST_FACTOR 

table. 

Field Name Data Type Description 

STATE_CODE NUMBER(2) Numerical code for State or Province. U.S. codes are 

consistent with Federal Information Processing Standards. 

SHRP_ID VARCHAR2(4

) 

Test section identification number assigned by the LTPP 

program. Must be combined with STATE_CODE to be 

unique. 

ALDF_USE_RAT

ING 

VARCHAR 

(255) 

Code indicating source of data and method for developing 

ALDF estimate. 

AXLE_GROUP NUMBER(1) Type of axle for which these percentages of axles apply. 

MONTH NUMBER(2) Numeric value for each calendar month. 
240

Field Name Data Type Description 

MONTH_NAME VARCHAR2(1

0) 

Name of month, per AASHTOWare Pavement ME 

Design software format. All 12 mo must be present for a 

site to be used as an input for AASHTOWare Pavement 

ME Design software. 

VEHICLE_CLAS

S 

NUMBER(2) Vehicle classification code using 13 bins as described in 

the TMG. 

TOTAL NUMBER(3) Total axle percentage computed as a summation of values 

in load bins MEPDG_LG01 through MEPDG_LG39. This 

field value must equal to 100 percent to be used as an 

input for AASHTOWare Pavement ME Design software. 

MEPDG_LG01 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 0–2,999 lb

for single axles; 0–5,999 lb for tandem axles; and 0–

11,999 lb for tridem and quad axles. 

MEPDG_LG02 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 3,000–3,999 

lb for single axles; 6,000–7,999 lb for tandem axles; and 

12,000–14,999 lb for tridem and quad axles. 

MEPDG_LG03 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 4,000–4,999 

lb for single axles; 8,000–9,999 lb for tandem axles; and 

15,000–17,999 lb for tridem and quad axles. 

MEPDG_LG04 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 5,000–5,999 

lb for single axles; 10,000–11,999 lb for tandem axles; 

and 18,000–20,999 lb for tridem and quad axles. 

MEPDG_LG05 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 6,000–6,999 

lb for single axles; 12,000–13,999 lb for tandem axles; 

and 21,000–23,999 lb for tridem and quad axles. 

MEPDG_LG06 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 7,000–7,999 

lb for single axles; 14,000–15,999 lb for tandem axles; 

and 24,000–26,999 lb for tridem and quad axles. 

MEPDG_LG07 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 8,000–8,999 

lb for single axles; 16,000–17,999 lb for tandem axles; 

and 27,000–29,999 lb for tridem and quad axles. 

MEPDG_LG08 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 9,000–9,999 

lb for single axles; 18,000–19,999 lb for tandem axles; 

and 30,000–32,999 lb for tridem and quad axles. 

MEPDG_LG09 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 10,000–

10,999 lb for single axles; 20,000–21,999 lb for tandem 

axles; and 33,000–35,999 lb for tridem and quad axles. 

MEPDG_LG10 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 11,000–

11,999 lb for single axles; 22,000–23,999 lb for tandem 

axles; and 36,000–38,999 lb for tridem and quad axles. 

MEPDG_LG11 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 12,000–

12,999 lb for single axles; 24,000–25,999 lb for tandem 

axles; and 39,000–41,999 lb for tridem and quad axles. 
241

Field Name Data Type Description 

MEPDG_LG12 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 13,000–

13,999 lb for single axles; 26,000–27,999 lb for tandem 

axles; and 42,000–44,999 lb for tridem and quad axles. 

MEPDG_LG13 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 14,000–

14,999 lb for single axles; 28,000–29,999 lb for tandem 

axles; and 45,000–47,999 lb for tridem and quad axles. 

MEPDG_LG14 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 15,000–

15,999 lb for single axles; 30,000–31,999 lb for tandem 

axles; and 48,000–50,999 lb for tridem and quad axles. 

MEPDG_LG15 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 16,000–

16,999 lb for single axles; 32,000–33,999 lb for tandem 

axles; and 51,000–53,999 lb for tridem and quad axles. 

MEPDG_LG16 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 17,000–

17,999 lb for single axles; 34,000–35,999 lb for tandem 

axles; and 54,000–56,999 lb for tridem and quad axles. 

MEPDG_LG17 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 18,000–

18,999 lb for single axles; 36,000–37,999 lb for tandem 

axles; and 57,000–59,999 lb for tridem and quad axles. 

MEPDG_LG18 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 19,000–

19,999 lb for single axles; 38,000–39,999 lb for tandem 

axles; and 60,000–62,999 lb for tridem and quad axles. 

MEPDG_LG19 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 20,000–

20,999 lb for single axles; 40,000–41,999 lb for tandem 

axles; and 63,000–65,999 lb for tridem and quad axles. 

MEPDG_LG20 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 21,000–

21,999 lb for single axles; 42,000–43,999 lb for tandem 

axles; and 66,000–68,999 lb for tridem and quad axles. 

MEPDG_LG21 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 22,000–

22,999 lb for single axles; 44,000–45,999 lb for tandem 

axles; and 69,000–71,999 lb for tridem and quad axles. 

MEPDG_LG22 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 23,000–

23,999 lb for single axles; 46,000–47,999 lb for tandem 

axles; and 72,000–74,999 lb for tridem and quad axles. 

MEPDG_LG23 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 24,000–

24,999 lb for single axles; 48,000–49,999 lb for tandem 

axles; and 75,000–77,999 lb for tridem and quad axles. 

MEPDG_LG24 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 25,000–

25,999 lb for single axles; 50,000–51,999 lb for tandem 

axles; and 78,000–80,999 lb for tridem and quad axles. 

MEPDG_LG25 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 26,000–

26,999 lb for single axles; 52,000–53,999 lb for tandem 

axles; and 81,000–83,999 lb for tridem and quad axles. 

MEPDG_LG26 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 27,000–

27,999 lb for single axles; 54,000–55,999 lb for tandem 

axles; and 84,000–86,999 lb for tridem and quad axles. 
242

Field Name Data Type Description 

MEPDG_LG27 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 28,000–

28,999 lb for single axles; 56,000–57,999 lb for tandem 

axles; and 87,000–89,999 lb for tridem and quad axles. 

MEPDG_LG28 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 29,000–

29,999 lb for single axles; 58,000–59,999 lb for tandem 

axles; and 90,000–92,999 lb for tridem and quad axles. 

MEPDG_LG29 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 30,000–

30,999 lb for single axles; 60,000–61,999 lb for tandem 

axles; and 93,000–95,999 lb for tridem and quad axles. 

MEPDG_LG30 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 31,000–

31,999 lb for single axles; 62,000–63,999 lb for tandem 

axles; and 96,000–98,999 lb for tridem and quad axles. 

MEPDG_LG31 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 32,000–

32,999 lb for single axles; 64,000–65,999 lb for tandem 

axles; and 99,000–101,999 lb for tridem and quad axles.

MEPDG_LG32 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 33,000–

33,999 lb for single axles; and 66,000–67,999 lb for 

tandem axles. 

MEPDG_LG33 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 34,000–

34,999 lb for single axles; and 68,000–6,9999 lb for 

tandem axles. 

MEPDG_LG34 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 35,000–

35,999 lb for single axles; and 70,000–71,999 lb for 

tandem axles. 

MEPDG_LG35 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 36,000–

36,999 lb for single axles; and 72,000–73,999 lb for 

tandem axles. 

MEPDG_LG36 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 37,000–

37,999 lb for single axles; and 74,000–75,999 lb for 

tandem axles. 

MEPDG_LG37 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 38,000–

38,999 lb for single axles; and 76,000–77,999 lb for 

tandem axles. 

MEPDG_LG38 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 39,000–

39,999 lb for single axles; and 78,000–79,999 lb for 

tandem axles. 

MEPDG_LG39 NUMBER(10,

7) 

Percent of axles whose weight falls in the bin 40,000–

40,999 lb for single axles; and 80,000–81,999 lb for 

tandem axles. 

MEPDG_AXLE_PER_TRUCK 

The MEPDG_AXLE_PER_TRUCK table contains representative APT for each truck class 

(FHWA Classes 4–13) and axle group for use with AASHTOWare Pavement ME Design 

software, including source of data or method used to develop APT. For LTPP sites with site-
243

specific factors in the MEPDG_AXLE_LOAD_DIST_FACTOR table, APT was computed using 

the same years as data used to develop ALDF. For LTPP sites with default factors in the 

MEPDG_AXLE_LOAD_DIST_FACTOR table, all years with available axle count data were 

used. The MEPDG_AXLE_PER_TRUCK table was designed to resemble the AASHTOWare 

Pavement ME Design input table using a copy–paste operation. 

Table 88. Field names and descriptions for the MEPDG_AXLE_PER_TRUCK table. 

Field Name Field Type Description 

STATE_CODE NUMBER(2) Numerical code for State or Province. U.S. codes are 

consistent with Federal Information Processing Standards. 

SHRP_ID VARCHAR2(4

) 

Test section identification number assigned by the LTPP 

program. Must be combined with STATE_CODE to be 

unique. 

APT_USE_RATI

NG

VARCHAR 

(255) 

Code indicating source of APT data. 

VEHICLE_CLAS

S 

NUMBER(2) Vehicle classification code using 13 bins as described in 

the TMG. 

SINGLE_AXLES NUMBER(4,2) Average number of single axles for each truck class 

(FHWA Classes 4–13). 

TANDEM_AXLE

S 

NUMBER(4,2) Average number of tandem axles for each truck class 

(FHWA Classes 4–13). 

