Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

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

PUBLICATION NO. FHWA-RD-03-094 MARCH 2005
Foreword 

In 1998, the Federal Highway Administration (FHWA) sponsored a two-phase study to develop 

traffic load estimates for Long-Term Pavement Performance (LTPP) sites. This report describes 

the results of the Phase 2 study. The Phase 1 study resulted in the development of methodology 

for estimating axle load spectra for all years the LTPP sites were in service. Phase 2 used this 

methodology to estimate axle loads for all LTPP sites that had acceptable site-specific axle 

weight data. In total, traffic load estimates were made for 558 LTPP traffic sites. 

This report will be of interest to engineers involved in pavement management, design, 

maintenance, and rehabilitation and in traffic data collection and analysis. 

 T. Paul Teng, P.E. 

 Director, Office of Infrastructure 

 Research and Development 

Notice 

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

in the interest of information exchange. The U.S. Government assumes no liability for the use of 

the information contained in this document. This report does not constitute a standard, 

specification, or regulation. 

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. 
 Technical Report Documentation Page 

1. Report No. 

FHWA-RD-03-094

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

5. Report Date 

March 2005 

4. Title and Subtitle 

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 6. Performing Organization Code 

7. Author(s) 

Jerry J. Hajek, Olga I. Selezneva, Goran Mladenovic, and Y. Jane Jiang

8. Performing Organization Report No. 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

ERES Consultants, Inc. 

9030 Red Branch Road, Suite 210 

Columbia, MD 21045 11. Contract or Grant No. 

DTFH61-96-C-00003

13. Type of Report and Period Covered 

Final Report

12. Sponsoring Agency Name and Address

Office of Infrastructure Research and Development 

Federal Highway Administration 

6300 Georgetown Pike 

McLean, VA 22101-2296

14. Sponsoring Agency Code 

15. Supplementary Notes 

Contracting Officers Technical Representative (COTR): Cheryl Allen Richter, HRDI 

Work was conducted as part of the LTPP Data Analysis Technical Support Contract.

16. Abstract 

In 1998, the Federal Highway Administration sponsored a study to estimate traffic loads on Long-Term Pavement Performance (LTPP) 

sites. This report contains findings of the second phase of the study. Phase 1 encompassed the development of the estimation 

methodology, including numerical examples, and was documented in report FHWA-RD-00-054 issued in July 2000. Phase 2, 

described in this report, included the assessment of the overall quality of traffic data for all 890 LTPP traffic sites, and the projection of 

axle loads for all LTPP sites with adequate traffic data. Phase 2 also included the distribution of comprehensive traffic data reports to 

all participating agencies and the incorporation of comments regarding traffic projections received from the agencies. 

Axle load projections were developed for all in-service years up to 1998 for 558 LTPP traffic sites that had adequate traffic monitoring 

data in the IMS database. The axle load projections were expressed as annual axle load spectra for single, tandem, and triple axles, and 

were placed into IMS computed parameter tables. The projection results for all LTPP sites are summarized in appendix A. 

To overcome the difficulty of estimating traffic loads for the remaining 332 LTPP sites, it was proposed to develop the LTPP 

Pavement Loading Guide (PLG). The report contains a description of the purpose, design parameters, and functionality of the PLG, a 

blueprint for the development of the PLG, and two examples of using the PLG to obtain traffic load projections for LTPP sites without 

site-specific truck class and/or axle load data. 

The recommended traffic analysis activities include the development of the LTPP PLG, completing traffic load projections for all 

LTPP sites, and the development of a comprehensive action plan for better utilization of the existing traffic data. The recommended 

components of the action plan include a comprehensive quality assurance process, use of monthly traffic data for estimating traffic 

loads, and regional traffic modeling utilizing both LTPP traffic data and other traffic data.

17. Key Words 

Pavements, traffic estimates, axle loads, LTPP

18. Distribution Statement 

No restrictions. This document is available to the public 

through the National Technical Information Service, 

Springfield, VA 22161.

19. Security Classification (of this report) 

Unclassified

20. Security Classification (of this page) 

Unclassified

21. No. of Pages 

133 

22. Price 

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

 

SI* (MODERN METRIC) CONVERSION FACTORS 

APPROXIMATE CONVERSIONS TO SI UNITS

Symbol When You Know Multiply By To Find Symbol 

LENGTH 

in inches 25.4 millimeters mm 

ft feet 0.305 meters m 

yd yards 0.914 meters m 

mi miles 1.61 kilometers km 

AREA 

in2 square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m2

yd2 square yard 0.836 square meters m2

ac acres 0.405 hectares ha 

mi2 square miles 2.59 square kilometers km2

VOLUME 

fl oz fluid ounces 29.57 milliliters mL 

gal gallons 3.785 liters L 

ft3 cubic feet 0.028 cubic meters m3 

yd3 cubic yards 0.765 cubic meters m3 

NOTE: volumes greater than 1000 L shall be shown in m3

MASS 

oz ounces 28.35 grams g

lb pounds 0.454 kilograms kg

T short tons (2000 lb) 0.907 megagrams (or "metric ton") Mg (or "t") 

TEMPERATURE (exact degrees) o

F Fahrenheit 5 (F-32)/9 Celsius oC 

or (F-32)/1.8 

ILLUMINATION 

fc foot-candles 10.76 lux lx 

fl foot-Lamberts 3.426 candela/m2 cd/m2

FORCE and PRESSURE or STRESS 

lbf poundforce 4.45 newtons N 

lbf/in2 poundforce per square inch 6.89 kilopascals kPa 

APPROXIMATE CONVERSIONS FROM SI UNITS 

Symbol When You Know Multiply By To Find Symbol 

LENGTH

mm millimeters 0.039 inches in 

m meters 3.28 feet ft 

m meters 1.09 yards yd 

km kilometers 0.621 miles mi 

AREA mm2

 square millimeters 0.0016 square inches in2 

m2 square meters 10.764 square feet ft2 

m2 square meters 1.195 square yards yd2 

ha hectares 2.47 acres ac 

km2 square kilometers 0.386 square miles mi2 

VOLUME 

mL milliliters 0.034 fluid ounces fl oz 

L liters 0.264 gallons gal 

m3 cubic meters 35.314 cubic feet ft3 

m3 cubic meters 1.307 cubic yards yd3 

MASS 

g grams 0.035 ounces oz

kg kilograms 2.202 pounds lb

Mg (or "t") megagrams (or "metric ton") 1.103 short tons (2000 lb) T 

TEMPERATURE (exact degrees) o

C Celsius 1.8C+32 Fahrenheit oF 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2 candela/m2 0.2919 foot-Lamberts fl

FORCE and PRESSURE or STRESS 

N newtons 0.225 poundforce lbf 

kPa kilopascals 0.145 poundforce per square inch lbf/in2

*SI is the symbol for th International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E e 380. 

