Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

PUBLICATION NO. FHWA-HRT-24-062 MARCH 2024

Guide to Select Long-Term 

Pavement Performance Traffic 

Data for Multiple Uses
FOREWORD

This Guide is designed to help users of the Long-Term Pavement Performance (LTPP) database 

navigate the available traffic data and computed parameters in the LTPP InfoPaveTM web portal. 

The Guide identifies traffic parameters suitable for different pavement analyses and details how 

to use InfoPave to extract the desired data.

The Guide consists of two parts. Part 1 describes traffic parameters available through LTPP 

program sources and provides details about the methods used to collect traffic data and compute 

traffic parameters. It also contains recommendations for the most applicable traffic parameters 

for different types of pavement analyses. Part 2 provides practical examples and details on how 

to use InfoPave to identify and extract various traffic parameters for LTPP sites.

The methodologies presented in this report can be applied beyond the LTPP program to assist 

highway agencies in the computation of traffic statistics necessary to support pavement design, 

research, management, and forensic investigations. Contractors, researchers, and consultants can 

also benefit from this research.

Jean A. Nehme, 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. 

Cover Graphic

Source: FHWA.
TECHNICAL REPORT DOCUMENTATION PAGE

1. Report No.

FHWA-HRT-24-062

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

4. Title and Subtitle

Guide to Select Long-Term Pavement Performance Traffic 

Data for Multiple Uses

5. Report Date

March 2024 

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.

DTFH6114C00018

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

July 2014—September 2023; Final Report

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

The purpose of this Guide is to help Long-Term Pavement Performance (LTPP) data users determine traffic 

parameters suitable for their analyses and identify LTPP tables containing such parameters. This Guide describes 

available tools and specific procedures for extracting these parameters from the LTPP database and provides 

practical examples with step-by-step instructions for identifying and extracting traffic parameters for different 

types of LTPP and other research analyses. This Guide consists of two parts. Part 1 includes a description of 

traffic data and parameters available through LTPP program sources and provides recommendations on what 

traffic parameters are most applicable for different types of pavement analyses. Part 1 also provides information 

about methods used to collect traffic data and compute traffic parameters. Part 2 contains practical examples of

how to identify and extract different traffic data and parameters from the LTPP database using the InfoPaveTM 

web portal. In summary, this Guide is designed to help LTPP users maximize use of LTPP traffic data for

achieving their pavement analysis objectives.

17. Key Words

Traffic data, traffic loading, pavement design, 

Long-Term Pavement Performance, MEPDG, 

WIM, vehicle class, truck volume, axle weight

18. Distribution Statement

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

https://www.ntis.gov

19. Security Classif. (of this report)

Unclassified

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

160 

22. Price

N/A

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

 

 

 

 

 

 

 
iii

TABLE OF CONTENTS

PART 1. LTPP TRAFFIC DATA AND PARAMETERS: DATA SOURCES, 

METHODS, TOOLS, AND RECOMMENDATIONS FOR USE ........................................... 1

CHAPTER 1. INTRODUCTION ................................................................................................ 3

Background ............................................................................................................................. 3

Purpose..................................................................................................................................... 3

Guide Overview ....................................................................................................................... 3

CHAPTER 2. LTPP TRAFFIC DATA AND SOURCES ......................................................... 5

LTPP Traffic Databases ......................................................................................................... 5

Traffic Databases Included in the SDR .............................................................................. 5

Traffic Databases Associated with LTPP Traffic Analysis Software ................................. 5

LTPP Traffic Data Tables and Parameters.......................................................................... 7

Traffic Tables Associated with the Pavement Performance Database ............................... 7

Traffic Tables From LTAS Databases .............................................................................. 13

Ancillary LTPP Traffic Data and Information .................................................................. 17

CHAPTER 3. LTPP TRAFFIC METADATA ......................................................................... 19

Methods of Traffic Data Collection ..................................................................................... 19

LTPP Traffic Data Sources ............................................................................................... 19

Data Shared by LTPP Traffic Sites ................................................................................... 20

Information on Traffic Data Collection Equipment .......................................................... 21

Traffic Data Accuracy .......................................................................................................... 21

Traffic Data Availability ...................................................................................................... 22

Methods Used to Compute Traffic Parameters ................................................................. 22

Codes Describing Data and Methods Used for Parameter Computation and 

Parameter Usability .............................................................................................................. 32

CHAPTER 4. SELECTING LTPP TRAFFIC DATA AND PARAMETERS FOR 

LTPP ANALYSES ...................................................................................................................... 39

Overview of Traffic Parameters Used for Pavement Analysis and Design ..................... 39

Traffic Parameters Recommended for Analyses Requiring Detailed 

Characterizations of Traffic Loading ................................................................................. 40

Parameters for Pavement Response Prediction Based on Mechanistic Models ............... 40

Parameters for Mechanistic–Empirical Pavement Performance Predictions.................... 42

Traffic Summary Parameters Recommended for High-Level and Empirical 

Analyses ................................................................................................................................. 44

Traffic Loading Summary Parameters .............................................................................. 44

Traffic and Truck Volume Summary Parameters ............................................................. 46

Recommendations for Selecting Summary Traffic Loading Parameters Based on the 

Purpose of the Analysis .................................................................................................... 46

LTPP Tables Containing Traffic Loading Summary Parameters ..................................... 48

Traffic Parameters For MEPDG Applications Using AASHTOWare Pavement ME 

Design Software ..................................................................................................................... 51

Recommendations for Assessing Axle Loading Distribution Data Rationality .............. 54

CHAPTER 5. WHERE TO GET LTPP TRAFFIC DATA .................................................... 57
iv

LTPP InfoPave ...................................................................................................................... 57

Custom Requests ................................................................................................................... 58

PART 2. PLAYBOOK ................................................................................................................ 59

OVERVIEW .......................................................................................................................... 59

CHAPTER 6. GENERIC STEPS TO SELECT AND EXTRACT LTPP TRAFFIC 

PARAMETER TABLES USING LTPP INFOPAVE ............................................................. 61

Generic Example of LTPP Traffic Parameter Extraction Using InfoPave ....................... 61

CHAPTER 7. SCENARIO 1: OBTAIN TRUCK TRAFFIC AND TRUCK VOLUME 

INFORMATION ......................................................................................................................... 69

Parameter 1.1: Annual Average Daily Traffic Volume ..................................................... 69

Option 1: Download AADT Values from LTPP Database Tables ................................... 69

Option 2: View AADT Values in InfoPave Section Summary Report ............................ 69

Example 1.1: Obtain an Estimate of AADT for an LTPP Test Site ................................. 71

Parameter 1.2: Annual Average Daily Truck Traffic Volume for the LTPP Lane........ 73

Example 1.2.1: Extract AADTT for Each Year in Service or Analysis Period ................ 74

Example 1.2.2: Extract or Estimate AADTT for the Year Selected for Analysis ............ 76

Parameter 1.3: Annual Total Truck Volume ..................................................................... 77

Example 1.3.1: Obtain Annual Total Truck Volume for Each Year in Service or 

Analysis Period ................................................................................................................. 77

Parameter 1.4: Cumulative Truck Volume for the Analysis Period ................................ 79

Example 1.4.1: Extract Cumulative Heavy Truck Traffic Volume Through the End 

of LTPP Section Participation in the Experiment or Last Reporting Year ....................... 80

Example 1.4.2: Compute Cumulative Heavy Truck Traffic Volume Based on 

User-Specified Start and End Dates.................................................................................. 81

CHAPTER 8. SCENARIO 2: OBTAIN VEHICLE CLASSIFICATION 

INFORMATION ......................................................................................................................... 83

Parameter 2.1: Annual Truck Volume by Vehicle Class .................................................. 83

Example 2.1.1: Obtain Annual Truck Volume by Vehicle Class for Each Year in 

Service or Analysis Period ................................................................................................ 83

Parameter 2.2: Normalized Vehicle Class Distribution .................................................... 87

Example 2.2.1: Obtain Representative Normalized VCD ................................................ 88

Parameter 2.3: Monthly Truck Volume by Vehicle Class ................................................ 89

Example 2.3.1: Obtain Monthly Truck Volume by Vehicle Class ................................... 89

CHAPTER 9. SCENARIO 3: OBTAIN AXLE OR TRUCK LOADING 

INFORMATION ......................................................................................................................... 95

Parameter 3.1: Axle Load Distribution or Axle Load Spectra ......................................... 95

Example 3.1.1: Obtain Annual Axle Load Spectra........................................................... 96

Example 3.1.2: Compute Monthly Axle Load Spectra ..................................................... 96

Example 3.1.3: Obtain Normalized Monthly Axle Load Spectra..................................... 99

Example 3.1.4: Obtain Daily Axle Load Spectra ........................................................... 101

Example 3.1.5: Obtain Normalized Annual Load Spectra ............................................. 102

Example 3.1.6: Estimated NALS for Sites with Limited or No Site-Specific Axle 

Load Spectra ................................................................................................................... 104

Parameter 3.2: Gross Vehicle Weight Distribution ......................................................... 105
v 

CHAPTER 10. SCENARIO 4: OBTAIN SUMMARY TRAFFIC LOADING 

INFORMATION (ESAL OR ALTERNATIVE STATISTICS) ........................................... 107

Parameter 4.1: Annual ESAL for Each Year in the Analysis Period ............................ 108

Example 4.1.1: Obtain Annual ESAL for Each Year in the Analysis Period ................. 109

Parameter 4.2: Cumulative ESAL for the Analysis Period ............................................ 111

Example 4.2.1: Obtain Cumulative ESAL for the Analysis Period ................................ 112

Parameter 4.3: Annual GESAL for Each Year in the Analysis Period ......................... 113

Example 4.3.1: Obtain Annual GESAL for Each Year in the Analysis Period .............. 114

Example 4.3.2: Obtain Cumulative GESAL for the Analysis Period ............................. 115

Parameter 4.4: GESAL per Truck, per Vehicle Class, and per Axle ............................ 116

Parameter 4.5: RPPIF per Truck, per Vehicle Class, and per Axle .............................. 117

Parameter 4.6: ATL for Each Year in the Analysis Period ............................................ 118

Example 4.6.1: Obtain ATL for Each Year in the Analysis Period ................................ 119

Parameter 4.7: Cumulative Total Load for the Analysis Period .................................... 120

CHAPTER 11. SCENARIO 5: OBTAIN MEPDG TRAFFIC INPUTS FOR USE IN 

AASHTOWARE PAVEMENT ME DESIGN SOFTWARE ................................................ 123

Parameter 5.1: MEPDG Base Year AADTT.................................................................... 125

Example 5.1.1: Obtain Base Year AADTT for Use in the AASHTOWare Pavement 

ME Software ................................................................................................................... 125

Parameter 5.2: MEPDG Number of Lanes in Design Direction .................................... 126

Parameter 5.3: MEPDG Percentage of Trucks in Design Direction (percent) ............. 127

Parameter 5.4: MEPDG Percent of Trucks in Design Lane (percent) .......................... 127

Parameter 5.5: MEPDG Vehicle Class Distribution ....................................................... 127

Example 5.5.1: Obtain Normalized VCD for the Base Year Specified for the 

Analysis........................................................................................................................... 127

Parameter 5.6: MEPDG Annual Vehicle Volume Growth Rate and Growth 

Function by Vehicle Class .................................................................................................. 128

Parameter 5.7: MEPDG Monthly Adjustment FactorS .................................................. 129

Parameter 5.8: MEPDG Hourly Distribution Factors .................................................... 132

Parameter 5.9: MEPDG Axle Load Distribution Factors ............................................... 136

Example 5.9.1: Enter ALDF Values in AASHTOWare Software From 

MEPDG_AXLE_LOAD_DIST_FACTOR Table .......................................................... 137

Example 5.9.2: Importing ALDF XML File to AASHTOWare Pavement ME Design 

Project ............................................................................................................................. 140

Parameter 5.10: MEPDG Number of Axles Per Truck................................................... 142

Parameter 5.11: MEPDG Axle Spacing for Tandem, Tridem, and Quad Axles .......... 144

Parameter 5.12: MEPDG Average Axle Width ............................................................... 145

Parameter 5.13: MEPDG Dual Tire Spacing ................................................................... 145

Parameter 5.14: MEPDG Tire Pressure ........................................................................... 145

Parameter 5.15: MEPDG Mean Wheel Location ............................................................ 145

Parameter 5.16: MEPDG Truck Wander Standard Deviation ...................................... 146

Parameter 5.17: MEPDG Operational Speed .................................................................. 146

Parameter 5.18: MEPDG Average Spacing of Short, Medium, and Long Wheelbase 

Axles and Corresponding Percentage of Trucks ............................................................. 146

REFERENCES .......................................................................................................................... 149
vi 

LIST OF FIGURES

Figure 1. Screenshot. InfoPave Table Export page....................................................................... 57

Figure 2. Screenshot. InfoPave Data screen with the DATA drop-down box displayed and 

“Table Export” option selected. .......................................................................................... 62

Figure 3. Screenshot. InfoPave Table Export menu with checkboxes selecting the 

“Section” label and the traffic table label “Monitored Traffic Axle Distribution 

(TRF_MONITOR_AXLE_DISTRIB).” ............................................................................. 63

Figure 4. Screenshot. InfoPave Section pop-up screen with site IDs displayed for the 

selected State (Arizona is selected). .................................................................................... 64

Figure 5. Screenshot. InfoPave screen displaying the top bars with the “Data Bucket” label 

displayed. ............................................................................................................................. 65

Figure 6. Screenshot. InfoPave Data Bucket screen with data export format options.................. 66

Figure 7. Screenshot. InfoPave File Download screen with a clickable hyperlink to 

download data. .................................................................................................................... 67

Figure 8. Screenshot. Excel screen capture showing the downloaded and saved data from 

the TRF_MONITOR_AXLE_DISTRIB table. ................................................................... 68

Figure 9. Screenshot. Example of the “State/Province Summary Reports” option selected. ....... 70

Figure 10. Screenshot. Example of AADT values available in the “Section Summary 

Report.” ............................................................................................................................... 71

Figure 11. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Annual Trends in Traffic Characteristics 

(TRF_TREND).” ................................................................................................................. 75

Figure 12. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Annual Trends in Traffic Characteristics 

(TRF_TREND)” and next to the field name 

“CMLTV_VOL_HEAVY_TRUCKS_TREND.” ............................................................... 78

Figure 13. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Annual Trends in Traffic Characteristics 

(TRF_TREND)” and next to the field name “AADTT_VEH_CLASS_#_TREND.” ........ 84

Figure 14. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Representative Site Traffic Parameters (TRF_REP).” ........ 88

Figure 15. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label, “Monthly Axle Counts (MM_AX).” .................................... 97

Figure 16. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label, “Mechanistic-Empirical Pavement Design Guide 

Traffic Axle Distribution (TRF_MEPDG_AX_DIST).” .................................................. 100

Figure 17. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label, “NALS Monthly Axle Distribution 

(NALS_MONTHLY_DISTRIB).” ................................................................................... 101

Figure 18. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Daily Axle Counts (DD_AX).” ......................................... 102

Figure 19. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Mechanistic-Empirical Pavement Design Guide 

Annual Traffic Axle Distribution (TRF_MEPDG_AX_DIST_ANL).” ........................... 103
vii 

Figure 20. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “NALS Annual Axle Distribution 

(NALS_ANNUAL_DISTRIB).” ....................................................................................... 104

Figure 21. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “AASHTOWare Pavement ME Axle Distributions 

(MEPDG_AXLE_LOAD_DIST_FACTOR).” ................................................................. 105

Figure 22. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Yearly Aggregate of GVW (YY_GVW).” ........................ 106

Figure 23. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Computed Traffic ESALs 

(TRF_ESAL_COMPUTED).” .......................................................................................... 109

Figure 24. Screenshot. InfoPave Table Export menu with checkboxes showing “selected” 

next to the traffic table label “Annual Trends in Traffic Characteristics 

(TRF_TREND)” and ANNUAL_ESAL_TREND field. .................................................. 110

Figure 25. Screenshot. InfoPave Table Export menu with checkboxes showing “selected” 

next to the traffic table label “Annual Trends in Traffic Characteristics 

(TRF_TREND)” and ANNUAL_GESAL_TREND field name. ...................................... 114

Figure 26. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Representative Site Traffic Parameters (TRF_REP).” ...... 117

Figure 27. Screenshot. InfoPave Table Export menu with checkboxes showing “selected” 

next to the traffic table label “Representative Site Traffic Parameters (TRF_REP)” 

and REP_RPPIF field names. ............................................................................................ 118

Figure 28. Screenshot. InfoPave Table Export menu with checkboxes showing “selected” 

next to the traffic table label “Annual Trends in Traffic Characteristics 

(TRF_TREND)” and ANNUAL_TOTAL_GVW_TREND field name. .......................... 119

Figure 29. Screenshot. AASHTOWare Pavement ME Design software Project 1: Traffic 

input screen. ...................................................................................................................... 123

Figure 30. Screenshot. AASHTOWare Pavement ME Design software showing AADTT 

input screen. ...................................................................................................................... 126

Figure 31. Screenshot. AASHTOWare Pavement ME Design software showing AADTT 

input screen with modified input values............................................................................ 126

Figure 32. Screenshot. AASHTOWare Pavement ME Design software Vehicle Class 

Distribution and Growth input screen. .............................................................................. 128

Figure 33. Screenshot. AASHTOWare Pavement ME Design software Monthly 

Adjustment input screen. ................................................................................................... 130

Figure 34. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Mechanistic-Empirical Pavement Design Guide Traffic 

Monthly Adjustment Factor (TRF_MEPDG_MONTH_ADJ_FACTR).” ........................ 131

Figure 35. Screenshot. AASHTOWare Pavement ME Design software Hourly Adjustment 

input screen with default values. ....................................................................................... 133

Figure 36. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Mechanistic-Empirical Pavement Design Guide Traffic 

Hourly Distribution (TRF_MEPDG_HOURLY_DIST).” ................................................ 134

Figure 37. Screenshot. AASHTOWare Pavement ME Design software Hourly Adjustment 

input screen with values entered for LTPP site 24-0501................................................... 136
viii

Figure 38. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “AASHTOWare Pavement ME Axle Distributions 

(MEPDG_AXLE_LOAD_DIST_FACTOR).” ................................................................. 137

Figure 39. Screenshot. AASHTOWare Pavement ME Design software Explorer menu with 

traffic options expanded. ................................................................................................... 139

Figure 40. Screenshot. AASHTOWare Pavement ME Design software screen showing the 

ALDF inputs. ..................................................................................................................... 140

Figure 41. Screenshot. AASHTOWare Pavement ME Design software screen showing 

import options for Axle Load Distributions. ..................................................................... 141

Figure 42. Screenshot. AASHTOWare Pavement ME Design software screen showing the 

Open XML file dialog screen. ........................................................................................... 142

Figure 43. Screenshot. AASHTOWare Pavement ME Design software screen showing 

successful file import message. ......................................................................................... 142

Figure 44. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “AASHTOWare Pavement ME Axles per Truck 

(MEPDG_AXLE_PER_TRUCK).” .................................................................................. 143

Figure 45. Screenshot. AASHTOWare Pavement ME Design software showing table with 

APT values. ....................................................................................................................... 144

Figure 46. Screenshot. AASHTOWare Pavement ME Design software showing Axle 

Configuration input screen. ............................................................................................... 145

Figure 47. Screenshot. AASHTOWare Pavement ME Design software showing Lateral 

Wander input screen. ......................................................................................................... 146

Figure 48. Screenshot. AASHTOWare Pavement ME Design software showing AADTT 

input screen. ...................................................................................................................... 146

Figure 49. Screenshot. AASHTOWare Pavement ME Design software Wheelbase input 

screen. ................................................................................................................................ 148
ix

LIST OF TABLES

Table 1. LTPP WIM data accuracy criteria for research-quality data. ......................................... 20

Table 2. Reference sources describing methods used to develop different traffic 

parameters. .......................................................................................................................... 23

Table 3. Fields describing sources and usability of traffic parameters included in 

analysis-ready tables. .......................................................................................................... 32

Table 4. Codes for AADTT_SOURCE fields............................................................................... 32

Table 5. Codes for VEH_CLASS_SOURCE fields. .................................................................... 33

Table 6. Codes for the ESAL_SOURCE field. ............................................................................. 33

Table 7. Codes for the GESAL_SOURCE field. .......................................................................... 33

Table 8. Codes for the GVW_SOURCE field. ............................................................................. 34

Table 9. Codes for the REP_AADTT_USE_RATING field. ....................................................... 34

Table 10. Codes for REP_VEH_CLASS_USE_RATING. .......................................................... 34

Table 11. Codes for ALDF_USE_RATING fields. ...................................................................... 35

Table 12. Codes for REP_LOAD_USE_RATING fields. ............................................................ 36

Table 13. Codes for the APT_USE_RATING field. .................................................................... 37

Table 14. Codes for the VEH_CLASS_GROWTH_USE_RATING field. .................................. 38

Table 15. Traffic parameters for mechanistic pavement response modeling. .............................. 41

Table 16. Traffic parameters for generic mechanistic–empirical pavement performance 

modeling. ............................................................................................................................. 43

Table 17. Traffic summary parameters. ........................................................................................ 48

Table 18. Traffic input parameters required by AASHTOWare Pavement ME Design 

software. .............................................................................................................................. 51

Table 19. AADT values for Arizona site 1024, GPS-1 experiment. ............................................ 73

Table 20. AADTT values for Arizona test site 7613, construction event 1. ................................. 76

Table 21. Annual total truck volume values for Arizona test site 7613, construction 

event 1. ................................................................................................................................ 79

Table 22. Cumulative heavy truck traffic volume for Arizona test site 7613, construction 

event 1. ................................................................................................................................ 81

Table 23. Annual cumulative total truck volume values for Arizona test site 7613, 

construction event 1. ........................................................................................................... 82

Table 24. TRF_TREND truck volumes for Arizona test site 7613, construction event 1. ........... 85

Table 25. TRF_TREND truck volumes for Arizona test site 7613, construction event 2. ........... 86

Table 26. Normalized VCD from the TRF_REP table for Arizona site 7613. ............................. 89

Table 27. Annual average daily truck volumes by classification from the TRF_TREND 

table. .................................................................................................................................... 91

Table 28. Illustrative monthly truck volume adjustment factors from the 

TRF_MEPDG_MONTH_ADJ_FACTR table. ................................................................... 92

Table 29. Total single-axle counts for class 9 vehicles for each DOW in June 1998 for 

Arizona site 7613 extracted from the MM_AX table.......................................................... 97

Table 30. Average single-axle counts for class 9 for each DOW in June 1998 for Arizona 

site 7613 extracted from the MM_AX table. ...................................................................... 98

Table 31. Average daily single-axle counts for class 9 for June 1998 for Arizona site 7613. ..... 98

Table 32. Average monthly single-axle counts for class 9 for June 1998 for Arizona site 

7613. .................................................................................................................................... 99
x 

Table 33. Annual ESAL trend table for Arizona site 7613 extracted from the TRF_TREND 

table. .................................................................................................................................. 111

Table 34. Cumulative ESAL for construction event 1 for Arizona site 7613. ............................ 112

Table 35. Annual GESAL trend for Arizona site 7613 from the TRF_TREND table. .............. 115

Table 36. Cumulative GESAL for construction event 1 for Arizona site 7613. ......................... 116

Table 37. ATL trend table for Arizona site 7613 extracted from the TRF_TREND table. ........ 120

Table 38. Cumulative total load computed for the years during construction event 1 for 

Arizona site 7613. ............................................................................................................. 121

Table 39. AADTT for the base design year from 

MEPDG_TRUCK_VOL_PARAMETERS for Arizona site 7613. ................................... 126

Table 40. Normalized VCD for the base design year from the 

MEPDG_TRUCK_VOL_PARAMETERS table for Arizona site 7613. .......................... 128

Table 41. Annual vehicle volume growth rate and growth function by vehicle class from 

the MEPDG_TRUCK_VOL_PARAMETERS table for Arizona site 7613. .................... 129

Table 42. MAFs for class 9 vehicles for 2016 for LTPP site 24-0501. ...................................... 132

Table 43. HDF for 2016 for LTPP site 24-0501. ........................................................................ 135

Table 44. ALDF sample for LTPP site 04-6713. ........................................................................ 138

Table 45. APT values from MEPDG_AXLE_PER_TRUCK table for LTPP site 01-4073. ...... 144

Table 46. Average axle spacing in inches for multi-axle groups. ............................................... 144

Table 47. Distribution of axle spacing on power units (tractor) for FHWA vehicle classes 

8–13. .................................................................................................................................. 147

Table 48. Distribution of axle spacing by vehicle class using sample of SPS TPF-5(004) 

WIM data (excluding power-unit wheelbase spacing for FHWA vehicle classes 8–

13). ..................................................................................................................................... 148
xi 

LIST OF ACRONYMS AND ABBREVIATIONS

AADT annual average daily traffic

AADTT annual average daily truck traffic 

AASHO American Association of State Highway Officials

AASHTO American Association of State Highway and Transportation Officials

AC asphalt concrete

AIMS Ancillary Information Management System 

ALDF axle load distribution factors 

APT axles per truck

ATL annual total truck load

ATR automatic traffic recorder

AVC automatic vehicle classifier

CTL cumulative total truck load 

CTV cumulative truck volume 

DOW day of the week 

ESAL equivalent single-axle load

FHWA Federal Highway Administration

GESAL general equivalent single-axle load 

GPS General Pavement Study 

GVW gross vehicle weight 

HDF hourly distribution factors 

ID identification

IMS Information Management System

IMS Guide Information Management System User Guide

IVR individual vehicle record 

JPCP jointed plain concrete pavement

LEF load-equivalency factor

LTAS Long-Term Pavement Performance Traffic Analysis Software

LTPP Long-Term Pavement Performance

MAF monthly adjustment factor 

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

QC quality control 

RPPIF Relative Pavement Performance Impact Factor

SDR Standard Data Release

SN structural number 

SPS Specific Pavement Study 

SQL Structured Query Language 

TMG Traffic Monitoring Guide

TPF Transportation Pooled-Fund Study 

VCD vehicle class distribution

WIM weigh-in-motion

1 

PART 1. LTPP TRAFFIC DATA AND PARAMETERS: DATA SOURCES, METHODS, 

TOOLS, AND RECOMMENDATIONS FOR USE

This Guide consists of two parts. Part 1 helps Long-Term Pavement Performance (LTPP)

(1) users 

make informed decisions about selecting traffic parameters suitable for their pavement analysis 

goal(s) and includes the following: 

• Overview (chapter 1). 

• Description of the traffic data and parameters available through LTPP program sources 

(chapter 2). 

• Description of methods used by the LTPP program to collect traffic data and compute 

traffic parameters (chapter 3). 

• Recommendations on what traffic parameters are most applicable for different types of 

pavement analyses (chapter 4). 

• Information on where to get the LTPP traffic data (chapter 5). 

In summary, part 1 of this Guide provides detailed descriptions of the traffic parameters 

available from LTPP sources and their applicability for different types of pavement analyses.

(2,3)

3 

CHAPTER 1. INTRODUCTION

BACKGROUND

Traffic data support LTPP program(1) experiments and data analysis studies by providing a 

measure of traffic loads applied to individual LTPP pavement test sections being studied. Most 

LTPP test sites are located on the existing highways that have accumulated traffic loads prior to 

the installation of traffic monitoring equipment. The exceptions are selected Specific Pavement 

Study (SPS)(1) test sites for new flexible and rigid pavements that had traffic monitoring 

equipment installed from the beginning of the pavement’s service life. Over the years, a large 

quantity of traffic data have been collected at LTPP test sites. Most data were collected by U.S. 

State and Canadian Provincial transportation agencies and submitted to the LTPP program. 

These data were collected using a variety of equipment and data collection methods. As a result,

both data quantity and quality vary among the LTPP test sites. Over the years, based on the 

collected data, many traffic parameters have been developed and stored in different LTPP 

database tables(2,3,4) The format, location, and contents of these tables have been modified over 

time to make the traffic data easier to access. Currently, these tables are accessible through the 

InfoPaveTM web portal.(3) However, users not familiar with the history and evolution of the LTPP 

traffic data collection effort often face challenges identifying relevant data tables and reliable 

data suitable for their analyses. This document will remedy those challenges. 

PURPOSE

The purpose of this Guide to Select Long-Term Pavement Performance Traffic Data for Multiple 

Uses (herein referred to as the “Guide”) is to help LTPP users identify traffic parameters for their 

analyses and identify LTPP database tables containing such parameters. The Guide contains 

information about methods used to collect traffic data and compute traffic parameters. The Guide 

also describes how to extract these parameters using the InfoPave web portal and provides 

practical examples with step-by-step instructions for different types of LTPP and other pavement 

analyses.(3)

 In summary, the Guide is designed to help LTPP users maximize use of LTPP traffic 

data for achieving their pavement analysis objectives. 

GUIDE OVERVIEW

This Guide consists of two parts. Part 1 describes traffic parameters available through LTPP 

program sources and provides recommendations about what traffic parameters are most 

applicable for different types of pavement analyses. It also provides details about methods used 

to collect traffic data and compute the traffic parameters. Part 2 contains practical examples of 

how to identify and obtain different traffic data and parameters using the InfoPave web portal.

(3)

For parameters not available through the web portal, examples of computational procedures are

provided showing how available LTPP traffic data could be used to compute the 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.
4 

Part 1 of the Guide includes the following chapters: 

• Chapter 1 describes the purpose, scope, and organization of the Guide. 

• Chapter 2 describes the traffic data and computed parameters with references to the LTPP 

database tables containing these parameters.

• Chapter 3 describes the sources of LTPP traffic data and methods used to collect the data, 

as well as provides references to the methods used to compute the traffic parameters. It 

describes indices and codes available in LTPP traffic tables that could 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 provides recommendations about types of traffic data and parameters most 

suitable for common pavement analyses, including references to traffic parameter names 

and LTPP traffic tables containing these parameters.

• Chapter 5 describes the methods for obtaining LTPP traffic data and parameters using the 

InfoPave web portal or by contacting the LTPP Customer Support Service Center. 

Part 2 of the Guide includes the following traffic data selection scenarios: 

• Scenario 1 shows how to obtain traffic volume information. 

• Scenario 2 shows how to obtain vehicle classification information. 

• Scenario 3 shows how to obtain detailed axle or truck loading information. 

• Scenario 4 shows how to obtain summary traffic loading information. 

• Scenario 5 shows how to obtain traffic inputs for use in the AASHTOWare® Pavement

ME DesignTM software.

(5)

In summary, part 1 helps LTPP users make informed decisions about selecting traffic parameters 

suitable for their pavement analysis goal(s), and part 2 provides detailed instructions on how to 

obtain these parameters from LTPP sources.(3)
5 

CHAPTER 2. LTPP TRAFFIC DATA AND SOURCES

The LTPP Information Management System [IMS] User Guide (IMS Guide) is a comprehensive 

resource that describes various databases maintained by LTPP program.(2) Chapter 12 and 

chapter 16 of the IMS Guide contain descriptions of the traffic tables included in the LTPP 

program’s annual Standard Data Release (SDR).

(6)

 The SDR contains data and information for

researchers, highway agency personnel, and others interested in pavement performance-related 

research and is downloadable through the LTPP InfoPave web portal.

(3)

 This chapter provides 

information from the IMS Guide pertinent to using LTPP traffic data and parameters. 

LTPP TRAFFIC DATABASES

The LTPP traffic tables are updated annually and available for download from the InfoPave web

portal.(3) The LTPP traffic data tables come from the two LTPP data sources described in the 

following subsections. 

Traffic Databases Included in the SDR

LTPP traffic data and parameters used for most pavement analyses are included in the Traffic 

module of the Pavement Performance section of the SDR.

(6)

 The contents of the Traffic module 

are described in chapter 12 of the IMS Guide.(2) The Traffic module contains several tables with 

annual estimates of traffic volume, vehicle classification, and axle loading in the LTPP lane. In 

addition to traffic parameters commonly used in transportation engineering and planning, traffic 

input parameters formatted for use with the Mechanistic-Empirical Design Guide (MEPDG) 

method are included in the Traffic module, as well as traditional equivalent single-axle load 

(ESAL) parameter used in pavement design prior to the development of the MEPDG

method.(2,7,8)

 These tables support pavement analyses based on empirical methods, as well as 

mechanistic–empirical analyses.

The Pavement Performance section of the SDR(6) contains the following traffic databases, which 

are available in Microsoft® Access® format: 

• Traffic: This database contains all SDR traffic tables, excluding the 

TRF_MEPDG_AX_DIST table, which stores MEPDG axle load distributions. 

• TRF_MEPDG_AX_DIST_*: This series of databases, organized by State code (each 

database name has the State’s two-letter abbreviation instead of “*”). Each database 

contains the TRF_MEPDG_AX_DIST table detailing axle load distributions binned in 

MEPDG-compatible format. The data in this table are stored by LTPP section 

identification (ID) (including STATE_CODE and SHRP_ID), year, vehicle class, and 

axle group for years with at least 210 days of accepted traffic loading data for the LTPP 

lane. 

Traffic Databases Associated with LTPP Traffic Analysis Software 

Another source of LTPP traffic data includes databases associated with the LTPP Traffic 

Analysis Software (LTAS) database.

(2) LTAS data are mostly used by traffic researchers 
6 

interested in the analysis of traffic characteristics and their changes over time on a more granular, 

disaggregated level. Chapter 16 of the IMS Guide contains detailed descriptions of the LTAS 

database’s structure and tables.(2) The LTAS databases contain daily and monthly traffic data 

used for computing annual traffic estimates stored in the pavement performance database. LTAS 

data also contain information on the locations of traffic monitoring equipment, statistical 

summaries used in the quality review of traffic data, information about identified data errors, and 

other information used in the traffic data review and analysis. In addition, traffic data from 

adjacent or non-LTPP lanes are stored in LTAS databases. States may provide traffic data from

additional lanes adjacent to the LTPP test section or lane. These data are stored but not 

processed.

Starting in 2017, LTAS data also include normalized axle load spectra (NALS) at monthly and 

annual levels and the Relative Pavement Performance Impact Factor (RPPIF). Due to size 

limitations, LTAS tables may be in either Microsoft Access or Microsoft Structured Query 

Language (SQL) Server database format. 

LTAS data in Microsoft Access format include the following traffic databases (two-letter State 

name abbreviation is used instead of “*” in database names).

(2)

• Annual_Traffic_*: Series of databases, organized by State code containing summarized

count data and annual traffic estimates for the LTPP test sections. 

• Daily_Count_ERR_*: Series of databases, organized by State code containing daily 

vehicle classification, weight, and volume counts along with the associated error counts 

for class and weight data for the LTPP test sections. Traffic data purges and the record 

status of the traffic data are also included. 

• Hourly_Class_Counts: Contains volume of trucks by hour table for SPS sites with a 

weigh-in-motion (WIM) system that has been field validated by the LTPP program. 

• LTAS_Administration: Contains LTAS-specific database metadata and other control 

tables used by the LTPP program. 

• Monthly_Axle_*: Series of databases, organized by State code, containing monthly axle 

load distributions and supporting information for the LTPP test sections. The database 

may contain LTPP sections for a part of a State, a single State, or multiple States, 

depending on the size limitations of Microsoft Access. 