TRIDEM_AXLES NUMBER(4,2) Average number of tridem axles for each truck class 

(FHWA Classes 4–13). 

QUAD_AXLES NUMBER(4,2) Average number of quad axles for each truck class 

(FHWA Classes 4–13). 

CODES DESCRIBING DATA AND METHODS USED FOR PARAMETER 

COMPUTATION, AND PARAMETER USABILITY 

To help LTPP data users assess the accuracy and applicability of available LTPP traffic loading 

data and computed parameters, a set of codes was developed and included in each CPT. LTPP 

CPT names and fields containing data–usability codes are summarized in table 89. Table 90

through table 99 provide a description of codes used for each field. Using these codes, LTPP 

researchers can identify LTPP sites with traffic parameters meeting analysis criteria based on 

data source and quality information.

Table 89. CPT fields describing sources of data and methods used to develop different 

traffic parameters. 

LTPP CPT CPT Field 

TRF_TREND AADTT_SOURCE 

VEH_CLASS_SOURCE 

ESAL_SOURCE 

GESAL_SOURCE 

GVW_SOURCE 

TRF_REP REP_AADTT_USE_RATING 
244

LTPP CPT CPT Field 

REP_VEH_CLASS_USE_RATIN

G 

REP_LOAD_USE_RATING 

MEPDG_TRUCK_VOL_PARAME

TERS 

VEH_CLASS_GROWTH_USE_R

ATING 

MEPDG_AXLE_LOAD_DIST_FA

CTOR 

ALDF_USE_RATING 

MEPDG_AXLE_PER_TRUCK APT_USE_RATING 

Table 90. Codes for AADTT_SOURCE and VEH_CLASS_SOURCE fields.

Code Description Short Description Long 

M Monitored AADTT value submitted in monitoring data. 

Mc Monitored 

calculated 

AADTT value computed from vehicle classification data obtained 

from the monitoring data collection program. 

S State-supplied Based on State-supplied historical data collection efforts prior to 

the LTPP traffic monitoring program. 

H Historical State-supplied historical AADTT value. 

Ec Estimated 

compound 

Estimated based on other years of data at a site to fill in gaps in data 

coverage using a best fit compound growth equation. 

El Estimate linear Estimated based on other years of data at a site to fill in gaps in data 

coverage using a best fit linear growth equation. 

D Default Default value based on an external source. 

A AADTT-based Calculated based on AADTT value and mean vehicle class 

percentage. 

N Not open to traffic A site has not yet opened to traffic. 

Table 91. Codes for ESALS_SOURCE, GESALS_SOURCE, and GVW_SOURCE fields. 

Code Description Short Description Long 

0 No ESAL for *00 

SPS sites 

No annual ESAL provided for the virtual SPS site (*00) or any site 

with no pavement structure information. 

1 Site- and yearspecific data Annual estimate computed by the LTPP program based on site- and 

year-specific WIM data. 

2 Based on average 

ESAL per truck 

and year-specific 

annual truck 

volume 

Annual ESAL computed based on average ESAL-per-truck values 

computed using selected years of site-specific WIM data and siteand year-specific annual truck volume from the TRF_TREND 

table. 

3 Agency-supplied 

historical annual 

ESAL 

Agency-supplied annual ESAL for years during the LTPP traffic 

monitoring program.

4 Agency-supplied 

annual ESAL 

Agency-supplied annual ESAL for years prior to the LTPP traffic 

monitoring program.
245

Code Description Short Description Long 

5 Based on 

representative 

per-truck loading 

value and site- and 

year-specific truck 

volume 

Annual estimate based on representative per-truck loading value 

from the TRF_REP table and site- and year-specific annual truck 

volume by vehicle class from the TRF_TREND table. 

6 No truck volume Annual estimate is “0” due to no truck volume. 

Table 92. Codes for the ESALS_SOURCE field. 

Code Description Short Description Long 

0 No ESAL for *00 

SPS sites 

No annual ESAL provided for the virtual SPS site (*00) or any site 

with no pavement structure information. 

1 Site- and yearspecific data Annual estimate computed by the LTPP program based on siteand year-specific WIM data. 

2 Based on average 

ESAL per truck 

and year-specific 

annual truck 

volume 

Annual ESAL computed based on average ESAL-per-truck values 

computed using selected years of site-specific WIM data and siteand year-specific annual truck volume from the TRF_TREND 

table. 

3 Agency-supplied 

historical annual 

ESAL 

Agency-supplied annual ESAL for years during the LTPP traffic 

monitoring program.

4 Agency-supplied 

annual ESAL 

Agency-supplied annual ESAL for years prior to the LTPP traffic 

monitoring program.

5 Based on 

representative 

per-truck loading 

value and site- and 

year-specific truck 

volume 

Annual estimate based on representative per-truck loading value 

from the TRF_REP table and site- and year-specific annual truck 

volume by vehicle class from the TRF_TREND table.

6 No truck volume Annual estimate is “0” due to no truck volume. 

Table 93. Codes for the GESALS_SOURCE field. 

Code Description Short Description Long 

1 Site- and yearspecific data Annual estimate computed by the LTPP program based on siteand year-specific WIM data. 

5 Based on 

representative 

per-truck loading 

value and site- and 

year-specific truck 

volume 

Annual estimate based on representative per-truck loading value 

from the TRF_REP table and site- and year-specific annual truck 

volume by vehicle class from the TRF_TREND table. 

6 No truck volume Annual estimate is “0” due to no truck volume. 
246 

Table 94. Codes for the GVW_SOURCE field.

Code Description Short Description Long

1 Site- and yearspecific dataAnnual GVW estimate computed by the LTPP program based on 

site- and year-specific WIM data.

5 Based on 

representative 

per-truck loading 

value and site- and 

year-specific truck 

volume 

Annual GVW based on representative per-truck loading value from 

the TRF_REP table and site- and year-specific annual truck volume 

by vehicle class from the TRF_TREND table.

6 No truck volume Annual GVW estimate is “0” due to no truck volume. 
247 

Table 95. Codes for the REP_AADTT_USE_RATING field. 

Code Description Short Description Long

1 Best More than 75 percent of annual AADTT estimates used for 

REP_AADTT computation have an AADTT_Source of “M” and 

no years during that period experience a year-to-year change in 

AADTT both greater than 25 percent and larger than 50 trucks. 

2 Better More than 75 percent of annual AADTT estimates used for 

REP_AADTT computation have an AADTT_Source of either “M” 

or “S” and no years during that period experience a year-to-year 

change in AADTT both greater than 25 percent and larger than 50 

trucks. 

3 Good More than 50 percent of annual AADTT estimates used for 

REP_AADTT computation have an AADTT_Source of “M,” but 

less than 75 percent of AADTT_Source values are either “M” or 

“S” and no years during that period experience a year-to-year 

change in AADTT both greater than 25 percent and larger than 

50 trucks. 

4 Fair More than 25 percent of annual AADTT estimates used for 

REP_AADTT computation have an AADTT_Source of “M,” but 

less than 50 percent of AADTT_Source values are either “M” or 

“S,” or 1 yr or more during that period experience a year-to-year 

change in AADTT both greater than 25 percent and larger than 

50 trucks and more than 25 percent of annual AADTT estimates 

used to compute the equation have an AADTT_Source of either 

“M” or “S.” 

5 Poor Less than 25 percent of annual AADTT estimates used for 

REP_AADTT computation have an AADTT_Source of either “M” 

or “S.” 

Table 96. Codes for REP_VEH_CLASS_USE_RATING and 

VEH_CLASS_GROWTH_USE_RATING fields. 

Code Description Short Description Long

1 Best More than 75 percent of years of data used to determine vehicle 

growth by class function have vehicle class data from the traffic 

monitoring program and during no year of traffic monitoring data

does the year-to-year percentage of truck traffic falling within one 

FHWA class change by more than 15 percent (e.g., FHWA Class 9 

trucks does not grow from 50 percent to more than 65 percent of 

total truck traffic). 

2 Better More than 50 percent of years of data used to determine vehicle 

growth by class function have vehicle class data from the traffic 

monitoring program and during no year of traffic monitoring data

does the year-to-year percentage of truck traffic falling within one 

FHWA class change by more than 15 percent no years during that 
248 

Code Description Short Description Long

period experience a year-to-year change in AADTT both greater 

than 25 percent and larger than 50 trucks. 

3 Good More than 50 percent, but less than 75 percent, of years of data 

used to determine vehicle growth by class function have vehicle 

class data from the traffic monitoring program and during no year 

of traffic monitoring data does the year-to-year percentage of truck 

traffic falling within one FHWA class change by more than 15 

percent, but the site experienced a year-to-year change in AADTT 

both greater than 25 percent and larger than 50 trucks. 

4 Fair No Good, Better, or Best code was assigned, and more than 25 

percent of years of data used to determine vehicle growth by class 

function have vehicle class data from the traffic monitoring 

program. 

5 Poor Less than 25 percent of years of data used to determine vehicle 

growth by class function have data from the traffic monitoring 

program, but at least 1 yr of traffic monitoring data per FHWA 

vehicle classification is present during the experiment. 

6 Bad No monitoring data are present. 

Table 97. Codes for the ALDF_USE_RATING field.

Code Description Short Description Long

1 Best Based on WIM data satisfying the performance requirements of 

Type I WIM systems for ASTM E1318-09 and passing data 

reasonableness checks. Data are available for at least one 

occurrence of each of 12 calendar months with at least one 

occurrence for each DOW for each calendar month. All computed 

parameters are based on monthly axle loading data summaries 

developed by the LTPP program. These data are acceptable for sitespecific MEPDG analyses and developing traffic loading defaults.