(Revised March 2003)
iii 

TABLE OF CONTENTS

 

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

 Objectives of Phase 2 ................................................................................................................1 

 Background................................................................................................................................1 

 Results of Phase 1......................................................................................................................7 

 Report Overview........................................................................................................................9 

CHAPTER 2. PROCEDURES FOR TRAFFIC DATA ASSESSMENT AND PROJECTION..11 

 Step 1—Preliminary Assessment of LTPP Traffic Data .........................................................13 

 Step 2—Development of LTPP Traffic Projection Procedure ................................................14 

 Step 3—Validation of the LTPP Projection Procedure Using Case Studies...........................17 

 Step 4—Development of LTPP Traffic Feedback and Resolution Package ...........................17 

 Step 5—Preparation of LTPP Traffic Feedback and Resolution Packages for All 

 Participating Agencies ...............................................................................................37 

 Step 6—Review of LTPP Traffic Feedback and Resolution Packages by RCOs ...................62 

Step 7—Review of LTPP Traffic Feedback and Resolution Packages by 

 Participating Agencies ...............................................................................................64 

Step 8—Implementation of Review Comments Received from Participating 

 Agencies.....................................................................................................................65 

CHAPTER 3. RESULTS OF DATA ASSESSMENT AND TRAFFIC PROJECTION .............67 

 Availability of Traffic Projections...........................................................................................67 

 Scope of the LTPP Traffic Data ..............................................................................................76

 Historical Trends in LTPP Traffic Data Quantity and Quality ...............................................80 

 Data Analysis/Operations Feedback Reports ..........................................................................81 

CHAPTER 4. COMPUTED PARAMETER TABLES FOR PROJECTED TRAFFIC DATA ..83 

 Description of Computed Parameter Tables............................................................................83 

 Generation of Projected Annual Axle Load Spectra ...............................................................84 
iv 

TABLE OF CONTENTS (Continued)

CHAPTER 5. PROTOTYPE LTPP PAVEMENT LOADING GUIDE ......................................91 

 The Need to Estimate Missing Traffic Data............................................................................91 

 The Need to Assess the Quality of Monitoring Traffic Data...................................................92 

 Scope of Pavement Loading Guide: LTPP PLG or General PLG...........................................92 

 Objectives of the PLG and Purpose of This Chapter...............................................................94 

 Role of PLG in Traffic Projection ...........................................................................................94

 Conceptual Outline of PLG .....................................................................................................95 

 Functionality of LTPP PLG.....................................................................................................98 

 Development of Generic Truck Characteristics and Data-Selection Guidelines...................105 

 Prototype Demonstration Software .......................................................................................107 

 Example Use of PLG.............................................................................................................108 

CHAPTER 6. SUMMARY AND RECOMMENDATIONS .....................................................117 

 Summary................................................................................................................................117 

 Conclusions ...........................................................................................................................118 

 Recommendations .................................................................................................................119 

APPENDIX A..............................................................................................................................123 

REFERENCES ............................................................................................................................151 
v 

LIST OF FIGURES

 1 Overview of main traffic data assessment and projection activities..................................11 

 2 Initial site-specific report for Mississippi ..........................................................................24 

 3 Feedback and Data Resolution Sheet for site 285805 .......................................................28 

 4 Site map for site 285805 ....................................................................................................29 

 5 Annual Traffic Projection Sheet for site 285805 ...............................................................30

 6 Projected AADT volumes for site 285805.........................................................................31 

 7 Annual vehicle class distribution for site 285805..............................................................32 

 8 Annual load spectra for site 285805 ..................................................................................33 

 9 Average annual load spectrum for site 285805..................................................................34 

10 Projected annual ESALs for site 285805...........................................................................35 

11 Projection of truck volumes using historical and monitoring data ....................................39 

12 Comparison of AADT volumes for Class 14 vehicles with AADT volumes 

for all trucks .......................................................................................................................43 

13 Use of the mean of all annual axle load spectra to obtain base annual spectrum..............49 

14 Use of the mean of 1991, 1992, and 1995 annual axle load spectra to obtain base 

annual spectrum .................................................................................................................50 

15 Use of the mean of 1991 and 1992 annual axle load spectra to obtain base annual 

spectrum.............................................................................................................................51 

16 Rejection of all available annual axle load spectra............................................................52 

17 Projected AADT truck volumes for site 124057 ...............................................................56 

18 Annual Traffic Projection Sheet for site 063042 ...............................................................59 

19 Projected AADT truck volumes (initial) for site 473104 ..................................................63 

20 Summary of LTPP sites sorted by data presence and data usability for projection...........68 

21 Geographical distribution of LTPP sites with acceptable projection confidence codes....75 

22 Mean vehicle class distribution for LTPP sites with acceptable and questionable 

projection confidence codes located on rural highways ....................................................79 

23 Mean vehicle class distribution for LTPP sites with acceptable and questionable 

projection confidence codes located on urban highways...................................................79 

24 Trends in the number of monitoring axle load spectra ......................................................81 

25 Overview of the IMS Traffic Module showing the proposed addition of projected 

traffic data ..........................................................................................................................84 