• Monthly_Count: Contains monthly vehicle count data by month for the LTPP test 

sections. 

• Monthly_GVW_*: Series of databases, organized by State code, containing monthly 

gross vehicle weight (GVW) data for the LTPP test sections by year, month, lane, 

direction, vehicle classification, and day of the week (DOW). Number of days of data in 

the month for that DOW is also included. 
7 

LTAS data available in SQL Server format include the following databases:

(2)

• Daily_Axles_*: Series of databases, organized by State code containing daily data on the 

number of axles by axle load bin, vehicle class, and axle group for the LTPP test sections. 

• Daily_GVW_*: Series of databases, organized by State code, containing daily data on 

GVWs aggregated by weight bin and vehicle class for the LTPP test sections. 

LTPP TRAFFIC DATA TABLES AND PARAMETERS

A detailed description of the structures and contents of LTPP traffic data tables can be found 

using LTPP Table Navigator, accessible through the InfoPave web portal (select LTPP Table 

Navigator option on the Data menu, then select the LTPP traffic table name of interest under the 

Traffic subsection and click on the “Export to Excel” button).(3) The following sections 

summarize traffic tables available in the pavement performance database. Discussion of specific 

LTPP traffic parameters recommended for different LTPP analyses, with references to 

appropriate LTPP traffic data tables, is included in chapter 4. Traffic data and parameters are 

reported in LTPP traffic data tables for individual LTPP General Pavement Study (GPS) or SPS 

test sections. 

Traffic Tables Associated with the Pavement Performance Database 

Table Naming Convention 

Names of tables(3) were developed to help users recognize the source of data or identify datasets 

developed for a specific application, such as the MEPDG.

(7) The “*” character used in the 

following table names means that more than one table name has the prefix shown before the “*” 

character.

TRF_HIST* Tables 

Tables(3) starting with the prefix TRF_HIST include traffic information provided to the LTPP 

program by State and Provincial highway agencies for the years prior to the installation of traffic

monitoring equipment and for the years when traffic monitoring equipment was not collecting 

data. Most GPS test sections are existing highways that have accumulated traffic loads prior to 

the installation of traffic monitoring equipment. For pavement research, knowledge of these 

historical loadings is important. Whenever available, the methodologies that State highway 

agencies use to derive historical estimates have been documented by the agencies and provided 

to LTPP (as metadata). In some cases, little data were available, and histories are “best guess” 

estimates. Historical data include annual estimates of total and truck volumes and the total 

ESALs. 

TRF_MON* or TRF_MONITOR* Tables 

Tables(3) starting with prefixes TRF_MON or TRF_MONITOR include traffic information based 

on traffic monitoring data collected at LTPP sites. This traffic information consists of two types. 

The first type includes simple statistical summaries of actual measurements from automatic 

traffic recorders (ATRs), automatic vehicle classifiers (AVCs), and WIM equipment. The second 
8 

type is computed values or annualized estimates based on the traffic monitoring data, including 

annual estimates of total and truck volumes, vehicle class distributions (VCDs), axle loading 

distributions, and total ESALs calculated using American Association of State Highway and 

Transportation Officials (AASHTO) methodology. 

TRF_MEPDG* Tables 

Tables(3) starting with the prefix TRF_MEPDG include traffic parameters that are computed to 

be used with the MEPDG method. TRF_MEPDG parameters are computed for the LTPP lane 

only for years with at least 210 days of accepted traffic loading data. The data included in 

TRF_MEPDG* tables require additional manipulation to develop representative values for use in 

the AASHTOWare Pavement ME Design software.

(5) TRF_MEPDG traffic parameters include 

annual average daily truck traffic (AADTT), vehicle class and axle loading distributions, hourly 

truck volume adjustment factors, monthly truck volume adjustment factors, and the number of 

axles per vehicle class.

MEPDG* Tables 

Tables(3) starting with the prefix MEPDG include traffic parameters that are computed for use in 

AASHTOWare Pavement ME Design.

(5) MEPDG traffic parameters include base year (the first 

year the LTPP site opened to traffic) AADTT, representative VCD, truck traffic growth 

parameters by vehicle class, representative axle load distribution factors (ALDF), and the 

representative number of axles per vehicle class.

TRF_ESAL* Tables 

Tables(3) starting with the prefix TRF_ESAL include annual ESAL estimates computed by the 

LTPP program using the methodology in the 1993 edition of the AASHTO Guide for Design of 

Pavement Structures.

(8) The estimates are based on section-specific pavement structures. These 

estimates are available for each year that has accepted monitored axle-loading information for a 

section.

NALS* and RPPIF* Tables

Tables(3) starting with prefixes NALS or RPPIF(4) resulted from LTPP research that produced the 

Long-Term Pavement Performance Pavement Loading Users Guide (PLUG)(9) and LTPP traffic 

loading defaults(10) for AASHTOWare Pavement ME Design software. NALS tables contain 

normalized axle-load spectra by vehicle class and axle group. RPPIF tables contain traffic

loading summary statistics used to group LTPP sites and vehicle classes with similar loading per 

axle. 

Traffic Volume Table 

The TRF_HIST_VOLUME_COUNT table(3) contains results of vehicle volume counts that were 

taken by State and Provincial highway agencies prior to the start of LTPP program traffic 

monitoring and were used to estimate traffic volumes at a given site. These counts were not 

necessarily taken at the sites. 
9 

Vehicle Classification Tables 

The vehicle classification data can be found in the following tables accessible through 

InfoPave:(3)

• TRF_HIST_CLASS_DATA: This table contains results of vehicle classification counts 

that were taken by the State and Provincial highway agencies prior to the start of LTPP 

program traffic monitoring and were used to estimate vehicle distributions at a given site. 

These counts were not necessarily taken at the sites. 

• TRF_MONITOR_LTPP_LN: This table contains information on the amount of traffic

monitoring data collection, which occurred at each site for each year for each vehicle 

class. This table also includes the estimated annual volume of trucks by class and the 

estimated number of axles associated with those truck volumes. Values are reported only 

for the LTPP lane. 

Traffic Loading Summary Tables 

The traffic loading data can be found in the following tables accessible through InfoPave:(3)

• TRF_HIST_EST_ESAL: This table contains estimates of ESALs at the LTPP section

level for each year from the date of that test section’s construction (or 1965, whichever is 

later) until its inclusion in the LTPP program (or 1989, whichever is earlier). 

• TRF_MON_EST_ESAL: This table contains annual estimates of the number of ESALs 

in the LTPP lane and estimates of truck and total vehicle volumes supplied by 

participating highway agencies. Data in this table are from 1990 (or when the LTPP lane 

became open to traffic, whichever is later) until the test section was instrumented with 

traffic monitoring equipment or for any year in which the traffic monitoring equipment 

was not operational. 

• TRF_ESAL_COMPUTED: This table contains estimates of annual ESAL calculations 

for LTPP lanes. These ESAL estimates are provided only for sites that have acceptable 

samples of axle load measurements contained in the LTPP database in the indicated year. 

The axle load sample is expanded to an annual estimate using a time-based multiplier. 

Estimates are contained in the KESAL_YEAR field with units of kESAL/year or 

1,000 ESAL/year. Thus, a value of 1 in this field should be interpreted as 

1,000 ESAL/year in the LTPP lane.

• TRF_MONITOR_AXLE_DISTRIB: This table contains the annualized number of axles 

in each weight range (i.e., weight bin) for each axle group (i.e., single, tandem, triple, 

quad, and quad+ (more than four axles)). This information is obtained using the data 

collected by WIM equipment installed at or near LTPP test sections. Note that steeringaxle weight distributions are not recorded separately from other single axles in this table. 

The WEIGHT_BIN_SIZE field contains the size of the weight bins used to describe the 

weight distribution by axle type. This distribution is for the LTPP lane only. 
10 

Tables Used for ESAL Computation

The data used for ESAL computation can be found in the following tables accessible through 

InfoPave:(3)

• TRF_ESAL_INPUTS_SUMMARY: This table contains a summary of all of the input 

data used for the annual ESAL estimate, including pavement type, structural number 

(SN) for asphalt concrete (AC) pavements, effective thickness for portland cement 

concrete (PCC) pavements, terminal service index value, functional road classification, 

climate characterizations such as annual average precipitation and freeze index, LTPP 

experimental climate region, and start and end dates associated with the construction 

number to which these values apply. 

• TRF_ESAL_AC_THICK: This table contains values used to compute SNs for 

AC-surfaced test sections. It includes the thickness, type of layer, layer coefficient, 

average resilient modulus, and drainage layer coefficient for base and subbase layers. 

This table also includes start and end dates to which these values apply. 

• TRF_ESAL_PCC_COMP_THICK: This table contains values used to compute values of 

the effective thickness of PCC layers used in the ESAL calculation. This table includes 

information on the thickness of multiple PCC layers and whether they are bonded. 

MEPDG Traffic Inputs Tables 

The MEPDG traffic input parameters can be found in the following tables accessible through 

InfoPave:(3)

• TRF_MEPDG_AADTT_LTPP_LN: This table contains estimates of AADTT in LTPP 

test lane computed by three alternate computation methods based on a combination of 

classification and weight data, only classification, or only weight data. Estimates are 

provided by year. 

• TRF_MEPDG_VEH_CLASS_DIST: This table contains percentages of trucks by vehicle 

class within truck population (See Federal Highway Administration (FHWA) Traffic 

Monitoring Guide for definition of FHWA vehicle classes 4–13) in LTPP lane.(11) Each 

estimate is based on classification counts, weight data, or a combination of both 

classification and weight data as indicated by the code contained in the 

TRF_DATA_TYPE field. Estimates are provided by year. 

• TRF_MEPDG_MONTH_ADJ_FACTR: This table contains adjustment factors for 

average daily truck traffic by month for each truck class based on either vehicle 

classification or weight data, as indicated by the code contained in the 

TRF_DATA_TYPE field. Estimates are provided by year. 

• TRF_MEPDG_HOURLY_DIST: This table contains data describing the annual average 

hourly distribution of trucks by each hour of the day in LTPP lane, based on available 
11 

classification data. Computations are performed using the MEPDG algorithm.(7) This 

table contains data for sites with at least 210 days of classification data in a calendar year.

• TRF_MEPDG_AX_DIST_ANL: This table contains the annual normalized axle load 

distributions by class and axle group for LTPP sites that satisfy data availability and data 

review criteria. Records in this table are generated from the LTPP 

TRF_MONITOR_AXLE_DISTRIB table where matching records in the 

TRF_MONITOR_LTPP_LN table have a RECORD_STATUS equal to D or E. Per site, 

at least 2 years with more than 210 days of WIM data must exist in the 

TRF_MONITOR_LTPP_LN table for data to be present in this table. 

• TRF_MEPDG_AX_DIST_ANL_VAR: This table contains means and variances of the 

elements of the normalized axle load distributions by vehicle class and axle type for all 

years of available site-specific monitoring data. Per site, at least 2 years with more than 

210 days of WIM data must exist for data to be present in this table. 

• TRF_MEPDG_AX_DIST: This table contains normalized axle load distributions by 

month, truck class, and axle group for each LTPP site and year with more than 210 days 

of WIM data in the TRF_MONITOR_LTPP_LN table. This table is provided in multiple 

databases due to its large size, organized by State or Provincial code.

• TRF_MEPDG_AX_PER_TRUCK: This table contains the annual average number of 

axles by vehicle class and axle type by year for LTPP sites. These data are computed 

from axles weighed as summed in the TRF_MONITOR_LTPP_LN table. Estimates are 

provided by year. 

AASHTOWare Pavement ME Design Input Tables

The AASHTOWare Pavement ME design input parameters can be found in the following tables 

accessible through InfoPave:(3)

• MEPDG_TRUCK_VOL_PARAMETERS: This table contains input parameters used in 

AASHTOWare Pavement ME Design software(5) to estimate traffic volumes of FHWA 

vehicle classes 4–13(11) for pavement analyses or design periods, including the following 

for each LTPP site and experiment: LTPP lane AADTT for the first year the site was 

opened to traffic, percentile VCD factors, and truck volume growth information (fitting 

either a linear or compound growth function). The table is designed to resemble the 

AASHTOWare Pavement ME Design software input table to facilitate data entry using a 

copy–paste operation. 

• MEPDG_AXLE_LOAD_DIST_FACTOR: This table contains ALDF for use with 

AASHTOWare Pavement ME Design. One set of values is provided for each LTPP site, 

including the source of the data or method used to develop the ALDF. This table is 

designed to resemble the AASHTOWare Pavement ME Design input table to facilitate 

data entry using a copy–paste operation. 
12 

• MEPDG_AXLE_PER_TRUCK: This table contains the representative number of axles 

per truck (APT) for vehicles in FHWA vehicle classes 4–13 for use with AASHTOWare 

Pavement ME Design software. It includes the source of the data or method used to 

develop the APT. This table is designed to resemble the AASHTOWare Pavement ME 

Design input table to facilitate data entry using a copy–paste operation. 

Analysis-Ready Traffic Summary Parameters

The analysis-ready traffic summary parameters can be found in the following tables accessible 

through InfoPave:(3)

• TRF_TREND: This table contains annual truck volume estimates for LTPP lanes by 

vehicle classification and year for each test site for each in-service and in-experiment 

year. The table also includes estimates of AADTT (computed for FHWA vehicle 

classes 4–13(11)

 combined), cumulative truck volumes (CTVs), cumulative FHWA

vehicle class 9 truck volumes, ESAL, general equivalent single-axle load (GESAL), and 

total annual load estimates for each year from the time each test section opened for 

traffic. These values are calculated based on data consolidated from the multiple traffic 

data sources described in this chapter; missing values are estimated. The source of the 

data or estimation method for each year is also provided in this table. 

• TRF_REP: This table contains representative AADTT values for LTPP sites, as well as 

the percentage of AADTT value corresponding to each FHWA vehicle class 4–13. The 

table also includes representative loading parameters for each truck class. One set of 

values is provided per LTPP site and experiment combination. Metadata describing the 

confidence associated with the traffic parameters included in TRF_REP table are also 

included. 

Traffic Metadata Tables 

The traffic metadata data can be found in the following tables accessible through InfoPave:(3)

• TRF_BASIC_INFO: This table contains basic information on the location of the LTPP 

test sites.

• TRF_CALIBRATION_AVC: This table contains information on the calibration activities

associated with AVC equipment used to collect vehicle classification data at LTPP test 

sites. 

• TRF_CALIBRATION_WIM: This table contains information on the calibration activities

associated with equipment used to collect WIM data at LTPP test sites. 

• TRF_EQUIPMENT_MASTER: This table contains information on both AVC and WIM 

equipment during calibration events. 

• TRF_HIST_WEIGHT_MASTER: This table contains all available general information 

on the roadways and equipment used for historical truck weighing sessions. 
13 

• TRF_HIST_CLASS_MASTER: This table contains information on classification counts 

that furnished data for the TRF_HIST_CLASS_DATA table. This table also contains the 

total volumes recorded during each count. 

Traffic Tables From LTAS Databases 

LTAS database tables that containing detailed traffic count data at various levels of data 

aggregation are detailed in the following sections. The following LTAS tables are accessible 

through InfoPave web portal.(3)

Vehicle Classification and Traffic Volume Count Data Tables 

The following LTAS vehicle classification and traffic volume count data tables can be found on 

InfoPave:(3)

• YY_CT: This table contains count data by site, year, lane, direction, DOW, and data 

source for each year for which classification and/or weight data were accepted for 

estimating volumes. The number of days of data in the year for a specific DOW is also 

included. This table is created by summing, for each data type, the number of days and 

counts for each DOW in a month using data from the MM_CT table. Included in the table 

is the number of vehicles observed during the count, but these data could not be 

classified.

• MM_CT: This table contains count data by site, year, month, lane, direction, DOW, and 

data source for each month for which classification and/or weight data were accepted for 

estimating volumes. This table is created by summing, for each data type, the number of 

days and counts for each DOW in a month using data from the relevant DD_CT table. 

The vehicle classes are converted from a State or Provincial agency-specified method 

into the FHWA vehicle classes. (See FHWA TMG for description of 13 FHWA vehicle 

classes.

(11)

) This table also includes the number of unclassified vehicles.

• DD_CL_CT: This table summarizes the number of vehicles by class for each day based 

on classification records. This table contains count data by site, year, month, day, lane, 

and direction for each day for which classification data were accepted for estimating 

volumes. This table is created by summing, for each day, the counts over all hours in a 

defined day. 

• DD_VOL: This table summarizes the number of vehicles by site, year, month, day, lane 

and direction. 

• DD_WT_CT: This table summarizes the number of vehicles by class based on weight 

records. This table contains count data by site, year, month, day, lane, and direction for 

each day for which weight data exist for estimating loads. 

• HH_CL_CT: This table stores the hourly volumes by vehicle class in the input method 

for classification data for selected sites. These data are used to generate inputs to the 

TRF_MEPDG_HOURLY_DIST table.
14 

Traffic Loading Data Tables 

The following LTAS traffic loading data tables can be found on InfoPave:(3)

• YY_AX: This table contains the number of axle counts by LTPP site, year, lane, 

direction, vehicle class, DOW, and load bin. The number of days of data in the year for a 

specific DOW is also included. This table is created by summing the number of days and 

the number of axles in each load bin by axle group for each DOW and each year using 

data from the MM_AX table. Only vehicles in FHWA vehicle classes 4–13(11) are 

included. 

• MM_AX: This table contains the number of axle counts by LTPP site, year, month, lane, 

direction, vehicle classification, axle group, DOW, and load bin. This table is created by 

summing the number of days and the number of axles in each load bin by axle group for 

each DOW, each month, and each year using data from the DD_AX table. Only vehicles 

in FHWA vehicle classes 4–13 are included. Data provided to LTPP in a State or 

Provincial agency-specific vehicle classification method are converted into 13 FHWA 

vehicle classes. 

• DD_AX: This table contains the number of axle counts by LTPP site, year, month, day, 

lane, direction, vehicle class, axle group, and load bin. Each record in this table is created 

by summing the number of axle counts for all hours in a calendar day for each load bin, 

axle group, and vehicle class. All vehicle classes, including passenger vehicles, may be in 

this table. Data are presented in vehicle classes that follow a State or Provincial 

agency-specified vehicle classification method. 

• YY_GVW: This table contains GVW data by LTPP site, year, lane, direction, vehicle 

classification, DOW, and weight bin. The number of days of data in the year for a 

specific DOW is also included. This table is created by summing the number of days and 

GVW data by weight bin for the DOWs for a year using data from the MM_GVW table. 

Only vehicles in FHWA vehicle classes 4–13 are included. 

• MM_GVW: This table contains GVW data by LTPP site, year, month, lane, direction, 

vehicle classification, DOW, and weight bin. This table is created by summing the 

number of days and GVW data by weight bin for the DOWs for each month and each 

year using data from the DD_GVW table. Only vehicles in FHWA vehicle classes 4–13 

are included. Data provided to LTPP in a State or Provincial agency-specific vehicle 

classification method are converted into the 13 FHWA vehicle classes. 

• DD_GVW: This table contains GVW data by LTPP site, year, month, day, lane, 

direction, vehicle class, and weight bin. This table is created by summing GVW data by 

weight bin over all hours in a calendar day for each vehicle class. All vehicle classes, 

including passenger vehicles, may be in this table. Data are presented in vehicle classes 

that follow a State or Provincial agency-specified vehicle classification method.
15 

Axle and Vehicle Loading Characterization Tables

The following LTAS axle and vehicle loading characterization tables can be found on 

InfoPave:(3)

• NALS_ANNUAL_DISTRIB: This table contains NALS data by LTPP site, year, lane, 

direction, vehicle class, and axle group. This table is created by averaging values from

the NALS_MONTHLY_DISTRIB table. This table is limited to FHWA vehicle classes 

4–13.(11)

• NALS_ANNUAL_EVAL: This table contains record status values by LTPP site, year, 

lane, direction, vehicle class, and axle group for each associated NALS in the 

NALS_ANNUAL_DISTRIB table. Record status values are based on the size of the 

NALS distribution tails. 

• NALS_MONTHLY_DISTRIB: This table contains percentile NALS values by LTPP 

site, year, month, lane, direction, vehicle class, axle group, and load bin. This table is 

created by normalizing values from the MM_AX table. This table is limited to FHWA 

vehicle classes 4–13. 

• NALS_MONTHLY_EVAL: This table contains record status values by LTPP site, year, 

month, lane, direction, vehicle class, and axle group for each associated NALS in the 

NALS_ANNUAL_DISTRIB table. Record status values are based on the size of the 

NALS distribution tails. 

• RPPIF_NALS_ANNUAL: This table contains the RPPIF by LTPP site, year, lane, 

direction, vehicle class, and axle group. This table is created by applying values from the 

RPPIF_WIJ_FACTOR table to the annual NALS distribution. 

• RPPIF_NALS_MONTHLY: This table contains the RPPIF by LTPP site, year, month, 

lane, direction, vehicle class, and axle group. This table is created by applying values 

from the RPPIF_WIJ_FACTOR table to the monthly NALS distribution. 

• RPPIF_WIJ_FACTOR: This table contains axle groups, weight bins (defined by the low 

weight bin boundary), and the factor values for each weight bin. Factors were developed 

to support grouping similar NALS using RPPIF values. 

• RPPIF_VEHICLE_CLASS_ANNUAL: This table contains the RPPIF per vehicle class

values by LTPP site, year, lane, direction, and FHWA vehicle classes 4–13. These values 

are created using data from RPPIF_NALS_ANNUAL and 

VEHICLE_CLASS_AVG_AX_ANL tables. 

• RPPIF_VEHICLE_CLASS_MONTHLY: This table contains the RPPIF per vehicle class 

values by LTPP site, year, month, lane, direction, and vehicle class. These values are 

created using data from RPPIF_NALS_MONTHLY and 

VEHICLE_CLASS_AVG_AX_MONTH tables. 
16 

• RPPIF_ANNUAL_AVG_TRUCK: This table contains the RPPIF for a representative 

truck by LTPP site, year, lane, and direction. The value is created using data from 

RPPIF_VEHICLE_CLASS_ANNUAL and VEHICLE_CLASS_ADT_ANNUAL tables. 

• RPPIF_MONTHLY_AVG_TRUCK: This table contains the RPPIF value for a 

representative truck by LTPP site, year, month, lane, and direction. These values are 

created using data from RPPIF_VEHICLE_CLASS_MONTHLY and 

VEHICLE_CLASS_ADT_MONTH tables. 

Axle Per Truck Tables 

The following LTAS axle per truck tables can be found on InfoPave:(3)

• VEHICLE_CLASS_AVG_AX_ANL: This table contains the average number of axles by 

LTPP site, year, lane, direction, vehicle class, and axle group. These values are created 

using data from VEHICLE_CLASS_AVG_AX_MONTH table. 

• VEHICLE_CLASS_AVG_AX_MONTH: This table contains the average number of 

axles by LTPP site, year, month, lane, direction, vehicle class, and axle group. These 

values are created using data from are VEHICLE_CLASS_TOTAL_AXLE and 

VEHICLE_CLASS_TOTAL_COUNT tables. 

• VEHICLE_CLASS_TOTAL_AXLES: This table contains the total number of axles by 

LTPP site, year, month, lane, direction, vehicle class, and axle group. These values are 

created using data from MM_AX table. 

Traffic Metadata Tables 

The following LTAS traffic metadata tables can be found on InfoPave:(3)

• TRAFFIC_CLASS_CONVERT_MASTER: This table indicates which classification 

scheme is used by an agency or a specific site.

• TRAFFIC_CLASS_CONVERT_DATA: This table contains information on matching a 

State or Provincial highway agency’s classification method to the 13 FHWA vehicle 

classes.

(11)

• SITE_EQUIPMENT_INFO: This table identifies types of equipment installed and 

classification schemes used with that equipment.
17 

ANCILLARY LTPP TRAFFIC DATA AND INFORMATION

Additional sources of information about data and parameters included in LTPP traffic tables can 

be found in the following tables included in the Administrative module of the IMS Guide:(2)

• CODES: This table contains codes and associated descriptions for coded values in the 

IMS.

• CODETYPES: This table contains definitions and sources of information for all code 

tables stored in the LTPP IMS.

Additional traffic data and information is available in the LTPP Ancillary Information 

Management System (AIMS) archives.

(12)

 Chapter 17 of the IMS Guide provides information 

about available ancillary LTPP traffic data and information.

(2)

 Ancillary LTPP traffic data 

include traffic output files from LTAS and those created by legacy LTPP Traffic Quality Control 

software and files from the iANALYZE® software.

(2) LTAS-related ancillary traffic data files 

are in the FHWA TMG file format.

(11)

In addition, AIMS archives contain electronic images of scanned hardcopy data forms used to 

report basic traffic information, traffic scale calibration, historical traffic, and traffic estimates 

produced by agencies when no onsite measurements were performed. This information also 

includes images of vehicles at selected WIM sites with corresponding vehicle classification

results. 

Most of the ancillary information from AIMS archives can be obtained through InfoPave. Image 

files (and other data not found through InfoPave) can be obtained by contacting the LTPP 

Customer Support Service Center at ltppinfo@dot.gov or by calling 202–493–3035. 

19 

CHAPTER 3. LTPP TRAFFIC METADATA

This chapter describes sources of LTPP traffic data. It also provides references to methods used 

to collect traffic data and compute traffic parameters and describes codes available in LTPP

traffic data tables(3)

 to identify data sources and computational methods. Information in this 

chapter will help users’ understanding of the data and methods used in the traffic data collection 

and computation of LTPP traffic parameters, as well as limitations associated with the data 

sources and methods. 

METHODS OF TRAFFIC DATA COLLECTION

LTPP Traffic Data Sources

Sources of LTPP traffic data include the following: 

• Historical data provided by States and Provinces. 

• Monitoring data collected by States and Provinces for the LTPP program.(13)

• Monitoring data collected by the program through the LTPP SPS Traffic Data Collection 

Pooled-Fund Study, TPF-5(004) (14)

 and the LTPP warm-mix asphalt overlay experiment.

• LTPP vehicle classification data collection effort. 

The Guide to LTPP Traffic Data Collection and Processing provides a detailed description of the 

LTPP traffic data collection and processing methods and procedures.(13) The following sections 

provide a brief summary of LTPP traffic data sources and data collection methods, updated with 

information about recent efforts of LTPP traffic data collection.

Data Collected by State and Provincial Agencies Prior to the LTPP Program 

Traffic data or estimates of traffic collected for each LTPP test site prior to its participation in the 

LTPP experiment are referenced within LTPP data sources as historical data. Historical estimates 

and supporting data were submitted by local agencies to the LTPP program via a series of forms 

referred to as LTPP Traffic Data Sheets (available under the Ancillary Data Selection and 

Download section of InfoPave.(3)

) Historical estimates include annual average daily traffic 

(AADT) values for the entire roadway and test lane broken out for all vehicles as well as trucks 

only. ESAL estimates for the test section lane were also obtained. 

Data Collected by State and Provincial Agencies as Part of the LTPP Program

After a test site becomes part of the LTPP experiment, an effort is made to collect site-specific 

traffic data at or near each site. States and Provinces collect data using automated traffic data 

collection equipment, such as ATRs, AVCs, and WIM systems. Equipment can be permanently 

installed at LTPP sites, or portable equipment can be used to take short-duration counts. 

Equipment from different manufacturers can be used. 
20 

States and Provinces follow procedures in the FHWA TMG(11)

 for collecting and reporting traffic 

data, and they submit the data (i.e., traffic volume, vehicle classification, and axle loading) to the 

LTPP program for further processing. The program processes and quality checks the data before

making the data available on the InfoPave web portal.

(3)

 Due to differences in methods and 

equipment States and Provinces use to collect these data, the quality and quantity of available 

traffic data vary among LTPP sites.

Data From Select LTPP SPS WIM Sites

Since 2003, 26 LTPP SPS WIM sites(14) have been installed as part of the LTPP SPS Traffic 

Data Collection Pooled-Fund Study TPF-5(004). These sites have been installed to collect 

research-quality traffic loading data at select SPS-1, -2, -5, and -6 sites.(15) From 2016 to 2018, 

the LTPP program expanded the number of LTPP SPS WIM sites to include the new SPS-10 

sites. 

To meet the study’s research-quality standards, 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 days within a year. Details 

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

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

(16) Table 1 shows criteria 

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

accuracy criteria of research-quality data. 

Table 1. LTPP WIM data accuracy criteria for research-quality data. 

Parameter 95-Percent Confidence Limit of Error

Steering axles +20 percent

Tandem axles +15 percent

GVW +10 percent

Vehicle length ±3 percent (or 2.2 ft)

Axle length +0.5 ft 

Vehicle Classification Data Collected by LTPP Program 

To provide at least a sample of classification data for all pavement sections that remained in 

study in 2005, the LTPP program acquired a week’s worth of classification data from 

approximately 140 sites. Data were collected during 2005–2006 from agencies and LTPP 

program contractors using the 13 FHWA vehicle classes.

(11)

Data Shared by LTPP Traffic Sites

For some LTPP sites, no traffic data are provided in LTPP traffic tables because one set of traffic 

data is used for multiple sites. The record of shared data is maintained in SHRP_INFO and 

SPS_GPS_LINK tables.

(3) To identify reference traffic sites for an LTPP site that is not included 

in LTPP traffic tables, the following fields should be used in the SHRP_INFO table for sites that 

are not part of the SPS-3 or -4 experiments: 
21 

• The CLASS_SITE field identifies the reference LTPP site for vehicle classification and 

volume information. 

• The WIM_SITE field identifies the reference LTPP site for axle loading and truck weight 

information. 

For sites that are part of the SPS-3 or -4 experiments, the related GPS section, found using 

SPS_GPS_LINK, is used as the traffic data source. For some LTPP sites, the source of 

classification or weight data changes over time, with data coming from one location for some 

years and a different location for other years. Similarly, classification data may come from one 

site while weight data may come from another.

INFORMATION ON TRAFFIC DATA COLLECTION EQUIPMENT

The SITE_EQUIPMENT_INFO table (accessible through InfoPave web portal) contains 

information about traffic monitoring equipment used at LTPP sites.(3) This table identifies the 

types of data being collected (i.e., volume, classification, and weight), types of sensors being 

used, and vehicle classification schemes being used. The table does not provide information on 

the installation, maintenance, validation, or calibration of that equipment. However, some of this 

information is available in TRF_CALIBRATION_AVC, TRF_CALIBRATION_WIM, and 

TRF_EQUIPMENT_MASTER tables. Equipment installation and maintenance information are 

available in LTPP Traffic Data Sheets 14 and 15 under the Ancillary Data Selection and 

Download section of InfoPave.

(3)

TRAFFIC DATA ACCURACY

The following two LTPP tables (accessible through InfoPave(3)) contain information about 

calibration efforts performed on traffic monitoring equipment used at some LTPP sites:

• The TRF_CALIBRATION_AVC table contains calibrations of automated vehicle 

classifiers. 

• The TRF_CALIBRATION_WIM table contains calibrations of WIM devices. 

These tables include statistics quantifying measurement errors observed from calibration test 

truck runs, including mean measurement errors and standard deviations of errors for WIM sites. 

This information could be used to infer measurement accuracy of traffic monitoring equipment. 

In addition, LTPP Traffic Data Sheet 11, available from the LTPP AIMS, contains information 

about the calibration of traffic volume counters.(12)

Not all LTPP WIM sites have information in the TRF_CALIBRATION_WIM table, and the 

available information does not always cover all data collection periods. Due to the variety of data 

collection equipment and systems being used, along with the limited availability of supporting 

data (such as WIM equipment performance parameters, calibration records and criteria, and 

WIM performance characteristics measured after calibration), the accuracy of most LTPP traffic 

loading data submitted by State and Provincial agencies is unknown. 
22 

TRAFFIC DATA AVAILABILITY

Information on traffic data availability allows a researcher to quickly identify LTPP sites with 

detailed traffic data that could be used in a pavement performance analysis. Information about 

the number of days for which traffic data were collected and accepted at each site can be 

obtained from the following fields in the LTPP TRF_MONITOR_LTPP_LN table using 

InfoPave web portal:(3)

• The COUNT_DAYS field shows the number of days with vehicle classification data per 

year for each LTPP site. 

• The WEIGHT_DAYS field shows number of days with vehicle and/or axle weight data 

per year for each LTPP site. 

The TRF_MONITOR_LTPP_LN table contains only LTPP sites with traffic monitoring data. 

Additional information may be available in TRF_HIST_VOLUME_COUNT and 

TRF_HIST_CLASS_DATA tables for the years when historical data were provided by 

participating State and Provincial highway agencies. 

METHODS USED TO COMPUTE TRAFFIC PARAMETERS

Table 2 details reference sources that describe methods used to develop different LTPP traffic 

parameters.
23 

Table 2. Reference sources describing methods used to develop different traffic parameters. 

Parameter Name Parameter Description LTPP Field Name LTPP Table Reference Source

AADT LTPP lane historical

count (prior to 1990)

Estimated AADT for the 

LTPP lane from count data

COUNT_AADT_LTPP_LN TRF_HIS_VOLUME_

COUNT

Values are computed by State and 

Provincial agencies prior to the start 

of LTPP monitoring (before 1990); 

values are based on traffic-volume 

count data; procedures are State

specific, and methods are not 

documented.

AADT two-way historical 

count (prior to 1990)

Estimated two-way AADT

in all lanes from count data

COUNT_AADT TRF_HIST_VOLUME_

COUNT

Values are computed by State and 

Provincial agencies prior to the start 

of LTPP monitoring (before 1990); 

values are based on traffic-volume 

count data; procedures are State

specific, and methods are not 

documented.

AADT LTPP lane historical

estimate (prior to 1990)

Estimated AADT for the 

LTPP lane

AADT_ALL_VEHIC TRF_HIST_EST_ESAL See METH_EST_AADT_LTPP 

code in the TRF_HIST_EST_ESAL

table.

(3)

AADT two-way historical 

estimate from local State or 

Provincial agency (prior to 

1990)

Estimated AADT in all 

lanes, two-way

AADT_ALL_VEHIC_

2WAY

TRF_HIST_EST_ESAL See METH_EST_AADT_TOT code

in the TRF_HIST_EST_ESAL table.

AADT LTPP lane during

LTPP experiment (1990 or 

later); estimate from local 

State or Provincial agency

Estimated AADT for the 

LTPP lane

AADT_ALL_VEHIC TRF_MON_EST_ESAL See METH_EST_AADT_LTPP 

code in the TRF_MON_EST_ESAL

table. 

AADT two-way during LTPP 

experiment (1990 or later); 

estimate from local State or 

Provincial agency

Estimated two-way AADT

in all lanes

AADT_ALL_VEHIC_

2WAY

TRF_MON_EST_ESAL See METH_EST_AADT_TOT code 

in the TRF_MON_EST_ESAL

table. 

AADTT LTPP lane during

LTPP experiment (1990 or 

later); estimate from local 

State or Provincial agency

Estimated AADTT in the 

LTPP lane

AADT_TRUCK_COMBO TRF_MON_EST_ESAL See METH_EST_TRK_LTPP code 

in the TRF_MON_EST_ESAL

table. 
24 

Parameter Name Parameter Description LTPP Field Name LTPP Table Reference Source

AADTT two-way during 

LTPP experiment (1990 or 

later); estimate from local 

State or Provincial agency

Estimated two-way

AADTT in all lanes

AADT_TRUCK_COMBO_

2WAY

TRF_MON_EST_ESAL See METH_EST_TRK_TOT code 

in the TRF_MON_EST_ESAL

table. 