2 Better Based on WIM data satisfying the performance requirements of 

Type I WIM systems for ASTM E1318-09 and passing data 

reasonableness checks. Data are available for at least 1 yr but not 

all 12 mo are present in the dataset. All computed parameters are 

based on annual axle loading data summaries developed by the 

LTPP program. These data are acceptable for site-specific MEPDG 

analyses and developing traffic loading defaults if no additional 

usability codes are assigned to this ALDF. 

3 Good Based on WIM data collected by equipment without a calibration 

record, thus data quality cannot be quantified. Computed 

parameters are based on annual axle loading data summaries 

developed by the LTPP program using data passing QC checks, 

research team data reasonableness checks, and minimum data 

availability requirements. These data are acceptable for site-specific 

MEPDG analyses and developing traffic loading defaults if no 

additional usability codes are assigned to this ALDF. 
249 

Code Description Short Description Long

4 Fair Based on WIM data collected by equipment without a calibration 

record, thus data quality cannot be quantified. Computed 

parameters are based on annual axle loading data summaries 

developed by the LTPP program using data passing QC checks, 

minimum data availability requirements, and most research team

data reasonableness checks. Annual axle load distributions selected 

for parameter computation were manually reviewed and specific 

years were selected to minimize the effects of low precision and 

bias observed in WIM data. Potential WIM data issues were noted 

for each site to inform users about lower-quality data. These lowerquality data may have low to moderate effects on MEPDG analysis 

results and should be used with caution. Using default values 

following similar axle loading distribution may be an alternative 

approach. 

10 Agency ESALbasedNo directly usable axle loading distribution. Agency ESAL 

distribution is more reasonable than distribution computed from 

single available distribution and was used as the basis for loading 

distribution selection.

11 ESALCalc-based No directly usable axle loading distribution. ESALCalc estimate 

distribution is more reasonable than the agency estimate and was 

used as the basis for loading distribution selection. 

12 Quantitative 

acceptance

Single available axle loading distribution quantitatively screened 

and accepted for direct use. No better than “Fair” quality.

13 Location-based No axle loading data or agency-provided ESAL estimates. Axle 

loading distributions were selected based on location information 

only. 

AA Atypical axle type Axle group is not typical for a given vehicle class. ALDF values are 

not recommended for developing defaults.

ALC Low axle count Computed values are based on low axle count (fewer than 200 

axles) and may not be representative of typical loading conditions 

for a given site. ALDF values are not recommended for developing 

defaults.

ALS Based on small 

axle sample

Computed values are based on a low number of days with data and 

may not be representative of typical loading conditions for a given 

site. ALDF values are not recommended for developing defaults.

NA Not applicable No ALDF; vehicle class–axle combination is not present in 

supporting data. 

DEF Default for low 

truck volume 

Vehicle class is not present in dataset. Defaults are selected using 

low-volume criterion for the vehicle class.

20 Typical as 

representative

Multiple years of unknown data quality with typical distribution 

accepted as representative.

21 ESAL-based 

acceptance of 

typical

Multiple years of data of unknown quality. Agency ESAL estimates 

are consistent with ESALs computed from distribution. Typical 

distribution is accepted for use.
250 

Code Description Short Description Long

22 Typical more 

reasonable than 

ESAL-based

Multiple years of data of unknown quality. Agency ESAL estimates 

are reasonable but smaller than ESALs computed from distribution. 

Typical distribution is accepted for use.

23 Estimated 

ESAL-based

Multiple years of data of unknown quality. Agency ESAL estimates 

are reasonable but larger than ESALs computed from distribution. 

Distributions are selected based on estimated ESALs.

24 Default using 

typical distribution 

ESALs 

Multiple years of data of unknown quality. Agency ESAL estimates 

are not reasonable. Distributions are selected based on ESALs 

computed from distributions. 

25 Default using 

agency ESAL 

estimate higher 

than distribution 

ESALs

Multiple years of data of unknown quality. Agency ESAL estimates 

are reasonable. ESALs computed from distributions are lower than 

reasonable. Axle distributions are based on agency ESALs. 

26 Default using 

agency ESAL 

estimate lower 

than distribution 

ESALs

Multiple years of data of unknown quality. Agency ESAL estimates 

are reasonable. ESALs computed from distributions are higher than 

reasonable. Axle distributions are based on agency ESALs. 

27 Location-based, 

loading provided 

rejected 

Multiple years of data of unknown quality. Agency ESAL estimates 

and ESALs computed from distributions are higher than reasonable. 

Axle distributions are selected based on location.

Table 98. Codes for the APT_USE_RATING field.

Code Description Short Description Long

1 Extensive data 

availability

APT based on a sample of more than 200 trucks collected over 

more than 365 d in total. Recommended for site-specific analysis. 

APT values are recommended to develop default values if no “A” 

codes are assigned.

2 Good data 

availability

APT based on a sample of more than 200 trucks collected over 

more than 210 and less than 365 d in total. Recommended for sitespecific analysis. APT values are recommended to develop default 

values if no “A” codes are assigned.

3 Sufficient data 

availability

APT based on a sample of more than 200 trucks collected over less 

than 210 d in total. APT values are recommended for site-specific 

analysis.

4 Limited data 

availability

APT based on a sample of less than 200 trucks. Use with caution in 

site-specific analysis or consider using defaults. APT values are not 

recommended to develop default values.

5 Marginal data 

availability

APT based on a sample of less than 100 trucks. Use with caution in 

site-specific analysis or consider using defaults. APT values are not 

recommended to develop default values.

0 No information on 

data availability

No information is available on the extent of availability of data 

used to compute the APT parameter. 
251 

Code Description Short Description Long

A0 Atypical total 

axles

Total number of axles per truck does not follow FHWA 13-bin 

vehicle classification rules for a given vehicle class. APT values are 

not recommended to develop default values. These lower-quality 

APT may have low to moderate effects on MEPDG analysis results 

and should be used with caution. Using default values may be an 

alternative approach.

A1 Atypically low 

single axles

Less than 0.95 single axles per truck (all trucks should have at least 

one single steering axle). Values are not recommended to develop 

default values. These lower-quality APT may have low to moderate 

effects on MEPDG analysis results and should be used with 

caution. Using default values may be an alternative approach. 

A2 Atypical tandem 

axle type 

Tandem axle is atypical for a given vehicle class. Values are not 

recommended to develop default values.

A3 Atypical tridem 

axle type 

Tridem axle is atypical for a given vehicle class. Values are not 

recommended to develop default values.

A4 Atypical quad axle 

type 

Quad axle is atypical for a given vehicle class. Values are not 

recommended to develop default values.

AW Low-quality 

weight data 

The quality of associated data is too low to estimate weight but 

does not affect classification and axle-count data applicability. 

NA Not applicable No APT; vehicle class is not present in supporting data. 

D1 Agency and 

roadway type 

default

Agency- and route-based default using average axles for routes in 

the same Agency with the same type of route signing (e.g., 

Interstate, U.S.). 

D2 Country and 

roadway type 

default

Country- and route-based default using average axles for routes in 

the same country with the same type of route signing (e.g., 

Interstate, U.S.). 

D3 Agency average 

default

Agency-based default using average axles for all routes in the same 

State/Province. 

Table 99. Codes for the REP_LOAD_USE_RATING field.

Code

DefinitionDescription 

Short Description Long

1 Best Based on WIM data satisfying the performance requirements of 

Type I WIM systems for ASTM E1318-09 and passing data 

reasonableness checks. Data are available for at least one 

occurrence of each of the 12 calendar months with at least one 

occurrence of each DOW for each calendar month. All computed 

parameters are based on monthly WIM data summaries developed 

by the LTPP program. These data are acceptable for site-specific 

MEPDG analyses and developing traffic loading defaults.

2 Better Based on WIM data that satisfying the performance requirements 

of Type I WIM systems for ASTM E1318-09 and passing data 

reasonableness checks. Data are available for at least 1 yr but not 

all 12 mo are present in the dataset. All computed parameters are 

based on annual WIM data summaries developed by the LTPP
252 

Code

DefinitionDescription 

Short Description Long

program. These data are acceptable for site-specific MEPDG 

analyses and developing traffic loading defaults if no additional 

usability codes are assigned to this ALDF. 

3 Good Based on WIM data collected by equipment without a calibration 

record, thus data quality cannot be quantified. Computed 

parameters are based on annual data summaries developed by the 

LTPP program using data passing QC checks, research team data 

reasonableness checks, and minimum data availability 

requirements. These data are acceptable for site-specific MEPDG 

analyses and developing traffic loading defaults if no additional 

usability codes are assigned to this ALDF. 

4 Fair Based on WIM data collected by equipment without a calibration 

record, thus data quality cannot be quantified. Computed 

parameters are based on annual data summaries developed by the 

LTPP program using data passing QC checks, minimum data 

availability requirements, and most research team data 

reasonableness checks. Annual axle load distributions selected for 

parameter computation were manually reviewed and specific years 

were selected to minimize the effects of low precision and bias 

observed in WIM data. Potential WIM data issues were noted for 

each site to inform users about lower-quality data. These lowerquality data may have low to moderate effects on MEPDG analysis 

results and should be used with caution. Using default values 

following similar axle loading distributions may be an alternative 

approach. 

10 ESAL selected axle 

distribution

Estimate based on axle distributions selected using ESAL values 

and truck volumes as inputs. 