26 Flowchart used for calculating computed parameter tables...............................................85 

27 Flowchart for computation of the normalized base annual load spectra ...........................86 

28 Flowchart for computation of the base annual axle load summary ...................................87 

29 Flowchart for computation of the annual projection factors..............................................88 

30 Flowchart for computation of projected annual axle load spectra.....................................89 

31 Overview of PLG functions...............................................................................................99 

32 Comparison of truck class distributions for sites 062040, 066044, and 068150 .............109 
vi 

LIST OF FIGURES (Continued)

33 Comparison of single axle load distributions for vehicle Class 9 for sites 

062040 and 066044 with computed mean distribution....................................................110 

34 Comparison of tandem axle load distributions for vehicle Class 9 for sites 

062040 and 066044 with computed mean distribution....................................................110 

35 Comparison of site-specific and surrogate base annual spectra for site 068150 .............112 

36 Comparison of projected, historical, and monitoring annual ESALs for site 068150.....113 

37 Comparison of truck class distributions for sites 041007 and 041017 ............................114 

38 Comparison of site-specific and surrogate base annual spectra for 041017....................115 

39 Comparison of projected, historical, and monitoring annual ESALs for site 041017.....116 
vii 

LIST OF TABLES

 1 FHWA commercial vehicle classification schema ..............................................................3 

 2 Example of possible axle-per-class coefficients for Class 9 vehicles..................................4 

 3 Arizona LTPP sites near other sites on the same highway and in the same direction.......21 

 4 Traffic loading parameters for Mississippi LTPP sites with axle weight projections .......22 

 5 Traffic loading parameters for Mississippi sites without axle weight projections ............22 

 6 Characteristic values of axle load spectra..........................................................................45 

 7 Availability of traffic projections.......................................................................................69 

 8 Availability of traffic projections by experiment type and projection confidence code....70 

 9 Availability of traffic projections by highway functional class and projection 

confidence codes................................................................................................................71 

 10 Presence of traffic data by LTPP region............................................................................72 

 11 Availability of traffic projections by LTPP region and projection confidence codes .......72 

 12 Availability of traffic projections by participating agencies and projection 

confidence codes................................................................................................................73 

 13 Traffic characteristics of LTPP sites with acceptable and questionable projection 

confidence codes................................................................................................................77 

 14 Truck class distribution by highway functional classes for LTPP sites with 

acceptable and questionable projection confidence codes.................................................78 

 15 Availability of annual axle weight data and their use for projection.................................81 

 16 Summary of data analysis/operations feedback report prepared during the study ............82 

 17 List of main variables stored in PLG ...............................................................................101 

 18 Example of truck class distribution..................................................................................104 

 19 Codes in the IMS database...............................................................................................123

 20 Code indicating projection category (2 to 5) ...................................................................124 

 21 Summary of traffic projection results. .............................................................................125 

 

1 

CHAPTER 1. INTRODUCTION 

In 1998, the Federal Highway Administration (FHWA) sponsored a two-phase study to develop 

traffic load estimates for Long-Term Pavement Performance (LTPP) sites. This report describes 

the results of the Phase 2 study. The Phase 1 study resulted in the development of a 

methodology for estimating axle load spectra for all years the LTPP sites were in service.[1] 

Phase 2 used this methodology to estimate axle loads for all LTPP sites that had acceptable sitespecific axle weight data. In total, traffic load estimates were made for 558 LTPP traffic sites. 

Objectives of Phase 2

The original goal of Phase 2 was to apply the methodology developed in Phase 1 to obtain 

annual axle load spectra for 500 LTPP sections. During the course of Phase 2, this goal was 

refined to encompass the following specific objectives: 

• Review traffic data for all LTPP sites in terms of quality and quantity. 

• Carry out initial truck volume projections for all LTPP sites and submit them for review 

by participating agencies. 

• Carry out initial axle load spectra projections for all LTPP sites with monitoring sitespecific axle loads and submit them for review by participating agencies. 

• Modify the initial traffic projections according to the review comments provided by 

participating agencies. 

• Develop computed parameter tables for storage of the projected axle load spectra and 

supplemental projection data in the LTPP Information Management System (IMS) and 

upload the traffic projections into the database. 

• Develop a prototype Pavement Loading Guide (PLG). 

• Develop reliability or variability indicators for the projected traffic loads. 

Background

A principal objective of the LTPP program is to answer key questions about pavement design 

and rehabilitation characteristics that will help the States and Provinces achieve pavement 

performance that is both long lived and cost effective.[2] For interstate and other major 

highways, this objective is directly related to the need to quantify the relationship between 

pavement performance (deterioration of pavement structure with time) and traffic loads. 

Consequently, traffic data collection and analysis, required to obtain traffic loads, is the key 

activity within the LTPP program. 

Since the inception of the LTPP program, traffic data collection has been the responsibility of the 

participating highway agencies, while the storage and analysis of traffic data have been done by 

LTPP program. Over the course of the program, participating agencies received a series of 

guidelines on how traffic data should be collected and reported to the LTPP. Briefly, the 

guidelines recommended that truck volume data should be collected using continuously 

operating automatic vehicle classifiers (AVC), and that truck axle weights should be collected 

using weigh-in-motion (WIM) scales or other scales operating during specified time periods.[3, 4]
2 

Traffic data collected in the field are sent by the participating agencies to LTPP Regional 

Coordination Offices (RCOs) in an electronic format as individual vehicle records for processing 

and storage. The traffic data processing includes quality assurance (QA) checks and factoring. 

Factoring is used to obtain annual traffic data, such as annual average daily truck volumes, from 

the data collected during a portion of the year only. After processing by RCOs, data are stored in 

the Central Traffic Database (CTDB), and selected aggregated traffic data are also stored in the 

IMS.[5]

At some LTPP sites, traffic monitoring equipment was not installed, was not operational, or was 

not calibrated. Also, in spite of the best plans and intentions of the participating agencies, traffic 

data collection in the field has been prevented by such factors as equipment malfunction, power 

failure, inclement weather, lack of funding, and lack of personnel. As a result, the amount and 

quality of traffic data collected or measured in the field vary considerably from agency to 

agency, from site to site, and from year to year. Typically, the amount of measured traffic data 

(the number of trucks that have been classified and weighed) represents only a fraction of the 

traffic that a typical LTPP site carried during the course of the LTPP program, and an even a 

smaller fraction of the traffic that occurred over the entire time the pavements were in service. 