AADTT LTPP lane historical

estimate from local State or 

Provincial agency (prior to 

1990)

Estimated AADTT in the 

LTPP lane

AADT_TRUCK_COMBO TRF_HIST_EST_ESAL See METH_EST_TRK_LTPP code 

in the TRF_HIST_EST_ESAL table. 

AADTT two-way historical 

estimate from local State or 

Provincial agency (prior to 

1990)

Estimated two-way

AADTT in all lanes

AADT_TRUCK_COMBO_

2WAY

TRF_HIST_EST_ESAL See METH_EST_TRK_TOT code 

in the TRF_HIST_EST_ESAL table. 

AADTT in the LTPP lane; 

estimated for all years

Estimated AADTT for the 

LTPP lane for each 

in-service year up to the 

end of site participation in 

the LTPP experiment

AADTT_ALL_TRUCKS_

TREND

TRF_TREND See AADTT_SOURCE code in the 

TRF_TREND table. See Facilitating 

Analysts’ Use of Traffic Data from 

the Long-Term Pavement 

Performance (LTPP) Program final 

report.

(4)

Annual total truck volume 

trend; estimated for all years

Estimated total annual 

truck volume for each year 

of participation in the 

LTPP experiment

ANNUAL_TRUCK_

VOLUME_TREND

TRF_TREND See AADTT_SOURCE code in the 

TRF_TREND table. See Facilitating 

Analysts’ Use of Traffic Data from 

the Long-Term Pavement 

Performance (LTPP) Program final 

report. 

AADTT by vehicle class

trend for FHWA vehicle 

classes 4–13;

(11) estimated for 

all years

AADTT by vehicle class 

for FHWA classes 4–13

estimated for each site for 

each in-service year of 

participation in the LTPP 

experiment

AADTT_VEH_CLASS_* 

_TREND

TRF_TREND See VEH_CLASS_SOURCE code 

in the TRF_TREND table. See 

Facilitating Analysts’ Use of Traffic 

Data from the Long-Term Pavement 

Performance (LTPP) Program final 

report. 

Cumulative FHWA vehicle 

class 9 truck volume since 

site opened to traffic; 

estimated annually

Estimated cumulative 

FHWA vehicle class 9 

truck volume to have 

crossed this test section 

since it opened to traffic 

through the end of the year 

indicated

CUMULATUVE_VOLUME_

VEH_CLASS_9_TREND

TRF_TREND See VEH_CLASS_SOURCE code 

in the TRF_TREND table. See

Facilitating Analysts’ Use of Traffic 

Data from the Long-Term Pavement 

Performance (LTPP) Program final 

report. 
25 

Parameter Name Parameter Description LTPP Field Name LTPP Table Reference Source

Cumulative heavy truck 

volume since site opened to 

traffic; estimated annually

Estimated cumulative 

volume for FHWA vehicle 

classes 4 and 6–13 to have 

crossed the test section 

since it opened to traffic 

through the end of the year 

indicated

CUMULATUVE_VOLUME_

HEAVYTRUCKS_TREND

TRF_TREND See VEH_CLASS_SOURCE code 

in the TRF_TREND table. See 

Facilitating Analysts’ Use of Traffic 

Data from the Long-Term Pavement 

Performance (LTPP) Program final 

report.

Representative AADTT 

value for each LTPP site

Single AADTT value 

useful for identifying test 

sites with truck traffic

volumes falling within 

selected ranges

REP_AADTT TRF_REP See REP_AADTT_CLASS_USE_

RATING code in the TRF_REP

table. See Facilitating Analysts’ Use 

of Traffic Data from the Long-Term 

Pavement Performance (LTPP) 

Program final report.

Representative vehicle 

classification percentage 

Estimated percentage of 

total daily truck traffic that 

occurs within the specified 

FHWA truck class at this 

site over the course of its 

participation in the LTPP 

experiment

REP_PERCENT_VEH_

CLASS_#

TRF_REP See REP_VEH_CLASS_USE

_RATING code in the TRF_REP

table. See Facilitating Analysts’ Use 

of Traffic Data from the Long-Term 

Pavement Performance (LTPP) 

Program final report.

Annual estimate of total truck 

volume for years with 

monitoring data (1990 or 

later)

Annual estimate of trucks 

in the LTPP lane

TRUCKS_LTPP_LN TRF_MONITOR_LTPP_L

N 

See LTAS Volume 4, Functional 

Specifications.

1

Annual estimate of the total 

number of axles for years 

with monitoring data (1990 

or later)

Annual estimate of the 

number of single, tandem, 

tridem, and quad+ axles

SINGLE_AX_EST, 

TANDEM_AX_EST, 

TRIDEM_AX_EST, 

QUADPLUS_AX_EST 

TRF_MONITOR_LTPP_L

N 

See LTAS Volume 4, Functional 

Specifications. 

Annualized axle load 

distribution for years with 

WIM data (1990 or later)

Annual estimate of the 

number of axles measured 

in each weight range for 

each axle group (i.e., 

single, tandem, triple, and 

quad+) and each FHWA 

vehicle class 4–13 

AX_CT_01, ...AX_CT_40 TRF_MONITOR_AXLE_

DISTRIB

See LTAS Volume 4, Functional 

Specifications.

1

A copy of LTAS volume 4 can be requested by contacting LTPP Customer Support Services Center at ltppinfo@dot.gov. 
26 

Parameter Name Parameter Description LTPP Field Name LTPP Table Reference Source

Axle load distribution 

summarized by DOW, 

month, and year for each 

month with weight data

Axle load summaries by 

load range, year, month, 

DOW, lane, direction, 

FHWA vehicle classes 

4–13, axle group, and site 

for each month with load 

data

AX_CT_01, ...AX_CT_40 MM_AX This table is from LTAS software 

database. See LTAS Volume 4, 

Functional Specifications.

GVW distribution 

summarized by DOW, 

month, and year for each 

month with weight data

GVW summaries by load 

range, year, month, DOW, 

lane, direction, vehicle 

class, axle group, and site 

for each month with load 

data

BIN01, ...BIN50 MM_GVW This table is from LTAS software 

database. See LTAS Volume 4, 

Functional Specifications.

VCD summarized by DOW, 

month, and year for each 

month with classification 

and/or weight data

Vehicle count summaries 

by site, year, month, DOW, 

lane, direction, and data 

source for each month with 

classification and/or weight 

data

CT_SUM_01, ...CT_SUM_15 MM_CT This table is from LTAS software 

database. See LTAS Volume 4, 

Functional Specifications. 

Axle load distribution 

summarized by DOW and 

year for each year with 

weight data

Axle load summaries by 

load range, year, DOW, 

lane, direction, vehicle 

class, axle group, and site 

for each year with load 

data

AX_CT_01, ...AX_CT_40 YY_AX This table is from LTAS software 

database. See LTAS Volume 4, 

Functional Specifications. 

GVW distribution 

summarized by DOW and 

year for each year with 

weight data

GVW summaries by load 

range, year, DOW, lane, 

direction, vehicle class, 

axle group, and site for 

each year with load data

BIN01, ...BIN50 YY_GVW This table is from LTAS software 

database. See LTAS Volume 4, 

Functional Specifications.

VCD summarized by DOW

and year for each year with 

classification and/or weight 

data

Vehicle count summaries 

by site, year, month, DOW, 

lane, direction, and data 

source for each year with 

classification and/or weight 

data

CT_SUM_01, ...CT_SUM_15 YY_CT This table is from LTAS software 

database. See LTAS Volume 4, 

Functional Specifications.
27 

Parameter Name Parameter Description LTPP Field Name LTPP Table Reference Source

Annual normalized axle load 

distribution for years with at 

least 210 days of WIM data 

for computing MEPDG 

ALDF

Annual normalized axle 

distribution by FHWA 

vehicle classes 4–13 and 

axle group for sites that 

have at least 2 years with 

more than 210 days of 

WIM data in the 

TRF_MONITOR_

LTPP_LN table

PERCENT_AXLES TRF_MEPDG_AX_DIST_

ANL

See SQL scripts and procedures for 

TRF_MEPDG tables.

Monthly normalized axle 

load distribution for years 

with at least 210 days of 

WIM data for computing 

MEPDG ALDF

Normalized axle load

distribution by month, 

FHWA vehicle classes

 4–13, and axle group for 

years with at least 210 days

of WIM data

PERCENT_AXLES TRF_MEPDG_AX_DIST Values are computed from weight 

data in MM_AX LTAS table. The 

process is documented in SQL 

scripts and procedures for 

TRF_MEPDG tables.2

MEPDG ALDF ALDF in MEPDG format 

representing the percentage 

of total axle applications 

within each load interval 

for a specific axle type 

(i.e., single, tandem, 

tridem, and quad+) and 

FHWA vehicle classes

 4–13; one set of factors 

represents each LTPP site

MEPDG_LG01, 

...MEPDG_LG39

MEPDG_AXLE_LOAD_

DIST_FACTOR

See field ALDF_USE_RATING for 

quality assessment and source. See 

Facilitating Analysts’ Use of Traffic 

Data from the Long-Term Pavement 

Performance (LTPP) Program final 

report. 

AADTT LTPP lane for years 

with 210 days or more of 

weight or count data for 

computing MEPDG first year 

AADTT

AADTT based on FHWA 

vehicle classes 4–13 for the 

LTPP lane for years with 

210 days or more of weight 

or count data

AADTT TRF_MEPDG_AADTT_

LTPP_LN

See field TRF_DATA_TYPE for 

data source; the computational 

method is based on the NCHRP 

1-37A Report (MEPDG).(7)

MEPDG first year AADTT 

LTPP lane

Estimated AADTT for the 

first year when the 

pavement at the LTPP site 

location was opened to 

traffic

AADTT_FIRST_YEAR_

LTPP_LANE

MEPDG_TRUCK_VOL_

PARAMETERS 

See Facilitating Analysts’ Use of 

Traffic Data from the Long-Term 

Pavement Performance (LTPP) 

Program final report. 

2

A copy of the SQL scripts and procedures can be requested by contacting LTPP Customer Support Service Center at ltppinfo@dot.gov. 
28 

Parameter Name Parameter Description LTPP Field Name LTPP Table Reference Source

Vehicle class volume 

distribution by year for 

computing MEPDG vehicle 

class volume distribution

Percentage of trucks by 

FHWA vehicle classes 

4–13 within the truck 

population (in the LTPP 

lane reported for years with 

210 days or more of weight 

or count data

PERCENT_OF_TRUCKS TRF_MEPDG_VEH_

CLASS_DIST

See SQL scripts and procedures for 

TRF_MEPDG tables.

MEPDG vehicle class 

volume distribution

Representative percentage

of trucks in each FHWA 

vehicle class 4–13; one set 

of factors represents each 

LTPP site

VEH_CLASS_DIST_

PERCENT

MEPDG_TRUCK_VOL_

PARAMETERS 

See Facilitating Analysts’ Use of 

Traffic Data from the Long-Term 

Pavement Performance (LTPP) 

Program final report. 

MEPDG truck growth 

function and growth rate by 

FHWA vehicle classes 4–13

Linear or compound truck

traffic growth rate for 

FHWA vehicle classes

 4–13, expressed as a 

percentage, from the first 

year when the pavement at 

LTPP site location was 

opened to traffic until the 

end of site participation in 

the LTPP experiment

VEH_CLASS_GROWTH_

FUNCTION

VEH_CLASS_GROWTH_

RATE

MEPDG_TRUCK_VOL_

PARAMETERS 

See the codes in VEH_CLASS_

GROWTH_USE_RATING field in 

the MEPDG_TRUCK_

VOL_PARAMETERS table and 

computational procedure in the

Facilitating Analysts’ Use of Traffic 

Data from the Long-Term Pavement 

Performance (LTPP) Program final 

report.

MAFs by year for computing 

MEPDG MAFs 

Adjustment factors for 

AADTT for each FHWA 

vehicle class 4–13 by 

month reported for years 

with 210 days or more of 

weight or count data

MONTHLY_RATIO TRF_MEPDG_MONTH_

ADJ_FACTR

See SQL scripts and procedures for 

TRF_MEPDG tables; additional 

data manipulations are needed to 

compute representative values for 

use in AASHTOWare Pavement ME 

Design software.(5)

HDF by year for computing 

MEPDG HDF

Adjustment factors for 

annual average hourly 

distribution of trucks by 

hour in the LTPP lane

PCT_HOURLY TRF_MEPDG_HOURLY_

DIST

Computational method is based on 

NCHRP 1-37A Report (MEPDG);

(7)

additional data manipulations are 

needed to compute representative 

values for use in AASHTOWare 

Pavement ME Design software.
29 

Parameter Name Parameter Description LTPP Field Name LTPP Table Reference Source

Axles per truck by year for 

computing MEPDG APT

Average number of axles in 

each axle group for a 

vehicle in FHWA vehicle 

classes 4–13 reported for 

each year with 210 days or 

more of weight or count 

data

AXLES_TRUCK TRF_MEPDG_AX_PER_

TRUCK

Values are computed from the axles 

summed in the TRF_MONITOR_

LTPP_LN LTPP table; SQL scripts 

and procedures for TRF_MEPDG 

tables.

MEPDG APT Representative number of 

axles for each truck class 

(i.e., FHWA vehicle 

classes 4–13) for each axle 

type (i.e., single, tandem, 

tridem, and quad); one set 

of factors represents per 

LTPP site

SINGLE_AXLES, 

TANDEM_AXLES, 

TRIDEM_AXLES, and

QUAD_AXLES

MEPDG_AXLE_PER_

TRUCK

See the APT_USE_RATING field 

of the MEPDG_AXLE_PER_

TRUCK table. See Facilitating 

Analysts’ Use of Traffic Data from 

the Long-Term Pavement 

Performance (LTPP) Program final 

report.

ESAL historical estimate 

(prior to 1990)

Annual ESALs in 

thousands in the LTPP lane 

for years prior 1990

ANL_KESAL_LTPP_LN_

YR

TRF_HIST_EST_ESAL From State or Provincial agency. 

Method unknown.

ESAL monitored (1990 or 

later)

Annual ESALs in 

thousands in the LTPP lane 

for 1990 or later

ANL_KESAL_LTPP_LN_

YR

TRF_MON_EST_ESAL From State or Provincial agency. 

Method unknown. 

Annual ESAL for years with 

acceptable WIM data

Annual ESALs in 

thousands in the LTPP lane 

for sites with an acceptable 

sample of axle load 

measurements in a given 

year

KESAL_YEAR TRF_ESAL_COMPUTED See methodology from 1993 

AASHTO Guide for Design of 

Pavement Structures.

(8)

Annual ESAL trend

estimated for all years

Annual ESAL estimate for 

each in-service year up to 

the end of site participation 

in the LTPP experiment

ANNUAL_ESAL_TREND TRF_TREND See Facilitating Analysts’ Use of 

Traffic Data from the Long-Term 

Pavement Performance (LTPP) 

Program final report. 

Annual GESAL trend Annual GESAL estimate 

for each in-service year up 

to the end of site 

participation in the LTPP 

experiment

ANNUAL_GESAL_TREND TRF_TREND See Facilitating Analysts’ Use of 

Traffic Data from the Long-Term 

Pavement Performance (LTPP) 

Program final report. 
30 

Parameter Name Parameter Description LTPP Field Name LTPP Table Reference Source

Annual total load trend 

estimated for all years 

Annual total load estimate 

for each in-service year up 

to the end of site 

participation in the LTPP 

experiment

ANNUAL_TOTAL_LOAD_T

REND

TRF_TREND See Facilitating Analysts’ Use of 

Traffic Data from the Long-Term 

Pavement Performance (LTPP) 

Program final report. 

Representative GESAL per 

APT

Representative number of 

GESAL per APT

REP_GESAL_SINGLE_

AXLE, REP_GESAL_

TANDEM_AXLE,

REP_GESAL_TRIDEM

_AXLE, REP_GESAL_

QUAD_AXLE

TRF_REP See LTPP IMS, Database Schema 

for Analysis Ready Traffic 

Computed Parameters, February

2019 (version 27), Working Draft 

Database Specifications.

3

Representative GESAL per 

vehicle class

Representative number of 

GESAL per vehicle class 

for FHWA vehicle classes 

4–13

REP_GESAL_PER_VEH_

CLASS_4, REP_GESAL_

PER_VEH_CLASS_13

TRF_REP See LTPP IMS Database Schema for 

Analysis Ready Traffic Computed 

Parameters, February 2019. 

Representative GESAL per 

truck

Representative number of 

GESAL per truck

REP_GESAL_PER_TRUCK TRF_REP See LTPP IMS Database Schema for 

Analysis Ready Traffic Computed 

Parameters, February 2019. 

Representative ESAL per 

APT

Representative number of 

ESAL per APT

REP_ESAL_SINGLE_AXLE, 

REP_ESAL_

TANDEM_AXLE,

REP_ESAL_TRIDEM_

AXLE,

REP_ESAL_QUAD_AXLE

TRF_REP See LTPP IMS Database Schema for 

Analysis Ready Traffic Computed 

Parameters, February 2019. 

Representative ESAL per 

vehicle class

Representative number of 

ESAL per vehicle class for 

FHWA vehicle classes 

4–13

REP_ESAL_PER_VEH_CLA

SS_4, ...REP_ESAL_PE

R_VEH_CLASS_13

TRF_REP See LTPP IMS Database Schema for 

Analysis Ready Traffic Computed 

Parameters, February 2019. 

Representative ESAL per 

truck

Representative number of 

ESAL per truck

REP_ESAL_PER_TRUCK TRF_REP See LTPP IMS Database Schema for 

Analysis Ready Traffic Computed 

Parameters, February 2019. 

Representative GVW per 

truck

Representative GVW per 

truck

REP_GVW_TRUCK TRF_REP See LTPP IMS Database Schema for 

Analysis Ready Traffic Computed 

Parameters, February 2019. 

3

A copy of LTPP IMS, Database Schema for Analysis Ready Traffic Computed Parameters, can be requested by contacting LTPP Customer Support Service 

Center at ltppinfo@dot.gov. 
31 

Parameter Name Parameter Description LTPP Field Name LTPP Table Reference Source

Representative GVW per 

vehicle class

Representative GVW per 

vehicle class for FHWA 

vehicle classes 4–13

REP_GVW_VEH_CLASS_4, 

...REP_GVW_VEH

_CLASS_13

TRF_REP See LTPP IMS Database Schema for 

Analysis Ready Traffic Computed 

Parameters, February 2019. 

Average monthly RPPIF per 

axle per vehicle class

Monthly average RPPIF 

per APT

RPPIF RPPIF_NALS_MONTHLY See LTPP IMS—LTAS Database 

Schema for NALS Tables and 

Derivative Computed Parameters, 

August 2016 (version 6), Working 

Draft Database Specification.

4

Annual average RPPIF per 

axle per vehicle class

Annual average RPPIF per 

APT

ANNUAL_RPPIF RPPIF_NALS_ANNUAL See LTPP IMS—LTAS Database 

Schema for NALS Tables and 

Derivative Computed Parameters, 

August 2016. 

Average monthly RPPIF per 

vehicle class

Monthly average RPPIF by 

vehicle class

VEHICLE_CLASS_RPPIF RPPIF_VEHICLE_CLASS

_MONTHLY

See LTPP IMS—LTAS Database 

Schema for NALS Tables and 

Derivative Computed Parameters, 

August 2016.

Annual average RPPIF per 

vehicle class

Annual average RPPIF by 

vehicle class

VEHICLE_CLASS_RPPIF_A

NL

RPPIF_VEHICLE_CLASS

_ANNUAL

See LTPP IMS—LTAS Database 

Schema for NALS Tables and 

Derivative Computed Parameters, 

August 2016. 

Monthly average RPPIF per 

truck

Monthly average RPPIF 

per truck

TRUCK_RPPIF RPPIF_MONTHLY_AVG_

TRUCK

See LTPP IMS—LTAS Database 

Schema for NALS Tables and 

Derivative Computed Parameters, 

August 2016. 

Annual average RPPIF per 

truck

Annual average RPPIF per 

truck

ANNUAL_TRUCK_RPPIF RPPIF_ANNUAL_AVG_

TRUCK

See LTPP IMS—LTAS Database 

Schema for NALS Tables and 

Derivative Computed Parameters, 

August 2016. 

MAFs = monthly adjustment factors; HDF = hourly distribution factors; NCHRP = National Cooperative Highway Research Program.

4

A copy of LTPP IMS—LTAS Database Schema for NALS Tables and Derivative Computed Parameters can be requested by contacting LTPP Customer 

Support Service Center at ltppinfo@dot.gov. 
32 

CODES DESCRIBING DATA AND METHODS USED FOR PARAMETER 

COMPUTATION AND PARAMETER USABILITY

To provide LTPP users with some means of assessing accuracy and applicability of available 

analysis-ready traffic parameters, a set of codes is included in each table containing 

analysis-ready traffic parameters. Table 3 through table 14 provide descriptions of codes 

associated with different parameters. Using these codes, LTPP researchers could identify LTPP 

sites that have traffic parameters that meet the analysis criteria based on the data source and data 

quality information. These codes can be downloaded from the InfoPave web portal(3)

 by selecting 

the following InfoPave options: 

1. Select Data option from the main InfoPave menu. 

2. Select LTPP Table Navigator option from the Data menu. 

3. Select Codes (CODES) from the list of tables.

4. Click on “Export to Excel” button located at the bottom of the screen. 

Table 3. Fields describing sources and usability of traffic parameters included in 

analysis-ready tables. 

LTPP Field LTPP Table

AADTT_SOURCE TRF_TREND

VEH_CLASS_SOURCE TRF_TREND

ESAL_SOURCE TRF_TREND

GESAL_SOURCE TRF_TREND

GVW_SOURCE TRF_TREND

REP_AADTT_USE_RATING TRF_REP

REP_VEH_CLASS_USE_RATING TRF_REP

REP_LOAD_USE_RATING TRF_REP

VEH_CLASS_GROWTH_USE_RATING MEPDG_TRUCK_VOL_PARAMETERS

ALDF_USE_RATING MEPDG_AXLE_LOAD_DIST_FACTOR

APT_USE_RATING MEPDG_AXLE_PER_TRUCK

Table 4. Codes for AADTT_SOURCE fields. 

Code Description

E Estimated value

EC AADTT calculated using compound growth function 

EL AADTT calculated using linear growth function 

H Historical AADTT value

M Monitored AADTT value 

MC Monitored AADTT value calculated from monitored class data (when available)

N No data; site not open to traffic 

S State-provided AADTT value

WA Derived from publicly available AADT data on agency website

WC Derived from publicly available AADT and vehicle classification data on agency 

website
33 

Table 5. Codes for VEH_CLASS_SOURCE fields.

Code Description

A AADTT: Based on classification volumes calculated using AADTT and median class 

percentages.

D Default: Based on an external source.

EC Estimated compound: Estimated based on other years of data at that site to fill in the 

gaps in data coverage using a best fit compound growth equation.

EL Estimated linear: Estimated based on other years of data at that site to fill in the gaps 

in data coverage using a best fit linear growth equation.

H Historical: AADTT value supplied by the State department of transportation.

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.

N Not open to traffic: This site has not yet opened to traffic.

S State-supplied: Based on State-supplied historical record data collection efforts prior 

to the LTPP traffic monitoring program.

Table 6. Codes for the ESAL_SOURCE field.

Code Description

0 No annual ESAL is provided for the virtual SPS site (*00) or any site that has no 

pavement structure information.

1 Annual ESAL is computed by LTPP based on site- and year-specific WIM data.

2 Annual ESAL is computed based on the 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 Agency-supplied annual ESAL is for years during LTPP monitoring program.

4 Agency-supplied annual ESAL is for years prior to LTPP monitoring program.

5 Annual ESAL is estimated based on the representative ESAL-per-truck value from the 

TRF_REP table and the site- and year-specific annual truck volume by vehicle class 

from the TRF_TREND table.

6 Annual ESAL is 0 due to zero truck volume.

Table 7. Codes for the GESAL_SOURCE field. 

Code Description

1 Annual GESAL is computed by LTPP based on site- and year-specific WIM data.

5 Annual GESAL is estimated based on the representative GESAL-per-vehicle-class 

value from the TRF_REP table and the site- and year-specific AADTT by vehicle class 

from the TRF_TREND table.

6 Annual GESAL is 0 due to zero truck volume.
34 

Table 8. Codes for the GVW_SOURCE field. 

Code Description

1 Annual total GVW is computed by LTPP based on site- and year-specific WIM data.

5 Annual total GVW is estimated based on the representative GVW per truck value from 

the TRF_REP table and the site and year-specific annual truck volume from 

TRF_TREND table.

6 Annual total GVW is 0 due to zero truck volume.

Table 9. Codes for the REP_AADTT_USE_RATING field. 

Code Description

1 Best: >75 percent of AADTT estimates used have an AADTT_SOURCE of M, and no 

years during that period experience a year-to-year change in AADTT >25 percent and 

> 50 trucks.

2 Better: >75 percent of the AADTT estimates used have an AADTT_SOURCE of either 

M or S, and no years during that period experience a year-to-year change in AADTT >25 

and percent >50 trucks.

3 Good: >50 percent of the AADTT estimates used have an AADTT_SOURCE of M, but 

<75 percent are M or S; no years have a year-to-year change in AADTT >25 percent and 

>50 trucks.

4 Fair: >25 percent of the AADTT estimates used have an AADTT_SOURCE of M, but 

< 50 percent are either M or S or have large year-over-year AADTT changes.

5 Poor: <25 percent of the AADTT estimates used have an AADTT_SOURCE of either M 

or S.

Table 10. Codes for REP_VEH_CLASS_USE_RATING. 

Code Description Long

1 Best: >75 percent of years used are monitored vehicle class data, and no year-to-year 

percentage any class changes by more than 15 percent in the period. 

2 Better: >50 percent of years used are monitored vehicle class data, and no year-to-year 

percentage change in any class >15 percent and no years have a large year-to-year 

change in AADTT.

3 Good: >50 percent but <75 percent of years used are monitored vehicle class data and no 

year-to-year change in any class >15 percent but at least one large year-to-year AADTT 

change.

4 Fair: >25 percent but <50 percent of years used are monitored vehicle class data or at 

least one year-to-year change in any truck class >15 percent.

5 Poor: <25 percent of the years used are monitored vehicle class data.

6 No monitoring data were present.
35 

Table 11. Codes for ALDF_USE_RATING fields. 

Code Description

1 Best: Based on WIM data that satisfy accuracy requirements for ASTM E1318-09(17)

Type I WIM systems passing data reasonableness checks. Data include at least one of 

each DOW in each month.

2 Better: Based on WIM data that satisfies accuracy requirements for ASTM E1318-09 

Type I WIM systems passing data reasonableness checks. Data available for at least 

1 year, not necessarily all months.

3 Good: Based on WIM data collected by the equipment that does not have a calibration 

record. Data quality cannot be quantified. Values based on annual data summaries 

passing QC checks.

4 Fair: Based on WIM data collected by the equipment that does not have a calibration 

record. Data quality cannot be quantified. Values based on selected annual data 

summaries passing QC checks.

5 Default-State pattern: Available WIM data are of unknown quality and did not pass 

study descriptive checks for acceptable precision and bias.

6 Default-PLUG pattern: Available WIM data are of unknown quality and did not pass 

study descriptive checks for acceptable precision and bias. Loading pattern used to 

identify LTPP PLUG defaults.

7 Default-no pattern PLUG: No WIM or information about loading pattern and/or weights 

is available. LTPP PLUG default values were assigned using information other than 

site-specific weight data.

8 Default-no pattern State: No WIM or information about loading pattern and/or weights 

is available. Default values based on the data from the other WIM sites in the same 

State.

10 Agency ESAL based: No directly usable annual axle distribution. Agency ESAL 

estimate more reasonable than ESALs computed from annual distribution and used as 

basis for selecting loading distribution.

11 ESALCalc based: No directly usable annual axle distribution. ESALCalc estimate more 

reasonable than agency ESAL estimate and used as basis for selecting loading 

distribution.

12 Quantitative acceptance: Single available annual axle distribution quantitatively 

screened and accepted for direct use. No better than “fair” quality.

13 Location based: No axle data or agency provided ESAL estimates. Loading distributions 

selected based on location information only.

20 Multiple years of unknown data quality, with typical distribution accepted as 

representative.

21 Multiple years of data of unknown quality: Agency ESAL estimates are consistent with 

ESALs computed from distribution. Typical distribution accepted for use.

22 Multiple years of data of unknown quality: Agency ESAL estimates are reasonable but 

smaller than ESALs computed from distribution. Typical distribution accepted for use.

23 Multiple years of data of unknown quality: Agency ESAL estimates are reasonable but 

larger than with ESALs computed from distribution. Distributions selected based on 

estimated ESALs.
36 

Code Description

24 Multiple years of data of unknown quality: Agency ESAL estimates are not reasonable. 

Distributions selected based on ESALs computed from distributions. 

25 Multiple years of data of unknown quality: Reasonable agency ESAL estimates. ESALs 

computed from distributions are lower than reasonable. Axle distributions selected 

based on agency ESAL estimates.

26 Multiple years of data of unknown quality: Reasonable agency ESAL estimates. ESALs 

computed from distributions are higher than reasonable. Axle distributions selected 

based on agency ESAL estimates.

27 Multiple years of data of unknown quality: Agency ESAL estimates and ESALs 

computed from distributions are higher than reasonable. Axle distributions selected 

based on location. 

AA Atypical axle type: Reported axle group is not typical for a given vehicle class. ALDF 

values are not recommended for development of the defaults. 

ALC Low axle count: Computed values are based on low axle count (<200 axles) and may 

not be representative of typical loading condition for a given site. 

ALS Small axle sample: Computed values are based on a low number of days with data and 

may not be representative of typical loading condition for a given site. 

DEF Vehicle class-axle combination not present in dataset: Default picked using the 

low-volume criterion for the class.

NA Not applicable: Zero ALDF; vehicle class is not present.

QC = quality control.

Table 12. Codes for REP_LOAD_USE_RATING fields. 

Code Description

1 Best: Based on WIM data that satisfies accuracy requirements for ASTM E1318-09(17)

Type I WIM systems passing data reasonableness checks. Data include at least one of 

each DOW in each month. 

2 Better: Based on WIM data that satisfies accuracy requirements for ASTM E1318-09 

Type I WIM systems passing data reasonableness checks. Data available for at least 

1 year, not necessarily all months. 

3 Good: Based on WIM data collected by the equipment that does not have a calibration 

record. Data quality cannot be quantified. Values based on annual data summaries 

passing QC checks. 

4 Fair: Based on WIM data collected by the equipment that does not have a calibration 

record. Data quality cannot be quantified. Values based on selected annual data 

summaries passing QC checks. 

10 Estimate based on axle distributions selected using ESAL values and truck volumes as 

inputs 

12 Based on typical site axle distribution. 

13 Estimate based on axle distributions selected as a function of site location. 

99 Linked site quality undetermined. 
37 

Table 13. Codes for the APT_USE_RATING field. 

Code Description

1 Extensive data availability: APT is based on >200 truck sample size from more than 

365 days, recommended for site-specific analysis. Use for development of defaults if no 

A codes exist.

2 Good data availability: APT is based on >200 truck sample size from between 210 and 

364 days, recommended for site-specific analysis. Use for development of defaults if no 

A codes exist.

3 Sufficient data availability: APT is based on a sample of more than 200 trucks collected 

over <210 days in total. APT values are recommended for site-specific analysis.

4 Limited data availability: APT is based on <200 truck sample, use with caution in 

site-specific analysis or consider use of defaults. Values are not recommended for 

default value development.

5 Marginal data availability: APT is based on <100 truck sample, use with caution in 

site-specific analysis or consider use of defaults. Values are not recommended for 

default value development.

A0 Atypical total axles: Total number of APT does not follow FHWA 13-bin vehicle 

classification rules for given vehicle class. Values are not recommended for 

development of defaults.

A1 Atypically low single axles: <0.95 single APT (all trucks should have at least one single 

steering axle). Values are not recommended for development of default values.

A2 Atypical tandem axle type: Tandem axle is atypical for a given vehicle class. Values are 

not recommended for development of default values.

A3 Atypical tridem axle type: Tridem axle is atypical for a given vehicle class. Values are 

not recommended for development of default values.

A4 Atypical quad axle type: Quad axle is atypical for a given vehicle class. Values are not 

recommended for development of default values.

AW Low-quality weight data: The quality of the associated data is too low for estimating 

weight but does not affect classification and axle count data applicability.

D0 Based on available annual estimated truck and axle counts.

D1 State- and route-based default using average axles for routes in the same State/Province

with the same type of route signing (Interstate, U.S., etc.).

D2 Country- and route-based default using average axles for routes in the same country 

with the same type of route signing (Interstate, U.S., etc.).

D3 State-based default using average axles for all routes in the same State/Province.

NA Not applicable zero APT: Vehicle class is not present in supporting data.
38 

Table 14. Codes for the VEH_CLASS_GROWTH_USE_RATING field. 

Code Description

1 Best: >75 percent of years used are monitored vehicle class data, and no year-to-year 

percentage in any class changes by >15 percent in the period.

2 Better: >50 percent of years used are monitored vehicle class data, and no year-to-year 

percentage change in any class >15 percent, and no years have a large year-to-year 

change in AADTT.

3 Good: >50 percent but <75 percent of years used are monitored vehicle class data, and

no year-to-year change in any class >15 percent in <3 years.

4 Fair: >25 percent but <50 percent of years used are monitored vehicle class data or at 

least one year-to-year change in any truck class >15 percent.

5 Poor: <25 percent of the years used are monitored vehicle class data.

6 Bad: No monitoring data are present.
39 

CHAPTER 4. SELECTING LTPP TRAFFIC DATA AND PARAMETERS FOR LTPP 

ANALYSES 

This chapter contains recommendations for selecting traffic data and parameters, including 

references to LTPP traffic parameter names and LTPP tables containing these parameters,

suitable for most common pavement analyses. 

OVERVIEW OF TRAFFIC PARAMETERS USED FOR PAVEMENT ANALYSIS AND

DESIGN

The LTPP InfoPave portal contains multiple tables with traffic parameters.

(3) These parameters

are summary statistics or application-specific input parameters that support a broad range of 

research topics related to pavement response and performance modeling and analysis, and 

pavement design. Based on the intended application, LTPP traffic parameters could be 

categorized as follows:

• Data and parameters for a detailed characterization or study of traffic loading effects. 

• Traffic summary parameters for high-level empirical pavement analyses. 

• Parameters for use in specialized pavement analysis and design software (such as 

AASHTOWare Pavement ME Design).(5)

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

mechanistic–empirical pavement response and performance modeling. These models require 

detailed information about traffic loading, including information about wheel- and/or axle load 

magnitude, load position and configuration (i.e., axle configuration and the position of the 

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 whole analysis period is needed (i.e., the 

number and magnitudes of loads reported for specified time increments used in the analysis). 