12 Typical basis Based on typical axle distribution for a site. 

13 Location-based 

distribution

Estimate based on axle distributions selected as a function of site 

location. 
253

CHAPTER 12. OVERVIEW OF THE USER GUIDE FOR SELECTING AND USING 

LONG-TERM PAVEMENT PERFORMANCE TRAFFIC DATA 

PURPOSE OF THE GUIDE 

An outcome of this study is the Guide, which helps LTPP data users navigate available LTPP 

traffic data and computed parameters.(1) Users can also refer to the Guide for assistance with the 

following: 

• Understanding the meaning of LTPP traffic parameters, along with the data and 

computational principals used to develop these parameters. 

• Identifying traffic parameters suitable for different pavement analyses. 

• Using InfoPave to identify LTPP tables containing these parameters and extract desired 

LTPP traffic data. 

The Guide also provides practical examples with step-by-step instructions for obtaining traffic 

parameters for different types of LTPP analyses. In summary, the Guide helps streamline LTPP 

users’ identification of desired LTPP traffic data and parameters and maximizes the use of LTPP 

traffic data in achieving their pavement analysis objectives. 

OVERVIEW OF THE GUIDE 

This Guide consists of two parts. Part one describes traffic parameters available from the LTPP 

program sources and provides details about the methods used to collect traffic data and compute 

the parameters. Part one also contains recommendations for the most applicable traffic 

parameters for different types of pavement analyses. Part one of the Guide includes the following 

chapters: 

• Chapter 1, Introduction—describes the purpose, scope, and organization of the Guide. 

• Chapter 2, Overview of LTPP Traffic Data and Parameters—describes LTPP traffic data 

and computed parameters with references to database tables containing these parameters. 

• Chapter 3, LTPP Traffic Metadata—describes sources of LTPP traffic data and methods 

used to collect traffic data and provides references to methods used to compute traffic 

parameters. It describes indices and codes available in LTPP database tables that can be 

used to identify data sources and computational methods and how to use this information 

as an aid in traffic data and parameter selection. 

• Chapter 4, Selecting LTPP Traffic Data and Parameters for Analysis—provides 

recommendations about types of traffic data and parameters suitable for various common 

pavement analyses, including references to traffic parameter names and LTPP tables 

containing these parameters. 

• Chapter 5, Tools and Procedures for Extracting LTPP Traffic Data—describes available 

tools for selecting and extracting LTPP traffic data and parameters. It discusses using 
254

default values for parameters not included in LTPP database tables, including 

applicability and limitations of default values. 

Part two of the Guide is a playbook of practical examples showing different traffic data selection 

and extraction scenarios. It shows examples of how to use InfoPave to obtain various types of 

traffic parameters for LTPP sites. For parameters not included in the LTPP database, examples of 

computational procedures are provided showing how available LTPP traffic data can be used to 

compute or estimate a desired parameter. For traffic parameters that cannot be computed using 

available LTPP traffic data, references to available default values and alternative data sources are 

provided. Traffic data-selection scenarios included in the playbook are as follows: 

• Scenario 1—Obtain traffic volume information. 

• Scenario 2—Obtain vehicle classification information. 

• Scenario 3—Obtain detailed axle or truck loading information.

• Scenario 4—Obtain summary traffic loading information. 

• Scenario 5—Obtain MEPDG traffic inputs for use in AASHTOWare Pavement ME 

Design software. 
255

CHAPTER 13. SUMMARY OF STUDY RESULTS 

OVERVIEW OF STUDY OUTCOMES 

The goal of this study was to develop analysis-ready traffic parameters necessary to support most 

LTPP analyses and help LTPP users identify and obtain LTPP traffic parameters necessary for 

different pavement analysis objectives. 

The following sections provide information about the analysis-ready traffic parameters 

developed in this study, including statistics about the number of LTPP traffic sites for which 

these parameters were developed. LTPP sites with traffic loading data issues identified during 

the study and sites lacking site-specific data were not included in Phase III of this study. The 

analysis-ready traffic parameters for these sites are under development using other LTPP 

program resources. 

To provide LTPP data users with guidance about the traffic data and computed parameters, the 

Guide was developed. See chapter 13 for an overview of the Guide. 

To help users apply traffic inputs based on HPMS traffic data to LTPP prediction models , a 

procedure for estimating truck volume by class in the design lane (i.e., truck lane) using HPMS 

directional traffic data was developed and presented in chapter 13. 

NEW TRAFFIC PARAMETERS DEVELOPED IN THIS STUDY 

Analysis-ready traffic parameters developed in this study include the following: 

• MEPDG base (first) year AADTT LTPP lane (AASHTOWare Pavement ME Design 

software input). 

• MEPDG truck traffic growth function by vehicle class (AASHTOWare Pavement ME 

Design software input). 

• MEPDG truck traffic growth rate by vehicle class (AASHTOWare Pavement ME Design 

software input). 

• MEPDG vehicle class volume distribution (AASHTOWare Pavement ME Design 

software input). 

• MEPDG Axle Load Distribution Factors (AASHTOWare Pavement ME Design software 

input). 

• MEPDG number of axles per truck (AASHTOWare Pavement ME Design software 

input). 

• AADTT in LTPP lane for each year, for vehicles in FHWA Classes 4–13 combined and 

for each truck class (FHWA Classes 4–13). 

• Cumulative volume of heavy trucks (vehicles in FHWA Classes 4 and 6–13 combined), 

estimated annually. 

• Cumulative volume of FHWA Class 9 trucks, estimated annually. 

• Annual ESAL for LTPP lane for each year for vehicles in FHWA Classes 4–13 

combined.
256

• Annual GESAL for LTPP lane for each year for vehicles in FHWA Classes 4–13 

combined.

• Annual total GVW for LTPP lane for each year for vehicles in FHWA Classes 4–13 

combined.

• Annual total truck volume for LTPP lane for each year for vehicles in FHWA Classes 

4–13 combined.

• Representative AADTT in LTPP lane, all classes combined. 

• Representative percent of trucks in each class for FHWA Classes 4–13 for LTPP lane. 

• Codes describing traffic data sources and methods used for parameter estimation. 

In addition, recommendations were made to the LTPP program to include the following 

additional analysis-ready traffic parameters in the LTPP database. These parameters were 

described in detail in chapters 2 and 3 of this report. The following parameters were developed 

by the LTPP program outside of this study: 

• Representative ESAL per truck, considering vehicles in FHWA Classes 4–13 for LTPP 

lane. 

• Representative EESAL per each vehicle in FHWA Classes 4–13 for LTPP lane. 

• Representative ESAL per single, tandem, tridem, and quad axle, considering vehicles in 

FHWA Classes 4–13 for LTPP lane. 

• Representative GESAL-per-truck, considering vehicles in FHWA Classes 4–13 for LTPP 

lane. 

• Representative GESAL per each vehicle class in FHWA Classes 4–13 for LTPP lane. 

• Representative GESAL per single, tandem, tridem, and quad axle, considering vehicles in 

FHWA Classes 4–13 for LTPP lane. 

• Representative RPPIF per truck, considering vehicles in FHWA Classes 4–13 for LTPP 

lane. 

• Representative RPPIF per each vehicle in FHWA Classes 4–13 for LTPP lane.

• Representative RPPIF per single, tandem, tridem, and quad axle, considering vehicles in 

FHWA Classes 4–13 for LTPP lane. 

• Representative GVW, considering all vehicles in FHWA Classes 4–13 for LTPP lane.

• Representative GVW for each vehicle in FHWA Classes 4–13 for LTPP lane. 

AVAILABILITY OF ANALYSIS-READY TRAFFIC PARAMETERS FOR LTPP 

TRAFFIC SITES 

Truck Volume, Growth, and Vehicle Classification Parameters for AASHTOWare 

Pavement ME Design Software Input 

The MEPDG_TRUCK_VOL_PARAMETERS table includes traffic input sets for use in 

AASHTOWare Pavement ME Design software. Each traffic input set includes the AADTT for 

the first year at each LTPP test site, the percentage distribution of that truck volume occurring 

within each of the 10 FHWA vehicle classes, and a growth formula (annual percentage change) 

for each of those classes. Each formula takes either a linear or compound shape. Which formula 

should be used for each equation is also described. The type of formula used (linear or 

compound) can be different for different vehicle classifications at a site (i.e., the appropriate 
257

growth formula for FHWA Class 9 trucks can be a linear function, but the formula for FHWA 

Class 10 can be a compound function). As a result, growth equations for each site come in sets of 

10, with a separate equation for each classification of trucks. 

The MEPDG_TRUCK_VOL_PARAMETERS table includes a total of 1,632 unique sets of 

traffic inputs for use in AASHTOWare Pavement ME Design software. In general, these traffic 

input sets are present for every SPS experiment site and every GPS test section. For SPS sites, 

inputs are provided at the site-level (i.e., SHRP_CODE is *00). Each SPS site typically contains 

more than one LTPP test section (e.g., SPS-1 site 010100 consists of a series of test sections 

(010101–010163.)) One set of MEPDG traffic inputs is provided in the 

MEPDG_TRUCK_VOL_PARAMETERS table for SPS-1 site 010100. Those equations are 

applicable across test sections 010101–010163.

A total of 247 SPS sites have traffic inputs in the MEPDG_TRUCK_VOL_PARAMETERS 

table. SPS-10 sites (SHRP_IDs in the form of “AAxx”) are not included in the 

MEPDG_TRUCK_VOL_PARAMETERS table. These sites did not have traffic data in SDR 30.