To quantify the relationship between pavement performance and traffic loads, it is necessary to 

estimate the total amount of traffic loads that pavement sections carried since opening to traffic. 

The estimating process relies on: (a) traffic data provided by the participating agencies and (b) 

mathematical modeling procedures that utilize available traffic data to fill in gaps in data. This 

report describes the results of the modeling procedures applied to the 558 LTPP sites for which 

participating agencies supplied acceptable site-specific axle weight data. 

Description of Traffic Variables and Terms 

The following description of traffic variables and terms provides background information to 

facilitate understanding of this report. 

Classification of Highway Vehicles

LTPP uses the vehicle classification schema recommended by the FHWA Traffic Monitoring 

Guide and shown in table 1.[6]

Trucks are defined as highway vehicles having dual tires on one or more axles. Buses are 

defined as highway vehicles with two axles and six tires, or three or more axles, manufactured to 

carry passengers. Trucks and buses are called commercial vehicles. Because the proportion of 

buses in the traffic flow is usually small, traffic composition is expressed in the form of a truck 

percentage that also includes buses. Similarly, the term truck volume is assumed to include both 

trucks and buses. 

Truck class distribution is the distribution of commercial vehicles into the vehicle classes defined 

in table 1. 
3 

Table 1. FHWA commercial vehicle classification schema. 

Vehicle Class Schema Description 

4 Buses 

5 Two-axle, six-tire, single-unit trucks 

6 Three-axle single-unit trucks 

7 Four- or more than four-axle single-unit trucks 

8 Four- or less than four-axle single trailer trucks 

9 Five-axle single trailer trucks 

10 Six- or more than six-axle single trailer trucks 

11 Five- or less than five-axle multi-trailer trucks 

12 Six-axle multi-trailer trucks 

13 Seven- or more than seven-axle multi-trailer trucks 

Classification of LTPP Sites by Highway Type

LTPP sites are classified according to the functional class of the highway on which they are 

located. At present, the LTPP sites are classified into six rural highway functional classes and 

five urban functional classes, listed below: 

• Rural Principal Arterial—Interstate. 

• Rural Principal Arterial—Other. 

• Rural Minor Arterial. 

• Rural Major Collector. 

• Rural Minor Collector. 

• Rural Local Collector. 

• Urban Principal Arterial—Interstate. 

• Urban Principal Arterial—Other Freeways or Expressways. 

• Urban Other Principal Arterial. 

• Urban Minor Arterial. 

• Urban Collector. 
4 

Axle Loads and Axle Load Spectra

Equivalent single axle load (ESAL) is a quantity that is related to pavement damage caused by a 

standard axle load of 80 kilonewtons (kN) (18,000 poundforce (lbf)) carried by a single axle with 

dual tires. Because LTPP uses pounds (lb) as the unit for measurement and storage of axle 

weights, this unit is the primary unit used to report traffic projection results in this report. 

Truck Factor (TF) is the number of ESALs per truck. 

Axle load spectrum is defined as a frequency distribution of axle weights, of a given axle type, 

into weight ranges. Axle types are classified by the spacing between consecutive axles. Axles 

that are far apart (usually more than 2.44 meters (m) (8 feet (ft)) are called single axles. Two 

axles close together are called tandem axles; three axles spaced close together are triple axles; 

and four axles closely spaced are quadruple axles. Axle load spectrum is also referred to as axle 

load distribution. 

Normalized axle load spectrum provides proportions of total axle loads that occur within 

designated load ranges. For example, the portion of 0.10 in the load range of 5,448 to 5,902 

kilograms (kg) (12,000 to 12,999 pounds (lb)) for tandem axles means that 10 percent of all 

tandem axles are in the load range of 5,448 to 5,902 kg (12,000 to 12,999 lb). The normalized 

spectra (rather than actual spectra) are used to facilitate comparison of axle load spectra obtained 

for different truck volumes or sample sizes. 

Axle-per-class coefficients provide the number of single, tandem, and triple axles for each 

vehicle class. Because the LTPP IMS database does not contain non-zero quadruple axle counts, 

no axle-per-class coefficients for the quadruple axles are discussed in this report. An example of 

axle-per-class coefficients for vehicle Class 9 (5-axle single trailer trucks) is presented in table 2. 

Table 2. Example of possible axle-per-class coefficients for Class 9 vehicles. 

Axle Type Axle-Per-Class 

Coefficient Comments 

Single 1.1 

The number is higher than 1 because some Class 9 vehicles 

may have, in addition to the ever-present steering axle, 

additional single load axles (on the semi-trailer or trailer). 

Tandem 1.9 The number is less than 2 because of the presence of additional 

single axles or the presence of triple axles in place of tandems. 

Triple 0.1 The number is small because the number of 5-axle single trailer 

trucks with triple axles is small. 

Types of LTPP Traffic Data

The three main categories of LTPP traffic data are historical, monitoring, and supporting. 
5 

Historical data were estimated by the participating agencies for the LTPP sections before the use 

of monitoring equipment. Typically, historical data include annual average daily traffic (AADT) 

volumes, AADT truck volumes, TFs, and annual ESALs. 

The years from the time the site was open to traffic to the time the site was included in the LTPP 

program are referred to as historical years. The years from the time a site was assumed by the 

LTPP program to present are referred to as monitoring years.

Monitoring data are data that have been submitted by the participating agencies for the years 

since the LTPP experiment began. There are two types of traffic monitoring data: measured and 

estimated. Measured monitoring data were obtained by field measurements using AVCs and 

WIM scales. Measured monitoring data are commonly referred to as monitoring data and 

typically include measured axle load data. Estimated monitoring data are data estimated by the 

participating agencies for monitoring years without measured monitoring data. Estimated 

monitoring data typically include annual truck volumes, TFs, and annual ESAL estimates. 