Aggregated traffic summary parameters are used for empirical pavement performance analyses 

and modeling, empirical pavement designs, and high-level analyses supporting pavement 

management models and decision-support tools. Typically, for these analyses, a single-value 

traffic summary statistic is desired, like ESAL, AADTT, CTV, or total load. Also, these 

summary statistics are used to identify and group LTPP sites into categories representing 

different levels of traffic.

Another set of traffic parameters available through the InfoPave web portal(3) is parameters that 

can be used as a direct input for specialized pavement analysis or design software, such as those 

used in the AASHTOWare Pavement ME Design software. 
40 

TRAFFIC PARAMETERS RECOMMENDED FOR ANALYSES REQUIRING 

DETAILED CHARACTERIZATIONS OF TRAFFIC LOADING

Parameters for Pavement Response Prediction Based on Mechanistic Models

Pavement response analysis and modeling studies are focused on stresses, strains, and deflections 

that pavements experience under each traffic load application. Pavement responses can be 

predicted using static or dynamic mechanistic modeling methods. Static analysis methods 

assume that the truck load is constantly applied to the pavement. The dynamic methods consider 

changes over time in traffic loads and pavement responses, as loads move over the pavement. In 

the latter case, in addition to static load, a vertical load component caused by truck bounce, road 

geometry, and pavement surface irregularities is considered, along with the speed or time history 

of the load application. 

Pavement responses predicted by static models (i.e., elastic, viscoelastic, and elastoplastic) 

depend on the following traffic loading parameters: 

• Magnitude of the load transferred by each wheel. 

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

the pavement surface). 

• Load distribution within the tire footprint. 

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

• Area and shape of load application (i.e., tire footprint). 

• Sequence of loads. 

Pavement responses predicted by dynamic models, in addition to these parameters, also require 

the following additional inputs: 

• Load duration. 

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

• Speed of moving load. 

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

tire footprint over time as each wheel passes over a specific pavement location). 
41 

LTPP Tables Containing Traffic Parameters for Mechanistic Pavement Response Modeling 

Table 15 summarizes the traffic parameters necessary for mechanistic pavement response 

evaluation and modeling and the LTPP sources for these parameters. Often, significant data 

processing is required to compute these parameters, and not all LTPP sites have the necessary 

data for parameter computation. For parameters that do not have supporting LTPP data, 

alternative data sources, when available, are recommended.

Table 15. Traffic parameters for mechanistic pavement response modeling. 

Input Parameter Parameter Description LTPP Data Source

Vehicle class for each vehicle 

passage. 

Vehicle class (FHWA vehicle 

classes 4–13)(11) or truck

configuration for each vehicle 

passage, including the number and 

spacing of axles.

LTPP WIM IVR files are stored offline

in the AIMS.

(12)

Axle-to-axle spacing for each 

vehicle passage, or annual average 

for each FHWA vehicle class 4–13.

Distance between pairs of 

consecutive vehicles’ axles for each 

vehicle passage.

LTPP WIM IVR files are stored offline

in the AIMS. 

Axle load for each axle for each 

vehicle passage or annual average 

for each FHWA vehicle class 4–13. 

Static weight estimated by the WIM 

system for each axle for each 

vehicle passage. 

LTPP WIM IVR files are stored offline

in AIMS and could be used to obtain 

axle load for each axle for each vehicle 

passage; NALS tables available 

through InfoPave(3) could be used to 

compute average loads.

Wheel load for each axle for each 

vehicle passage or annual average 

for each FHWA vehicle class 4–13. 

Static weight estimated by the WIM 

system for each axle for each 

vehicle passage; can be converted to 

an estimate of wheel load given 

assumptions about the number of 

tires on each axle.

This information is available from 

some WIM equipment but is not stored 

by LTPP in the AIMS; LTPP NALS 

tables could be used to compute 

average wheel loads. 

Duration of each axle- or wheelload application. Time during which the axle load 

was applied on the monitored 

pavement section; used for dynamic 

pavement response modeling only.

This information is not collected by 

LTPP; data could be obtained from 

some WIM controller records. 

Time history of changes in load 

magnitude for each axle or wheel 

passage at a point of interest. 

Dynamic-load amplitude estimated 

based on the 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 

LTPP; data could be obtained from 

some WIM controller records but is 

not routinely reported by WIM 

equipment. 

Wheel location on the pavement 

associated with each axle and 

vehicle passage, or annual average 

for each FHWA vehicle class 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 

LTPP; data could be obtained from 

specially configured quartz-piezo 

sensor arrays and from the advanced 

WIM sensors that record tire footprint 

and tire position data.

Tire footprint area for each axle 

associated with each vehicle 

passage, or annual average for each 

FHWA vehicle class 4–13. 

Tire footprint area of each axle and 

vehicle passage. 

This information is not collected by 

LTPP; the advanced WIM sensors that 

capture tire footprint and tire position 

data are currently available for pilot 

implementations.
42 

Input Parameter Parameter Description LTPP Data Source

Load distribution within a tire 

footprint. 

Load distribution over the footprint 

area for each axle and vehicle 

passage. 

This information is not collected by 

LTPP; the advanced WIM sensors 

capable of recording pressure 

distribution under the tire footprint are 

currently available for pilot 

implementations. 

Axle width from each vehicle 

passage, or annual average for each 

FHWA vehicle class 4–13. 

Distance in feet between two 

outside edges of an axle. 

This information is not collected by 

LTPP; default values may be 

appropriate due to expected low 

variability of this parameter. 

Dual-tire spacing from each vehicle 

passage, or annual average for each 

FHWA vehicle class 4–13. 

Distance in feet between two tires. This information is not collected by 

LTPP; default values may be 

appropriate due to expected low 

variability of this parameter. 

Tire pressure for the wheels of each 

vehicle, or annual average for each 

FHWA vehicle class 4–13. 

Tire pressure (could be used as 

alternative means for computing 

size of tire footprint). 

This information is not collected by 

LTPP. 

Truck speed for each vehicle 

passage with each vehicle passage, 

or annual average for each FHWA 

vehicle class 4–13. 

Truck speed. Value could be obtained from 

available LTPP WIM IVRs recorded 

for each vehicle passage; LTPP WIM 

IVR files are stored offline in the

AIMS. 

IVR = individual vehicle record.

Parameters for Mechanistic–Empirical Pavement Performance Predictions 

While pavement response analysis and modeling studies are focused on stresses, strains, and 

deflections that pavements experience under each traffic load application, pavement performance 

analysis and modeling studies are focused on pavement distresses (cracking, rutting, faulting, 

etc.) that develop over time. Many pavement distresses develop from incremental or cumulative 

changes in pavement structure over time due to material aging, environmental impacts, and 

traffic loading. Therefore, for traffic loading characterization, in addition to information about 

individual traffic load applications, it is important to know the sequence and cumulative total 

number of traffic load applications that lead to pavement deterioration over time.

Many mechanistic–empirical pavement performance analyses are carried out using the MEPDG

method and software products, such as AASHTOWare Pavement ME Design.

(5) This software 

uses a defined set of traffic input parameters in a specific format. These parameters are described 

later in this chapter in section, Traffic Parameters for MEPDG Applications Using 

AASHTOWARE Pavement ME Design Software. The following sections detail

recommendations for traffic parameters needed for generic mechanistic–empirical pavement 

performance analysis and modeling. The traffic parameters that are formatted specifically for use

in the AASHTOWare Pavement ME Design software are described in a separate section. 

Axle Loading Characterization 

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

loads are summarized in the form of an axle load spectrum (note that, in some pavement 

applications, “axle load spectrum” is referred to as “axle load distribution”). An axle load 

spectrum represents a frequency distribution of axle loads, where counts of axle load applications 
43 

observed during a specified period of time are summed and reported using predefined load bins. 

Recognizing the importance of axle load configuration, separate axle load spectra are used to 

summarize axle load counts for typical axle groups: single, tandem, tridem, and quad. Depending 

on the intended use, load spectra could be created for an individual truck class or for all truck 

classes combined. In summary, input from axle load spectra provides information about axle 

load magnitudes, the number of axle load applications over a specified period of time at that 

magnitude, and axle load configuration (i.e., the number of axles in each axle load group). If no 

site-specific axle weight data are available to compute an axle load spectrum, default axle 

weights could be used. 

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

pavement is also important, especially for jointed rigid pavements. 

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 is predicted.

LTPP Tables Containing Traffic Parameters for Mechanistic–Empirical Pavement 

Performance Modeling 

Table 16 summarizes traffic parameters recommended for generic mechanistic–empirical 

pavement performance modeling using pavement response models with static loads. LTPP 

sources for these parameters are also provided in the table.

Table 16. Traffic parameters for generic mechanistic–empirical pavement performance 

modeling. 

Input Parameter Parameter Description LTPP Data Source

Axle load spectrum Frequency distribution, where a number of 

axle load applications observed during a 

specified period of time is reported in

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 could 

represent daily, monthly, or annual traffic 

loading summaries. This input must cover the 

whole analysis period, using time increments 

specified for analysis, so that the number of 

axle load applications can be used to model 

incremental changes in pavement structure 

over the selected analysis period.

This parameter is available in InfoPave(3)

tables DD_AX (daily), MM_AX (monthly), 

YY_AX (annual), and 

TRF_MONITOR_AXLE_DISTRIB

(annualized) for each year with WIM data, 

MEPDG_AXLE_LOAD_DIST_FACTOR

(representative values for a typical day of 

the month or year). 

Number of APT Annual or representative number of single, 

tandem, tridem, and quad axles for each truck 

class (FHWA vehicle classes 4–13).(11)

This parameter is available in InfoPave(3)

table TRF_MEPDG_AX_PER_

TRUCK for each year with sufficient WIM 

data or MEPDG_AXLE_PER

_TRUCK (representative set of values per 

LTPP site). Additional values could be 

computed from LTPP WIM IVR files.
44 

Input Parameter Parameter Description LTPP Data Source

Axle spacing for 

tandem, tridem, and 

quad axle groups 

Average axle spacing in inches for tandem, 

tridem, and quad+ axles. 

This parameter is not available through

LTPP sources. It could be computed using 

LTPP WIM IVR files.

Axle spacing 

distribution

Frequency of longitudinal spacing of 

consecutive axles in feet, excluding spacing 

within tandem, tridem, and quad+ axles. 

Used to model locations of the load for JPCP 

pavements.

This parameter is not available in 

InfoPave.

(3) It could be computed using 

LTPP WIM IVR files.

Average axle width The distance in feet between two outside 

edges of an axle. Only needed for rigid 

pavement analysis.

This information is not collected by LTPP. 

Use manufacturers’ truck specifications to 

find typical values.

Operational speed Average truck speed. This information could be obtained from 

WIM IVR records that follow the 2016 or 

later TMG data submission format.

Dual-tire spacing Dual tire spacing. This information is not collected by LTPP.

Use manufacturers’ truck specifications to 

find typical values.

Tire pressure One value representing hot tire-inflation 

pressure.

This information is not collected by LTPP.

Mean wheel location The distance from the outer edge of the 

wheel to the pavement marking. Used to 

model location of the load.

This information is not collected by LTPP.

Truck wander Standard deviation from the mean wheel 

location, based on wheel-location 

measurements from the lane marking. Used

to model location of the load.

This information is not collected by LTPP.

JPCP = jointed plain concrete pavement.

TRAFFIC SUMMARY PARAMETERS RECOMMENDED FOR HIGH-LEVEL AND

EMPIRICAL ANALYSES

Many LTPP studies of pavement performance use empirical methods or statistical models to 

correlate pavement performance parameters (e.g., road roughness) to traffic and environmental 

loads, site conditions, material properties, and construction practices. These studies frequently 

use a single traffic summary parameter to describe traffic in the LTPP lane at each LTPP site. 

These analyses may require a complete history of changes in the selected traffic summary 

parameter (computed annually for the duration of the pavement’s service life, LTPP experiment, 

or analysis period), a single cumulative value aggregated over the analysis period, or one 

representative traffic summary value for each LTPP site. The most frequently used traffic 

summary parameters for empirical analyses are AADTT and ESAL. More details about different 

summary statistics and recommendations for their uses are provided in the ensuing sections. 

Traffic Loading Summary Parameters

ESAL as a Traditional Traffic Loading Summary Statistic

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

applications since the 1960s. ESAL is a concept developed from data collected at the American 

Association of State Highway Officials (AASHO) Road Test to establish a relationship to 

compare the effects of axles carrying different loads on pavement damage.(18) In ESAL 
45 

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 ESALs or LEFs do not 

precisely describe the relationship between axle loads and specific pavement distresses like 

rutting or cracking, ESAL continues to be a convenient statistic for sizing and quantifying traffic 

loading levels for empirical pavement analysis and design. 

It is important to note that in addition to traffic loading, ESAL values depend on pavement type, 

pavement thickness, and road condition, expressed through a subjective pavement serviceability 

index. As a result, ESAL values representing the same traffic stream can vary due to a change in 

the pavement type or because the pavement was rehabilitated and, thus, its thickness and/or 

roughness changed. 

GESAL

GESAL is a parameter computed similar to ESAL, using LEFs for flexible pavements with an 

SN equal to 5 and pavement terminal serviceability index equal to 2.5.(19) Because LEFs are set 

to a constant, GESALs are independent of pavement type and thickness and the level and type of 

pavement distress. Therefore, any changes in GESAL values can be directly attributed to 

changes in traffic loads. This makes GESAL a more desired summary traffic loading statistic for 

comparing loads, correlating the effect of traffic loads to pavement performance or for 

comparing traffic loading between LTPP sites. This summary statistic is more sensitive to the 

importance of heavy loads on pavement performance compared to the average traffic load or 

total traffic load summary statistics. However, use of constant LEF parameters makes GESAL 

not applicable as a direct input to empirical pavement design using the methodology in the 1993 

version of the AASHTO Guide for Design of Pavement Structures.

(8)

RPPIF

RPPIF is a parameter designed to aid in identifying LTPP sites with similar traffic-loading 

levels.

(10)

 Like ESAL, RPPIF computation uses factors that are applied to load spectrum, but 

instead of using LEFs based on data from the AASHO Road Test,

(18) it uses a parameter called 

W-factor. W-factors were determined through MEPDG analysis, based on globally calibrated 

distress-prediction models included in the MEPDG report and software.(10) W-factors are not 

normalized with respect to 18-kip single-axle loads like LEFs, but instead are normalized with 

respect to a fully loaded tandem-axle load, which is 34 kips. The main purpose or usability of 

this statistic is to compare axle loading distributions between different sites. As MEPDG models 

evolve, W-factors used for RPPIF computation may need to be updated, or distress-specific Wfactors and RPPIF statistics may be desired for a particular pavement performance modeling 

task. As with GESAL, the RPPIF statistic is independent of pavement type and thickness and 

level of pavement distress.
46 

Annual Total Load and Cumulative Total Load 

The annual total truck load (ATL) parameter is an estimate or summation of all truck traffic 

loads accumulated over a year. The cumulative total truck load (CTL) parameter is an estimate or 

summation of all truck traffic loads accumulated over the entire analysis period. The main 

advantage of ATL and CTL parameters is that they are independent of empirically derived 

relationships that relate load to pavement damage, like ESAL. However, these parameters cannot 

be used to infer whether trucks are empty or loaded and whether their values are affected by the 

number or the weight of trucks (i.e., a small number of heavy trucks and large number of light 

trucks may produce the same ATL value). These limitation makes ATL and CTL parameters less 

desirable for analyses of pavement performance that have a nonlinear relationship with load

magnitude. 

Traffic and Truck Volume Summary Parameters

For analyses focused on characterizing total traffic or truck volumes at LTPP sites, several 

statistical parameters are available through InfoPave web portal.(3) The most widely used traffic 

volume parameters are AADT and AADTT in LTPP lane. The AADTT parameter is more 

relevant for pavement analysis and management applications than AADT because trucks have a 

much higher contribution to pavement damage than the lighter vehicles that make up most of the 

AADT number. The AADT parameter may be more appropriate as an input in road prioritization 

decision algorithms within pavement or road maintenance management applications.

Other traffic volume statistics used in pavement analyses are total annual truck volume, annual 

truck volume by vehicle class, the ratio of FHWA vehicle class 5 to class 9 truck volumes, 

cumulative volume of FHWA vehicle class 9 vehicles, and cumulative volume of heavy vehicles

(FHWA vehicle classes 4 and 6–13(11)). In some empirical analyses, annual volume of FHWA 

vehicle class 9 vehicles or the portion of AADTT attributed to FHWA vehicle class 9 vehicles 

may be used. Typically, in the United States, FHWA vehicle class 9 vehicles carry the largest 

portion of total load due to their heavy weight, and they typically make up a high percentage of 

the truck population. 

Recommendations for Selecting Summary Traffic Loading Parameters Based on the 

Purpose of the Analysis 

The following recommendations are for the situations when a single traffic loading summary 

parameter is a desired input for pavement analysis. No single traffic loading summary parameter 

works equally well for all pavement analysis applications, mostly due to the differences in 

sensitivity of different pavement distresses to load magnitude versus the number of axle load 

applications. The choice of a traffic loading statistic should be based on the intent of the analysis 

and perceived relationship between the load and pavement distress. The following is guidance 

for selecting a traffic loading summary parameter based on the type of pavement distress being 

analyzed: 

• If a pavement distress is primarily caused by the repeated heavy axle loads (as in the case 

of fatigue cracking), then a summary loading statistic that properly accounts for the 

number of heavy-load applications should be used, such as ESAL or GESAL. 
47 

• If a pavement distress is caused by overloaded trucks or axles (as in the case of rigid

pavement slab cracking), then the summary loading statistic should accurately describe 

the number of fully loaded and overloaded axles and average weight of these axles, such 

as the number of fully loaded and overloaded axles or the number and average weight of 

heavy trucks (FHWA vehicle classes 4 and 6–13(11)

). 

• If a pavement distress is primarily caused by repeated load application (as in the case of 

raveling and, to some degree, rutting), then a summary loading statistic that accurately 

describes the number of load applications should be used, such as AADTT, cumulative 

total truck volume, CTL, or the total number of axle loads for FHWA vehicle classes

4–13. 

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

traffic loading summary parameter is desired for the analysis, then GESAL or RPPIF may 

be the traffic loading summary statistic of choice. The reasoning behind this 

recommendation is that these statistics are independent of pavement-related variables but 

recognize the higher significance of heavier traffic loads in pavement deterioration. These 

parameters are based on an actual axle load spectrum, as well as truck volume. Another 

alternative is to use a parameter that represents the total number of heavy axle load 

applications (those that are at or above of 50, 75, or 100 percent of the Federal legal load 

limit per axle or per truck).

• If a traffic loading statistic that is free of any adjustments with respect to significance of 

load magnitude to pavement damage development is needed, either the ATL or CTL 

summary statistic can be used. These parameters are suitable for higher level pavement 

network-level performance analyses.

• If no site-specific vehicle weight data are available to compute axle or truck weights for 

use as a loading traffic summary statistic, default axle weights can be selected for 

individual truck FHWA vehicle classes 4–13,(11) such as defaults provided in LTPP 

PLUG.

(9)

 These default weights, in combination with site-specific truck volume and 

vehicle classification data, can be used to estimate traffic loads and compute a traffic 

loading summary statistic of choice for any LTPP site. 

• If a parameter to characterize or differentiate traffic loading intensity between LTPP sites

is desired, the average truck weight, GESAL per truck, or RPPIF per truck can be used as 

a traffic loading summary statistic. Another alternative is to characterize traffic loading 

intensity between LTPP sites with respect to the percentage of heavy axles (75 percent or 

more of the Federal legal load limit per axle) that are present on a test section. For 

example, a test site might be characterized to have light loading (i.e., less than one-third

of axle applications are heavy), moderate loading (i.e., between one-third and one-half of 

axle applications are heavy), or heavy loading (i.e., greater than one-half of axle 

applications are heavy).
48 

LTPP Tables Containing Traffic Loading Summary Parameters

Table 17 summarizes different traffic summary parameters, specifies the types of analyses for 

which these parameters are appropriate to use, and provides references to LTPP traffic data 

tables (accessible through InfoPave web portal(3)

) containing these parameters.

Table 17. Traffic summary parameters.

Parameter Description

Recommended Use and 

Limitations LTPP Data Sources

AADT Traffic summary statistic used 

to measure average daily road 

use by all vehicular traffic. 

Can be used to quantify road 

importance for pavement 

management applications to 

quantify the level of facility 

use and as an input for safety 

and congestion studies; should 

not be used for evaluating the 

effect of traffic loads on 

pavement performance. 

AADT_ALL_VEHIC_2WAY

and AADT_ALL_VEHIC

fields from TRF_MON_EST_

ESAL and TRF_HIST_EST_

ESAL tables. Data are not 

available for all LTPP test 

sites or all in-service years. 

Additional data interpolation 

and extrapolation required to 

estimate AADT for all years.

AADTT Truck traffic summary statistic 

used to measure average daily 

road use by heavy vehicles 

(FHWA vehicle classes

 4–13).

(11).

Can be used to quantify road 

importance for pavement 

management applications; not 

sufficient as a single summary 

statistic for evaluating the 

effect of traffic loads on 

pavement performance.

AADTT_ALL_TRUCKS_

TREND field from 

TRF_TREND table (for LTPP 

lane only). 

Cumulative truck 

traffic volume

Traffic summary statistic used 

to measure total road use by 

heavy vehicles (FHWA 

vehicle classes 4–13) from the 

pavement’s open to traffic date 

to the end of pavement service 

life or end of the LTPP 

experiment. 

Can be used as an input in 

analyses of effects of traffic on 

nonstructural pavement 

distresses and as supplemental 

input in analyses of effects of 

traffic on structural pavement 

response and performance; not 

sufficient as a single summary 

statistic for evaluating the 

effect of traffic on structural 

pavement performance.

Compute by summing 

ANNUAL_TRUCK_

VOLUME_TREND values 

from TRF_TREND table. 

Cumulative 

volume of

FHWA vehicle 

class 9 trucks

Traffic summary statistic used 

to measure road use by FHWA 

vehicle class 9 vehicles from

the pavement’s open to the 

end of pavement service life or 

end of the LTPP experiment. 

Can be used as a primary or 

supplemental input for 

analyses of pavement 

performance for LTPP sites 

with dominant FHWA vehicle 

class 9 trucks; limitations 

associated with variable loads 

carried by FHWA vehicle 

class 9 trucks should be 

understood.

CUMULATIVE_VEH_

CLASS_9_TREND field from 

TRF_TREND table. 

Cumulative 

volume of heavy 

trucks (FHWA 

vehicle classes 4 

and 6–13)

Traffic summary statistic used 

to measure road use by FHWA 

vehicle classes 4 and 6–13 

from the pavement’s open to 

traffic date to the end of 

pavement service life or end of 

the LTPP experiment.

Can be used as a primary or 

supplemental input for 

analyses of pavement 

performance; limitations 

associated with variable loads 

carried by these trucks should 

be understood.

CMLTV_VOL_HEAVY_

TRUCKS_TREND field from

TRF_TREND table. 
49 

Parameter Description

Recommended Use and 

Limitations LTPP Data Sources

Annual ESAL Traffic loading summary 

statistic that uses coefficients 

developed from the AASHO 

Road Test to convert traffic 

stream to an equivalent 

number of 18,000-lb

single-axle loads. 

Historically used as a primary 

input parameter to relate 

pavement performance to 

traffic loading; can be used to 

characterize traffic loading at 

the site, but can be affected by 

nontraffic parameters (i.e., 

pavement structure, thickness, 

and serviceability); if used as a 

direct input to analyze 

pavement response or 

performance, limitations 

associated with the ESAL 

statistic should be understood. 

ANL_KESAL_LTPP_LN_YR 

field from TRF_HIST_EST_

ESAL or TRF_MON_EST_

ESAL tables; 

TRF_ESAL_COMPUTED

table KESAL_YEAR field;

TRF_TREND table

ANNUAL_ESAL_TREND

field. 

Cumulative 

ESAL

Traffic loading summary 

statistic that provides total 

measure of traffic load 

accumulated from the 

pavement’s open to traffic date 

to the end of pavement service 

life or end of the LTPP 

experiment; uses coefficients 

developed from the AASHO 

Road Test to convert traffic 

stream to an equivalent 

number of 18,000-lb

single-axle loads. 

Historically used as a 

parameter to relate pavement 

performance to traffic loading; 

used as traffic input to the 

AASHTO guide from 1993(8)

and earlier pavement design 

methods; can be used as a 

general estimate of cumulative 

traffic loading; however, 

because it is affected by 

nontraffic parameters (i.e., 

pavement structure, thickness, 

and serviceability), its 

applicability may be limited. 

Compute by summing the 

annual ESAL values from 

TRF_HIST_EST_ESAL, 

TRF_MON_EST_ESAL, 

TRF_ESAL_COMPUTED, or

TRF_TREND tables. 

Annual GESAL Similar to ESAL computation

for flexible pavements but has 

pavement structure and 

pavement condition inputs set 

to a constant value. 

Can be used as a general 

estimate of traffic loading at 

the site and as an input 

parameter to relate pavement 

performance to traffic loading 

when multiple sites are 

considered in the analysis; if 

used in the analysis, 

limitations and assumptions 

associated with the GESAL 

formulation must be taken in 

consideration. 

ANNUAL_GESAL_TREND

field from TRF_TREND table. 

Cumulative 

GESAL

Traffic loading summary 

statistic similar to cumulative 

ESAL but has pavement 

structure and pavement 

condition inputs set to constant 

values. 

Can be used as a general 

estimate of traffic loading at 

the site and as an input 

parameter to relate pavement 

performance to traffic loading 

when multiple sites are 

considered in the analysis; if 

used in the analysis, 

limitations and assumptions 

associated with GESAL 

formulation need to be 

considered by the analyst. 

Compute by summing the 

annual GESAL values from 

ANNUAL_GESAL_TREND 

field of TRF_TREND table. 
50 

Parameter Description

Recommended Use and 

Limitations LTPP Data Sources

Representative 

RPPIF per truck 

axle

Summary loading statistic 

similar to ESAL per axle (i.e., 

single, tandem, tridem, and 

quad) but with LEF estimated 

based on MEPDG simulations 

and normalized to fully loaded 

34,000-lb tandem-axle loads. 

Used to quantify differences in 

axle loading between different 

axle load spectra; can be used 

to identify load spectra likely 

to produce different levels of 

pavement distresses, 

especially for distress that are 

sensitive to heavy axle load 

applications. 

REP_RPPIF_SINGLE_

AXLE, REP_RPPIF_

TANDEM_AXLE, REP_

RPPIF_TRIDEM_AXLE, and

REP_RPPIF_QUAD_AXLE

fields from TRF_REP table. 

Representative

RPPIF per truck

Similar to ESAL per truck 

statistic but with LEF 

estimated based on MEPDG 

simulations and normalized to 

fully loaded 34,000-lb

tandem-axle loads. 

Can be used to quantify 

differences in loading between 

different truck classes or for 

the same truck class between 

different LTPP sites. 

REP_RPPIF_PER_TRUCK

and REP_RPPIF_PER_

VEH_CLASS_# fields from 

TRF_REP table. 

ATL Summary loading statistic that 

represents an estimate of the 

total weight of all vehicles in 

FHWA vehicle classes 4–13 

applied to the pavement during 

the reporting year. 

Can be used as an input 

parameter to relate pavement 

performance to traffic loading; 

major limitation is that it does 

not contain information on the 

number of heavy loads that are 

especially damaging for 

pavements. 

ANNUAL_TOTAL_LOAD_

TREND field from

TRF_TREND table. 

CTL Summary loading statistic that 

represents an estimate of a 

total weight of all vehicles in 

FHWA vehicle classes 4–13 

applied to the pavement from 

the pavement’s open to traffic 

date to the end of pavement 

service life or end of the LTPP 

experiment. 

Can be used as an input 

parameter to relate pavement 

performance to traffic loading; 

major limitation is that it does 

not contain information on the 

number of heavy loads that are 

especially damaging for 

pavements. 

Compute by summing annual 

traffic load values from 

ANNUAL_TOTAL_LOAD_

TREND field of TRF_TREND 

table. 

Representative 

average GVW 

for each FHWA 

vehicle class

 4–13

Representative average GVW 

for each FHWA vehicle class 

4–13. 

Describes typical weight of 

different types of trucks but 

does not contain information 

about truck volume or total 

loading experienced by the 

site; in combination with 

AADTT by vehicle class 

information, can be used to 

define traffic loading 

associated with each FHWA 

vehicle class 4–13 at the site. 

REP_GVW_VEH_CLASS_#

field from TRF_REP table. 
51 

Parameter Description

Recommended Use and 

Limitations LTPP Data Sources

Representative 

average GVW 

for FHWA 

vehicle classes 

4–13 combined

Representative average GVW 

for the site for FHWA vehicle 

classes 4–13 combined. 

Can be used to identify or 

group LTPP sites with a 

similar rate of traffic loading; 

provides a description of 

expected traffic loading but 

does not contain information 

about truck volume or total 

loading experienced by the 

site; in combination with 

AADTT information, can be 

used to characterize total 

traffic at the site. 

REP_GVW_TRUCK field

from TRF_REP table. 

TRAFFIC PARAMETERS FOR MEPDG APPLICATIONS USING AASHTOWARE 

PAVEMENT ME DESIGN SOFTWARE

A complete list of input traffic parameters required by AASHTOWare Pavement ME Design 

software(5) is shown in table 18. This software provides a means to analyze pavement response 

and performance using MEPDG models. The parameters shown in table 18 are also used for the 

local calibration of MEPDG models and for developing and testing new performance prediction 

models. 

Table 18. Traffic input parameters required by AASHTOWare Pavement ME Design 

software.

(5)

Input Parameter Parameter Description LTPP Data Source

ALDF for single, 

tandem, tridem, 

and quad axles 

(percent) 

ALDF represents a percentile axle load 

distribution for a typical day for each 

calendar month for a typical design/analysis 

year; one set of ALDF is provided for each 

vehicle class (FHWA vehicle classes

 4–13),

(11) axle group type (i.e., single, 

tandem, tridem, and quad+), and calendar 

month (January through December); ALDF 

stay constant between analysis years.

This parameter is available in MEPDG_

AXLE_LOAD_DIST_FACTOR table in

MEPDG_LG01... MEPDG_LG39 fields. 

Vehicle class 

volume 

distribution

(percent) 

One percentile distribution of vehicles in 

FHWA vehicle classes 4–13 is provided to 

represent an average VCD for the base 

design/analysis year.

This parameter is available in 

MEPDG_TRUCK_VOL_PARAMETERS 

table in VEH_CLASS_DISTRIBUTION

_PERCENT field, where vehicle class is 

defined by the VEH_CLASS field. 

Monthly 

adjustment factors

One set of 12 monthly coefficients is 

provided for each FHWA vehicle class 4–13 

to represent differences in truck volumes 

between different calendar months for the 

base design/analysis year; the sum of factors 

for all months for one truck class should 

equal 12.

Information to compute this parameter is 

available for some sites through the 

InfoPave web portal(3) in the form of 

monthly summary statistics for each year 

with data; see the 

TRF_MEPDG_MONTH_ADJ_FACTR 

table and MONTHLY_RATIO field;

compute the average factors, considering 

all or selected years of data; one 

representative set of factors should be used 

in AASHTOWare Pavement ME Design

software. 
52 

Input Parameter Parameter Description LTPP Data Source

Hourly adjustment 

factors (percent)

One set of 24 hourly factors is provided, 

showing the representative percentage of 

total truck traffic for each hour; values are 

the same for all truck classes and only apply 

to truck volumes; the sum of factors for all 

hours should equal 100; this input parameter 

only applies to PCC pavements.

Information to compute this parameter is 

available through the InfoPave web 

portal(3) for each year with monitored 

traffic data; see 

TRF_MEPDG_HOURLY_DIST table and 

PCT_HOURLY field; compute the average 

factors, considering all or selected a years 

of data; one representative set of factors 

should be used in AASHTOWare

Pavement ME Design software.

Number of APT One representative set of values is provided,

showing the average number of single, 

tandem, tridem, and quad axles for each 

truck class (FHWA vehicle classes 4–13).

This parameter is available through 

InfoPave web portal(3) in 

MEPDG_AXLE_PER_TRUCK table in

SINGLE_AXLES, TANDEM_AXLES, 

TRIDEM_AXLES, and QUAD_AXLES

fields. 

Base year 

AADTT for LTPP 

lane

One value representing the annual average

daily volume of vehicles in FHWA vehicle 

classes 4–13 for the first full (base) 

design/analysis year is provided. If this 

input parameter is used in the 

AASHTOWare Pavement ME Design

software in place of two-way AADTT, enter 

the following values: percent trucks in 

design direction = 100, and percent trucks in 

design lane = 100; alternative input to 

AADTT for LTPP lane: base year two-way 

AADTT.

This parameter is available through 

InfoPave web portal(3) in MEPDG_

TRUCK_VOL_PARAMETERS table in

AADTT_FIRST_YEAR_LTPP_LANE

field. 

Base year

two-way AADTT

Two-way AADTT is provided, computed 

for the first full (base) design/analysis year.

Information to compute this parameter 

using a limited number of years is 

available in the LTPP database for many

LTPP sites; instead of this parameter, a 

better approach is to use the parameter and 

corresponding instructions for base year 

AADTT for LTPP lane.

Number of lanes 

in design direction

Number of lanes in the design direction 

(direction of LTPP lane) is provided. 

This parameter is available in 

SHRP_INFO table in LANES_LTPP_DIR

field. 

Percentage of 

trucks in design 

direction 

Percentage of trucks in the design direction 

(direction of LTPP lane) is provided for the 

base design/analysis year.

This parameter is not available in the 

LTPP sources; instead, use the parameter 

and corresponding instructions for base 

year AADTT for LTPP lane. 

Percentage of 

trucks in design 

lane 

Percentage of trucks in the design lane 

(LTPP lane) is provided for the base 

design/analysis year.

This parameter is not available in the 

LTPP sources; instead, use the parameter 

and corresponding instructions for base 

year AADTT for LTPP lane. 

Growth rate by 

vehicle class 

(percent)

Annual growth rate (percent) for each truck 

class (FHWA vehicle classes 4–13) is 

provided. This parameter is used together 

with the growth function parameter (linear 

or compound) to estimate truck volume 

from AADTT values provided for the base 

design/analysis year for each year of the 

analysis/design period.

This parameter is available in 

MEPDG_TRUCK_VOL_PARAMETERS 

table in VEH_CLASS_GROWTH_RATE

field. 
53 

Input Parameter Parameter Description LTPP Data Source

Vehicle class 

growth function

by vehicle class

Type of truck volume growth function

(linear or compound) is provided by FHWA 

vehicle classes 4–13. This parameter is used

together with the growth rate parameter to 

estimate truck volume over the 

analysis/design period from the base 

design/analysis year AADTT values.

This parameter is available in 

MEPDG_TRUCK_VOL_PARAMETERS 

table in VEH_CLASS_GROWTH

_FUNCTION field. 

Operational speed 

(mph)

This parameter is defined as the posted 

speed limit or the average speed of heavier 

trucks through the project limits.

This parameter is not available in the 

LTPP sources but could be computed from 

WIM IVR files submitted in the 2016 

TMG(11) format; alternatively, use the 

posted speed limit.