Some LTPP test sites experience significant construction activity when they are part of the LTPP 

program. When construction activity is significant enough to cause a test section’s structural 

characteristics to change, an additional set of traffic inputs is developed for that new structural 

condition. Often, but not always, this second set of traffic inputs is associated with a change in 

what LTPP experiment a given section is associated with (e.g., as an SPS test section nears the 

end of its useful life, that section receives an overlay and becomes part of a GPS experiment). 

When this occurs, a second set of MEPDG traffic inputs is provided for that test section (e.g., at 

LTPP site 390900, test section 390901 was overlaid with a 1.5-inch AC pavement in 2011 and 

moved to GPS-6S). That section was then overlaid a second time 1 yr later with an additional 

2-inch AC pavement. Test section 390902 was never overlaid, and test section 390903 was 

overlaid once in 2012 when it was moved to GPS-6S. Thus, these test sections are treated 

differently in the MEPDG_TRUCK_VOL_PARAMETERS table. Specifically, a single set of 

MEPDG traffic volume input parameters is present in the MEPDG table and should be applied to 

the time each of these test sites participated in the SPS experiment. Two additional sets of 

MEPDG traffic volume input parameters are present for test section 390901. Both are labeled as 

applying to the GPS-6S experiment, with one set of parameters present for each of the two 

different overlay events. For test section 390902, only the original set of MEPDG traffic volume 

input parameters (labeled 390900) is present. Test section 390903 has an additional set of 

MEPDG traffic volume input parameters. These apply to the time this site spends in the GPS-6S 

experiment. 

A total of 1,376 GPS sections (including those moving from SPS experiments) have traffic 

inputs in the MEPDG_TRUCK_VOL_PARAMETERS table. As noted in the previous 

paragraph, if a GPS test section received a treatment significantly changing the structural 

strength of that pavement, an additional set of traffic inputs are developed for that GPS section.

With all LTPP experiments, some test sections have better, more reliable, data than others. This 

is particularly true with traffic monitoring data, where State and Provincial agencies collected

considerable data at some sites and relatively little data at others. To help users select the best 

sites for their research and analysis, CPTs contains a code describing the usability or confidence 
258

associated with a given set of traffic parameters. One code is present for each set of traffic 

growth parameters. The code accounts for two basic factors: the amount of quality traffic 

monitoring data available for calibrating traffic growth equations and the continuity (or lack of 

continuity) in truck volumes at a site. 

More data and more year-to-year continuity in data increases the degree to which the traffic 

growth equation—the method selected by the MEPDG to input truck volume data—can replicate 

actual truck volumes traveling over the pavement. 

Users should have confidence in the reliability of analyses using forecasts rated “Best” and 

“Better.” “Good” and “Fair” ratings can mean that results need testing for sensitivity to traffic 

inputs, as the lack of data, or high level of variation observed in truck volumes at a site, means 

that actual truck volumes experienced by a test pavement may be substantially different from 

those input into the model. 

Truck traffic growth parameters rated “Poor” should only be used with extreme caution. 

Truck traffic growth parameters rated “Bad” mean no data are available for a site. For SPS-8 

experiments, this may correctly define the level of truck traffic present—no trucks. However, 

with no data collected, this cannot be confirmed. Users should proceed accordingly. 

Axle Loading Distribution Factors for AASHTOWare Pavement ME Design Software 

Input 

At the beginning of this study, 647 LTPP traffic sites (including GPS sections and SPS sites) 

were identified by the LTPP program as candidates for developing site-specific representative 

ALDF values. These sites are included in appendix B of this report.

Available WIM data for 647 sites were reviewed to determine if data quality and quantity are 

sufficient to compute site-specific ALDF values for use as an AASHTOWare Pavement ME 

Design software input. WIM data availability and reasonableness checks described in chapter 8 

were used to evaluate and categorize monthly and annual axle load distributions. The following 

data quality and usability categories were developed and used to categorize WIM data for each 

LTPP site: 

• Best—WIM data satisfying the performance requirements of Type I WIM systems for 

ASTM E1318-09 and data reasonableness requirements described in chapter 8 are 

available for at least one occurrence for each calendar month in a dataset with at least one 

occurrence for each DOW for each month. These data were used to develop 

AASHTOWare Pavement ME Design software ALDF with unique values for each 

calendar month. 

• Better—WIM data satisfying the performance requirements of Type I WIM systems for 

ASTM E1318-09 and data reasonableness requirements described in chapter 8 are 

available for at least 1 yr, but not all 12 mo have data present in the dataset. These data 

were used to develop ALDF with the same average annual values repeated for each 

month in the AASHTOWare Pavement ME Design software ALDF input. 
259

• Good—available WIM data are of unknown quality, but at least 1 yr is available that 

passed QC checks and data reasonableness checks described in chapter 8. These data 

were used to develop ALDF representing a typical day of the year. The same 

representative annual values were repeated for each month in the AASHTOWare 

Pavement ME Design software ALDF input. Sites in this category required additional 

manual review and selection of specific years to compute ALDF. 

• Fair—available WIM data are of unknown quality but passed QC checks and most data 

reasonableness checks described in chapter 8. Annual axle load distributions for these 

sites were manually reviewed and specific years were selected to compute ALDF to 

minimize the effects of low precision and bias observed in WIM data. Potential WIM 

data issues were noted for each site to inform users about lower-data quality. These data 

were used to develop ALDF representing a typical day of the year. The same 

representative annual values were repeated for each month in the AASHTOWare 

Pavement ME Design software ALDF input. These lower-quality data may have low to 

moderate effects on MEPDG analysis results and should be used with caution. Using 

default values may be an alternative approach for LTPP sites with ALDF based on load 

distributions labeled as “Fair.” 

• Poor, site-specific loading pattern—available WIM data are of unknown quality and did 

not pass data reasonableness checks described in chapter 8. These data show an 

identifiable loading pattern for dominant truck classes (typically FHWA Class 9). 

Loading pattern information can be used to identify alternative LTPP WIM sites with 

similar loading patterns with better quality WIM data to compute AASHTOWare 

Pavement ME Design software ALDF or select ALDF default values with similar loading 

patterns. Default values were assigned for these sites. 

• Bad, no site-specific loading pattern—available WIM data are of unknown quality and 

did not pass data reasonableness checks described in chapter 8. These data do not follow 

any rational patterns. Sites in this category required assignment of default values using 

methodology not based on site-specific WIM data. Default values were assigned for these 

sites. 

Out of 647 LTPP traffic sites with WIM data, 460 had data suitable to compute site-specific 

ALDF inputs. For these sites, ALDF parameters were computed and included in the 

MEPDG_AXLE_LOAD_DIST_FACTOR table. Another 161 sites had enough WIM data to 

support developing site-specific loading patterns for dominant heavy truck classes (primarily 

FHWA Class 9) that can be used to select appropriate default values from the LTPP PLUG or 

other sources. Another observation showed that only 26 of the 647 LTPP WIM sites identified 

for this study had useable data regarding a site’s traffic loading patterns. In general, LTPP SPS 

sites had better quality WIM data compared to GPS sites. 

The distribution of LTPP traffic sites by confidence in computed ALDF parameters is presented 

in table 100. 
260

Table 100. ALDF assessment results. 

ALDF Use Category Number of LTPP WIM Sites 

Best 23

Better 36

Good 177

Fair 224

Default value, site-specific loading pattern 161

Default value, no site-specific loading pattern 26

Information in table 100 is based on WIM data assessment for FHWA Class 9 vehicles. Because 

the number of axle counts observed at each LTPP site varies by vehicle class and axle group, not 

all vehicle classes and axle groups for a given LTPP site have sufficient data to compute unique 

monthly or annual ALDF values. If monthly axle counts were below 200 (using all available 

years of data combined for a given month) for a given site and class–axle combination, available 

annual values were used to compute ALDF. If the class–axle combination for a given site did not 

have axle count data to compute ALDF (i.e., vehicle class and/or axle group were not present in 

the ALDF computation dataset), the ALDF factor was reported as “0.” 

For LTPP sites without necessary data to compute site-specific ALDF, default values were 

assigned using LTPP PLUG default NALS axle load and additional defaults described in the 

forthcoming Predicting Truck and Axle Load Patterns.

(22) ALDF values based on default NALS 

were developed outside of this study. As a result, all LTPP sites should have ALDF values 

available in the MEPDG_AXLE_LOAD_DIST_FACTOR table. 

Axles Per Truck Parameters for AASHTOWare Pavement ME Design Software Input

APT parameters were computed for 791 LTPP traffic sites with axle count data reported in the 

VEHICLE_CLASS_AVG_AX_ANL table. LTPP traffic sites with site-specific APT inputs are 

ready for use in AASHTOWare Pavement ME Design software. The parameters are stored in the 

MEPDG_AXLE_PER_TRUCK table. Sites without axle count data were assigned APT default 

values from LTPP PLUG in the MEPDG_AXLE_PER_TRUCK table. The default APT 

assignment was performed outside of this study. As a result, all LTPP sites should have APT 

values available in the MEPDG_AXLE_PER_TRUCK table. 