Supporting data include site-specific characteristics such as site location, highway number, and 

pavement type. 

Site-Specific, Site-Related, Regional, and Generic Data

Site-specific data are traffic data collected at or near the LTPP site using equipment that 

measures actual traffic that crosses the site. Site-specific data include truck class and axle load 

distributions. 

Site-related data are traffic data collected on the same highway as the LTPP site but some 

distance from the site so that the collected data may be influenced by the presence of a major 

truck traffic generator (e.g., an intersection). 

Regional data are traffic data collected on highways of the same functional class and located in 

the same region as the LTPP site. The site of the regional data and the LTPP site should be the 

subject of the same truck size and weight regulations. 

Generic data are traffic data that represent typical traffic conditions, for example typical truck 

class distribution on rural interstates. 

Projected Traffic Data

Traffic projection is a mathematical modeling process used to estimate traffic loads from 

samples of monitoring traffic data and other information. In the context of this study, the 

objective of traffic projection was to obtain axle load distributions for all years the LTPP sites 

were in service. 

Projected traffic data are data that have been obtained by the projection process involving 

factoring or expanding sampled traffic data to obtain traffic data for an entire period. If the 

period used to factor or expand the data is a year, the result is annual estimates. If the period is 
6 

the entire period the pavement was open to traffic, the result is cumulative estimates. In this 

report, the period for which traffic estimates were carried out is from the date the pavement was 

open to traffic to the end of 1998. 

LTPP Traffic Data Structure 

LTPP traffic data reside in two locations—the CTDB and the IMS. 

The CTDB stores traffic data in five levels. Levels 1 through 4 store only measured monitoring 

data, whereas level 5 stores historical and supporting data. Level 1 features annual axle load 

spectra for all vehicle classes combined. Level 2 data contain annual axle load spectra for 

individual vehicle classes (FHWA Classes 4 through 13). Level 3 data feature daily axle load 

spectra for individual vehicle classes. Up to 365 tables (1 for each day of the year) may appear 

in level 3 for each of the 10 vehicle classes and for each monitoring year. Level 4 contains raw 

data submitted by the participating agencies. Level 5 contains supporting data. 

The IMS contains level 1 data of the CTDB, including the monitoring axle load spectra for all 

vehicle classes combined, as well as annual ESALs. 

Traffic Data Sources

Traffic data used in this report were obtained from the following IMS tables (first quarter of 

2000, Level E release): 

• TRF_BASIC _INFO (F01)—Basic information about site characteristics (sheet 1). 

• TRF_EST_ANL_TOT_GPS_LN (F02)—Estimate of annual totals (volume and ESAL) 

in study lane when traffic monitoring equipment was not in service. 

• TRF_MONITOR_BASIC INFO (F00)—Summary information concerning data 

collection and traffic characteristics (volume and ESAL) on a yearly basis. 

• TRF_MONITOR_AXLE_DISTRIB (F04)—Annual axle load distribution by weight 

range and axle group from monitoring data, all vehicle classes combined. 

• TRF_MONITOR_VEHICLE_DIST (F05)—Annual vehicle type distribution by FHWA 

vehicle class from monitoring data. 

• TRF_MONITOR_AXLE_SUMMARY (F06)—Annual number of axles in each axle 

group from monitoring data. 

In addition, CTDB level 2 data were utilized to investigate axle load spectra for individual 

vehicle classes. 

The Challenge of Estimating Traffic Loads for All In-Service Years

Pavement damage caused by traffic loads is cumulative. Consequently, to quantify the 

relationship between pavement performance and traffic loads, all traffic loads imposed on the 

pavement during its service life must be taken into account. Estimating traffic loads for all the 

years the pavement was in service requires knowledge of both historical and monitoring traffic 

data. 
7 

The quantity and quality of available historical and monitoring data vary considerably between 

the LTPP sites and also between years for the individual LTPP sites. Historical data do not 

contain any truck class and axle load distribution data, and are unavailable for some sites for all 

or some of the historical years. Monitoring data are in the form of samples of uneven duration 

and quality taken during the monitoring years. To obtain axle loads for all years the pavement 

was in service (i.e., for both historical and monitoring years), appropriate mathematical modeling 

procedures must be used that fully utilize the available fragmented historical and monitoring 

data. 

In addition, because emerging mechanistically based pavement performance models (such as the 

2002 Pavement Design Guide[7]) require knowledge of axle loads, axle loads must be estimated 

in terms of axle load spectra for all in-service years. 

The estimation of cumulative axle loads for LTPP sites is done in two steps involving annual 

estimates and cumulative estimates. 

1. Annual estimates—The sampled monitoring data are expanded or factored to obtain 

annual monitoring data. The shortest duration of a traffic sample that can be used to 

estimate annual monitoring data (e.g., annual axle load spectra) in the IMS database is 24 

consecutive hours. The longest duration of a traffic sample is 365 days during the year. 

The quality and reliability of annual monitoring data depend on the quantity and quality 

of traffic samples and on the procedures used to expand the samples to obtain annual 

traffic data estimates. The procedure used to expand or project sampled data to obtain 

annual data is outlined in reference 5. The estimated annual data are stored in the CTDB 

and IMS databases. 

2. Cumulative estimates—The annual monitoring data, available for some of the monitoring 

years, are combined with historical data and are projected for all the years the pavement 

was in service. Cumulative traffic estimates for any specified period expressed in 

number of years may be obtained by summation of the projected annual traffic data for 

these years. 

Traffic data collection is essential to estimate traffic loads reliably. However, traffic data 

collection alone is not sufficient to obtain cumulative traffic loads because it is not possible to 

collect past data or to collect traffic data 100 percent of the time. To obtain pavement loads for 

all years the LTPP sections were in service, it is necessary to use a combination of traffic data 

(historical and monitoring) and traffic modeling procedures (traffic projection). 