Axle spacing for 

tandem, tridem, 

and quad axles

(inches) 

Average representative axle spacing

(inches) is provided for tandem, tridem, and 

quad axles. 

This parameter is not available in the 

LTPP sources but could be computed from 

WIM IVR files; alternatively, use the 

LTPP default in the LTPP PLUG report.

(9) 

Percentage of

trucks with short, 

medium, and long

wheelbases

(percent)

This parameter provides percentages of 

trucks with wheelbases that fall in the 

following three categories: short (≤12 ft), 

medium (>12 and ≤15 ft), and long (>15 

and ≤20 ft); for multiunit and combination 

trucks, only the wheelbase of the truck 

power-unit (i.e., first unit) is considered; 

used for top–down JPCP cracking model 

only.

This parameter is not available in the 

LTPP sources but could be computed from 

WIM IVR files; alternatively, use the 

LTPP default in the LTPP PLUG report. 

Average axle 

width (ft)

The average distance (feet) between two 

outside edges of an axle is provided as a 

representative value for all truck classes; 

only needed for rigid pavement designs.

This parameter is not available in the 

LTPP sources; use the default in 

AASHTOWare Pavement ME Design

software.

Mean wheel 

location (inches) 

The mean distance (inches) from the outer 

edge of the wheel to the pavement marking 

is kept constant between all truck classes 

and does not change over time.

This parameter is not available in the 

LTPP sources; use the default in 

AASHTOWare Pavement ME Design

software.

Truck wander 

standard deviation

(inches) 

Standard deviation (inches) from the mean 

wheel location is computed based on 

measurements from the lane marking.

This parameter is not available in the 

LTPP sources; use the default in 

AASHTOWare Pavement ME Design

software.

Dual-tire spacing

(inches) 

Average spacing (inches) of dual tires is 

kept constant between all truck classes and 

does not change over time.

This parameter is not available in the 

LTPP sources; use the default in 

AASHTOWare Pavement ME Design

software.

Tire pressure (psi) One value representing hot tire inflation 

pressure is kept constant between all truck 

classes and does not change over time.

This parameter is not available in the 

LTPP sources; use the default in 

AASHTOWare Pavement ME Design

software.

MEPDG Parameters Requiring Special Input Formats 

Most input parameters can be manually entered into AASHTOWare Pavement ME Design

software user interface.

(5)

 However, two input parameters—axle load distribution factors and 

monthly truck volume adjustment factors—consist of a large set of values. Due to the large size 

of these input parameters, the preferable way to enter them into AASHTOWare Pavement ME

Design software is to upload files in a format readable by the software. Alternatively, these input 
54 

parameters could be entered into the software by copying values from the 

MEPDG_AXLE_LOAD_DIST_FACTOR table and pasting into the AASHTOWare Pavement 

ME Design software user interface. 

RECOMMENDATIONS FOR ASSESSING AXLE LOADING DISTRIBUTION DATA 

RATIONALITY

For successful use in pavement analyses, axle loading distributions factors or NALS must be 

computed using accurate axle loading data. Accurate estimates of heavy axle loads are especially 

important for load-related pavement response and distress modeling. Therefore, WIM data 

should be collected by a calibrated WIM system that satisfies ASTM E1318-09 requirements for 

Type I WIM systems for the duration of the data collection period.(17)

Only a limited number of LTPP WIM sites have the necessary information in 

TRF_EQUIPMENT_MASTER and TRF_CALIBRATION_WIM tables (available through 

InfoPave web portal(3)

) to quantify WIM data quality and conclude that the available data satisfy 

the ASTM E1318-09 requirements for Type I WIM systems. To overcome this limitation, 

several WIM data rationality checks have been developed to help LTPP users evaluate the 

reasonableness and usability of available axle loading data. 

The WIM data rationality checks detailed in this section could be used to identify axle loading 

distributions likely affected by a strong measurement bias (due to a lack of calibration or 

calibration drift) and/or low precision in heavy weight measurements or by vehicle 

misclassification. These checks apply to monthly and annual single and tandem NALS for 

FHWA vehicle class 9 trucks.(11) FHWA vehicle class 9 NALS are used because this vehicle 

class has a well-known and predictable axle loading distribution. Changes in NALS over time or 

unusual distributions are used to identify data with suspected quality issues. 

These checks are designed to identify high percentages of unusually light or heavy axle loads for 

FHWA vehicle class 9 vehicles, as well as atypical weights corresponding to loaded and 

unloaded tandem axles of FHWA vehicle class 9 vehicles. High percentages of very light loads 

typically indicate a vehicle misclassification issue or calibration drift. Very high percentages of 

loads exceeding the legal load limit on tandem axles typically indicate calibration drift. Annual 

or monthly NALS that have high percentages of both very light and very heavy loads could also 

indicate low precision or temperature sensitivity of WIM weight measurements. Knowledge of 

local truck traffic trends and commodities is important for making decisions about usability of 

WIM datasets flagged through reasonableness checks. 

The following are class 9 single-axle NALS checks:

• Ten percent or greater are <5,000 lb. 

• Three percent or greater are ≥20,000 lb. 

• Average single-axle weight or peak load in NALS is <9,000 lb. 

• Average single-axle weight or peak load in NALS is >12,500 lb. 
55 

The following are class 9 tandem-axle NALS checks: 

• U.S. sites: 10 percent or greater are <8,000 lb. 

• U.S. sites: 20 percent or greater are ≥34,000 lb. 

• Canadian sites: 10 percent or greater are <8,000 lb. 

• Canadian sites: 20 percent or greater are ≥38,000 lb.

• WIM sites located on a road with predominantly empty class 9 trucks have >7 percent of 

overloaded tandems. 

• Calibration drift check: Sites are likely to be out of calibration if the following applies: 

o Percentage of axles between 30,000 and 35,999 lb is less than the percentage of axles 

between 36,000 and 41,999 lb. (Note: This condition is likely an overestimation of 

loads.) 

o Percentage of axles between 26,000 and 33,999 lb is less than the percentage of axles 

between 20,000 and 25,999 lb for sites with less than 30 percent of axles between 

10,000 and 15,999 lb. (Note: This condition is likely an underestimation of loads.) 

• The first loading peak (for tandem axles on empty trucks) is not between 10,000 and 

15,999 lb. 

• The second loading peak (for tandem axles on loaded trucks) is not between 30,000 and 

33,999 lb. 

• Average loaded tandem-axle weight (this parameter is computed as the average weight of 

class 9 tandem axles weighing 26,000 lb or more) is <29,000 lb. 

• Average loaded tandem-axle weight is ≥34,000 lb.

57 

CHAPTER 5. WHERE TO GET LTPP TRAFFIC DATA

LTPP INFOPAVE 

The InfoPave web portal is one way to get LTPP traffic data and information (figure 1).(3) The 

web portal provides a means to search for and download specific LTPP data tables and 

parameters. The Help feature, shown in the top right corner of figure 1, provides information on 

how to use and navigate the InfoPave web portal. Detailed instructions on how to use InfoPave 

to extract traffic parameters are provided in part 2 of this Guide. 

Source: FHWA.

Figure 1. Screenshot. InfoPave Table Export page.(3)

The following InfoPave features are most useful for obtaining traffic data: 

• Search bar: This feature, shown in the top right corner of figure 1 underneath the Help 

feature, allows users to find information, documents, and specific data tables by 

searching for a keyword or phrase. This feature is especially helpful if users do not know 

what LTPP table or document contains the desired traffic information.

• Data module: This module contains the InfoPave table export feature, which allows 

LTPP users to quickly locate and download LTPP data tables that have the desired traffic 

information. In addition to downloading data using this module, users may download 

documents and other ancillary traffic information. 
58 

CUSTOM REQUESTS

For data or information not included in LTPP data tables or not found through the InfoPave web 

portal, a custom request can be sent via e-mail to the LTPP Customer Support Service Center at 

ltppinfo@dot.gov. Other contact information is posted on the LTPP program website.

(20)
59 

PART 2. PLAYBOOK

OVERVIEW

Part 2 of the Guide serves as a playbook showing practical examples of how to identify and 

extract the desired LTPP traffic data and parameters for different pavement analysis applications. 

The examples are presented for the following scenarios in the corresponding chapters: 

• Chapter 6—Generic Steps to Select and Extract Data Tables Using LTPP InfoPave.

• Chapter 7—Scenario 1: Obtain Truck Traffic and Volume Information.

• Chapter 8—Scenario 2: Obtain Vehicle Classification Information.

• Chapter 9—Scenario 3: Obtain Axle or Truck Loading Information. 

• Chapter 10—Scenario 4: Obtain Summary Traffic Loading Information (ESAL or 

Alternative Statistics).

• Chapter 11—Scenario 5: Obtain MEPDG Traffic Inputs for Use in AASHTOWare 

Pavement ME Design software.

61 

CHAPTER 6. GENERIC STEPS TO SELECT AND EXTRACT LTPP TRAFFIC 

PARAMETER TABLES USING LTPP INFOPAVE

The generic steps to select and extract a table with the desired LTPP traffic parameter from 

InfoPave are listed as follows.(3)

 A detailed data extraction example using these steps is provided 

in the next section. 

1. From the DATA tab on the InfoPave application Data screen, select “Table Export” 

option. This action will display the Table Export menu in the InfoPave application. 

2. Select the “+” to the left of the word “Traffic” from the Table Export menu, which will 

reveal the list of tables containing traffic parameters that can be downloaded. 

3. Select the table name that contains the desired parameter.

4. Click on the button “Add to Data Bucket” located at the bottom of the Table Export menu. 

5. Locate the “Data Bucket” menu label on the top right of the main InfoPave screen and 

click on it. Select the “Data” option from the drop-down Data Bucket menu.

6. On the displayed Data Bucket form, specify the email address to receive data extraction 

notifications and the desired data extraction format (typically Microsoft Excel® or 

Access) and click on the “Submit for Data Extraction” button shown on the bottom of the 

Data Bucket form. 

7. Open the email account specified on the Data Bucket form. 

8. Find a new email from InfoPave (noreply@infopave.com) containing Data Bucket unique 

ID number and a web link to check the status of data extraction. 

9. Use the weblink and the Data Bucket ID number to access File Download form. 

10. Click on the word “Download” displayed on the pop-up File Download form to download 

the data table to a desired location on a local computer. 

Generic Example of LTPP Traffic Parameter Extraction Using InfoPave 

The generic data extraction steps described in the following example can be used to extract 

LTPP data tables using the InfoPave web portal.(3)

1. Open the InfoPave website: https://infopave.fhwa.dot.gov.

(3)

2. On the InfoPave screen, click on the DATA tab displayed on the top toolbar. When the 

drop-down list appears under DATA tab, click on “Table Export” option located on the 

drop-down list, as shown in figure 2. 
62 

Source: FHWA.

Figure 2. Screenshot. InfoPave Data screen with the DATA drop-down box displayed and 

“Table Export” option selected.(3) 

3. When the Table Export menu is displayed, find the word “Traffic” and click on the circle 

with “+” sign displayed to the left of the word “Traffic.” This action will display a list of 

available traffic parameters and associated traffic data tables. It will also change the “+” 

sign to a “−” sign to the left of the word “Traffic,” as shown in figure 3. 
63 

Source: FHWA.

Figure 3. Screenshot. InfoPave Table Export menu with checkboxes selecting the “Section” 

label and the traffic table label “Monitored Traffic Axle Distribution 

(TRF_MONITOR_AXLE_DISTRIB).”(3)

4. Scroll down the list until you find the desired parameter and/or table name. The list item

“Monitored Traffic Axle Distribution (TRF_MONITOR_AXLE_DISTRIB)” is used in 

this example.

5. Click on the checkbox to the left of the desired table name, as shown in figure 3. No 

additional selection is needed to download the whole table. However, if data are needed 

for a specific LTPP section or a set of sections, look on the left-hand side of the screen 

shown in figure 3 and find the word “Section” displayed on the left side of the screen 

under headers “Find Sections,” and “General.” 

6. Click on the checkbox displayed to the left of the word “Section.” A pop-up screen will be 

displayed with LTPP section IDs, filtered by the State name, as shown in figure 4. 
64 

Source: FHWA.

Figure 4. Screenshot. InfoPave Section pop-up screen with site IDs displayed for the

selected State (Arizona is selected).

(3)

7. Select the desired State and LTPP section ID by placing a checkmark to the left of the 

section ID and click the “Apply” button. 

8. Scroll to the bottom of the Table Export form and click on the “Add to Data Bucket” 

button located at the bottom. 

9. Locate the “Data Bucket” label on the top of the main InfoPave screen (figure 5) and put 

mouse pointer over it. A drop-down list will be displayed, as shown in figure 5. Find the 

“Data” option on that list and click on it. 
65 

Source: FHWA.

Figure 5. Screenshot. InfoPave screen displaying the top bars with the “Data Bucket” label 

displayed.(3)

10. On the Data Bucket form that will show up (figure 6), specify your email address and the 

desired data extraction format (typically Excel or Access) and click on the “Submit for 

Data Extraction” button shown at the bottom of that screen. 
66 

Source: FHWA.

Figure 6. Screenshot. InfoPave Data Bucket screen with data export format options.(3)

11. Open the email account provided on the Data Bucket form. Find a new email from 

InfoPave (noreply@infopave.com) containing a Download URL. 

12. Click on the weblink provided on the Download URL line in the email. A File Download 

web form within the InfoPave website will open as illustrated in figure 7. 
67 

Source: FHWA.

Figure 7. Screenshot. InfoPave File Download screen with a clickable hyperlink to 

download data.(3)

13. Click on the word “Download” displayed on the File Download form to download the data 

table to a desired location on a local computer. 

14. Review the downloaded data table by opening the downloaded file with the appropriate 

software. Figure 8 illustrates this step using Excel.
68 

Source: FHWA.

Figure 8. Screenshot. Excel screen capture showing the downloaded and saved data from 

the TRF_MONITOR_AXLE_DISTRIB table.(3)
69 

CHAPTER 7. SCENARIO 1: OBTAIN TRUCK TRAFFIC AND TRUCK VOLUME 

INFORMATION 

PARAMETER 1.1: ANNUAL AVERAGE DAILY TRAFFIC VOLUME

By definition, “AADT” refers to the annual average daily traffic volume occurring in all lanes 

and in both directions of travel at a location. AADT is typically used only for high-level 

pavement management analyses either to characterize the daily use of a highway facility by all 

vehicular traffic or as a preliminary traffic statistic. AADT can be used along with other 

available traffic statistics, such as “percent trucks” and “number of lanes,” to estimate truck 

traffic volumes on a specific lane of pavement for which estimates of expected pavement 

performance are desired. Because these more desirable detailed truck volume statistics are 

readily available within the LTPP traffic data tables, use of the AADT parameter for LTPP 

analyses is very limited. 

Due to its modest connection to changes in LTPP lane pavement performance over time, AADT 

was not a focus of the LTPP traffic data collection program. Thus, AADT is not always present 

in the current LTPP database tables. The AADT values available in the LTPP database are either 

computed by the LTPP traffic data processing software using State-submitted traffic data or are 

State-computed and -submitted parameters. These values can be extracted and used as overall 

estimates of roadway traffic.

Option 1: Download AADT Values from LTPP Database Tables

AADT values can be found in several LTPP traffic data tables available on InfoPave:

(3)

• TRF_HIST_VOLUME_COUNT (contains State-computed AADT values). 

• TRF_HIST_EST_ESAL (contains State-computed AADT values that may not be in 

TRF_HIST_VOLUME_COUNT). 

• TRF_MON_EST_ESAL (contains State-computed AADT values for years without 

monitoring data or without monitored loading data). 

To download these three tables, use the instructions provided in chapter 6, Generic Steps to 

Select and Extract LTPP Traffic Parameter Tables Using LTPP InfoPave. Once data have been 

extracted, open the downloaded files, and use the AADT values included in the following fields 

in the three tables as follows: 

• Table = TRF_HIST_VOLUME_COUNT, field = COUNT_AADT. 

• Table = TRF_HIST_EST_ESAL, field = AADT_ALL_VEHIC_2WAY. 

• Table = TRF_MON_EST_ESAL, field = AADT_ALL_VEHIC_2WAY. 

Option 2: View AADT Values in InfoPave Section Summary Report

In addition to the data download option described in option 1, the AADT values can be viewed in 

InfoPave on the Section Summary Report screen. The AADT values shown on the Section 
70 

Summary Report screen are not roadway AADT values. The InfoPave values are AADT in the 

direction of travel of the LTPP test section. The InfoPave AADT values are roughly half of the 

AADT for the road section that contains the LTPP test section. To differentiate from the standard 

AADT definition, these values are referred to as “Directional AADT” in this Guide. 

To use InfoPave to view Directional AADT values, first select the “State/Province Summary 

Report” option under the DATA menu (figure 9), and then select the State and Section ID for the 

site of interest on the Section Summary Report form. Select the “+” sign next to “Climate and 

Traffic” heading shown in the middle of the Section Summary Report form. InfoPave will 

respond with an image that looks like figure 10. 

Source: FHWA.

Figure 9. Screenshot. Example of the “State/Province Summary Reports” option selected.

(3)
71 

Source: FHWA.

Figure 10. Screenshot. Example of AADT values available in the “Section Summary 

Report.”(3)

If Directional AADT values are available at this site, they will be found in the first traffic 

estimate column. For example, an InfoPave screen capture in figure 10 shows Directional AADT 

estimates for 1986 to 1990 for Maryland section 1632. 

Example 1.1: Obtain an Estimate of AADT for an LTPP Test Site

The availability of that data may vary considerably between LTPP test sites. No simple 

mechanism exists for obtaining a single representative AADT value for a given site, nor does one 

exist for obtaining AADT values for all pavement in-service years for all sites. If a user requires 

a representative AADT value, the recommended option is to download the available AADT 

values from the three tables listed under option 1 and analyze the data found in those tables. A 

representative value can then be constructed. Although there are many ways to develop such a 

value, the suggested method is to: 
72 

1. Determine the years for which the representative value is to be applied.

2. Take the values available on InfoPave(3) and determine a linear equation for those data. 

(For example, use Excel’s “Add Trendline” feature and select the “Linear” option and the 

“Display equation on chart” option.) 

3. Use that equation to estimate the AADT value for any years for which the data are 

missing.

4. Compute an average of the AADT values for all years, including both the downloaded 

data and estimates for the missing years. This helps remove biases that might result from 

more data being present in some portions of a pavement’s life compared to other years. 

Other methods for interpolating missing years of data (such as compound growth) can also be 

used and may be more appropriate if the AADT trend line is nonlinear. 

For example, to obtain a representative AADT value (both directions of traffic) for Arizona site 

1024, during that site’s participation in the GPS-1 experiment, use the instructions in chapter 6 to 

extract the available AADT data from the three tables listed under option 1. Once the tables are 

downloaded, then extract the values for each year from the following table fields: 

• Table = TRF_HIST_VOLUME_COUNT, field = COUNT_AADT. 

• Table = TRF_HIST_EST_ESAL, field = AADT_ALL_VEHIC_2WAY. 

• Table = TRF_MON_EST_ESAL, field = AADT_ALL_VEHIC_2WAY. 

The result is a table that contains the data in the AADT Extracted from LTPP Data Tables

column in table 19. Running a linear regression on that data results in the following formula: 

AADT = (294.37 * Year) – 576,971 (1)

Apply this equation to estimate the missing AADT values for 1977, 1993, 1998, and 1999 (the 

last year this site was part of the GPS-1 experiment). These values are shown in the right-hand 

column of table 19. The average of the AADT values provided in the right-hand column is 8,220. 

This number (8,220) is the representative AADT value for Arizona site 1024 for the GPS-1 

experiment.
73 

Table 19. AADT values for Arizona site 1024, GPS-1 experiment.

Table Data were Taken 

From Year

AADT Extracted from 

LTPP Data Tables

AADT Including 

Estimated Missing Values

— 1977 — 4,998 

TRF_HIST_EST_ESAL 1978 5,500 5,500 

TRF_HIST_EST_ESAL 1979 5,500 5,500 

TRF_HIST_EST_ESAL 1980 4,700 4,700 

TRF_HIST_EST_ESAL 1981 5,600 5,600 

TRF_HIST_EST_ESAL 1982 6,000 6,000 

TRF_HIST_EST_ESAL 1983 6,000 6,000 

TRF_HIST_EST_ESAL 1984 6,600 6,600 

TRF_HIST_EST_ESAL 1985 7,300 7,300 

TRF_HIST_EST_ESAL 1986 8,600 8,600 

TRF_HIST_EST_ESAL 1987 9,300 9,300 

TRF_HIST_EST_ESAL 1988 9,300 9,300 

TRF_HIST_EST_ESAL 1989 9,800 9,800 

TRF_MON_EST_ESAL 1990 10,500 10,500 

TRF_MON_EST_ESAL 1991 9,400 9,400 

TRF_MON_EST_ESAL 1992 9,900 9,900 

— 1993 — 9,708 

TRF_MON_EST_ESAL 1994 9,600 9,600 

TRF_MON_EST_ESAL 1995 8,900 8,900 

TRF_MON_EST_ESAL 1996 9,400 9,400 

TRF_MON_EST_ESAL 1997 9,800 9,800 

— 1998 — 11,180 

— 1999 — 11,475 

—No data.

PARAMETER 1.2: ANNUAL AVERAGE DAILY TRUCK TRAFFIC VOLUME FOR 

THE LTPP LANE

AADTT is the key truck volume statistic used in the pavement analyses. AADTT values for the 

LTPP test lane are used in analyses that require information about typical daily truck volumes in 

a specific traffic lane. AADTT for the test lane is an easily understood, if imprecise, statistic that 

describes the general level of truck traffic loading a pavement is experiencing. Most of the LTPP 

data analyses involving prediction of pavement performance, service life, or design pavement 

thickness use this parameter. AADTT for the road segment is a different statistic, as it includes 

truck volume in all lanes and all directions of traffic on that road segment. AADTT for the LTPP 

test lane measures only truck traffic that actually drives over the test pavement.

The LTPP tables that contain the AADTT statistics for the LTPP test lane include: 

• TRF_TREND contains an analysis-ready complete history of AADTT estimates for all 

pavement in-service years. These values are based on either monitored traffic data or 

estimates and are periodically updated by the LTPP program. Special codes provide 

information about the data source for each statistic. 
74 

• MEPDG_TRUCK_VOL_PARAMETERS contains the estimate of AADTT for the test 

lane that serves as the first-year truck traffic volume statistic that should be input to 

MEPDG analyses for each site (field = AADTT_FIRST_YEAR_LTPP_LANE). 

• TRF_REP contains a single AADTT value for each LTPP test lane, which can be used as 

the best simple summary of the annual truck traffic volume crossing that test site during 

the time that site is part of the LTPP experiment.

• TRF_MON_EST_ESAL contains AADTT values submitted by State and Provincial 

highway agencies during LTPP monitoring years. 

• TRF_HIST_EST_ESAL contains AADTT values when provided by State and Provincial 

highway agencies for years prior to LTPP monitoring years. 

The TRF_TREND table contains an easily obtainable, LTPP lane-specific AADTT estimate for 

all years for which an experimental site is in service. This estimate is based on monitored data 

(where available) or historical data (where monitoring data were not collected but the State 

submitted an estimate), or it is estimated based on a mathematical extrapolation of those data 

(where the State did not submit data for a given year). The TRF_TREND table contains a 

metadata flag (AADTT_SOURCE field with codes) that indicates the source of each AADTT 

value: whether the data were submitted as a historical estimate (H or S) by a State or Provincial 

highway agency, a value computed from data collected during the LTPP traffic monitoring effort 

(M, Mc), or estimated for that year by LTPP because no other estimate exists (E). To download 

this entire table from InfoPave,

(3)

 use the instructions provided in chapter 6 and simply select the 

entire table.

In addition to AADTT for the LTPP lane, some State agencies collected or submitted data that 

described the total volume of trucks in both directions at a test site. Thus, two-directional truck 

traffic volume data are available but not for all LTPP sites. If these data are desired, the tables 

that contain these values are shown as follows along with the table field name that contains that 

information. All data present in these tables are year specific.

• Table = TRF_HIST_EST_ESAL, field = AADT_TRUCK_COMBO_2WAY. 

• Table = TRF_MON_EST_ESAL, field = AADT_TRUCK_COMBO_2WAY. 

The supporting data on truck volumes by FHWA vehicle class can be found in the table 

TRF_MONITOR_LTPP_LN, along with the number of days traffic monitoring data were

collected.

Example 1.2.1: Extract AADTT for Each Year in Service or Analysis Period

An estimate of annual total truck volume for each year in service can be found in the 

TRF_TREND table, in the field AADTT_ALL_TRUCKS_TREND. The InfoPave screen 

showing how to select this table is shown in figure 11. 
75 

Source: FHWA.

Figure 11. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Annual Trends in Traffic Characteristics (TRF_TREND).”(3)

For example, to obtain the AADTT values for all years during which Arizona test site 7613 was 

part of the LTPP experiment, follow the generic InfoPave data extraction instructions given in 

chapter 6 to download TRF_TREND table. 

The downloaded table will show that Arizona test site 7613 has data reported for two 

construction number events during its time with the LTPP experiment. The construction number 

field in TRF_TREND table is used to identify changes in the pavement section because of 

construction, rehabilitation, or maintenance events that occurred during the site’s participation in 

the LTPP experiment. The construction number 1 corresponds to the pavement structure at the 

start of that segment’s participation in an LTPP experiment. The subsequent construction 

numbers (2, 3, 4, etc.) are used to track changes in pavement structure or surface condition 

because of pavement rehabilitation or maintenance activities. Major changes in pavement 

structure typically result in the pavement test section being placed out of study from the LTPP 

experiment or being reassigned to another LTPP experiment. Once initiated, the construction 

number remains active and assigned to all years until the site is de-assigned from the specific 

LTPP experiment or changes LTPP experiments. 
76 

If the user would like to find AADTT values for the LTPP lane corresponding to the years when 

Arizona test site 7613 was part of construction event 1 (i.e., covering all years from the initial 

participation to the time when the LTPP site left the LTPP experiment), this could be 

accomplished by filtering the TRF_TREND table by STATE_CODE = 4, SHRP_ID = 7613, and 

CONSTRUCTION_NO = 1. Applying this filter would provide the AADTT values from 1979 to 

2001, as shown in table 20. 

Table 20. AADTT values for Arizona test site 7613, construction event 1. 

STATE_CODE SHRP_ID

CONSTRUCTION

_NO YEAR

AADTT_ALL_TRUCKS_

TREND

4 7613 1 1979 275 

4 7613 1 1980 350 

4 7613 1 1981 400 

4 7613 1 1982 575 

4 7613 1 1983 650 

4 7613 1 1984 650 

4 7613 1 1985 700 

4 7613 1 1986 825 

4 7613 1 1987 925 

4 7613 1 1988 925 

4 7613 1 1989 1,000 

4 7613 1 1990 817 

4 7613 1 1991 847 

4 7613 1 1992 876 

4 7613 1 1993 760 

4 7613 1 1994 1,056 

4 7613 1 1995 1,267 

4 7613 1 1996 1,274 

4 7613 1 1997 522 

4 7613 1 1998 941 

4 7613 1 1999 1,089 

4 7613 1 2000 916 

4 7613 1 2001 1,145 

Example 1.2.2: Extract or Estimate AADTT for the Year Selected for Analysis 

A user could extract an AADTT value for a specific year of analysis following the same 

procedure described in example 1.2.1. Once the data for all years have been extracted, a value for 

a specific year can be identified in the extracted table. For example, for Arizona site 7613, the 

AADTT value for the first year after pavement construction was 275, and for the last year 

participation in LTPP experiment it was 1,145. 
77 

PARAMETER 1.3: ANNUAL TOTAL TRUCK VOLUME 

Annual total truck volume values are used in LTPP analyses that require information about total 

truck volume for each year over the user-defined analysis period or for specific years. Typically 

these are the same years pavement condition or nondestructive-testing data were collected. 

An estimate of annual total truck volume for each year in service can be obtained from the field 

ANNUAL_TRUCK_VOLUME_TREND in the TRF_TREND table. This field provides annual 

total truck volume value for each year since the site was opened to traffic, until the end of site 

participation in the LTPP experiment or the last year for which the TRF_TREND table was 

updated. For those years when a site participated in the LTPP experiment only during some 

months, these annual values cover only those months when the site was part of an LTPP 

experiment. For example, if a new SPS site was opened to traffic on July 1, 2004, truck volumes 

for 2004 include only the period from July 1 to December 31 for that year. Similarly, if a site 

leaves the LTPP experiment on June 30, 2008, the annual truck volume statistic for 2008 

includes only the truck volumes crossing that site from January 1 to June 30, 2008. 

Example 1.3.1: Obtain Annual Total Truck Volume for Each Year in Service or Analysis 

Period

To obtain the annual total truck volume for each in-service year for Arizona test site 7613,

extract records from TRF_TREND table field ANNUAL_TRUCK_VOLUME_TREND 

(figure 12) using the generic InfoPave data extraction instructions given in chapter 6. Once the 

table has been extracted, filter records for STATE_CODE = 4, SHRP_ID = 7613, and 

CONSTRUCTION_NO = 1. Applying this filter provides the annual total truck volume values in 

ANNUAL_TRUCK_VOLUME_TREND column shown in table 21. 
78 

Source: FHWA.

Figure 12. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Annual Trends in Traffic Characteristics (TRF_TREND)” 

and next to the field name “CMLTV_VOL_HEAVY_TRUCKS_TREND.”(3)
79 

Table 21. Annual total truck volume values for Arizona test site 7613, construction event 1. 

STATE_CODE SHRP_ID

CONSTRUCTION

_NO YEAR

ANNUAL_TRUCK_

VOLUME_TREND

4 7613 1 1979 25,300 

4 7613 1 1980 128,100 

4 7613 1 1981 146,000 

4 7613 1 1982 209,875 

4 7613 1 1983 237,250 

4 7613 1 1984 237,900 

4 7613 1 1985 255,500 

4 7613 1 1986 301,125 

4 7613 1 1987 337,625 

4 7613 1 1988 338,550 

4 7613 1 1989 365,000 

4 7613 1 1990 298,205 

4 7613 1 1991 309,155 

4 7613 1 1992 320,616 

4 7613 1 1993 277,400 

4 7613 1 1994 385,440 

4 7613 1 1995 462,455 

4 7613 1 1996 466,284 

4 7613 1 1997 190,530 

4 7613 1 1998 343,465 

4 7613 1 1999 397,485 

4 7613 1 2000 335,256 

4 7613 1 2001 365,255 

PARAMETER 1.4: CUMULATIVE TRUCK VOLUME FOR THE ANALYSIS PERIOD

The total cumulative truck volume (CTV) data are needed for analyses that require information 

about the cumulative use of the LTPP test pavement by heavy vehicles or all trucks over a 

selected analysis period. Total truck traffic volume is based on counts of vehicles in FHWA 

vehicle classes 4–13.(11) This parameter is computed by summing annual total truck volume 

values available in the TRF_TREND table, field ANNUAL_TRUCK_VOLUME_TREND. 

In addition, for the convenience of LTPP users, the TRF_TREND table includes a total 

cumulative heavy truck volume statistic (CMLTV_VOL_HEAVY_TRUCKS_TREND) for 

analyses that require information about the cumulative use of the LTPP test pavement by heavy 

vehicles over a selected analysis period. (This statistic ignores class 5 trucks, which typically 

impose very little pavement damage because of their light weight.) An accumulation of class 9 

truck traffic is also provided (CMLTV_VOL_VEH_CLASS_9_TREND). A separate 

accumulated traffic statistic is present for each year, for each construction event, for each LTPP 

test site.
80 

Accumulated heavy truck traffic for construction event 1 starts from whenever that site opens to 

traffic, regardless of when that occurs. For each subsequent year that site and construction event 

is part of LTPP experiment, a cumulative traffic record will be present. The combination “State + 

SHRP_ID + Construction Number” is used as the unique identifier for the heavy truck traffic 

accumulation in the TRF_TREND table. When a new construction number event occurs for an 

LTPP site, the traffic accumulation starts over. Thus, the accumulated traffic for construction 

event 1 starts when the site opens to traffic, and it continues until construction event 1 ends 

(typically at the end of site participation in a given LTPP experiment). The accumulated traffic 

for construction event 2 at that same site starts when construction event 2 is assigned in the 

TRF_TREND table and continues until construction event 2 ends (typically at the end of site 

participation in a given LTPP experiment). For some SPS sites, records are present prior to the 

site opening to traffic because a record is present for each year the site is part of the LTPP 

experiment. All years present prior to the site opening to traffic are given zero traffic volumes. 

Example 1.4.1: Extract Cumulative Heavy Truck Traffic Volume Through the End of 

LTPP Section Participation in the Experiment or Last Reporting Year

Because the TRF_TREND table already has computed these values, researchers need to 

determine only the sites for which these values are desired. The user may select either the entire 

TRF_TREND table or a specific set of sites. The cumulative value is computed for the period 

starting from the date when the pavement is first opened to traffic and ending when the pavement 

is removed from participation in the LTPP experiment or the last reporting year, whichever is 

earlier. A CTV statistic is provided for every year that pavement exists. If the site ends active 

participation in one experiment (e.g., GPS-1) and joins a second experiment (e.g., GPS-6B), the 

cumulative traffic statistic for that site ends with the first experiment, and a new cumulative 

statistic starts over for the new experiment.

The annual estimate of CTV for each year in service can be found in the TRF_TREND table, 

field = CMLTV_VOL_HEAVY_TRUCKS_TREND (figure 12). These values are available for 

each construction event for each site.

For example, to obtain the cumulative heavy truck traffic volume for all years during which 

Arizona test site 7613 was part of the LTPP experiment, extract records for Arizona test site 

7613 from the TRF_TREND table using the generic InfoPave data extraction instructions given 

in chapter 6. 

Once the records have been extracted, review cumulative heavy truck traffic volume for each 

year during construction event 1, starting from 1979, when the site first open to traffic, and 

ending in 2001, when the site left the LTPP experiment. At the end of 1979, cumulative heavy 

traffic volume was 10,948 vehicles, and at the end of 2001, cumulative heavy truck traffic 

volume was 2,865,083 vehicles, as shown in table 22. 
81 

Table 22. Cumulative heavy truck traffic volume for Arizona test site 7613, construction 

event 1. 

STATE_CODE SHRP_ID

CONSTRUCTION

_NO YEAR

CMLTV_VOL_HEAVY 

TRUCKS_TREND

4 7613 1 1979 10,948 

4 7613 1 1980 65,848 

4 7613 1 1981 128,993 

4 7613 1 1982 219,513 

4 7613 1 1983 322,078 

4 7613 1 1984 424,924 

4 7613 1 1985 535,519 

4 7613 1 1986 665,459 

4 7613 1 1987 810,729 

4 7613 1 1988 956,397 

4 7613 1 1989 1,113,347 

4 7613 1 1990 1,241,827 

4 7613 1 1991 1,375,052 

4 7613 1 1992 1,513,034 

4 7613 1 1993 1,647,354 

4 7613 1 1994 1,781,674 

4 7613 1 1995 1,930,229 

4 7613 1 1996 2,120,183 

4 7613 1 1997 2,222,018 

4 7613 1 1998 2,377,143 

4 7613 1 1999 2,563,293 

4 7613 1 2000 2,707,497 

4 7613 1 2001 2,865,083 

Example 1.4.2: Compute Cumulative Heavy Truck Traffic Volume Based on 

User-Specified Start and End Dates

Although the cumulative total of heavy truck traffic volume can be read directly from the 

TRF_TREND table, the user can compute a cumulative total of all truck traffic (thus including 

class 5 light trucks), if desired. To make these computations, follow the directions for example 

1.3.1 to download the appropriate data (field AADTT_ALL_TRUCKS_TREND) from the LTPP 

table TRF_TREND, and then add the annual truck volume totals as shown in the right-hand 

column in table 23. 
82 

Table 23. Annual cumulative total truck volume values for Arizona test site 7613, 

construction event 1. 