Traffic Trend Parameters for LTPP Analyses 

The TRF_TREND table is the primary CPT for truck volume and traffic loading summary 

parameters (e.g., AADTT, ESAL, GESAL, total GVW). There are 2,743 unique STATE_CODE 

and SHRP_ID combinations in the TRF_TREND table. These identifiers are used to characterize 

1,353 GPS sites and 1,955 SPS sections. The same pavement section may participate in multiple 

GPS and SPS experiments during its service life, so the number of GPS sites and SPS sections is 

greater than the number of unique STATE_CODE and SHRP_ID combinations. Records of truck 

volume and summary loading statistics are present for each GPS and SPS section with traffic 

data reported in SDR 30. Because a variety of construction activities occur and may be of 

interest to researchers, records are present for each test site for every year to identify every 

construction event in the CONSTRUCTION_NO field in the TRF_TREND table. This allows 

users to directly select annual traffic conditions applying to specific test sections and events of 
261

interest to them. Note that no annual ESAL value is provided for virtual SPS WIM sites (i.e., 

sites with SHRP_ID ending in “00”) because no actual pavement structure is associated with 

these virtual sites. 

Cumulative total truck volumes are also provided in the TRF_TREND table. Because the 

cumulative total is a function of when the roadway section opened to traffic, this value changes 

from year to year depending on the life of that specific pavement section (i.e., when a pavement 

is overlaid, the life of that pavement is reset to zero, and the accumulation of truck traffic starts 

over). 

Note that some records in the TRF_TREND table report zero traffic. These are records for either 

SPS-8 sites with no truck traffic (some SPS-8 sites were specifically constructed to experience 

no truck traffic) or for years at an LTPP site that entered the LTPP experiment prior to opening 

to traffic. In these cases, a record is present for each year or partial year the site was part of the 

LTPP experiment, even if those years were prior to opening to traffic. For example, a site was 

assigned to the LTPP experiment in 1995 while still under construction. It did not open to traffic 

until spring 1997. The records for that site for 1995 and 1996 have no volumes in all 10 truck 

classes. 

Although no specific statistical accuracy is assigned to the truck volume statistics presented in 

the TRF_TREND table, some test sections have better, more reliable data than others. To help 

users select the best possible sites for their research and analysis, the TRF_TREND table 

contains several codes (e.g., AADTT_SOURCE, VEH_CLASS_SOURCE, ESAL_SOURCE, 

GESAL_SOURCE, and GVW_SOURCE) describing the basis for annual truck volume and 

traffic loading estimates recorded. 

In general, annual statistics based on traffic monitoring data (i.e., data collected specifically at a 

test site in response to the LTPP program) are considered the most reliable sources of truck 

volume. Estimates from State/Provincial highway agencies are based on short-duration counts 

made prior to the start of the LTPP program. State/Provincial highway agencies also provide 

historical estimates, but they are typically based on counts taken some distance from a test site 

and are often factored for growth. They are far less reliable than traffic monitoring counts but are 

often the only estimates of traffic loads prior to the start of the LTPP program. At some test sites, 

historical estimates can be substantially different from volumes observed in the traffic 

monitoring program. This can be a sign of inaccurate historical estimates; it can also be caused

by changing economic conditions or changes in the local roadway network resulting in shifting 

truck travel patterns. 

The TRF_TREND table is the best available estimate of annual truck traffic at LTPP test sites.

Where historical and monitored traffic data show different truck volume patterns, users should 

take additional steps to test the sensitivity of their results to variations in truck traffic volumes 

because some of these estimates have limited reliability. 

Representative Traffic Parameters for High-Level Pavement Performance Analyses 

The TRF_REP table is a CPT allowing LTPP data users to quickly categorize LTPP test sections 

by the type of truck traffic passing over that pavement (i.e., categorize a section as experiencing 
262

high, moderate, or low truck volumes). Because AADTT is present, a user can select their own 

definition of how many trucks are needed for a pavement to experience heavy truck traffic. In 

addition to providing a single representative AADTT value, the TRF_REP table records the 

nature of trucks passing over a test pavement, which allows a user to select pavements not only 

with high truck volumes but where those high volumes are specifically found within heavy, 

single-unit truck classes (e.g., FHWA Classes 6 or 7) or any other type of truck.

There are 2,743 unique STATE_CODE and SHRP_ID combinations in the TRF_REP table. 

These identifiers are used to characterize 1,353 GPS sites and 1,955 SPS sections. The same 

pavement section may participate in multiple GPS and SPS experiments during its service life, so 

the number of GPS sites and SPS sections is greater than the number of unique STATE_CODE 

and SHRP_ID combinations. One record is present for each experiment and test site combination 

(i.e., if a test site was only part of one LTPP experiment, only one record is present for that site).

However, if a test section was part of three different experiments, three different records are 

present for that site, one for each experiment. For SPS test sites where more than one test section 

is present (e.g., SPS test site 010100, which has attached sections 010101–010163) at least one 

record is present for each test section. 

Separate records are present to help account for changes in truck traffic over time. The nature of 

truck traffic occurring at a site during the first experiment may differ from truck traffic occurring 

during the last experiment.

Using only one statistic to represent a distribution of traffic conditions at each test section over 

the course of each experiment does not account for truck traffic changes over time. Where there 

is little variation in truck traffic patterns, representative values are good descriptors of truck 

travel patterns. Where traffic patterns vary heavily during the course of a pavement section’s 

participation in a given experiment, a single representative value is a less reliable descriptor of 

truck traffic for that pavement. 

To help users quickly understand the level of confidence they should have in these best available 

representative values, two metadata codes, REP_VEH_CLASS_USE_RATING and 

REP_AADTT_USE_RATING, are included in each data record. Each code accounts for two 

basic factors: the amount of quality traffic monitoring data available to compute representative 

values and the continuity (or lack of continuity) in truck volumes at a site during a specific 

experiment. 

The more LTPP-specific traffic monitoring data available to compute reported statistics and the 

more year-to-year continuity in those data, the more confidence a user should have in a statistic’s 

accuracy to describe traffic throughout the life of a pavement in an experiment.

Users should have higher confidence in the analyses outcomes using statistics rated as “Best” 

and “Better.” “Good” and “Fair” ratings can mean that results need testing for sensitivity to 

traffic inputs, as the lack of data, or high level of variation observed in truck volumes at a site,

means that actual truck volumes experienced by a test pavement may be substantially different 

from representative values reported. 

Data rated “Poor” or “Bad” should only be used with extreme caution.
263

APPENDIX A. SCREENSHOTS OF AASHTOWARE PAVEMENT ME DESIGN 

SOFTWARE GUIs FOR TRAFFIC INPUTS 

Figure 16 through figure 22 are screenshots of AASHTOWare Pavement ME Design software 

GUIs for different traffic input tables. 

265

Source: Screen capture performed by Applied Research Associates, Inc. from the AASHTOWare Pavement ME Design Software. Reference: AASHTOWare 

Pavement ME Design Software, Version 2.6.0, June 2020, American Association of State Highway and Transportation Officials 555 12th Street NW, Suite 1000, 

Washington, DC, 20004. 

Figure 16. Screenshot. Traffic inputs main screen.

267

Source: Screen capture performed by Applied Research Associates, Inc. from the AASHTOWare Pavement ME 

Design Software. Reference: AASHTOWare Pavement ME Design Software, Version 2.6.0, June 2020, 

American Association of State Highway and Transportation Officials 555 12th Street NW, Suite 1000, 

Washington, DC, 20004. 

Figure 17. Screenshot. Vehicle class distribution and growth inputs. 

Source: Screen capture performed by Applied Research Associates, Inc. from the AASHTOWare Pavement ME 

Design Software. Reference: AASHTOWare Pavement ME Design Software, Version 2.6.0, June 2020, 

American Association of State Highway and Transportation Officials 555 12th Street NW, Suite 1000, 

Washington, DC, 20004. 

Figure 18. Screenshot. Monthly adjustments inputs. 
268

Source: Screen capture performed by Applied Research Associates, Inc. from the AASHTOWare Pavement ME 

Design Software. Reference: AASHTOWare Pavement ME Design Software, Version 2.6.0, June 2020, 

American Association of State Highway and Transportation Officials 555 12th Street NW, Suite 1000, 

Washington, DC, 20004. 

Figure 19. Screenshot. APT inputs. 
269

Source: Screen capture performed by Applied Research Associates, Inc. from the 

AASHTOWare Pavement ME Design Software. Reference: AASHTOWare Pavement 

ME Design Software, Version 2.6.0, June 2020, American Association of State Highway 

and Transportation Officials 555 12th Street NW, Suite 1000, Washington, DC, 20004. 

Figure 20. Screenshot. AADTT, traffic capacity, axle configuration, lateral wander, and 

wheelbase inputs. 

Note 1: Average axle width, mean wheel location, design lane width, and all wheelbase inputs 

shown in Figure 20 are only used for rigid pavement analyses. 

Note 2: For analyses of LTPP sites, the AADTT values represents all trucks within the LTPP 

lane only. Therefore, the values entered in the AASHTOWare Pavement ME Design software for 

number of lanes, percent trucks in design direction, and percent trucks in design lane are 1, 100, 

and 100, respectively. Using these values ensures that the LTPP truck volume data are 

represented accurately. 

Note 3: The green checkmarks shown in Figure 20 mean that the values entered are within the 

minimum and maximum input values for which the software was designed. 

271

Source: Screen capture performed by Applied Research Associates, Inc. from the AASHTOWare Pavement ME Design Software. Reference: AASHTOWare 

Pavement ME Design Software, Version 2.6.0, June 2020, American Association of State Highway and Transportation Officials 555 12th Street NW, Suite 

1000, Washington, DC, 20004. 

Figure 21. Screenshot. ALDF inputs. 

Note 1: To access ALDF inputs for different axle groups, click on the green circles in the top left corner, identified by the red 

rectangle on the screenshot. 