Results of Phase 1 

The Phase 1 study encompassed preliminary assessment of the quantity and quality of LTPP 

traffic data.[1] Because of the large differences in the quantity and quality of traffic data 

available for the LTPP sites, the LTPP sites were divided into five projection categories based on 

available data: 
8 

• Category 1 was intended for LTPP sites that have sufficient truck class and axle weight 

distribution data to enable the projection of annual and monthly variation in traffic loads. 

• Category 2 was intended for LTPP sites with both truck class and axle weight 

distribution data; however, compared to Category 1, the amount and quality of data is 

insufficient for projection of monthly variation in traffic loads. 

• Category 3 represents sites with adequate truck class distribution data, but without sitespecific axle weight data. 

• Category 4 represents LTPP sites with truck volume data but without site-specific truck 

class and axle weight distribution data. 

• Category 5 represents LTPP sites without traffic data or with unacceptable traffic data. 

Traffic projection procedures for estimation of axle loads for all in-service years were developed 

for each projection category. Regardless of the projection category, the basic procedure for 

projecting axle load spectra for the LTPP was as follows: 

1. All available annual historical and monitoring data were used to establish a model 

predicting annual truck volumes for all years the section was in service. 

2. A base annual axle load spectrum, representing a typical annual axle load spectra, was 

established. 

3. For the years with missing annual monitoring axle load spectra, the missing spectra were 

obtained by multiplying the base annual spectrum by a factor related to annual truck 

volumes. 

The methodology for projecting axle load spectra was evaluated and demonstrated using case 

studies for specific LTPP sites. Altogether, 12 case studies were conducted (3 for each 

projection category except 5, where no data can be acquired).[1]

The main conclusions and recommendations of the Phase 1 study included the following: 

• Proceed with Phase 2 study to develop and make available projected axle load spectra for 

selected LTPP sites using the projection methodology developed in Phase 1. 

• Involving the participating agencies in the traffic projection process is crucial; many data 

problems cannot be resolved without input from local agencies. 

• The projection of axle load spectra for LTPP sites without site-specific data (Categories 3 

and 4) must be done judiciously and must be supported by a reference database source 

summarizing characteristic truck class and axle load distribution data. For this reason, 

the development of an LTPP PLG was proposed. 

• Traffic projection and modeling is a highly cost effective and necessary process required 

to extend limited LTPP sampling traffic data and to compensate for the lack of measured 

traffic data in the past. 
9 

Report Overview 

This report describes the findings and results obtained in Phase 2 of the FHWA study on the 

development of traffic loads for all years the LTPP sites were in service. It is organized into six 

chapters, including this one. Chapter 2 contains an outline of the projection procedure developed 

in Phase 1 and provides a detailed description of the process used to assess quality of traffic data, 

develop traffic projections in cooperation with the participating agencies, and assign projection 

confidence codes. It also includes the description of pilot studies that were used to develop 

procedures to involve participating agencies in the review of the initial projections and to 

facilitate the involvement of the regional LTPP data collection offices in the traffic data 

assessment and projection process. Chapter 3 summarizes the results of traffic data assessment 

and traffic load projection work. Chapter 4 describes the development of computed parameter 

tables used to store the projected traffic data in the IMS database. Chapter 5 describes the 

purpose, design parameters, and functionality of the proposed PLG. Finally, chapter 6 

summarizes the study results and gives recommendations for future traffic analysis work. 

This report also includes one appendix, appendix A, which tabulates traffic data assessment and 

projection results for all individual LTPP sites. 

11 

CHAPTER 2. PROCEDURES FOR TRAFFIC DATA ASSESSMENT AND 

PROJECTION 

This chapter describes the procedures used to carry out the assessment of the quantity and quality 

of traffic data, and to estimate traffic loads for all years the LTPP sites were in service. The 

process of estimating traffic loads is referred to as traffic projection. 

The assessment of the data quality and quantity was carried out for all LTPP sites. The 

projection of truck volumes was carried out for all sites with appropriate monitoring or historical 

truck volumes, and the projection of axle load spectra was done for all LTPP sites that had 

appropriate site-specific monitoring axle load data in the Level E release of the IMS database 

(first quarter of 2000). 

The assessment of traffic data and the development of traffic projections have been carried out in 

two phases over the course of 30 months; they involved eight main activities (see figure 1). 

Figure 1. Overview of main traffic data assessment and projection activities. 

1. Preliminary assessment of LTPP traffic data. 

2. Development of LTPP traffic projection procedure. 

3. Validation of LTPP traffic projection procedure using case studies. Phase 1 

4. Development of LTPP Traffic Feedback and Resolution Package. 

a. Development of the content of the package. 

b. Evaluation and testing of the package. 

 i. Review by RCO representatives and other parties. 

 ii. Review and enhancement by pilot studies. 

5. Preparation of LTPP Traffic Feedback and Resolution Packages for all 

participating agencies. 

a. Assessment of traffic data. 

b. Projection of truck volumes. 

c. Development of base annual spectra. 

d. Assignment of initial projection codes. 

e. Computation of annual axle load spectra. 

6. Review of LTPP Traffic Feedback and Resolution Packages by RCOs. 

7. Review of LTPP Traffic Feedback and Resolution Packages by 

participating agencies. 

8. Implementation of review comments received from participating 

agencies.

a. Responding to agencies. 

b. Preparation of revised traffic projections. 

c. Placing projected traffic data into computed parameter tables. 

Phase 2 

Recommended phase Phase 3 Recommendations for Phase 3 are provided in chapter 6.
12 

Of the eight traffic data assessment and projection activities, three were carried out in Phase 1 

and five in Phase 2. In this chapter, the activities are described in the order they are listed in 

figure 1 as steps 1 to 8. To enable the reader to follow the traffic data assessment and projection 

process better, a general outline of the eight activities is provided first, followed by a more 

detailed description of the activities. 