STATE_CODE SHRP_ID YEAR

ANNUAL_TRUCK_

VOLUME_TREND

Cumulative Total 

Truck Volume

4 7613 1979 25,300 25,300 

4 7613 1980 128,100 153,400 

4 7613 1981 146,000 299,400 

4 7613 1982 209,875 509,275 

4 7613 1983 237,250 746,525 

4 7613 1984 237,900 984,425 

4 7613 1985 255,500 1,239,925 

4 7613 1986 301,125 1,541,050 

4 7613 1987 337,625 1,878,675 

4 7613 1988 338,550 2,217,225 

4 7613 1989 365,000 2,582,225 

4 7613 1990 298,205 2,880,430 

4 7613 1991 309,155 3,189,585 

4 7613 1992 320,616 3,510,201 

4 7613 1993 277,400 3,787,601 

4 7613 1994 385,440 4,173,041 

4 7613 1995 462,455 4,635,496 

4 7613 1996 466,284 5,101,780 

4 7613 1997 190,530 5,292,310 

4 7613 1998 343,465 5,635,775 

4 7613 1999 397,485 6,033,260 

4 7613 2000 335,256 6,368,516 

4 7613 2001 365,255 6,733,771 
83 

CHAPTER 8. SCENARIO 2: OBTAIN VEHICLE CLASSIFICATION INFORMATION

PARAMETER 2.1: ANNUAL TRUCK VOLUME BY VEHICLE CLASS 

Annual truck volume by vehicle class values are used in LTPP analyses that require information 

about the volume of different types of trucks for each analysis year, such as 

mechanistic-empirical pavement analyses, or analyses that focus on the effect of certain vehicle 

types on pavement performance. 

The data fields AADTT_VEH_CLASS_#_TREND (where # refers to FHWA vehicle classes

4–13)(11) in the LTPP table TRF_TREND contain estimates of AADTT volume by vehicle class 

for each year from when the LTPP site either is open to traffic or joins the LTPP experiment 

(whichever is earlier) to the time when that test site is no longer part of an LTPP experiment (or 

the last year for which data were available when this table was produced, if the site is still in the 

experiment).

If the user needs very detailed data on truck volume distributions, the HH_CL_CT, DD_CL_CT, 

and MM_CT tables from the LTAS database (accessible through InfoPave(3)) provide data about 

detailed hourly, daily, or monthly variation in truck volumes by class when vehicle classification 

data were collected as part of the LTPP traffic monitoring program. 

Example 2.1.1: Obtain Annual Truck Volume by Vehicle Class for Each Year in Service or 

Analysis Period 

For example, to obtain annual truck volume by vehicle class values for each year in service or 

analysis period during which Arizona test site 7613 was part of the LTPP experiment, follow this 

sequence of steps:

1. Extract records for Arizona test site 7613 from the TRF_TREND table (figure 13) using 

the generic InfoPave data extraction instructions given in chapter 6. A portion of the 

extracted table for construction event 1 is shown in table 24. A second portion of the 

table for construction event 2 is shown in table 25. 
84 

Source: FHWA.

Figure 13. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Annual Trends in Traffic Characteristics (TRF_TREND)” 

and next to the field name “AADTT_VEH_CLASS_#_TREND.”(3)
85 

Table 24. TRF_TREND truck volumes for Arizona test site 7613, construction event 1. 

YEARAADTT_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

CMLTV_VOL

_VEH_CLASS_9_TREND

CMLTV_VOL

_HEAVYTRUCKS_TREND

AADTT_ALL

_TRUCKS_TREND

ANNUAL_TRUCK_VOLUMETREND

1979 5 156 24 1 26 57 1 3 0 2 5,244 10,948 275 25,300

1980 6 200 30 1 33 73 1 3 0 2 31,962 65,848 350 128,100

1981 7 227 35 1 38 83 2 4 0 3 62,257 128,993 400 146,000

1982 11 327 50 1 55 119 2 5 1 4 105,692 219,513 575 209,875

1983 12 369 56 2 62 135 3 6 1 4 154,967 322,078 650 237,250

1984 12 369 56 2 62 135 3 6 1 4 204,377 424,924 650 237,900

1985 13 397 61 2 67 145 3 6 1 5 257,302 535,519 700 255,500

1986 15 469 71 2 79 171 3 8 1 6 319,717 665,459 825 301,125

1987 17 527 80 2 88 192 4 8 1 6 389,797 810,729 925 337,625

1988 17 527 80 2 88 192 4 8 1 6 460,069 956,397 925 338,550

1989 18 570 87 2 95 207 4 9 1 7 535,624 1,113,347 1,000 365,000

1990 15 465 71 2 78 169 3 7 1 6 597,309 1,241,827 817 298,205

1991 16 482 73 2 81 175 3 8 1 6 661,184 1,375,052 847 309,155

1992 16 499 76 2 83 181 3 8 1 6 727,430 1,513,034 876 320,616

1993 24 392 76 0 73 179 2 8 1 5 792,765 1,647,354 760 277,400

1994 18 688 74 0 83 171 3 11 1 7 855,180 1,781,674 1,056 385,440

1995 5 860 79 1 101 202 2 11 1 5 928,910 1,930,229 1,267 462,455

1996 21 755 94 5 162 214 5 8 2 8 1,007,234 2,120,183 1,274 466,284

1997 11 243 60 1 65 128 3 5 1 5 1,053,954 2,222,018 522 190,530

1998 19 516 84 5 77 217 5 9 1 8 1,133,159 2,377,143 941 343,465

1999 20 579 104 6 85 271 6 10 1 7 1,232,074 2,563,293 1,089 397,485

2000 17 522 79 2 87 190 4 8 1 6 1,301,614 2,707,497 916 335,256

2001 21 651 99 3 109 237 5 11 1 8 1,377,217 2,865,083 1,145 365,255
86 

Table 25. TRF_TREND truck volumes for Arizona test site 7613, construction event 2.

YEARAADTT_VEH_CLASS_4_TRENDAADTT_VEH_CLASS_5_TRENDAADTT_VEH_CLASS_6_TRENDAADTT_VEH_CLASS_7_TRENDAADTT_VEH_CLASS_8_TRENDAADTT_VEH_CLASS_9_TRENDAADTT_VEH_CLASS_10_TRENDAADTT_VEH_CLASS_11_TRENDAADTT_VEH_CLASS_12_TRENDAADTT_VEH_CLASS_13_TRENDCMLTV_VOL_VEH_CLASS_9_TRENDCMLTV_VOL_HEAVY TRUCKS_TRENDAADTT_ALL_TRUCKS_TRENDANNUAL_TRUCK_VOLUME_TREND

1990 15 465 71 2 78 169 3 7 1 6 61,685 128,480 817 298,205

1991 16 482 73 2 81 175 3 8 1 6 125,560 261,705 847 309,155

1992 16 499 76 2 83 181 3 8 1 6 191,806 399,687 876 320,616

1993 24 392 76 0 73 179 2 8 1 5 257,141 534,007 760 277,400

1994 18 688 74 0 83 171 3 11 1 7 319,556 668,327 1,056 385,440

1995 5 860 79 1 101 202 2 11 1 5 393,286 816,882 1,267 462,455

1996 21 755 94 5 162 214 5 8 2 8 471,610 1,006,836 1,274 466,284

1997 11 243 60 1 65 128 3 5 1 5 518,330 1,108,671 522 190,530

1998 19 516 84 5 77 217 5 9 1 8 597,535 1,263,796 941 343,465

1999 20 579 104 6 85 271 6 10 1 7 696,450 1,449,946 1,089 397,485

2000 17 522 79 2 87 190 4 8 1 6 765,990 1,594,150 916 335,256

2001 21 651 99 3 109 237 5 11 1 8 841,593 1,751,736 1,145 365,255
87 

2. Once the records have been extracted, it can be seen that on the average day 156, class 5 

trucks crossed the LTPP test section in 1979. This grew to 651 class 5 trucks per day in 

2001. 

3. To compute the annual truck volume for a specific class of trucks, it is necessary to know 

the number of days during that year the LTPP site was open to traffic in that year. To 

obtain this number, for each year, divide the value found in the field 

ANNUAL_TRUCK_VOLUME_TREND by the value found in the field 

AADTT_ALL_TRUCKS_TREND. So, for the first year for construction event 1 (1979), 

the ANNUAL_TRUCK_VOLUME_TREND is 25,300 vehicles, and the 

AADTT_ALL_TRUCKS_TREND is 275. Thus, this LTPP section was open for 92 days 

(25,300/275 = 92). Since 1980 was a leap year, there are 366 days of truck volume data 

(128,100/350 = 366). Consequently, if the total volume of class 13 trucks passing over the 

site is desired for 1980, this can be computed by multiplying the value for 

AADTT_VEH_CLASS_13_TREND for 1980 by the number of days: 2 × 366 = 732. 

PARAMETER 2.2: NORMALIZED VEHICLE CLASS DISTRIBUTION

Another commonly used set of traffic inputs is the normalized VCD. For pavement analyses, 

these distributions typically consider only the heavy vehicle classes (FHWA vehicle classes

4–13).(11) These statistics indicate the percentage of truck traffic occurring in each truck class. 

The normalized VCDs are used in mechanistic-empirical pavement response and performance 

analyses and design. 

These distributions can be obtained in several ways. To obtain these values for specific years, 

use the AADTT by class value in the TRF_TREND table (examples of which were previously 

shown in table 24 and table 25) and simply divide the AADTT for each class value by sum of 

those values for all classes.

Alternatively, the table TRF_REP includes a single distribution of vehicle classification 

percentages that provides a single set of values that estimates the percentage of truck traffic that 

occurs in each class of vehicles. The values found in the TRF_TREND table illustrate the 

variation in what percentage of trucks falls within each truck class. For some sites, these 

percentages can vary considerably from year to year. In some cases, this variation is caused by 

differences in the equipment being used to collect these data. In other cases, changes in the 

economy result in significant changes to the truck volumes for some or all vehicle classes. Thus, 

the annual values found in the TRF_TREND table are particularly useful in examining changes 

in truck travel over a road segment, but the variables in the TRF_REP table are easier to use if 

the intent is to get a general understanding of the types of truck traffic present at a test site.

For a limited number of LTPP sites, normalized VCD values are available in the LTPP data table 

TRF_MEPDG_VEH_CLASS_DIST, for the years that had sufficient monitored vehicle 

classification or weight data (a minimum of 210 days per year of classification or weight data).

Finally, the representative or annual average condition of the normalized VCD, covering FHWA 

vehicle classes 4–13, is stored in the computed parameter table, 

MEPDG_TRUCK_VOL_PARAMETERS.
88 

Example 2.2.1: Obtain Representative Normalized VCD 

The annual average condition of the normalized VCD, covering FHWA vehicle classes 4–13,(11)

can be extracted from the table TRF_REP. The InfoPave screen showing how to select this table 

is shown in figure 14. 

Source: FHWA.

Figure 14. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Representative Site Traffic Parameters (TRF_REP).”(3)

For example, to obtain the representative normalized VCD for Arizona test site 7613 while it was 

a part of the LTPP experiment, extract records for Arizona test site 7613 from the TRF_REP 

table using the generic InfoPave data extraction instructions given in chapter 6. The results of 

this extract were used to develop table 26. 
89 

Table 26. Normalized VCD from the TRF_REP table for Arizona site 7613. 

Field in TRF_REP Table

Percentage of Trucks in That 

Vehicle Class

REP_PERCENT_VEH_CLASS_4 1.84 

REP_PERCENT_VEH_CLASS_5 56.9 

REP_PERCENT_VEH_CLASS_6 8.66 

REP_PERCENT_VEH_CLASS_7 0.25 

REP_PERCENT_VEH_CLASS_8 9.52 

REP_PERCENT_VEH_CLASS_9 20.72 

REP_PERCENT_VEH_CLASS_10 0.39 

REP_PERCENT_VEH_CLASS_11 0.92 

REP_PERCENT_VEH_CLASS_12 0.12 

REP_PERCENT_VEH_CLASS_13 0.68 

PARAMETER 2.3: MONTHLY TRUCK VOLUME BY VEHICLE CLASS

The total monthly truck volume by vehicle class parameters are used in the analyses that focus 

on evaluation of the effect of seasonal changes in truck traffic volume and environment on 

pavement response and performance.

Monthly truck volume for a selected calendar month, year, and FHWA vehicle class (trucks are 

FHWA classes are 4 to 13), can be computed by multiplying the AADTT value for that year and 

vehicle class by a monthly adjustment factor (MAF) for that truck class and month and then 

multiplying the resulting average day of month truck volume statistic by the number of days in 

that month. Each truck class has a different MAF because truck travel patterns can be quite 

different for each vehicle class. Monthly truck patterns can also change from year to year, given 

variations in economic activity occurring on that specific roadway. 

In addition, only those LTPP sites that have continuous traffic classifier or WIM equipment 

installed have data that support computation of this parameter. Many of these sites only have 

data for a limited set of years because of either delays in getting permanent equipment installed 

or equipment issues that result in some years of data not being present in the LTPP data tables 

available on InfoPave.(3)

 MAFs are stored in the TRF_MEPDG_MONTH_ADJ_FACTR table

and accessible through the InfoPave web portal.(3)

 Example 2.3.1: Obtain Monthly Truck Volume by Vehicle Class 

To compute this parameter for a specific site, first follow the InfoPave table extraction 

instructions given in chapter 6 to download two LTPP tables: 

• TRF_MEPDG_MONTH_ADJ_FACTR. 

• TRF_TREND. 
90 

The downloaded TRF_TREND file contains the AADTT values for each FHWA vehicle class 

(see data fields AADTT_VEH_CLASS_#_TREND) for each year and for each experiment and 

construction number for those sites requested. The TRF_MEPDG_MONTH_ADJ_FACTR table 

contains the monthly vehicle volume adjustment factors by vehicle class for each year for which 

sufficient data were collected at each site for which data were requested. 

Table 27 illustrates the TRF_TREND data for Arizona site 7614 for construction event 1. Only a 

portion of this table is shown. The data fields shown include the annual daily traffic volumes for 

each of the 10 FHWA vehicle classes.

(11)

As can be seen in table 27, Arizona site 7614 starts with an average daily class 9 volume of 

1,233 trucks per day in 1984. This number then declines to only 423 class 9 trucks per day in 

1994, before growing again to over 1,000 class 9 trucks in 2004. These annual values are the 

control totals used to estimate monthly truck volumes for class 9.
91 

Table 27. Annual average daily truck volumes by classification from the TRF_TREND table. 

STATE_CODESHRP_IDCONSTRUCTION_NOYEARAADTT_ALL_TRUCKS_TRENDAADTT_VEH_CLASS_4_TRENDAADTT_VEH_CLASS_5_TRENDAADTT_VEH_CLASS_6_TRENDAADTT_VEH_CLASS_7_TRENDAADTT_VEH_CLASS_8_TRENDAADTT_VEH_CLASS_9_TRENDAADTT_VEH_CLASS_10_TRENDAADTT_VEH_CLASS_11_TRENDAADTT_VEH_CLASS_12_TRENDAADTT_VEH_CLASS_13_TREND

4 7614 1 1984 2,080 13 371 117 7 195 1,233 16 91 28 9 

4 7614 1 1985 2,240 14 400 126 8 210 1,327 17 98 30 10

4 7614 1 1986 2,560 16 457 144 9 240 1,516 20 112 35 11

4 7614 1 1987 3,520 22 628 199 12 330 2,086 27 153 48 15

4 7614 1 1988 3,840 24 686 217 13 360 2,275 29 167 52 17

4 7614 1 1989 2,123 13 379 120 7 199 1,258 16 93 29 9 

4 7614 1 1990 2,066 13 369 117 7 194 1,223 16 90 28 9 

4 7614 1 1991 1,250 8 223 71 4 117 741 10 54 17 5 

4 7614 1 1992 1,325 8 237 75 5 124 784 10 58 18 6 

4 7614 1 1993 1,109 9 287 37 3 54 591 19 68 23 18

4 7614 1 1994 743 6 79 29 4 139 423 3 43 15 2 

4 7614 1 1995 1,218 7 192 48 9 131 720 8 74 25 4 

4 7614 1 1996 1,496 10 398 64 7 142 754 9 79 22 11

4 7614 1 1997 1,600 10 286 90 5 150 948 12 70 22 7 

4 7614 1 1998 1,501 2 320 114 5 106 859 13 63 13 6 

4 7614 1 1999 1,518 3 344 111 4 109 829 21 75 16 5 

4 7614 1 2000 1,466 9 262 83 5 138 868 11 64 20 6 

4 7614 1 2001 1,549 6 205 97 4 156 995 9 56 17 4 

4 7614 1 2002 1,552 10 230 99 4 127 1,002 9 50 17 4 

4 7614 1 2003 1,743 8 244 108 4 171 1,123 10 51 20 4 

4 7614 1 2004 1,742 14 260 106 4 164 1,110 10 52 18 4 
92 

Table 28 illustrates the MAFs (in MONTHLY_RATIO column) available for Arizona site 7614. 

Because of its size, only a portion of the extracted table is shown in table 28. This illustrative 

table shows only 2 months of data for 2 years (1994 and 1995) for all 10 vehicle classes, but it 

also shows all years of data available for class 9 for the months of April and May. At this site, 

MAFs are available only for 1994, 1995, 1996, 1998, 2003, and 2004. Also note that the factor 

for May is missing from 1994. The complete table contains data for all vehicle classes, but some 

months and years of data are missing because of the equipment issues. 

Table 28. Illustrative monthly truck volume adjustment factors from the 

TRF_MEPDG_MONTH_ADJ_FACTR table.

STATE

_CODE

SHRP

_ID YEAR MONTH

VEHICLE

_CLASS

VEHICLE_CLASS

_EXP

MONTHLY

_RATIO DATE_EXP

4 7614 1994 4 4 FHWA class 4 1.98 04/01/1994 

4 7614 1994 4 5 FHWA class 5 1.52 04/01/1994 

4 7614 1994 4 6 FHWA class 6 1.46 04/01/1994 

4 7614 1994 4 7 FHWA class 7 0.98 04/01/1994 

4 7614 1994 4 8 FHWA class 8 1.85 04/01/1994 

4 7614 1994 4 9 FHWA class 9 1.66 04/01/1994 

4 7614 1994 4 10 FHWA class 10 2.01 04/01/1994 

4 7614 1994 4 11 FHWA class 11 1.73 04/01/1994 

4 7614 1994 4 12 FHWA class 12 1.84 04/01/1994 

4 7614 1994 4 13 FHWA class 13 2.20 04/01/1994 

4 7614 1995 4 4 FHWA class 4 0.70 04/01/1995 

4 7614 1995 4 5 FHWA class 5 0.59 04/01/1995 

4 7614 1995 4 6 FHWA class 6 0.99 04/01/1995 

4 7614 1995 4 7 FHWA class 7 0.64 04/01/1995 

4 7614 1995 4 8 FHWA class 8 1.09 04/01/1995 

4 7614 1995 4 9 FHWA class 9 0.97 04/01/1995 

4 7614 1995 4 10 FHWA class 10 0.88 04/01/1995 

4 7614 1995 4 11 FHWA class 11 0.98 04/01/1995 

4 7614 1995 4 12 FHWA class 12 1.07 04/01/1995 

4 7614 1995 4 13 FHWA class 13 0.91 04/01/1995 

4 7614 1995 5 4 FHWA class 4 0.70 05/01/1995 

4 7614 1995 5 5 FHWA class 5 0.63 05/01/1995 

4 7614 1995 5 6 FHWA class 6 1.31 05/01/1995 

4 7614 1995 5 7 FHWA class 7 0.85 05/01/1995 

4 7614 1995 5 8 FHWA class 8 1.09 05/01/1995 

4 7614 1995 5 9 FHWA class 9 1.03 05/01/1995 

4 7614 1995 5 10 FHWA class 10 1.00 05/01/1995 

4 7614 1995 5 11 FHWA class 11 1.13 05/01/1995 

4 7614 1995 5 12 FHWA class 12 1.11 05/01/1995 

4 7614 1995 5 13 FHWA class 13 1.36 05/01/1995 
93 

STATE

_CODE

SHRP

_ID YEAR MONTH

VEHICLE

_CLASS

VEHICLE_CLASS

_EXP

MONTHLY

_RATIO DATE_EXP

4 7614 1996 4 9 FHWA class 9 0.97 04/01/1996 

4 7614 1996 5 9 FHWA class 9 1.00 05/01/1996 

4 7614 1998 4 9 FHWA class 9 0.96 04/01/1998 

4 7614 1998 5 9 FHWA class 9 0.95 05/01/1998 

4 7614 2003 4 9 FHWA class 9 1.00 04/01/2003 

4 7614 2003 5 9 FHWA class 9 1.00 05/01/2003 

4 7614 2004 4 9 FHWA class 9 1.02 04/01/2004 

4 7614 2004 5 9 FHWA class 9 1.01 05/01/2004 

To obtain an estimate of monthly truck volumes, take the AADTT value for each vehicle class 

for each desired year (from TRF_TREND as shown in table 27) and multiply that value by the 

appropriate monthly factor for that vehicle class and month. If data were collected for that 

specific year, use the value for that class, for that year from the 

TRF_MEPDG_MONTH_ADJ_FACTR table (using MONTHLY_RATIO field as shown in 

table 28). 

For example, if class 9 volumes for May 1995 were required, the AADTT value for class 9 in 

1995 is 720 (as shown in table 27). The MONTHLY_RATIO in table 28 is 1.03. Thus, the 

monthly class 9 AADTT volume in May 1995 is: 

720 * 1.03 = 742 (2)

To estimate total monthly class 9 volume, multiply this value by the number of days in the month 

of May (31):

742 * 31 = 23,002 (3)

If a monthly volume is required for a year in which MAFs are unavailable in the 

TRF_MEPDG_MONTH_ADJ_FACTR TREND table, create an average value for that vehicle 

class and month from the years for which factors are available and apply that average.

For example, if a class 9 volume was needed for May for 2002, the first step is to take all May 

factors for class 9 that are available (1.03, 1.00, 0.95, 1.00, and 1.01) and compute the average 

(1.00). This value is then used as the class 9 May factor for any year that lacks a value in the 

table. Thus, the class 9 AADTT volume for May 2002 would be computed as: 

1,002 * 1.00 = 1,002 (4)

And the total monthly volume for that month would be 31,062. 

Note that the sum of monthly truck volumes will at times be slightly off from the value found in 

the TRF_TREND table’s ANNUAL_TRUCK_VOLUME_TREND variable because of different 

rounding errors occurring in this process versus the process used to compute those values. 

95 

CHAPTER 9. SCENARIO 3: OBTAIN AXLE OR TRUCK LOADING INFORMATION

PARAMETER 3.1: AXLE LOAD DISTRIBUTION OR AXLE LOAD SPECTRA

Axle load distribution and axle load spectrum are two names frequently used by pavement 

engineers for the same parameter. Axle load distribution is a frequency distribution of axle loads, 

whereby counts of axle load applications, observed during a specified period of time, are 

reported using predefined load bins. When the distribution of loads is expressed as a percentile 

with the percentages of loads reported for each load bin instead of the axle counts, the 

distribution or axle load spectrum is called “normalized.” In the LTPP data tables,

(3) axle load 

spectra are reported separately for each vehicle class 4–13(11) and each axle group (i.e., single, 

tandem, tridem, and quad+).

Typically, an axle load spectrum is used to characterize the traffic loading for the 

mechanistic-empirical pavement response and performance modeling. Also, it is used to compute 

summary axle loading statistics such as ESAL, RPPIF, and cumulative total load. When the 

AASHTOWare Pavement ME Design software(5) is used, specifically formatted ALDF are used

as an input. The following examples are for the use of axle load spectra for analyses based on the 

MEPDG method not tied to AASHTOWare software.

Axle load spectra are available in several LTPP tables(3) at different level of aggregation or 

estimation:

• DD_AX (LTAS table)—Axle load distribution summarized for each day with weight 

data.

• MM_AX (LTAS table)—Axle load distribution summarized by DOW, month, and year 

for each month with weight data. 

• YY_AX (LTAS table)—Axle load distribution summarized by DOW and year for each 

year with weight data.

• TRF_MONITOR_AXLE_DISTRIB—Annualized axle load distribution for years with 

WIM data (1990 or later). 

• TRF_MEPDG_AX_DIST—Monthly normalized axle load distribution for years with at 

least 210 days of WIM data. 

• TRF_MEPDG_AX_DIST_ANL—Annual normalized axle load distribution for years 

with at least 210 days of WIM data. 

• MEPDG_AXLE_LOAD_DIST_FACTOR—ALDF or normalized axle load distribution 

representing a typical day of each calendar month formatted for use in AASHTOWare 

Pavement ME Design software, based on either site-specific WIM data or defaults.
96 

The accuracy of WIM data varies greatly from site to site. The 

MEPDG_AXLE_LOAD_DIST_FACTOR table contains representative NALS and a code field 

with WIM data usability rating based on the assessment of WIM data quality and reasonableness 

conducted by LTPP data analysis contractors. 

Example 3.1.1: Obtain Annual Axle Load Spectra 

Annual axle load spectra provide annualized counts of axle loads, stored by axle load bins. These 

parameters are used in analyses based on the MEPDG method when month-to-month variation in 

distribution of axle loads is random (i.e., for sites with no definable seasonal pattern) or when 

seasonal variations are unimportant or excluded from the analysis. Annual axle load spectra are 

also used for computation of annual ESAL. 

If axle load spectra are unavailable for the entire analysis period, values for missing periods 

could be estimated using available load spectra and information about truck volume growth for a 

given LTPP site. 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 

provides detailed information about a recommended methodology for estimating and projecting 

axle load spectra for the design period.(19)

An example of how to obtain annual axle load spectra for LTPP SPS-1 site, section 0113 in 

Arizona using the TRF_MONITOR_AXLE_DISTRIB table is provided in chapter 6. 

Once the data have been extracted, it is recommended to check the rationality of the axle load 

distribution using the procedure included in chapter 4 of part 1 of this Guide. 

Example 3.1.2: Compute Monthly Axle Load Spectra 

Monthly axle load spectra provide monthly counts of axle loads, stored by axle load bins. These 

parameters are used when month-to-month variation in distribution of axle loads is important for 

analysis.

The LTPP LTAS MM_AX table contains axle counts by site, year, month, lane, direction, 

vehicle classification, axle group, DOW, and the number of DOW occurrences in a month. This 

table is created by summing the number of daily axle counts in each load bin by DOW, axle 

group, vehicle class, and LTPP site for each month and year with WIM data.

To compute monthly axle load spectra for a selected LTPP site, vehicle class, axle group, year, 

and month: 

1. Obtain records of axle counts by load bin from the MM_AX table using the generic

InfoPave data extraction instructions given in chapter 6, as shown in figure 15. An 

example of the extracted records for Arizona site 7613 is shown in table 29. Because of a 

large volume of data, only partial records are shown, depicting single-axle counts for 

class 9 vehicles for each DOW in June 1998. 
97 

Source: FHWA.

Figure 15. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label, “Monthly Axle Counts (MM_AX).”(3)

Table 29. Total single-axle counts for class 9 vehicles for each DOW in June 1998 for 

Arizona site 7613 extracted from the MM_AX table.

STATE_CODESHRP_IDVEHICLE_CLASSAXLE_GROUPYEARMONTHDAY_OF_

W

E

E

K

NUM_DAY_OCCURRENCESAX_CT_01AX_CT_02AX_CT_03AX_CT_04AX_CT_05AX_CT_06AX_CT_07AX_CT_08AX_CT_09AX_CT_10AX_CT_11....*AX_CT_40

4 7613 9 1 1998 6 1 3 0 0 6 1 3 6 6 10 33 41 62 ... 0 

4 7613 9 1 1998 6 2 4 0 0 9 5 24 57 72 93 174 287 331 ... 0 

4 7613 9 1 1998 6 3 5 0 0 9 11 38 52 60 107 233 328 324 ... 0 

4 7613 9 1 1998 6 4 4 0 0 3 1 30 64 61 81 182 279 321 ... 0 

4 7613 9 1 1998 6 5 3 0 0 11 12 18 39 52 96 157 180 194 ... 0 

4 7613 9 1 1998 6 6 3 0 0 7 7 28 37 50 76 188 224 237 ... 0 

4 7613 9 1 1998 6 7 3 0 0 6 0 7 16 12 30 71 74 75 ... 0 

*Load bins AX_CT_12 to AX_CT_39 are not shown. 
98 

2. Divide the axle counts reported in each load bin and each DOW by the number of DOW 

occurrences reported in the MM_AX table. This step will produce an average axle count 

by load bin for each DOW, as shown in table 30. 

Table 30. Average single-axle counts for class 9 for each DOW in June 1998 for Arizona 

site 7613 extracted from the MM_AX table.

STATE_CODESHRP_IDVEHICLE_CLASSAXLE_GROUPYEARMONTHDAY_OF_WEEK

AX_CT_01AX_CT_02AX_CT_03AX_CT_04AX_CT_05AX_CT_06AX_CT_07AX_CT_08AX_CT_09AX_CT_10AX_CT_11....*AX_CT_40

4 7613 9 1 1998 6 1 0 0 2 0 1 2 2 3 11 14 21 ... 0 

4 7613 9 1 1998 6 2 0 0 2 1 6 14 18 23 44 72 83 ... 0 

4 7613 9 1 1998 6 3 0 0 2 2 8 10 12 21 47 66 65 ... 0 

4 7613 9 1 1998 6 4 0 0 1 0 8 16 15 20 46 70 80 ... 0 

4 7613 9 1 1998 6 5 0 0 4 4 6 13 17 32 52 60 65 ... 0 

4 7613 9 1 1998 6 6 0 0 2 2 9 12 17 25 63 75 79 ... 0 

4 7613 9 1 1998 6 7 0 0 2 0 2 5 4 10 24 25 25 ... 0 

*Load bins AX_CT_12 to AX_CT_39 are not shown.

3. Average the average DOW axle counts by load bin over seven DOW to compute an 

average daily axle counts for a given month and year. The results are shown in table 31. 

Table 31. Average daily single-axle counts for class 9 for June 1998 for Arizona site 7613. STATE_CODE SHRP_ID VEHICLE_CLASS AXLE_GROUP YEAR MONTH AX_CT_01 AX_CT_02 AX_CT_03 AX_CT_04 AX_CT_05 AX_CT_06 AX_CT_07 AX_CT_08 AX_CT_09 AX_CT_10 AX_CT_11 ....* AX_CT_40

4 7613 9 1 1998 6 0 0 2 1 6 10 12 19 41 54 60 ... 0 

*Load bins AX_CT_12 to AX_CT_39 are not shown. 

4. Multiply the average daily counts by the total number of days in the given calendar month 

to get average monthly axle counts, as shown in table 32. 
99 

Table 32. Average monthly single-axle counts for class 9 for June 1998 for Arizona site 

7613. 

STATE_CODESHRP_IDVEHICLE_CLASSAXLE_GROUP

YEARMONTHAX_CT_01AX_CT_02AX_CT_03AX_CT_04AX_CT_05AX_CT_06AX_CT_07AX_CT_08AX_CT_09AX_CT_10AX_CT_11....*AX_CT_40

4 7613 9 1 1998 6 0 0 63 44 170 314 365 581 1223 1629 1788 ... 0 

*Load bins AX_CT_12 to AX_CT_39 are not shown. 

Example 3.1.3: Obtain Normalized Monthly Axle Load Spectra 

Monthly NALS provide monthly percentages of axle loads by axle load bin, axle group, and 

vehicle class. These parameters are used when month-to-month variation in distribution of axle 

loads is important for analysis. Also, monthly NALS are the key traffic loading input for the 

AASHTOWare Pavement ME Design software(5) (See chapter 5 of this Guide for 

AASHTOWare Pavement ME Design software use example.) 

LTPP table NALS_MONTHLY_DISTRIB contains monthly NALS for months and years when 

WIM data were collected and passed LTPP level E checks (the highest QC hierarchy level for 

traffic data). In addition, the TRF_MEPDG_AX_DIST table contains monthly NALS for 

selected LTPP sites with sufficient number of days with monitored axle weight data (a minimum 

of 210 days per year of classification or weight data).

To get the data from these tables, use the InfoPave data extraction instructions given in chapter 6

and identify tables of interest, as shown in figure 16 and figure 17. 

Review the downloaded data for reasonableness using the procedure included in chapter 4 of 

part 1 of this Guide and decide about data usability for the intended pavement analysis. 
100 

Source: FHWA.

Figure 16. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label, “Mechanistic-Empirical Pavement Design Guide Traffic Axle 

Distribution (TRF_MEPDG_AX_DIST).”(3)
101 

Source: FHWA.

Figure 17. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label, “NALS Monthly Axle Distribution 

(NALS_MONTHLY_DISTRIB).”(3)

Example 3.1.4: Obtain Daily Axle Load Spectra 

Daily axle weight data are used to investigate variations in axle loading to gain a better 

understanding of the changes in truck loads occurring on each DOW. 

Daily axle load spectra are stored in the DD_AX table. These spectra provide counts of axle 

loads, stored by axle load bin, axle group, and vehicle class for each day with monitored traffic 

loading data. Vehicle classes provided in this table are based on classification schemes 

implemented by different State and Provincial agencies that are providing the data to LTPP and 

may differ from LTPP vehicle classification method (scheme).

To get the daily axle load spectra, use the generic InfoPave data extraction instructions given in 

chapter 6 and select the DD_AX table for download. Place a checkmark next to “Show 

Supplementary Tables” option on the Traffic bar, as shown in figure 18. 
102 

Review the downloaded data for reasonableness using procedure included in chapter 4 of part 1 

of this Guide. 

Source: FHWA.

Figure 18. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Daily Axle Counts (DD_AX).”(3)

Example 3.1.5: Obtain Normalized Annual Load Spectra 

NALS are used to investigate the differences in the shape of axle load distributions by removing 

the effect of truck volumes from the load spectra. This parameter allows users to analyze how 

heavy different axles are, the typical axle weights of loaded and unloaded trucks at the site, and 

how frequently light, moderate, heavy, and overloaded axles are observed for a given LTPP site 

or a group of sites. 

LTPP table NALS_ANNUAL_DISTRIB contains annual NALS for all LTPP sites with WIM 

data that passed LTPP level E checks. In addition, annual NALS are available in the database 

table TRF_MEPDG_AX_DIST_ANL for LTPP sites with a sufficient number of days with 

monitored axle weight data (a minimum of 210 days per year of classification or weight data).