273

Source: Screen capture performed by Applied Research Associates, Inc. 

from the AASHTOWare Pavement ME Design Software. 

Reference: AASHTOWare Pavement ME Design Software, Version 

2.6.0, June 2020, American Association of State Highway and 

Transportation Officials 555 12th Street NW, Suite 1000, Washington, 

DC, 20004. 

Figure 22. Screenshot. Hourly adjustment inputs. 

Note 1: Hourly adjustments are only used in rigid pavement analyses.

275

APPENDIX B. LTPP WIM SITES CONSIDERED FOR ALDF COMPUTATION 

Table 101 provides a list of LTPP WIM sites with axle loading data evaluated in this study. 

Table 101. LTPP WIM sites considered for ALDF computation. 

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

1 1001 Pattern L2 

1 1019 Pattern H1 

1 4073 Good, small sample 

1 4129 Fair, small sample 

1 6012 Pattern VH 

2 1001 Pattern L1 

2 1002 Pattern VL 

2 1004 Pattern VH 

2 1008 Pattern VL 

2 6010 Pattern LE 

2 9035 Pattern E 

4 0100 Best 

4 0200 Best 

4 0500 Fair 

4 0600 No pattern 

4 0900 Good 

4 1001 Good, small sample 

4 1002 Pattern H2 

4 1006 Good, small sample 

4 1007 Fair 

4 1016 Pattern E 

4 1017 Good, small sample 

4 1024 No pattern 

4 1025 Pattern H2 

4 1034 Fair, small sample 

4 1036 Good 

4 1062 Pattern H2 

4 6053 Pattern VH 

4 6055 Fair 

4 6060 Good 

4 7079 Fair 

4 7613 Good 

4 7614 Fair, small sample 

4 A900 Good 

4 B900 Fair 

5 0100 Good 

5 0200 Best 
276

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

5 0800 Pattern L1 

5 2042 Good, small sample 

5 3011 Fair 

5 3048 Fair 

5 3058 Good 

5 3059 Good 

5 3074 Fair 

5 4019 Pattern L2 

5 4021 Good, small sample 

5 4023 Fair 

5 4046 Fair 

5 5803 Good, small sample 

5 5805 Fair, small sample 

5 A600 Good 

6 0200 Best 

6 0500 Good 

6 0600 Good 

6 1253 Pattern L2 

6 2002 Good 

6 2040 Better 

6 2051 Pattern L2 

6 2053 Fair 

6 2647 Good 

6 3005 Good 

6 3021 Good 

6 3024 Good 

6 3030 Better 

6 3042 Good 

6 6044 Good 

6 7452 Good 

6 7454 Pattern E 

6 7455 Good 

6 7493 Good 

6 8149 Good 

6 8150 Good 

6 8151 Good 

6 8153 Pattern L2 

6 8156 Fair, small sample 

6 8201 Fair, small sample 

6 8202 Fair 

6 8534 Good 

6 8535 Good 

6 9049 Good 
277

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

6 9107 Good 

8 0200 Better 

8 0500 Fair 

8 1029 Pattern L1 

8 1047 Pattern L2 

8 1053 Pattern LE 

8 3032 Pattern H1 

8 6002 Fair 

8 7035 No pattern 

8 7776 Fair 

8 7780 No pattern 

8 7783 Pattern E 

8 9019 Fair 

8 9020 Fair 

9 0900 Better 

9 0960 Better 

9 1803 Fair 

9 4008 Fair 

9 4020 Pattern L1 

9 5001 Better 

10 0100 Better 

10 1201 Pattern L2 

10 1450 Fair 

10 4002 Good, small sample 

10 5004 Good, small sample 

10 5005 Fair 

12 0100 Best 

12 0500 Better 

12 1030 Pattern L2 

12 1060 No pattern 

12 1370 Good 

12 3804 No pattern 

12 3811 Good, small sample 

12 3996 No pattern 

12 3997 Pattern E 

12 4000 Good 

12 4057 Good 

12 4059 Pattern L1 

12 4096 No pattern 

12 4097 No pattern 

12 4099 Pattern H2 

12 4100 Good 

12 4103 Pattern H2 
278

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

12 4105 Pattern L1 

12 4106 Good 

12 4107 Pattern L2 

12 4108 No pattern 

12 4109 Good 

12 4135 No pattern 

12 4136 No pattern 

12 4137 Pattern E 

12 4138 Fair 

12 4154 Fair, small sample 

12 9054 No pattern 

13 3017 No pattern 

13 3020 No pattern 

13 4118 Pattern LE 

15 1003 Pattern VL 

15 1006 Pattern E 

15 1008 Pattern L1 

15 7080 No pattern 

16 1001 Fair 

16 1005 Fair 

16 1007 Pattern H1 

16 1009 Good 

16 1010 Pattern H1 

16 1020 Fair 

16 1021 Good 

16 3017 Good 

16 3023 Good 

16 5025 Fair 

16 6027 No pattern 

16 9032 Good 

16 9034 Fair 

17 0600 Best 

17 1002 Good 

17 1003 Fair 

17 4074 Pattern LE 

17 4082 Fair, small sample 

17 5151 Good 

17 5217 Pattern H1 

17 5423 Fair, small sample 

17 5453 Fair, small sample 

17 5843 Fair 

17 5849 Fair 

17 5854 Fair 
279

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

17 5869 Fair, small sample 

17 5908 Good 

17 7937 Fair 

17 9327 Pattern H1 

18 0600 Best 

18 0900 Fair 

18 1028 Good 

18 1037 Good 

18 2008 Good 

18 2009 Good 

18 3002 Good 

18 3030 Good 

18 3031 Good 

18 4021 Better 

18 5022 Fair 

18 5043 Fair 

18 5518 Good 

18 5528 Good 

18 6012 Fair 

18 9020 Pattern E 

18 A900 Fair 

19 0100 Good 

19 0200 Good 

19 0600 Fair 

19 0700 Fair 

19 1044 Fair 

19 3006 Fair 

19 3009 Good 

19 3028 Good 

19 3033 Good, small sample 

19 3055 Good 

19 5042 Fair 

19 6049 Fair 

19 6150 Fair 

19 9116 Fair 

19 9126 Fair 

20 0100 Good 

20 0200 Best 

20 1005 Pattern L1 

20 1006 Fair, small sample 

20 1009 Fair 

20 3015 Fair 

20 4016 Fair, small sample 
280

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

20 4052 Pattern LE 

20 4053 Pattern L2 

20 4063 Fair 

20 4067 Fair 

20 6026 Fair, small sample 

20 7085 Pattern E 

21 1010 Pattern L1 

21 1014 Pattern VL 

21 1034 Fair 

21 3016 Fair 

21 4025 Fair 

21 6040 Pattern L2 

21 6043 Pattern L1 

22 0100 Better 

22 3056 Fair, small sample 

23 0500 Better 

23 1009 Good, small sample 

23 1028 Good 

24 0500 Better 

24 0900 Pattern L2 

24 0960 Pattern L2 

24 1632 Pattern L1 

24 2401 Fair 

24 5807 Fair 

25 1002 Fair, small sample 

25 1004 Pattern L1 

26 0100 Better 

26 0200 Best 

26 0600 Fair 

26 0900 Best 

26 1001 Fair 

26 1004 Pattern L1 

26 1010 Pattern L1 

26 1012 Good 

26 1013 Fair, small sample 

26 3068 Fair 

26 4015 Best 

26 5363 Fair 

26 6016 Pattern E 

26 7072 Pattern VL 

26 9029 Better 

26 9030 Good 

27 0500 Better 
281

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

27 0700 Pattern VH 

27 1003 Good 

27 1016 Good 

27 1018 Pattern VH 

27 1019 Good 

27 1023 Fair 

27 1028 Fair 

27 1029 Pattern L1 

27 1085 Fair 

27 1087 Fair 

27 3003 Pattern E 

27 3013 Good 

27 4033 Good 

27 4037 Good 

27 4040 Fair 

27 4054 Good, small sample 

27 4055 Good, small sample 

27 5076 Fair 

27 6251 Fair, small sample 

27 6300 Pattern H2 

27 7090 Fair 

27 9075 Fair 

28 0500 Pattern H2 

28 0900 Good 

28 1001 Fair 

28 1016 Pattern L1 

28 1802 Fair 

28 2807 Fair 

28 3018 Good 

28 3019 Good 

28 3081 Good 

28 3082 Fair 

28 3087 Pattern L2 

28 3089 Fair 

28 3091 Fair 

28 3093 Good 

28 3094 Good 

28 3099 Fair 

28 4024 Fair 

28 5006 Fair 

28 5025 Pattern L2 

28 5803 Fair 

28 7012 Pattern H1 
282

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

28 9030 Good 

29 0600 Fair 

29 0700 Good 

29 0900 Good 

29 1002 Good, small sample 

29 1005 Fair 

29 1008 Fair, small sample 

29 1010 Fair 

29 4036 Good 

29 4069 Good, small sample 

29 5000 Good, small sample 

29 5047 Pattern E 

29 5393 Good 

29 5413 Fair, small sample 

29 5473 Good 

29 5483 Fair, small sample 

29 5503 Good 

29 6067 Fair 

29 7054 Fair 

29 7073 Pattern H1 

30 0100 Fair 

30 0500 Good 

30 0800 Good, small sample 

30 1001 Good 

30 6004 Good 

30 7066 Good 

30 7075 Good 

30 7088 Good 

30 8129 Good 

31 0100 Good 

31 1030 Pattern L2 

31 3018 Fair 