The main traffic data assessment and projection activities carried out in Phase 1, and described in 

Phase 1 report were:[1]

1. Preliminary assessment of LTPP traffic data—The objective of this activity was to obtain 

a basic understanding of the overall quantity and quality of traffic data. This 

understanding was necessary for the development of the traffic projection procedure. 

2. Development of LTPP traffic projection procedure—The projection procedure developed 

in Phase 1 was designed to utilize fully all available historical and monitoring traffic data 

and to incorporate additional traffic information obtained from local agencies. 

3. Validation of LTPP traffic projection procedure using case studies—The projection 

procedure was used to estimate axle loads for all in-service years of 12 LTPP sites with 

different amounts of historical and monitoring data. The objective was to validate the 

projection procedure using realistic examples. 

The main traffic data assessment and traffic projection activities carried out in Phase 2, and 

described in here, were: 

4. Development of LTPP Traffic Feedback and Resolution Package—The purpose of the 

LTPP Traffic Feedback and Resolution Package was to summarize and present historical 

and monitoring traffic data and the results of traffic data projections in a structured and 

user-friendly manner. The package, developed using a systematic procedure applied 

consistently to all LTPP sites, was the main means of communication and interaction 

with the participating agencies. The package contains traffic data summaries for the 

LTPP sites, easy-to-follow results of traffic projections, background information, and 

guidelines on how to assess traffic data and review traffic projections. During the 

development of the package, input and comments were received from the representatives 

of RCOs, members of the Expert Task Group (ETG) on Traffic Data Collection and 

Analysis, and others. To further evaluate suitability and ease of use, and to provide an 

opportunity for the RCOs to become familiar with the content and the purpose of the 

package, pilot studies involving four participating agencies were also carried out. 

5. Preparation of the LTPP Traffic Feedback and Resolution Packages for all participating 

agencies—Altogether, 62 LTPP Traffic Feedback and Resolution Packages were 

produced (one for each participating agency). Each agency package addresses all LTPP 

traffic sites that belong to the agency, and each LTPP site was processed in terms of: (a) 

the assessment of the amount and quality of historical and monitoring traffic data, (b) the 

development of traffic projections, provided that appropriate traffic data were available, 

and (c) the assignment of confidence codes to the initial traffic projections. 
13 

6. Review of LTPP Traffic Feedback and Resolution Packages by RCOs—The 

responsibilities of RCOs include initial traffic data quality checks, as well as processing 

and storage of traffic data supplied by the participating agencies. Because of a longstanding involvement of the RCOs with traffic data issues on the local level and the 

knowledge of local traffic issues, the packages were sent to the RCOs for review prior to 

being sent to the participating agencies. 

7. Review of Traffic Feedback and Resolution Packages by participating agencies—After 

the concerns of the RCOs were addressed, the updated packages were sent to the 

participating agencies for review. The involvement of the participating agencies has been 

crucial; many data problems cannot be resolved without local involvement. 

8. Implementation of review comments received from participating agencies—To facilitate 

communication with the participating agencies on an individual section basis, a 

standardized questionnaire (Feedback and Data Resolution Sheet) for each LTPP site was 

developed, and the participating agencies were asked to complete it. Based on the 

responses we received from the participating agencies, the initial traffic projections were 

revised and the initial projection confidence codes were changed to reviewed projection 

confidence codes. 

Step 1—Preliminary Assessment of LTPP Traffic Data 

The Phase 1 report contains the results of the evaluation of LTPP traffic characteristics.[1] The 

results include examples of spatial and temporal variation in truck volumes; comparison of 

trends in historical and monitoring annual truck volumes and annual ESALs; the evaluation of 

the potential of Class 9 vehicles for prediction of axle loads; and the evaluation of axle load 

distribution characteristics: The Phase 1 report contains a review of several previous studies that 

used LTPP traffic data to: characterize seasonal variation in truck volumes;[8] evaluate the 

consequences of alternative sampling plans on the estimated annual traffic loads;[9] and assess 

the potential of LTPP traffic data to develop models for predicting axle load distributions.[10] 

The results of an exploratory analysis of trends in traffic data were presented in an interim report 

that preceded the Phase 1 report. [11]

The review of LTPP traffic data carried out during Phase 1 identified the following major issues, 

concerns, and recommendations regarding quality and quantity of traffic data and the projection 

of traffic loads. 

• Trends in historical and monitoring data—The relationship between annual historical and 

monitoring ESALs, and between monitoring ESALs obtained for different years, 

investigated for all sites in the North Atlantic and North Central Regions, showed 

considerable variation. The differences between historical and monitoring ESALs, and 

between ESALs obtained for consecutive monitoring years, frequently exceeded 100 

percent. Because truck volumes and axle loads on interstates and other major highways 

are expected to show relatively steady growth over the years, the observed differences 

indicated important data concerns requiring resolution. 
14 

• Involvement of participating agencies—Considering the available historical and 

monitoring data and the challenges involved in carrying out traffic projection, the 

involvement of participating agencies in the traffic projection process was identified as 

being essential for knowledge-based traffic data assessment and projection of traffic 

loads. 

• Unavailability of monitoring data—Typically, only 3 or 4 years of monitoring axle load 

data are available for LTPP sites. Many sites do not have any axle load (WIM) or truck 

class distribution (AVC) data. 

• Calibration of AVC and WIM equipment—AVC and WIM scale calibration concerns 

have not been studied in sufficient detail. For example, the CTDB does not contain any 

data on quadruple axles even though such axles regularly occur in several participating 

agencies, including Michigan and Ontario. For many sites, axle load spectra exhibited 

unexpectedly large year-to-year variations. 

• Quality assurance—The QA techniques employed previously were characterized as an 

automated review to detect common equipment problems, rather than a comprehensive 

and detailed QA process. To ensure the integrity of the data used for traffic projections 

and for any subsequent analysis, the Phase 1 study recommended that a comprehensive 

QA process of underlying traffic data be carried out. 

• Development of PLG—The data QA process would greatly benefit from the development 

of a knowledge base documenting typical values and ranges of traffic variables, 

particularly truck class and axle load distributions. 