To get the data from these tables, use the generic InfoPave data extraction instructions given at 

the beginning of part 2 of this guide and identify tables of interest, as shown in figure 19 and 

figure 20. Place a checkmark next to “Show Supplementary Tables” option under the Traffic bar 

to see the NALS_ANNUAL_DISTRIB table.
103 

After download, review the downloaded NALS for reasonableness using the procedure included 

in chapter 4 of part 1 of this Guide. 

Source: FHWA.

Figure 19. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Mechanistic-Empirical Pavement Design Guide Annual 

Traffic Axle Distribution (TRF_MEPDG_AX_DIST_ANL).”(3)
104 

Source: FHWA.

Figure 20. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “NALS Annual Axle Distribution 

(NALS_ANNUAL_DISTRIB).”(3)

Example 3.1.6: Estimated NALS for Sites with Limited or No Site-Specific Axle Load 

Spectra 

LTPP traffic analysis-ready table MEPDG_AXLE_LOAD_DIST_FACTOR contains ALDF or 

NALS representing a typical day of the year, including the default values assigned for each 

LTPP site, including sites with limited or no site-specific axle load spectra. The values 

representing a typical day of the year have been assigned to each month of the year. This table

can be downloaded using the generic InfoPave data extraction instructions given in chapter 6. An 

example of selecting the MEPDG_AXLE_LOAD_DIST_FACTOR table for extraction from the 

InfoPave Table Export menu is shown in figure 21. 
105 

Source: FHWA.

Figure 21. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “AASHTOWare Pavement ME Axle Distributions 

(MEPDG_AXLE_LOAD_DIST_FACTOR).”(3)

PARAMETER 3.2: GROSS VEHICLE WEIGHT DISTRIBUTION

GVW distribution information may be needed to compute average truck weight for some of the 

analyses that use aggregated traffic loading summary statistics and to investigate variations in 

traffic loading that may be associated with WIM equipment calibration drift over time.

The GVW distributions are available for the selected LTPP sites with WIM data collected after 

2002. The following tables in LTPP LTAS database contain GVW distributions for FHWA 

vehicle classes 4–13:(11)

• DD_GVW—Daily aggregate of GVW distribution data (data reported using 

State-specific vehicle classification scheme).

• MM_GVW—Monthly aggregate of GVW distribution data by DOW (data reported using 

FHWA vehicle classification scheme).

• YY_GVW—Yearly aggregate of GVW distribution data (data reported using FHWA 

vehicle classification scheme).
106 

These tables can be downloaded using the generic InfoPave data extraction instructions given in 

chapter 6. An example of selecting the YY_GVW table for extraction from the InfoPave Table 

Export menu is shown in figure 22. 

Source: FHWA.

Figure 22. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Yearly Aggregate of GVW (YY_GVW).”(3)
107 

CHAPTER 10. SCENARIO 4: OBTAIN SUMMARY TRAFFIC LOADING 

INFORMATION (ESAL OR ALTERNATIVE STATISTICS)

The summary traffic loading parameters are used for analyses that require a single parameter to 

describe traffic loading for an LTPP site. These parameters are applied in situations when a quick 

judgment about traffic loading is needed (that does not include analysis of axle load 

distributions) or for high-level analyses that are not focused on the investigation of the specific 

mechanisms of pavement deterioration because of traffic but need some estimate of traffic 

loading. A number of such parameters are available through InfoPave,

(3)

 including the following: 

• ESAL—Most commonly use traffic loading summary statistic since the 1960s based on 

weighted averaging (heavier, more damaging loads carry higher weight compared to light 

loads). Its main drawback is that, in addition to traffic loading inputs, it also considers 

pavement type, structure, and serviceability rating. In ESAL computation, axle loads are 

statistically weighted based on their damaging potential to the pavement using test data 

from the AASHO Road Test.

(18)

 (Heavy loads have exponentially higher weights in the 

formula.) 

• GESAL—Similar to ESAL but with pavement type, structure, and serviceability rating 

set to constant values in the ESAL formula. This parameter is useful for comparing

loading between different sites.

• RPPIF—Similar to GESAL but with weight factors computed based on MEPDG model

predictions instead of test data from the AASHO Road Test.(10) This parameter is useful 

for comparing loading between different sites. RPPIF values are available for LTPP sites 

with WIM data on a monthly and annual basis and reported at different levels of 

aggregation: per axle, per vehicle class, and for all truck classes combined. 

• ATL—An estimate of ATL. It represents a simple summary of all the loads from heavy 

vehicles (computed as a summation of GVWs of the vehicles in FHWA vehicle classes

4–13(4)

 that traveled over the pavement during the year) to which a pavement was 

subjected over a year. Its main drawback is that it lacks the means for differentiating

between sites exposed to large numbers of low to moderate loads and sites exposed to 

fewer numbers of heavier loads. For some pavement distresses, the lower number of 

heavier loads may be more critical than the higher number of light loads because of a 

complex relationship between the load magnitude, the number of load applications, and 

the pavement responses and damage accumulation. 

• CTL—An estimate of cumulative total load for the period of interest (multiple years). It 

represents a simple summary of all the loads from heavy vehicles to which the pavement 

was subjected over the analysis period (computed as a summation of GVWs of all the 

vehicles in FHWA vehicle classes 4–13 that traveled over the pavement during the 

analysis period). It has advantages and disadvantages similar to those of the ATL 

parameter.
108 

PARAMETER 4.1: ANNUAL ESAL FOR EACH YEAR IN THE ANALYSIS PERIOD

Annual ESAL for each year in the analysis period are used in the empirical analyses correlating 

pavement performance parameters collected or computed for specific years with traffic loading 

obtained during the same years. In these analyses, ESAL is used as a single parameter 

characterizing traffic loading at the site. When using this parameter in an analysis involving 

multiple LTPP sites, the user must pay attention to the nontraffic factors affecting ESAL values, 

such as road functional class, pavement type, pavement thickness, drainage, and others. 

Annual ESAL values can be found in the following LTPP tables: 

• TRF_ESAL_COMPUTED—Values based on monitored traffic loading data, available 

for years with WIM data. 

• TRF_MON_EST_ESAL—Estimated ESAL values during the monitoring period 

(post-1989) when no weight data were collected. 

• TRF_HIST_EST_ESAL—Historical values provided by local highway agencies based on 

various data sources prior to 1989. 

• TRF_TREND—Estimated annual ESAL values for all in-service years based on all 

available monitored and historical values, as well as projections to fill in the gap years. 

These tables can be downloaded using the generic InfoPave data extraction instructions given in 

chapter 6. An example of selecting TRF_ESAL_COMPUTED table for extraction from the 

InfoPave Table Export menu is shown in figure 23. 

Note that ESAL values may be affected by errors because of incorrect vehicle classification, as 

well as weighing errors. Critical review and evaluation of changes in the annual ESAL values 

year to year is recommended. Comparison with the expected ESAL per truck values is 

recommended to identify outliers and identify reasonableness of ESAL values. 
109 

Source: FHWA.

Figure 23. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Computed Traffic ESALs (TRF_ESAL_COMPUTED).”(3)

Example 4.1.1: Obtain Annual ESAL for Each Year in the Analysis Period 

The following example demonstrates how to obtain the annual ESAL values for Arizona test site 

7613 for years corresponding to construction number 1. 

1. To obtain the annual ESAL values for each in-service year corresponding to 

CONSTRUCTION_NO = 1 for Arizona test site 7613, extract records from the 

TRF_TREND table field ANNUAL_ESAL_TREND using the generic InfoPave data 

extraction instructions given in chapter 6 and the example demonstrated in figure 24. 
110 

Source: FHWA.

Figure 24. Screenshot. InfoPave Table Export menu with checkboxes showing “selected” 

next to the traffic table label “Annual Trends in Traffic Characteristics (TRF_TREND)” 

and ANNUAL_ESAL_TREND field.(3)

2. Once the table has been extracted to an Excel or Access file, identify columns (fields) 

with headers STATE_CODE, SHRP_ID, YEAR, CONSTRUCTION_NO, and 

ANNUAL_ESAL_TREND. Filter records for STATE_CODE=4, SHRP_ID = 7613, and 

CONSTRUCTION_NO = 1. Applying this filter would provide the annual ESAL values 

in the ANNUAL_ESAL_TREND column shown in table 33. 
111 

Table 33. Annual ESAL trend table for Arizona site 7613 extracted from the TRF_TREND 

table.

STATE_CODE SHRP_ID

CONSTRUCTION

_NO YEAR

ANNUAL_ESAL_

TREND

4 7613 1 1979 14,168 

4 7613 1 1980 71,736 

4 7613 1 1981 81,760 

4 7613 1 1982 117,530 

4 7613 1 1983 132,860 

4 7613 1 1984 133,224 

4 7613 1 1985 143,080 

4 7613 1 1986 168,630 

4 7613 1 1987 189,070 

4 7613 1 1988 189,588 

4 7613 1 1989 204,400 

4 7613 1 1990 166,995 

4 7613 1 1991 190,000 

4 7613 1 1992 200,000 

4 7613 1 1993 187,000 

4 7613 1 1994 249,000 

4 7613 1 1995 275,000 

4 7613 1 1996 261,119 

4 7613 1 1997 106,697 

4 7613 1 1998 192,340 

4 7613 1 1999 223,000 

4 7613 1 2000 187,743 

4 7613 1 2001 204,543 

PARAMETER 4.2: CUMULATIVE ESAL FOR THE ANALYSIS PERIOD

Cumulative ESAL is used in analyses requiring correlation between pavement deterioration and 

traffic loading accumulated at certain time points (typically dates when pavement performance 

data have been collected). This also includes analyses when pavement performance is evaluated 

at the end of the analysis period and involves accumulation of ESALs from the year when the 

site was first opened to traffic to the last year in which the site was part of the experiment or the 

last year of pavement monitoring. 

Cumulative ESAL values can be computed by summing annual ESAL values extracted from the 

LTPP table TRF_TREND for the years identified for the analysis.
112 

Example 4.2.1: Obtain Cumulative ESAL for the Analysis Period 

Cumulative ESAL values can be computed by summing annual ESAL values extracted from the 

TRF_TREND table for the years identified for the analysis period. Typically, this includes years 

from the first year when the site was first opened to traffic to the last year in which the site was 

part of the experiment or the last year of pavement monitoring. 

An example of the cumulative ESAL computation for the years corresponding to construction 

event 1 for Arizona site 7613 is shown in table 34. Annual ESAL values for each year are shown 

in the ANNUAL_ESAL_TREND column. Cumulative ESAL values for each year are shown in 

the Cumulative ESAL column. Cumulative ESAL for any selected year is computed as a 

summation of annual ESAL values from all the previous years. For example, cumulative ESAL 

for 1981 is computed as a summation of annual ESAL values for 1979, 1980, and 1981. With the 

use of the annual ESAL values from the ANNUAL_ESAL_TREND column corresponding to 

the above-mentioned 3 years, the following computation can be made: 14,168 + 71,736 + 81,760 

= 167,664. For the example shown in table 34, cumulative total ESAL, accumulated over the 

years corresponding to construction event 1, could be found on the last row of the Cumulative 

ESAL column: 3,889,483. This value is a result of summation of all annual ESAL values from 

the ANNUAL_ESAL_TREND column. 

Table 34. Cumulative ESAL for construction event 1 for Arizona site 7613.

STATE_

CODE SHRP_ID

CONSTRUCTION

_NO YEAR

ANNUAL_ESAL_

TREND

Cumulative 

ESAL

4 7613 1 1979 14,168 14,168 

4 7613 1 1980 71,736 85,904 

4 7613 1 1981 81,760 167,664 

4 7613 1 1982 117,530 285,194 

4 7613 1 1983 132,860 418,054 

4 7613 1 1984 133,224 551,278 

4 7613 1 1985 143,080 694,358 

4 7613 1 1986 168,630 862,988 

4 7613 1 1987 189,070 1,052,058 

4 7613 1 1988 189,588 1,241,646 

4 7613 1 1989 204,400 1,446,046 

4 7613 1 1990 166,995 1,613,041 

4 7613 1 1991 190,000 1,803,041 

4 7613 1 1992 200,000 2,003,041 

4 7613 1 1993 187,000 2,190,041 

4 7613 1 1994 249,000 2,439,041 

4 7613 1 1995 275,000 2,714,041 

4 7613 1 1996 261,119 2,975,160 

4 7613 1 1997 106,697 3,081,857 

4 7613 1 1998 192,340 3,274,197 
113 

STATE_

CODE SHRP_ID

CONSTRUCTION

_NO YEAR

ANNUAL_ESAL_

TREND

Cumulative 

ESAL

4 7613 1 1999 223,000 3,497,197 

4 7613 1 2000 187,743 3,684,940 

4 7613 1 2001 204,543 3,889,483 

PARAMETER 4.3: ANNUAL GESAL FOR EACH YEAR IN THE ANALYSIS PERIOD 

This parameter is similar to ESAL but with constant values used for pavement thickness or SN

and serviceability inputs. Thus, this traffic summary statistic is independent of pavement 

structure and pavement condition. This parameter is recommended for LTPP empirical analyses 

where a single-value traffic loading statistic is desired. Also, GESAL could be used for 

quantifying and comparing traffic loads between sites. GESAL is not intended for use with the 

AASHTO 1993 pavement design procedure. 

An estimate of annual GESAL for each in-service and in-experiment year can be obtained from 

the TRF_TREND table, field ANNUAL_GESAL_TREND, using the InfoPave data table 

extraction instructions provided in chapter 6. ANNUAL_GESAL_TREND values are 

consolidated from multiple sources, with any data discrepancies between multiple sources 

resolved and missing values estimated. The source of data or estimation method for each year is 

also provided in the TRF_TREND table, field LOAD_SOURCE.

The TRF_TREND table can be downloaded using the generic InfoPave data extraction 

instructions given in chapter 6. An example of selecting TRF_TREND table and 

ANNUAL_GESAL_TREND field for extraction from the InfoPave Table Export menu is shown 

in figure 25. 
114 

Source: FHWA.

Figure 25. Screenshot. InfoPave Table Export menu with checkboxes showing “selected” 

next to the traffic table label “Annual Trends in Traffic Characteristics (TRF_TREND)” 

and ANNUAL_GESAL_TREND field name.(3)

Example 4.3.1: Obtain Annual GESAL for Each Year in the Analysis Period

This example demonstrates how to obtain the annual GESAL values for Arizona test site 7613 

for in-service years corresponding to LTPP construction event number 1. 

1. To obtain the annual GESAL values for each in-service year corresponding to 

CONSTRUCTION_NO = 1 for Arizona test site 7613, extract records from the 

TRF_TREND table field ANNUAL_GESAL_TREND using the generic InfoPave data 

extraction instructions given in chapter 6 (figure 25).

2. Once the table has been extracted to an Excel or Access file, identify columns (fields) 

with headers STATE_CODE, SHRP_ID, YEAR, CONSTRUCTION_NO, and 

ANNUAL_GESAL_TREND. Filter records for STATE_CODE = 4, SHRP_ID = 7613, 

and CONSTRUCTION_NO = 1. Applying this filter would provide the annual GESAL 

values in the ANNUAL_GESAL_TREND column shown in table 35. 
115 

Table 35. Annual GESAL trend for Arizona site 7613 from the TRF_TREND table.

STATE_CODE

SHRP_

ID

CONSTRUCTION

_NO YEAR

ANNUAL_GESAL_

TREND

4 7613 1 1979 11,808 

4 7613 1 1980 59,083 

4 7613 1 1981 68,116 

4 7613 1 1982 97,484 

4 7613 1 1983 110,239 

4 7613 1 1984 110,541 

4 7613 1 1985 118,999 

4 7613 1 1986 140,130 

4 7613 1 1987 156,692 

4 7613 1 1988 157,121 

4 7613 1 1989 169,546 

4 7613 1 1990 138,712 

4 7613 1 1991 143,743 

4 7613 1 1992 148,683 

4 7613 1 1993 133,580 

4 7613 1 1994 168,743 

4 7613 1 1995 196,663 

4 7613 1 1996 211,790 

4 7613 1 1997 95,157 

4 7613 1 1998 163,393 

4 7613 1 1999 189,561 

4 7613 1 2000 155,614 

4 7613 1 2001 169,964 

Example 4.3.2: Obtain Cumulative GESAL for the Analysis Period 

Cumulative GESAL values can be computed by summing annual GESAL values extracted from 

the TRF_TREND table ANNUAL_GESAL_TREND column for the years identified for the 

analysis period. Typically, this includes years from the first year when the site was first opened 

to traffic to the last year in which the site was part of the experiment or the last year of pavement 

monitoring. 

An example of the cumulative GESAL computation for the years corresponding to construction 

event 1 for Arizona site 7613 is shown in table 36. The last row in the Cumulative GESAL 

column shows the total GESAL, accumulated over the years corresponding to construction 

event 1. This value is a summation of all annual GESAL values shown in the 

ANNUAL_GESAL_TREND column. 
116 

Table 36. Cumulative GESAL for construction event 1 for Arizona site 7613. 

STATE

CODE

SHRP_

ID

CONSTRUCTION

_NO YEAR

ANNUAL_GESAL_

TREND Cumulative GESAL

4 7613 1 1979 11,808 11,808 

4 7613 1 1980 59,083 70,891 

4 7613 1 1981 68,116 139,007 

4 7613 1 1982 97,484 236,491 

4 7613 1 1983 110,239 346,730 

4 7613 1 1984 110,541 457,271 

4 7613 1 1985 118,999 576,270 

4 7613 1 1986 140,130 716,400 

4 7613 1 1987 156,692 873,092 

4 7613 1 1988 157,121 1,030,213 

4 7613 1 1989 169,546 1,199,759 

4 7613 1 1990 138,712 1,338,471 

4 7613 1 1991 143,743 1,482,214 

4 7613 1 1992 148,683 1,630,897 

4 7613 1 1993 133,580 1,764,477 

4 7613 1 1994 168,743 1,933,220 

4 7613 1 1995 196,663 2,129,883 

4 7613 1 1996 211,790 2,341,673 

4 7613 1 1997 95,157 2,436,830 

4 7613 1 1998 163,393 2,600,223 

4 7613 1 1999 189,561 2,789,784 

4 7613 1 2000 155,614 2,945,398 

4 7613 1 2001 169,964 3,115,362 

PARAMETER 4.4: GESAL PER TRUCK, PER VEHICLE CLASS, AND PER AXLE

GESAL per truck values could be used to judge how heavy different trucks are. These values are 

also known as truck factors and are being used by State highway agencies for estimating loads. 

LTPP table TRF_REP contains estimates of representative GESAL per truck, representative 

GESAL per vehicle class, and representative GESAL per axle values for all LTPP sites.

The TRF_REP table can be downloaded using the generic InfoPave data extraction instructions 

given in chapter 6. An example of selecting the TRF_REP table and related GESAL fields for 

extraction from the InfoPave Table Export menu is shown in figure 26. 
117 

Source: FHWA.

Figure 26. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Representative Site Traffic Parameters (TRF_REP).”(3)

PARAMETER 4.5: RPPIF PER TRUCK, PER VEHICLE CLASS, AND PER AXLE 

RPPIF values per truck, per vehicle class, and per axle could be used to judge how heavy 

different trucks and axles are and to compare axle loads between different sites. LTPP table 

TRF_REP contains representative RPPIF per truck, per vehicle class, and per axle values for 

LTPP sites.

The TRF_REP table can be downloaded using the generic InfoPave data extraction instructions 

given in chapter 6. An example of selecting the TRF_REP table and related RPPIF fields for 

extraction from the InfoPave Table Export menu is shown in figure 27. 
118 

Source: FHWA.

Figure 27. Screenshot. InfoPave Table Export menu with checkboxes showing “selected” 

next to the traffic table label “Representative Site Traffic Parameters (TRF_REP)” and 

REP_RPPIF field names.(3)

PARAMETER 4.6: ATL FOR EACH YEAR IN THE ANALYSIS PERIOD 

The ATL parameter provides an estimate of the total load transferred to the pavement by heavy 

vehicles over a year. This parameter is recommended for LTPP empirical analyses where a 

single-value traffic loading statistic is desired and for quantifying and comparing traffic loads 

between sites. It is computed or estimated. 

TRF_TREND table field ANNUAL_TOTAL_GVW_TREND contains ATL estimates for each 

year that a pavement site was in service, up to the end of the site’s participation in the LTPP 

experiment. The source of data or estimation method for each year is also provided in this table. 

The TRF_TREND table can be downloaded using the generic InfoPave data extraction 

instructions given in chapter 6. An example of selecting the TRF_TREND table and 

ANNUAL_TOTAL_GVW_TREND field for extraction from the InfoPave Table Export menu is 

shown in figure 28. 
119 

Source: FHWA.

Figure 28. Screenshot. InfoPave Table Export menu with checkboxes showing “selected” 

next to the traffic table label “Annual Trends in Traffic Characteristics (TRF_TREND)” 

and ANNUAL_TOTAL_GVW_TREND field name.(3)

Example 4.6.1: Obtain ATL for Each Year in the Analysis Period 

This example demonstrates how to obtain the ATL values for Arizona test site 7613 for 

in-service years corresponding to LTPP construction event number 1. 

1. To obtain the ATL values for each in-service year corresponding to 

CONSTRUCTION_NO = 1 for Arizona test site 7613, extract records from the 

TRF_TREND table field ANNUAL_TOTAL_GVW_TREND using the generic InfoPave 

data extraction instructions given in chapter 6 (figure 28).

2. Once the table has been extracted to an Excel or Access file, identify columns (fields) 

with headers STATE_CODE, SHRP_ID, YEAR, CONSTRUCTION_NO, and 

ANNUAL_TOTAL_GVW_TREND. Filter records for STATE_CODE = 4, SHRP_ID = 

7613, and CONSTRUCTION_NO = 1. Applying this filter would provide the ATL 

values in the ANNUAL_TOTAL_GVW_TREND column shown in table 37. 
120 

Table 37. ATL trend table for Arizona site 7613 extracted from the TRF_TREND table.

STATE_CODE SHRP_ID

CONSTRUCTION_

NO YEAR

ANNUAL_TOTAL_GVW_

TREND (lb)

4 7613 1 1979 651,165,328 

4 7613 1 1980 3,266,769,234 

4 7613 1 1981 3,760,213,940 

4 7613 1 1982 5,386,448,445 

4 7613 1 1983 6,101,174,290 

4 7613 1 1984 6,117,889,836 

4 7613 1 1985 6,575,295,420 

4 7613 1 1986 7,740,417,395 

4 7613 1 1987 8,664,241,155 

4 7613 1 1988 8,687,978,802 

4 7613 1 1989 9,367,088,805 

4 7613 1 1990 7,659,231,175 

4 7613 1 1991 7,936,237,340 

4 7613 1 1992 8,212,741,710 

4 7613 1 1993 7,408,415,950 

4 7613 1 1994 9,162,449,730 

4 7613 1 1995 10,764,915,435 

4 7613 1 1996 11,574,653,376 

4 7613 1 1997 5,315,578,585 

4 7613 1 1998 9,076,371,415 

4 7613 1 1999 10,694,238,000 

4 7613 1 2000 8,603,131,218 

4 7613 1 2001 9,388,623,618 

PARAMETER 4.7: CUMULATIVE TOTAL LOAD FOR THE ANALYSIS PERIOD 

CTL values can be computed by summing the ATL values extracted from the LTPP table 

TRF_TREND column ANNUAL_TOTAL_GVW_TREND for the years identified for the 

analysis period. Typically, this includes years from the first year when the site was first opened 

to traffic to the last year in which the site was part of the experiment or the last year of pavement 

monitoring. 

An example of the CTL computation for the years corresponding to construction event 1 for 

Arizona site 7613 is shown in table 38. The last row in the Cumulative Total Load column shows 

the total cumulative load, accumulated over the years corresponding to construction event 1. The 

value of 172,115,270,202 lb is a result of summing the values shown in 

ANNUAL_TOTAL_GVW_TREND column.
121 

Table 38. Cumulative total load computed for the years during construction event 1 for 

Arizona site 7613. 

STATE_

CODE SHRP_ID

CONSTRUCTION

_NO YEAR

ANNUAL_TOTAL_

GVW_TREND (lb)

Cumulative Total 

Load (lb)

4 7613 1 1979 651,165,328 651,165,328 

4 7613 1 1980 3,266,769,234 3,917,934,562 

4 7613 1 1981 3,760,213,940 7,678,148,502 

4 7613 1 1982 5,386,448,445 13,064,596,947 

4 7613 1 1983 6,101,174,290 19,165,771,237 

4 7613 1 1984 6,117,889,836 25,283,661,073 

4 7613 1 1985 6,575,295,420 31,858,956,493 

4 7613 1 1986 7,740,417,395 39,599,373,888 

4 7613 1 1987 8,664,241,155 48,263,615,043 

4 7613 1 1988 8,687,978,802 56,951,593,845 

4 7613 1 1989 9,367,088,805 66,318,682,650 

4 7613 1 1990 7,659,231,175 73,977,913,825 

4 7613 1 1991 7,936,237,340 81,914,151,165 

4 7613 1 1992 8,212,741,710 90,126,892,875 

4 7613 1 1993 7,408,415,950 97,535,308,825 

4 7613 1 1994 9,162,449,730 106,697,758,555 

4 7613 1 1995 10,764,915,435 117,462,673,990 

4 7613 1 1996 11,574,653,376 129,037,327,366 

4 7613 1 1997 5,315,578,585 134,352,905,951 

4 7613 1 1998 9,076,371,415 143,429,277,366 

4 7613 1 1999 10,694,238,000 154,123,515,366 

4 7613 1 2000 8,603,131,218 162,726,646,584 

4 7613 1 2001 9,388,623,618 172,115,270,202 

123 

CHAPTER 11. SCENARIO 5: OBTAIN MEPDG TRAFFIC INPUTS FOR USE IN 

AASHTOWARE PAVEMENT ME DESIGN SOFTWARE

The AASHTOWare Pavement ME Design software requires a large number of traffic input 

parameters.(5) This Guide demonstrates how LTPP traffic parameters can be used as 

AASHTOWare software inputs. The screenshot examples provided in this Guide are consistent 

with the AASHTOWare Pavement ME Design versions 2.2 through 2.6. A screenshot of the 

software with Traffic Inputs menu is shown in figure 29. 

© 2022 AASHTO. 

Figure 29. Screenshot. AASHTOWare Pavement ME Design software Project 1: Traffic

input screen.(5)

Most of the required traffic inputs for the AASHTOWare Pavement ME Design software can be 

found in the LTPP traffic data tables or could be computed using LTPP traffic data. Still, a 

number of the input parameters are unavailable from the LTPP database. For these parameters, 

the use of default values (either included in the AASHTOWare Pavement ME Design software(5)

or published by LTPP or State/Provincial highway agencies) is recommended. Table 18 in part 1 

of this Guide contains a complete listing of the AASHTOWare Pavement ME Design traffic 

input parameters and the LTPP sources where these parameters can be found. For parameters 

unavailable through the LTPP database, references to recommended default values are also 

provided in table 18. 

To make an informed selection of the MEPDG traffic input parameters, it is important to 

understand how each of these parameters is used by the AASHTOWare Pavement ME Design 

software.

(5) To estimate the number of axle load applications of different magnitudes applied 
124 

during a design or analysis time increment (the time increment typically equals 1 day for flexible 

pavement or 1 h for rigid pavements), the following inputs are multiplied inside the 

AASHTOWare Pavement ME Design software:

• Base year AADTT is the estimated total number of trucks for the first year in the analysis 

or design period. This parameter has a direct effect on the estimate of the total number of 

axle load applications. If a two-way AADTT is used, then it is also multiplied by the 

number of lanes, by the percentage of trucks in the design direction, and by the 

percentage of trucks in the design lane. 

• Vehicle Class Distribution (VCD) provides the percentile distribution by vehicle class of 

vehicle volume in FHWA vehicle classes 4–13(11)

 for the base design or analysis year. 

This parameter does not contain information about the total number of vehicles. The 

parameter contains just the percentile distribution of vehicles by vehicle type. The 

AASHTOWare Pavement ME Design program has a capability to change this 

distribution over the years and between different months by applying annual growth rates 

and monthly truck volume adjustment factors, separately for each vehicle class. 

• Annual Vehicle Volume Growth Rate and Growth Function by Vehicle Class are values 

applied against the base year AADTT value to estimate the total number of trucks for 

each year in the analysis or design period, starting with analysis year 2. These parameters 

have a direct effect on the estimate of the total number of axle load applications in every 

year of the analysis period.

• Monthly Adjustment Factors (MAFs) are multipliers used to adjust truck volume for each 

FHWA vehicle classes 4–13 between different calendar months within the 

design/analysis period. This monthly distribution stays constant over the design period. 

• Hourly Distribution Factors (HDF) (used for rigid pavements only) are multipliers used 

to estimate total truck volume occurring during each hour during the day. These values 

are the same for all truck classes and apply only to total truck volume. This distribution 

stays constant over the design period. These factors used for rigid pavement analysis 

only. 

• Axle Load Distribution Factors (ALDF) provide the percentile distribution of axle counts 

by load magnitude for each heavy vehicle class (FHWA vehicle classes 4–13) and axle 

type/group (single, tandem, tridem, and quad). This parameter is the same as the NALS. 

Both ALDF and NALS are used in conjunction with the MEPDG method 

interchangeably. Use of the term ALDF is limited primarily to the AASHTOWare 

Pavement ME Design software. This parameter does not contain information about the 

total number of axles. It contains just the percentile distribution of axles by load 

magnitude (i.e., it shows percentages of light, moderate, and heavy loads) that 

characterizes the base year condition, provided separately for each vehicle class and axle 

type. For a given vehicle class and axle type, this distribution stays constant over the 

design or analysis period. 
125 

• Number of axles per truck (APT) coefficients, provided for each vehicle class (FHWA 

vehicle classes 4–13) and axle type/group (single, tandem, tridem, and quad), is used as 

multipliers for estimating the total number of axle loads from the total number of trucks. 

An APT is needed for each truck class and axle type. To compute the total number of 

single, tandem, tridem, or quad axle loads, single, tandem, tridem, or quad APTs for each 

vehicle class are multiplied by the total number of trucks for each truck class; these 

products are then summed across different vehicle classes.

To determine the location of the load and load configuration, the following parameters are used 

inside the software:

• Axle group and axle spacing for tandem, tridem, and quad axles. 

• Average axle width.

• Dual tire spacing.

• Truck wander described using mean wheel location and standard deviation of wheel 

location. 

• Average spacing of short, medium, and long wheelbase axles and corresponding 

percentage of trucks.

The following sections provide examples of how to obtain individual MEPDG traffic parameters, 

listed in the order that these parameters are entered in the AASHTOWare Pavement ME Design 

software.

PARAMETER 5.1: MEPDG BASE YEAR AADTT

The AASHTOWare Pavement ME Design software requires input of two-way AADTT 

computed for the base design/analysis year.(5)

 However, the analysis is for one design lane only. 

The two-way AADTT is converted inside the AASHTOWare software to the design lane 

AADTT. In the case of the LTPP traffic data, AADTT for the LTPP lane is already known and 

does not need this conversion. 

To use the LTPP lane’s AADTT value in the AASHTOWare Pavement ME Design software, the 

following MEPDG parameters must be entered: 

1. Enter AADTT values for the LTPP lane.

2. Enter percent trucks in the design direction = 100 percent.

3. Enter percent trucks in the design lane = 100 percent. 

Example 5.1.1: Obtain Base Year AADTT for Use in the AASHTOWare Pavement ME 

Software

For example, to obtain base year AADTT for the analysis period during which Arizona test site 

7613 was part of the LTPP experiment, extract records for Arizona test site 7613 from the 

MEPDG_TRUCK_VOL_PARAMETERS table using the generic InfoPave data extraction 

instructions given in chapter 6. The result will be as shown in table 39. 
126 

Table 39. AADTT for the base design year from MEPDG_TRUCK_VOL_PARAMETERS 

for Arizona site 7613. 

STATE

_CODE

SHRP

_ID 

TRAFFIC_OPEN_

DATE_EXP_NO

TRAFFIC_OPEN_

YEAR_EXP_NO END_YEAR

AADTT_FIRST_

YEAR_LTPP_

LANE

4 7613 10/1/1979 1979 2001 275 

Once extracted from the TRF_TREND table, the design lane AADTT value then could be 

entered on the traffic input screen of the AASHTOWare Pavement ME Design software, as 

shown in figure 30.

(5)

© 

2022 AASHTO. 

Figure 30. Screenshot. AASHTOWare Pavement ME Design software showing AADTT 

input screen.(5)

The software will automatically issue a warning for the “Percent trucks in the design direction” 

input because it lacks an option for a one-way, one-lane road. To remove the warning, AADTT 

can be multiplied by 2, and then 50 percent can be used for the “Percent trucks in the design 

direction” input, as shown in figure 31. 

© 2022 AASHTO. 

Figure 31. Screenshot. AASHTOWare Pavement ME Design software showing AADTT 

input screen with modified input values.(5)

PARAMETER 5.2: MEPDG NUMBER OF LANES IN DESIGN DIRECTION

Because the software is designed for general application, not just for use in the analysis of LTPP 

data, the AASHTOWare Pavement ME Design software requires the input of the number of 

lanes in the design direction (i.e., the direction of LTPP lane).(5) The LTPP table 

TRF_BASIC_INFO contains information about the number of lanes in the direction of the LTPP 

section in the field NO_LTPP_LANES. The LTPP table SHRP_INFO has a similar field called 

LANES_LTPP_DIR. If all other input entries for the AASHTOWare Pavement ME Design 

software are set to LTPP lane only, use a value of 1 for number of lanes input. 
127 

PARAMETER 5.3: MEPDG PERCENTAGE OF TRUCKS IN DESIGN DIRECTION 

(PERCENT) 

For reasons stated in parameter 5.2, the AASHTOWare Pavement ME software(5) requires the 

input of the percentage of trucks operating in the design direction (i.e., the direction of LTPP 

lane) for the base design/analysis year. Because all LTPP traffic parameters are computed and 

reported for just the LTPP lane, the user needs to tell the software that the data entered are 

specifically for the LTPP lane. To do this, enter the Percent of Trucks in the Design Direction = 

100. 

PARAMETER 5.4: MEPDG PERCENT OF TRUCKS IN DESIGN LANE (PERCENT) 

Because many roads contain more than one lane in the design direction, the AASHTOWare 

Pavement ME Design software requires the input of the percentage of trucks in the design lane 

(LTPP lane) for the base design/analysis year.

(5) Because all LTPP traffic parameters are 

computed and reported for just the LTPP lane, the user needs to tell the software that the data 

entered are specifically for the LTPP lane. To do this, enter Percent of Trucks in Design Lane = 

100. 

PARAMETER 5.5: MEPDG VEHICLE CLASS DISTRIBUTION

VCD is one of the parameters used to estimate total traffic loading for MEPDG analysis. It 

represents an average VCD for a base design or analysis year. For LTPP sites, one set of 

normalized VCD values is stored in the LTPP table MEPDG_TRUCK_VOL_PARAMETERS. 