31 3023 Pattern H2 

31 3024 Pattern H2 

31 3028 Pattern L2 SHIFT S 

31 3033 Fair 

31 4019 Fair 

31 5052 Pattern H1 

31 6700 Pattern L1 

31 6701 Pattern L2 

31 6702 Fair 

31 7005 Fair 

31 7017 Pattern L2 
283

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

31 7040 Pattern H2 

31 7050 Pattern H1 

32 0100 Good 

32 0200 Good 

32 1020 Pattern E 

32 1030 Pattern L2 

32 3010 Pattern VH 

32 3013 Good 

32 7000 Good 

32 7084 Fair, small sample 

33 1001 Pattern L1 

34 0500 Better 

34 1003 Better 

34 1011 Best 

34 1030 Good, small sample 

34 1031 Better 

34 1033 Better 

34 1034 Better 

34 1638 Better 

34 4042 Good 

34 6057 Fair 

35 0100 Better 

35 0500 Best 

35 0900 Pattern VH 

35 1022 Good 

35 1112 Pattern L2 

35 2006 Good 

35 2007 Good 

35 2118 No pattern 

35 3010 Pattern L1 

35 6033 Pattern H2 

35 6401 No pattern 

36 1008 Pattern L1 

36 1011 Pattern E 

36 1643 Pattern H1 

36 1644 Pattern L1 

36 4018 Good, small sample 

37 0200 Good 

37 0900 Pattern L1 

37 1006 Fair 

37 1024 Fair 

37 1028 Pattern L1 

37 1030 Pattern L1 
284

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

37 1040 Fair, small sample 

37 1352 Fair, small sample 

37 1645 Better 

37 1801 Fair, small sample 

37 1803 Good, small sample 

37 1814 Fair 

37 1817 Fair 

37 1992 Fair 

37 2819 Better 

37 2824 Better, small sample 

37 2825 Fair 

37 3008 Fair, small sample 

37 3011 Good, small sample 

37 3044 Pattern E 

37 3807 Good 

37 3816 Fair 

37 5037 Fair, small sample 

37 5826 Fair, small sample 

37 5827 Good, small sample 

37 A900 Good 

38 2001 Pattern H1 

38 3005 Fair, small sample 

38 3006 Good 

38 5002 Fair 

39 0100 Best 

39 0200 Better 

39 3013 Good 

39 3801 Good 

39 4018 Good 

39 4031 Good 

39 5003 Good 

39 5010 Good 

39 5569 Good 

39 7021 Good 

39 9006 Better 

39 9022 Fair 

40 0100 Better 

40 0500 Good 

40 0600 Good 

40 1015 Pattern L2 

40 3018 Fair, small sample 

40 4154 Fair 

40 4155 Fair 
285

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

40 4157 Fair 

40 4158 Good 

40 4160 Fair 

40 4161 Fair 

40 4162 Pattern L1 

40 4163 Pattern L2 

40 4165 Good 

40 4166 Good, small sample 

40 5021 Fair 

40 6010 Pattern L2 

40 7024 Fair, small sample 

41 2002 Pattern E 

41 5005 Pattern H1 

41 5006 Fair 

41 5008 Fair, small sample 

41 5021 Good 

41 5022 Good 

41 6011 Fair, small sample 

41 7018 Good 

41 7019 Pattern L2 SHIFT S 

41 7025 Good, small sample 

41 7081 Good, small sample 

42 0600 Best 

42 1597 Pattern L2 

42 1598 Fair 

42 1599 Better 

42 1605 Good 

42 1606 Better 

42 1608 Pattern L1 

42 1610 Fair 

42 1613 Fair, small sample 

42 1614 Fair 

42 1618 Pattern E 

42 1623 Pattern L2 

42 1627 Good 

42 1690 Fair, small sample 

42 1691 Fair 

42 3044 Best 

42 5020 Fair 

42 7025 Fair 

42 7037 Pattern E 

42 9027 Best 

44 7401 Pattern VL 
286

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

45 1008 Fair 

45 1011 Fair 

45 1025 Pattern H2 

45 5035 Fair 

45 7019 Good 

46 0600 Good 

46 0800 Fair, small sample 

46 3010 Good 

46 3012 Good 

46 3052 Good 

47 0600 Best 

47 1028 Pattern L2 

47 2001 Pattern E 

47 2008 Good, small sample 

47 3075 No pattern 

47 3104 No pattern 

47 3108 Pattern H1 

47 3110 Fair, small sample 

47 6015 Pattern E 

47 6022 Pattern L1 

47 9025 Fair, small sample 

48 0001 Fair, small sample 

48 0100 Best 

48 0800 Fair, small sample 

48 0900 Fair 

48 1039 Fair, small sample 

48 1047 Pattern H2 

48 1060 Fair 

48 1068 Fair, small sample 

48 1069 Good 

48 1076 Pattern L1 TX 

48 1087 Pattern E TX 

48 1092 Good, small sample 

48 1093 Fair, small sample 

48 1094 Fair, small sample 

48 1096 Fair, small sample 

48 1119 Pattern L2 TX 

48 1122 Fair, small sample 

48 1123 Good 

48 1130 Pattern L1 TX 

48 1169 Pattern L2 TX 

48 1174 Pattern VL TX 

48 1181 Fair, small sample 
287

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

48 2108 Fair, small sample 

48 2133 Pattern E TX 

48 2172 Pattern H2 TX 

48 2176 Fair, small sample 

48 3003 Pattern L1 

48 3569 Pattern H1 TX 

48 3589 Fair, small sample 

48 3835 Fair, small sample 

48 3845 Good, small sample 

48 3855 Fair, small sample 

48 3865 Fair, small sample 

48 4142 Pattern L2 TX 

48 4146 Pattern L2 TX 

48 4152 Pattern L2 TX 

48 5024 Pattern L2 TX 

48 5035 Pattern L1 TX 

48 5154 Fair 

48 5274 Fair, small sample 

48 5278 Fair, small sample 

48 5283 Fair, small sample 

48 5284 Fair, small sample 

48 5287 Fair 

48 5301 Pattern L2 TX 

48 5310 Fair, small sample 

48 5317 Pattern L1 TX 

48 5323 Fair, small sample 

48 5328 Good, small sample 

48 5334 Fair 

48 5335 Fair 

48 5336 Good 

48 6079 Fair, small sample 

48 6086 Fair, small sample 

48 6179 Pattern H2 TX 

48 7165 Fair 

48 9005 Pattern L1 TX 

48 9167 Fair, small sample 

48 9355 Fair, small sample 

48 A500 Good 

48 A800 No pattern 

49 1006 No pattern 

49 3011 No pattern 

50 1002 Fair 

50 1004 Fair 
288

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

50 1681 Fair 

50 1682 Fair 

51 0100 Best 

51 1002 Pattern VL 

51 1023 Better 

51 1417 Pattern E 

51 1419 Fair 

51 1423 Pattern L1 

51 1464 Fair, small sample 

51 2004 Fair, small sample 

51 2021 Fair 

51 2564 Fair 

51 5008 Fair 

51 5009 Pattern L1 

51 5010 Fair 

53 0200 Best 

53 0800 No pattern 

53 1002 Good 

53 1005 Better 

53 1006 Fair 

53 1007 Best 

53 1008 Good 

53 1501 Fair 

53 1801 Good 

53 3011 Good 

53 3013 Good 

53 3014 Better 

53 3019 Good 

53 3812 Fair 

53 3813 Better 

53 6020 Fair 

53 6048 Fair 

53 6049 Fair 

53 6056 Good 

53 7322 Better 

53 7409 Good 

53 A800 Fair 

54 1640 Pattern L2 

54 4004 Pattern H1 

54 5007 Good 

54 7008 Fair 

55 0100 Best 

55 3009 Fair, small sample 
289

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

55 3010 Good, small sample 

55 3015 Good, small sample 

55 3019 Pattern L2 

55 6351 Good 

55 A900 Good, small sample 

55 B900 Good, small sample 

56 1007 Fair, low count 

56 2015 Fair 

56 2017 Good 

56 2019 Good 

56 2020 Fair 

56 2037 Fair 

56 3027 Good 

56 6029 Better, low count 

56 6031 Fair, low count 

56 6032 Pattern L1 

56 7772 Good 

56 7773 Fair 

56 7775 Good 

81 0500 Fair 

81 A900 Fair 

83 1801 No pattern 

83 3802 Pattern L2 

83 6450 Pattern H1 

83 6452 Pattern H1 

83 6454 Pattern E 

84 1684 Pattern L1 

84 1802 Pattern L1 

84 6804 Fair, small sample 

86 6802 Pattern VL 

87 1620 Pattern L1 

87 1622 Pattern E 

87 2811 Fair 

87 2812 Fair 

88 1645 Fair 

88 1646 Fair 

88 1647 Good 

89 1021 Fair 

89 1125 Fair 

89 1127 Good 

89 2011 Pattern E 

89 3001 Fair 

89 3002 Fair, small sample 
290

STATE_CODE SHRP_ID 

Data Availability 

Assessment for ALDF 

89 3015 Better 

89 3016 Pattern L1 

89 9018 Fair, small sample 

90 0900 Pattern E 

90 6400 Pattern E 

90 6405 Pattern L2 

90 6410 Fair, small sample 

90 6420 Fair, small sample 

L = light; VL = very light; H = heavy; VH = very heavy; E = equal; 

TX = specific to Texas.
291

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