Each LTPP site has a set of 10 values. If an LTPP site is included in more than one LTPP 

experiment, a set of 10 values is stored for each LTPP experiment. Each set of 10 values 

represents the percentage of trucks found in one of the FHWA vehicle classes 4–13.(11) The sum 

of the 10 values is equal to 100 percent. 

Example 5.5.1: Obtain Normalized VCD for the Base Year Specified for the Analysis 

The normalized VCD, for the base year corresponding to the opening to traffic date for a given 

LTPP site specified for the analysis, can be extracted from the table 

MEPDG_TRUCK_VOL_PARAMETERS using the generic InfoPave data extraction 

instructions given in chapter 6. For example, the normalized VCD representing the base year for 

the analysis period during which Arizona test site 7613 was part of the LTPP experiment is 

shown in table 40. 
128 

Table 40. Normalized VCD for the base design year from the 

MEPDG_TRUCK_VOL_PARAMETERS table for Arizona site 7613. 

STATE_CODE SHRP_ID VEHICLE_CLASS VEH_CLASS_DIST_PERCENT

4 7613 4 1.91 

4 7613 5 56.87 

4 7613 6 8.79 

4 7613 7 0.2 

4 7613 8 9.48 

4 7613 9 20.76 

4 7613 10 0.37 

4 7613 11 0.94 

4 7613 12 0 

4 7613 13 0.68 

Once the data have been extracted, copy the 10 VCD values from the field 

VEH_CLASS_DIST_PERCENT and paste them into the Vehicle Class Distribution and Growth 

input screen of the AASHTOWare Pavement ME Design software, as shown in figure 32.

(5)

© 2022 AASHTO. 

Figure 32. Screenshot. AASHTOWare Pavement ME Design software Vehicle Class 

Distribution and Growth input screen.(5)

PARAMETER 5.6: MEPDG ANNUAL VEHICLE VOLUME GROWTH RATE AND 

GROWTH FUNCTION BY VEHICLE CLASS 

The AASHTOWare Pavement ME Design software(5) requires input of growth rate (percent) and 

the designation of whether that growth takes the shape of a linear or composite growth function. 

Growth rates are applied to each truck class (FHWA vehicle classes 4–13(11)) from the base 

design/analysis year through the end of the required analysis period to compute annual truck 

traffic volume for each vehicle class. These parameters could be obtained from the LTPP table 
129 

MEPDG_TRUCK_VOL_PARAMETERS, fields VEH_CLASS_GROWTH_RATE and 

VEH_CLASS_GROWTH_FUNCTION.

For example, to extract annual vehicle volume growth rate and growth function records for 

Arizona test site 7613 from the MEPDG_TRUCK_VOL_PARAMETERS table, use the generic 

InfoPave data extraction instructions given in chapter 6. The results are shown in table 41. 

Table 41. Annual vehicle volume growth rate and growth function by vehicle class from the 

MEPDG_TRUCK_VOL_PARAMETERS table for Arizona site 7613. 

STATE

_CODE

SHRP

_ID

TRAFFIC

_OPEN_

YEAR_

EXP_NO

END_

YEAR

AADTT_

FIRST_

YEAR_

LTPP_

LANE

VEHICLE

_CLASS

VEH_CLASS_

DIST_

PERCENT

VEH_CLASS

_GROWTH_

FUNCTION

VEH_ 

CLASS_

GROWTH

_RATE

4 7613 1979 2001 275 4 1.91 Linear 5.44

4 7613 1979 2001 275 5 56.87 Compound 4.24

4 7613 1979 2001 275 6 8.79 Linear 5.96

4 7613 1979 2001 275 7 0.2 Linear 10.55

4 7613 1979 2001 275 8 9.48 Compound 4.24

4 7613 1979 2001 275 9 20.76 Linear 6.22

4 7613 1979 2001 275 10 0.37 Linear 7.32

4 7613 1979 2001 275 11 0.94 Linear 5.8

4 7613 1979 2001 275 12 0 Compound 41.37

4 7613 1979 2001 275 13 0.68 Linear 6.23

Once the data have been extracted, enter these values into the Vehicle Class Distribution and 

Growth input screen of the AASHTOWare Pavement ME Design software, as shown in 

figure 32. 

PARAMETER 5.7: MEPDG MONTHLY ADJUSTMENT FACTORS 

MAFs are used as multipliers to adjust the annual average truck volume statistic for each of the 

10 heavy vehicle classes (FHWA vehicle classes 4–13),(11) to reflect the monthly or seasonal 

variations present in the truck traffic stream. The monthly distribution pattern is assumed to 

remain constant over multiple years for any length analysis period. One set of 12 representative 

monthly factors is required for each vehicle class. These factors represent the difference in truck 

volume, relative to the annual total, for each of the 12 months in any given year. The sum of 

factors for all months for one truck class equals 12. 

The LTPP table TRF_MEPDG_MONTH_ADJ_FACTR contains MAFs for LTPP sites with 

sufficient vehicle classification data to allow computation of these factors for each calendar 

month. For all other LTPP sites, use default values provided in the AASHTOWare Pavement ME 

Design software.

(5)

 The default assumes no monthly variation in truck volume, and all MAFs are 

equal to 1, as shown in figure 33. 
130 

© 2022 AASHTO. 

Figure 33. Screenshot. AASHTOWare Pavement ME Design software Monthly Adjustment 

input screen.(5)

For example, to extract records from the TRF_MEPDG_MONTH_ADJ_FACTR table for LTPP 

test section 0501 in Maryland, use the generic InfoPave data extraction instructions given in 

chapter 6 and select the TRF_MEPDG_MONTH_ADJ_FACTR table from the Table Export 

menu, as shown in figure 34. Then use the following procedure.
131 

Source: FHWA.

Figure 34. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Mechanistic-Empirical Pavement Design Guide Traffic 

Monthly Adjustment Factor (TRF_MEPDG_MONTH_ADJ_FACTR).”(3)

1. Once the TRF_MEPDG_MONTH_ADJ_FACTR table is extracted for LTPP test section 

0501 in Maryland, open the table in Excel and review values in the field 

MONTHLY_RATIO. Note that for the LTPP sites that report the weight data and vehicle 

classification data separately, there may be two sets of MONTHLY_RATIO values for 

the same year (see Weight and/or Classification values in the TRF_DATA_TYPE_EXP

field of the TRF_MEPDG_MONTH_ADJ_FACTR table). Since the purpose of the 

MEPDG MAFs is to adjust the number of axle load applications, use the 

MONTHLY_RATIO values computed from the weight data, unless the weight data were 

not available for the whole year.

2. Select a year with MONTHLY_RATIO values representing vehicle volume distribution 

for each FHWA vehicle class 4–13 by calendar month, as shown in table 42 for vehicle 

class 9. Alternatively, compute the average MONTHLY_RATIO values by averaging the 

values across all the available or selected years for each calendar month and for each 

FHWA vehicle classes 4–13 (in this case, renormalize the computed average values so 

that sum of 12 monthly average factors is equal to 12 for each vehicle class). 

3. Enter computed or selected MONTHLY_RATIO values onto the Monthly Adjustment 

input screen of the AASHTOWare Pavement ME Design software (figure 33).

(5)
132 

Table 42. MAFs for class 9 vehicles for 2016 for LTPP site 24-0501. 

STATE_

CODE

SHRP_

ID YEAR MONTH

VEHICLE_

CLASS

MONTHLY_

RATIO* 

24 0501 2016 1 9 0.8 

24 0501 2016 2 9 0.85 

24 0501 2016 3 9 1.03 

24 0501 2016 4 9 1.02 

24 0501 2016 5 9 0.91 

24 0501 2016 6 9 1.16 

24 0501 2016 7 9 1.03 

24 0501 2016 8 9 1.12 

24 0501 2016 9 9 1.06 

24 0501 2016 10 9 1.04 

24 0501 2016 11 9 1.03 

24 0501 2016 12 9 0.95 

*This column contains the MAFs. 

PARAMETER 5.8: MEPDG HOURLY DISTRIBUTION FACTORS 

HDFs apply only to PCC pavements. These factors are used to account for curling and warping 

associated with PCC pavement during different times of the day. HDF are used as multipliers to 

estimate truck volume for each hour within a 24-h period. These values are the same for all truck 

classes and apply to total truck volume. The HDF stay constant over the design or analysis 

period. One set of 24-h factors is used as an input to the AASHTOWare Pavement ME Design 

software(5) to represent the fraction of total truck traffic for each hour. 

For a limited number of LTPP sites, this parameter is reported in the LTPP data table 

TRF_MEPDG_HOURLY_DIST in the field PCT_HOURLY. The PCT_HOURLY values are 

available for each year with hourly volume data submitted by vehicle class. For all other LTPP 

sites, use the default values from the AASHTOWare Pavement ME Design software (figure 35). 

It is expected that hourly adjustment factors will be different for the following two groups of 

roads: roads that serve primarily local businesses or deliveries, and roads primarily used by the 

throughway long-haul trucks. If local agency defaults are available for these two road types, use 

these defaults instead of the AASHTOWare Pavement ME Design software default.
133 

© 2022 AASHTO. 

Figure 35. Screenshot. AASHTOWare Pavement ME Design software Hourly Adjustment 

input screen with default values.(5)

For example, to obtain the HDF from the TRF_MEPDG_HOURLY_DIST table for LTPP test 

section 0501 in Maryland, use the generic InfoPave data extraction instructions given in 

chapter 6, and select the TRF_MEPDG_HOURLY_DIST table from Table Export menu, as 

shown in figure 36. Then use the following procedure. 
134 

Source: FHWA.

Figure 36. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “Mechanistic-Empirical Pavement Design Guide Traffic 

Hourly Distribution (TRF_MEPDG_HOURLY_DIST).”(3)

1. Open the Excel file with the extracted data and review values in the field 

PCT_HOURLY. Sort hours in ascending order from 0 to 24. 

2. Select a year with PCT_HOURLY values representing typical hourly distribution, as 

shown in table 43. Alternatively, compute the average PCT_HOURLY values across all 

the available or selected years (in the latter case, renormalize the computed average 

values so that the sum of percentile values from PCT_HOURLY over 24 h is equal to 

100 percent).
135 

Table 43. HDF for 2016 for LTPP site 24-0501. 

STATE_CODE SHRP_ID YEAR HOUR PCT_HOURLY

24 0501 2016 0 0.77 

24 0501 2016 1 0.71 

24 0501 2016 2 0.65 

24 0501 2016 3 0.74 

24 0501 2016 4 1.14 

24 0501 2016 5 2.36 

24 0501 2016 6 3.87 

24 0501 2016 7 5.21 

24 0501 2016 8 6.16 

24 0501 2016 9 6.53 

24 0501 2016 10 6.4 

24 0501 2016 11 6.44 

24 0501 2016 12 6.56 

24 0501 2016 13 7.26 

24 0501 2016 14 8.62 

24 0501 2016 15 8.68 

24 0501 2016 16 7.36 

24 0501 2016 17 6.12 

24 0501 2016 18 4.99 

24 0501 2016 19 3.36 

24 0501 2016 20 2.06 

24 0501 2016 21 1.53 

24 0501 2016 22 1.33 

24 0501 2016 23 1.15 

Enter selected or computed PCT_HOURLY values onto the Hourly Adjustment input screen of 

the AASHTOWare Pavement ME Design software, as shown in figure 37.

(5)
136 

© 2022 AASHTO. 

Figure 37. Screenshot. AASHTOWare Pavement ME Design software Hourly Adjustment 

input screen with values entered for LTPP site 24-0501.(5)

PARAMETER 5.9: MEPDG AXLE LOAD DISTRIBUTION FACTORS

The AASHTOWare Pavement ME Design software(5) uses ALDF to compute traffic loads. One 

set of normalized ALDF is available in the LTPP data table 

MEPDG_AXLE_LOAD_DIST_FACTOR for each LTPP site, vehicle class (classes 4–13),(11)

axle group type (single, tandem, tridem, quad), and calendar month (January through December). 

Depending on data availability and data quality, these ALDF represent axle load for a typical day 

of the year (same values are reported for each calendar month) or a typical day of each of the 12 

calendar months (unique values are reported for each calendar month) observed during pavement 

service life. Each LTPP site in the MEPDG_AXLE_LOAD_DIST_FACTOR table has an index 

that provides ALDF usability rating based on the quality and quantity of the data used for ALDF 

development. 
137 

Because of the large size of this input parameter, in addition to the direct input on the ALDF 

screen, the AASHTOWare Pavement ME Design software accepts ALDF inputs in specially 

formatted XML files. These files could be downloaded from InfoPave or requested from LTPP 

Customer Service.

Example 5.9.1: Enter ALDF Values in AASHTOWare Software From 

MEPDG_AXLE_LOAD_DIST_FACTOR Table 

To enter the ALDF values from the LTPP MEPDG_AXLE_LOAD_DIST_FACTOR table, use 

the following procedure: 

1. Use InfoPave table extraction steps provided in chapter 6 to extract the 

MEPDG_AXLE_LOAD_DIST_FACTOR table from the InfoPave Table Export menu, 

shown in figure 38, for Arizona site 7613. 

Source: FHWA.

Figure 38. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “AASHTOWare Pavement ME Axle Distributions 

(MEPDG_AXLE_LOAD_DIST_FACTOR).”(3)

2. Download the MEPDG_AXLE_LOAD_DIST_FACTOR table for LTPP site 04-7613 

to an Excel file.

3. Make sure that the exported data are sorted in ascending order by axle group, then by 

the calendar month (January to December), then by vehicle class. A portion of the 

extracted ALDF file for site 04-7613 single-axle group is shown in table 44. 
138 

Table 44. ALDF sample for LTPP site 04-6713. 

STATE_CODESHRP_IDAXLE_GROUP

MonthVEH_CLASSTOTALMEPDG_LG01MEPDG_LG02MEPDG_LG03MEPDG_LG04MEPDG_LG05MEPDG_LG06...*MEPDG_LG39

4 7613 1 January 4 100 0 0 0.17 2.33 10.69 13.86 ... 7.64

4 7613 1 January 5 100 3.91 1.39 19.03 14.46 11.61 9.78 ... 2.41

4 7613 1 January 6 100 0 0 1.75 2.71 5.56 10.6 ... 4.39

4 7613 1 January 7 100 0 0 0 1.96 4.69 9.62 ... 3.83

4 7613 1 January 8 100 3.73 1.82 10.84 8.58 10.81 12.87 ... 2.98

4 7613 1 January 9 100 1.68 2.82 4.76 3.54 5.38 13.92 ... 1.35

4 7613 1 January 10 100 0.14 0.28 0.75 0.87 2.53 9.22 ... 0.8

4 7613 1 January 11 100 1.73 10.24 12.18 11.61 16.67 13.74 ... 1.3

4 7613 1 January 12 100 1.35 5.81 6.43 13.37 18.51 11.04 ... 1.25

4 7613 1 January 13 100 1.15 0.43 1.77 2.09 4.84 20.49 ... 1.17

4 7613 1 February 4 100 0 0 0.17 2.33 10.69 13.86 ... 7.64

4 7613 1 February 5 100 3.91 1.39 19.03 14.46 11.61 9.78 ... 2.41

4 7613 1 February 6 100 0 0 1.75 2.71 5.56 10.6 ... 4.39

4 7613 1 February 7 100 0 0 0 1.96 4.69 9.62 ... 3.83

4 7613 1 February 8 100 3.73 1.82 10.84 8.58 10.81 12.87 ... 2.98

4 7613 1 February 9 100 1.68 2.82 4.76 3.54 5.38 13.92 ... 1.35

4 7613 1 February 10 100 0.14 0.28 0.75 0.87 2.53 9.22 ... 0.8

4 7613 1 February 11 100 1.73 10.24 12.18 11.61 16.67 13.74 ... 1.3

4 7613 1 February 12 100 1.35 5.81 6.43 13.37 18.51 11.04 ... 1.25

4 7613 1 February 13 100 1.15 0.43 1.77 2.09 4.84 20.49 ... 1.17

4 7613 1 March 4 100 0 0 0.17 2.33 10.69 13.86 ... 7.64

4 7613 1 March 5 100 3.91 1.39 19.03 14.46 11.61 9.78 ... 2.41

4 7613 1 March 6 100 0 0 1.75 2.71 5.56 10.6 ... 4.39

4 7613 1 March 7 100 0 0 0 1.96 4.69 9.62 ... 3.83

4 7613 1 March 8 100 3.73 1.82 10.84 8.58 10.81 12.87 ... 2.98

4 7613 1 March 9 100 1.68 2.82 4.76 3.54 5.38 13.92 ... 1.35

4 7613 1 March 10 100 0.14 0.28 0.75 0.87 2.53 9.22 ... 0.8

4 7613 1 March 11 100 1.73 10.24 12.18 11.61 16.67 13.74 ... 1.3

4 7613 1 March 12 100 1.35 5.81 6.43 13.37 18.51 11.04 ... 1.25

4 7613 1 March 13 100 1.15 0.43 1.77 2.09 4.84 20.49 ... 1.17

*Load bins MEPDG_LG07 to MEPDG_LG38 are not shown in the table. 
139 

4. Open or create a new project in AASHTOWare Pavement ME Design. 

5. On the AASHTOWare project main menu, locate the green circle and “Traffic” label in 

the upper left corner under the “Projects” folder. Left-click on the “+” sign to the left of 

the “Traffic” label, and four options for “Single,” “Tandem,” “Tridem,” and “Quad Axle” 

distribution inputs will appear, as shown in figure 39. 

© 2022 AASHTO. 

Figure 39. Screenshot. AASHTOWare Pavement ME Design software Explorer menu with 

traffic options expanded.(5)

6. Double click on each of the axle load distribution types (“Single,” “Tandem,” “Tridem,” 

and “Quad”) shown in figure 39. A window will appear to enter axle load distribution 

inputs shown in figure 40. The tabs on the top of the window show different axle groups. 

(The words “Single,” “Tandem,” “Tridem,” and “Quad” are shown at the end of each tab 

label.) 
140 

 © 2022 AASHTO. 

Figure 40. Screenshot. AASHTOWare Pavement ME Design software screen showing the 

ALDF inputs.(5)

7. For each axle group (one at a time), go back to the Excel file containing the 

MEPDG_AXLE_LOAD_DIST_FACTOR table (see example in table 44) and select 

values from 120 rows (for months January to December) and 39 columns (starting from 

column MEPDG_LG01 to column MEPDG_LG39) for a given axle type 

(AXLE_GROUP = 1 or Single is shown in table 44). Copy the selected information by 

right-clicking on the selected area and selecting “Copy” from the pop-up window (or use 

Ctrl + C keys).

8. To paste the copied cells into the AASHTOWare input table shown in figure 40, 

right-click on the top cell under the first load bin (load bin 3,000 is highlighted in blue in 

figure 40 for “Single” axle) and select the “Paste” option in the pop-up menu (or use 

Ctrl + V keys). 

9. Repeat steps 6 and 7 to paste ALDF for the other three axle types (tandem, tridem, and 

quad). 

10. Save results in the AASHTOWare Pavement ME Design project file.

Example 5.9.2: Importing ALDF XML File to AASHTOWare Pavement ME Design

Project

Use the following procedure to import an ALDF XML file saved on a local computer to an 

AASHTOWare Pavement ME Design project.
141 

1. Open or create a new project in AASHTOWare Pavement ME Design.

(5)

2. On the AASHTOWare project main menu, right click on the “Traffic” label located in the 

upper left corner under the “Projects” folder, as shown in figure 41. Use the drop-down 

menus under “Traffic” to click on the “Axle Load Distributions” option and then click on 

Import XML..., as shown in figure 41.

© 2022 AASHTO. 

Figure 41. Screenshot. AASHTOWare Pavement ME Design software screen showing 

import options for Axle Load Distributions.(5)

3. A standard “Open” file dialog window will appear, as shown in figure 42. Use the “Open” 

file dialog box to select the ALDF XML file and click on the “Open” button. 
142 

© 2022 AASHTO. 

Figure 42. Screenshot. AASHTOWare Pavement ME Design software screen 

showing the Open XML file dialog screen.(5)

4. If the software is able to successfully load the XML file, the following message 

(figure 43) will appear in the Output window of the AASHTOWare Pavement ME Design 

software.

(5)

© 2022 AASHTO. 

Figure 43. Screenshot. AASHTOWare Pavement ME Design software screen showing 

successful file import message.(5)

PARAMETER 5.10: MEPDG NUMBER OF AXLES PER TRUCK 

The number of APT parameter is used as a multiplier to estimate the total number of axle loads 

(single, tandem, tridem, and quad) based on the total number of trucks reported for each truck 

class (FHWA vehicle classes 4–13).(11) In the AASHTOWare Pavement ME Design software,

(5)

one set of values is used to represent the average number of single, tandem, tridem, and quad 

axles for each truck class. These values could be copied from the 

MEPDG_AXLE_PER_TRUCK table and pasted in the AASHTOware Pavement ME Design 

software input screen. 
143 

To obtain the APT for an LTPP site, use the following procedure: 

1. Use the InfoPave table extraction steps provided in chapter 6 to select the 

MEPDG_AXLE_PER_TRUCK table from the InfoPave Table Export menu, as shown in

figure 44. 

Source: FHWA.

Figure 44. Screenshot. InfoPave Table Export menu with a checkbox showing “selected” 

next to the traffic table label “AASHTOWare Pavement ME Axles per Truck 

(MEPDG_AXLE_PER_TRUCK).”(3)

2. Filter the APT dataset for the selected LTPP site. (Site 01-4073 was selected for this 

example.) 

3. Download the MEPDG_AXLE_PER_TRUCK table for LTPP site 01-4073 to an Excel 

file.

4. Copy the 40 APT values from the spreadsheet, as shown in table 45 in the columns 

“SINGLE,” “TANDEM,” “TRIDEM,” and “QUAD,” and paste them into the 

AASHTOWare Pavement ME Design software traffic input screen, as shown in figure 45.
144 

Table 45. APT values from MEPDG_AXLE_PER_TRUCK table for LTPP site 01-4073. 

STATE_CODE SHRP_ID VEHICLE_CLASS SINGLE TANDEM TRIDEM QUAD

1 4073 4 1.08 0.93 0 0 

1 4073 5 2 0 0 0 

1 4073 6 1 1 0 0 

1 4073 7 0.99 0 0.94 0 

1 4073 8 2.13 0.87 0 0 

1 4073 9 1.14 1.93 0 0 

1 4073 10 1 1.24 0.76 0 

1 4073 11 5 0 0 0 

1 4073 12 4 1 0 0 

1 4073 13 0 0 0 0 

© 2022 AASHTO. 

Figure 45. Screenshot. AASHTOWare Pavement ME Design software showing table with 

APT values.(5)

PARAMETER 5.11: MEPDG AXLE SPACING FOR TANDEM, TRIDEM, AND QUAD 

AXLES

For the average axle spacing values for tandem, tridem, and quad axles, use the default values 

from the LTPP PLUG(9) report developed using data from the LTPP TPF-5(004) SPS WIM 

sites(14) or the defaults from the National Cooperative Highway Research Program (NCHRP) 

1-37A project(7)

 (under which the MEPDG was developed) presented in table 46. 

Table 46. Average axle spacing in inches for multi-axle groups.

Default Source Tandem (inches) Tridem (inches) Quads (inches)

NCHRP 1-37A(5,7) 51.6 49.2 49.2 

LTPP SPS TPF-5(004)(10) 49.0 50.8 51.8 

This information is entered under the Axle Configuration section of the AASHTOWare traffic 

input screen, as shown in figure 46. 
145 

© 2022 AASHTO. 

Figure 46. Screenshot. AASHTOWare Pavement ME Design software showing Axle 

Configuration input screen.

(5)

PARAMETER 5.12: MEPDG AVERAGE AXLE WIDTH

Average axle width is the distance in feet between the two outside edges of an axle. One value, 

constant between all truck classes, is used by the AASHTOWare Pavement ME Design software, 

but only for rigid pavement designs.(5)

 For this parameter, use the default values provided in the 

AASHTOWare Pavement ME Design software, as shown in figure 46. 

PARAMETER 5.13: MEPDG DUAL TIRE SPACING

The AASHTOWare Pavement ME Design software(5) requires one input value for this 

parameter. This parameter is treated as constant between all truck classes and does not change 

over time. For this parameter, use the default values provided in the AASHTOWare Pavement

ME Design software, as shown in figure 46. 

PARAMETER 5.14: MEPDG TIRE PRESSURE

The AASHTOWare Pavement ME Design software(5) requires one value representing the 

average hot tire inflation pressure. This parameter is treated as constant between all truck classes 

and does not change over time. For this parameter, use the default values provided in the 

AASHTOWare Pavement ME Design software, as shown in figure 46. 

PARAMETER 5.15: MEPDG MEAN WHEEL LOCATION 

The AASHTOWare Pavement ME Design software(5) requires the distance in inches from the 

outer edge of the outer wheel path to the pavement marking. This parameter is treated as constant 

between all truck classes and does not change over time. For this parameter, use the default 

values provided in the AASHTOWare Pavement ME Design software. This information is 

entered under the “Lateral Wander” section of the AASHTOWare traffic input screen, as shown 

in figure 47. 
146 

© 2022 AASHTO. 

Figure 47. Screenshot. AASHTOWare Pavement ME Design software showing Lateral 

Wander input screen.

(5)

PARAMETER 5.16: MEPDG TRUCK WANDER STANDARD DEVIATION

The AASHTOWare Pavement ME Design software(5) uses a statistic that represents the standard 

deviation of the Mean Wheel Location statistic to represent the fact that different vehicles use 

different wheel paths as they travel down the road. This statistic is computed in inches and is 

based on measurements from the lane marking. For this parameter, use the default values 

provided in the AASHTOWare Pavement ME Design software, as shown in figure 47. 

PARAMETER 5.17: MEPDG OPERATIONAL SPEED 

This value is defined as the expected or observed speed of traffic traveling in the design lane. 

This input impacts design or analysis of pavements with an AC top layer. This parameter is 

available for LTPP SPS TPF-5(004) sites on LTPP Traffic Sheet 21. For other sites, use the 

posted speed limit, which is available from public domain sources. Operational speed 

information is entered under the AADTT section of the traffic input screen of the AASHTOWare 

Pavement ME Design software,

(5)

 as shown in figure 48. 

© 2022 AASHTO. 

Figure 48. Screenshot. AASHTOWare Pavement ME Design software showing AADTT 

input screen.

(5)

PARAMETER 5.18: MEPDG AVERAGE SPACING OF SHORT, MEDIUM, AND LONG

WHEELBASE AXLES AND CORRESPONDING PERCENTAGE OF TRUCKS 

This input is required for jointed plain concrete pavement (JPCP) only. The AASHTOWare 

Pavement ME Design software(5) requires the input of the average longitudinal spacing, in feet, 

of truck wheelbases with short (≤12 ft), medium (>12 ft and ≤15 ft), and long (>15 ft and ≤20 ft) 

axle spacings and the corresponding percentages of trucks with these wheelbases. This primarily 

applies to power units of tractor semitrailer trucks in FHWA vehicle classes 8–13(11) plus any 

other axles that fall in the same axle spacing categories, excluding the spacing within tandem, 

tridem, and quad+ axles.
147 

Table 47 and table 48 contain data extracted from the LTPP PLUG(9) report and can be used to 

compute the percentage of short, medium, and long axle spacings for the desired joint spacing. 

Table 47 shows the distribution of axle spacings on tractor units for FHWA vehicle classes 8–13. 

In addition, the axle spacing distribution for units other than tractor wheelbases for FHWA 

vehicle classes 4–13, based on the SPS TPF-5(004) data, are shown in table 48. These 

distributions provide information about vehicle classes that are likely to have axle spacings that 

could contribute to the development of top-down cracking in JPCP. 

Table 47. Distribution of axle spacing on power units (tractor) for FHWA vehicle

classes 8–13.

Axle Spacing (feet)

Percentage of Axle Spacings on 

the Tractor Unit

≤7 0.0

>7 and ≤8 0.0

>8 and ≤9 0.0

>9 and ≤10 0.1

>10 and ≤11 0.7

>11 and ≤12 3.5

>12 and ≤13 7.8

>13 and ≤14 5.4

>14 and ≤15 3.0

>15 and ≤16 8.1

>16 and ≤17 12.9

>17 and ≤18 32.9

>18 and ≤19 9.8

>19 and ≤20 7.3

>20 and ≤21 6.9

>21 and ≤22 0.9

>22 and ≤23 0.3

>23 and ≤24 0.2

>24 0.2
148 

Table 48. Distribution of axle spacing by vehicle class using sample of SPS TPF-5(004) 

WIM data (excluding power-unit wheelbase spacing for FHWA vehicle classes 8–13). 

Axle Spacing (feet)

Percentage of Axle Spacing by Class

Class 

4 

Class 

5 

Class 

6 

Class 

7 

Class 

8 

Class 

9 

Class 

10 

Class 

11 

Class 

12 

Class 

13 

≤8 37 13 49 66 25 47 62 0 20 50 

>8 and ≤9 0 1 0 0 3 0 0 2 6 4 

>9 and ≤10 0 1 0 1 1 0 1 17 11 5 

>10 and ≤11 0 2 0 3 1 3 1 3 3 3 

>11 and ≤12 0 12 1 11 2 1 1 2 5 3 

>12 and ≤13 0 7 2 9 8 1 2 12 2 3 

>13 and ≤14 0 21 3 3 8 1 2 7 0 2 

>14 and ≤15 0 12 3 2 4 1 1 2 0 2 

>15 and ≤16 0 4 6 1 2 2 2 2 2 3 

>16 and ≤17 0 3 6 0 2 4 2 2 3 3 

>17 and ≤18 0 4 9 0 4 9 3 4 6 3 

>18 and ≤19 0 3 6 1 4 3 4 2 2 5 

>19 and ≤20 0 3 5 0 4 2 2 1 5 4 

>20 and ≤21 0 4 6 0 6 2 1 6 13 2 

>21 and ≤22 0 5 2 0 5 0 1 24 8 2 

>22 and ≤23 1 3 1 0 3 0 0 15 12 1 

>23 and ≤24 20 1 0 0 1 0 0 1 1 1 

>24 42 2 0 0 16 25 15 0 0 5 

Average spacing of axles is entered under the “Wheelbase” section of the traffic input screen of 

the AASHTOWare Pavement ME Design software,

(5)

 as shown in figure 49. 

© 2022 AASHTO. 

Figure 49. Screenshot. AASHTOWare Pavement ME Design software Wheelbase input

screen.

(5) 
149 

REFERENCES

1. Federal Highway Administration. 2015. The Long-Term Pavement Performance 

Program. Report No. HRT-15-049. Washington, DC: Federal Highway Administration. 

https://www.fhwa.dot.gov/publications/research/infrastructure/pavements/ltpp/15049/150

49.pdf, last accessed September 5, 2023. 

2. Elkins, G.E. and B. Ostrom. 2021. Long-Term Pavement Performance Information 

Management System User Guide. Report No. FHWA-HRT-21-038. Washington, DC: 

Federal Highway Administration. 

3. Federal Highway Administration. 2019. “LTPP InfoPave” (web page).

https://infopave.fhwa.dot.gov, last accessed September 5, 2023. 

4. Selezneva, O. I., and M. E. Hallenbeck. 2022. Facilitating Analysts’ Use of Traffic Data 

from the Long-Term Pavement Performance (LTPP) Program. FHWA Report No. 

HRT-22-074. Washington, DC: Federal Highway Administration. 

https://highways.dot.gov/sites/fhwa.dot.gov/files/2022-06/FHWA-HRT-22-074.pdf, last 

accessed September 5, 2023. 

5. American Association of State Highway and Transportation Officials. 2022. 

AASHTOWare Pavement ME Design (software). Versions 2.2 through 2.6. 

6. Federal Highway Administration. n.d. “LTPP InfoPave: Data” (web page). 

https://infopave.fhwa.dot.gov/Data/StandardDataRelease/, last accessed September 5, 

2023. 

7. NCHRP. 2004. Guide for Mechanistic–Empirical Design of New and Rehabilitated 

Pavement Structures. NCHRP Report 1-37A. Washington, DC: National Cooperative 

Highway Research Program 

8. American Association of State Highway and Transportation Officials. 1993. Guide for 

Design of Pavement Structures. Washington, DC: American Association of State 

Highway and Transportation Officials. 

9. Selezneva, O.I., and M. Hallenbeck. 2013. Long-Term Pavement Performance Pavement 

Loading User Guide (LTPP PLUG). Report No. FHWA-HRT-13-089. Washington, DC: 

Federal Highway Administration. 

10. Selezneva, O.I., M. Ayres, M. Hallenbeck, A. Ramachandran, H. Shirazi, and H. Von 

Quintus. 2016. MEPDG Traffic Loading Defaults Derived from LTPP Traffic Pooled 

Fund Study. Report No. FHWA-HRT-13-090. Washington, DC: Federal Highway 

Administration.

11. Federal Highway Administration. 2016. Traffic Monitoring Guide. Report No. 

FHWA-PL-17-003. Washington, DC: Federal Highway Administration. 
150 

12. Elkins, G.E., B. Ostrom, B. Visintine, and J. Groeger. 2012. Long-Term Pavement 

Performance Ancillary Information Management System (AIMS) Reference Guide. 

Report No. FHWA-HRT-12-058. Washington, DC: Federal Highway Administration. 

13. Federal Highway Administration. 2001. Guide to LTPP Traffic Data Collection and 

Processing. Washington, DC: Federal Highway Administration. 

http://www.fhwa.dot.gov/publications/research/infrastructure/pavements/ltpp/trfcol/trfcol

.pdf, last accessed September 5, 2023. 

14. Transportation Pooled Fund. 2023. “Long-Term Pavement Performance (LTPP) Specific 

Pavement Study (SPS) Traffic Data Collection. Study Number: TPF-5(004)” (web page). 

https://www.pooledfund.org/Details/Study/123, last accessed September 5, 2023.

15. Walker, D., and D. Cebon. 2011. “The Metamorphosis of Long-Term Pavement 

Performance Traffic Data.” TR News 277 (November–December): 9–17. Washington, 

DC: Transportation Research Board. 

16. Federal Highway Administration. 2012. LTPP Field Operations Guide for SPS WIM 

Sites, Version 1.0. Washington, DC: Federal Highway Administration. 

17. ASTM International. 2009. ASTM E1318-09. Standard Specification for Highway 

Weigh-in-Motion (WIM) Systems with User Requirements and Test Methods. West 

Conshohocken, PA: ASTM International. 

18. Yoder, E. J., and M. W. Witczak. 1975. Principles of Pavement Design. New York: John 

Wiley and Sons. 

19. Hajek, J.J., O. I. Selezneva, G. Mladenovic, and Y. J. Jiang. 2005. 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. Report No. 

FHWA-RD-03-094. Washington, DC: Federal Highway Administration. 

20. Federal Highway Administration. n.d. “Long-Term Pavement Performance” (web page). 

https://highways.dot.gov/research/long-term-infrastructure-performance/ltpp/long-termpavement-performance, last accessed September 5, 2023. 

HRDI-30/03-24(WEB)E Recycled

Recyclable

Recommended citation: Federal Highway Administration,

Guide to Select Long-Term Pavement Performance Traffic Data for Multiple Uses 

(Washington, DC: 2024) https://doi.org/10.21949/1521493