The knowledge gained during the preliminary assessment of the amount and quality of available 

historical and monitoring data was instrumental in the development of the LTPP traffic 

projection procedure. 

Step 2—Development of LTPP Traffic Projection Procedure 

This section outlines the LTPP traffic projection procedure. The detailed description of the 

procedure, including numerical examples in the form of case studies, is contained in the Phase 1 

report.[1] To satisfy the following design requirements, the projection procedure was developed 

to ensure: 

• The ability to estimate annual axle loads for the majority of LTPP sites and for all years 

the sites were in service. 

• Compatibility with the available historical and monitoring traffic data and their full 

utilization. 

• Transparency (to be understandable to the users of projected traffic data) and modularity 

(for ease of future enhancements of the projection procedure, and for ease of updating the 

projections if more data become available). 

The procedure for projecting axle load spectra for the individual LTPP sites includes the 

following activities: 

• All available annual historical and monitoring data were used to establish a model for 

estimating annual truck volumes for all years the site was in service. 
15 

• The model for estimating annual truck volumes was used to obtain annual projection 

factors. 

• A base annual axle load spectrum, representing a typical axle load spectrum for the site, 

was established. 

• The projected annual axle load spectra for all in-service years were obtained by 

multiplying the base annual spectrum by the annual projection factors. 

Mathematically, the LTPP traffic load projection procedure is based on the following formula: 

 

(1)

Where: 

Annual Axle Load Spectrumy = Projected annual distribution of axle weights by load ranges for 

year y. Axle weights are reported separately for single, 

tandem, and triple axles. 

Base Annual Spectrum = A typical annual axle load spectrum chosen to represent traffic 

loads on the site. 

Annual Projection Factory = Annual truck volume adjustment factor for year y used to scale 

the Base Annual Spectrum according to the total volume of 

trucks in year y. 

The above formula means that once the base annual spectrum was established, it was scaled 

using the annual projection factor corresponding to the annual truck volume for the given year. 

Consequently, it was assumed that the truck class distribution remained constant over the years 

and that only the amount of trucks changed. 

Specific techniques, described in Phase 1 report, were developed to obtain base annual spectra 

for the five projection categories.[1] The five projection categories, classified by the amount of 

available monitoring data, are defined in chapter 1 of this report. Annual axle load spectra were 

projected also for the years with monitoring (measured) annual axle load spectra. For these 

years, both the monitoring and the projected axle load spectra will be available in the IMS 

database. 

To obtain cumulative axle loads, annual axle spectra could be summed as shown in equation 2. 

 

 (2)
16 

Where: 

Cumulative Axle Load Spectrum = Total amount of axle weights during n years. Axle weights 

are reported separately for single, tandem, and triple axles. 

n = The number of years from the opening of highway to traffic 

through (and including) 1998. 

Projections for Sites in Category 1 and 2 

Category 1 and 2 sites have monitoring annual axle load spectra in the IMS database; these 

spectra were used to obtain the base annual spectrum. Traffic load projections carried out in 

Phase 2 were done for Category 1 and 2 sites only, and were in terms of annual axle load spectra. 

Typically, the base annual spectrum was an average of all acceptable annual spectra for the given 

site. Techniques used to establish base annual spectra for Category 1 and 2 sites are summarized 

as part of step 5 (figure 1) in the section titled “Development of Base Annual Spectra.” 

The Phase 1 report also contains the description of procedures developed for estimating base 

annual spectra for Category 3 and 4 sites, as well as numerical examples of traffic projections for 

these sites.[1] Even though these procedures were not used in this report, they are outlined below 

because they are important for the understanding of the subsequent recommendation to develop 

the LTPP PLG (discussed in chapter 5 of this report). 

Projections for Sites in Category 3 

Category 3 includes sites with site-specific monitoring truck class distribution data but without 

acceptable site-specific monitoring axle load distribution data. Because these sites do not have 

annual axle load spectra in the database, the base annual spectrum must be estimated. According 

to the Phase 1 report, it is proposed to accomplish this by combining the following two inputs:[1]

• Site-specific monitoring truck class distribution. 

• Surrogate axle load spectra for individual truck types. 

Site-Specific Monitoring Truck Class Distribution

If there are several years for which the site-specific monitoring truck class distributions are 

available, a typical truck volume distribution (called the base truck distribution) is established by 

plotting and assessing all annual truck class distributions using similar techniques as those used 

to obtain the base annual spectrum. The objective is to obtain the truck class distribution that 

best represents the given site. 

Surrogate Axle Load Spectra for Individual Truck Types

The sources of surrogate axle load spectra include site-related data, regional data, and generic or 

typical data.
17 

Projections for Sites in Category 4 

Category 4 includes sites with annual truck volume data but without site-specific truck class and 

axle load distributions. Because these sites do not have annual axle load distribution in the 

database, the base annual axle load spectrum must be constructed. According to the Phase 1 

report, it is proposed to accomplish this by combining the following three inputs:[1]

• Site-specific total truck volume. 

• Surrogate monitoring truck class distribution. 

• Surrogate axle load spectra for individual truck types. 

The sources of surrogate truck class and axle load distribution data include site-related data, 

regional data, and generic or typical data. 

Step 3—Validation of the LTPP Projection Procedure Using Case Studies 

The methodology for projecting axle load spectra was evaluated and demonstrated in Phase 1 

using case studies for selected LTPP sites. Case studies are numerical example applications of 

the traffic projection procedure for actual LTPP sites. All together, 12 case studies were carried 

out (3 case studies in each projection category except Category 5). The following observations 

were based on the results of the case studies:[1]

• The procedure for projecting traffic loads developed in the course of the Phase 1 study 

can be used to estimate axle load spectra for all LTPP sites. 

• The involvement of participating agencies in the projection process is essential. 

• The selection of truck class and axle load distributions required for Category 3 and 4 

projections must be done judiciously and should be supported by a reference database 

summarizing characteristic truck class and axle load distribution data. 

• The QA process would greatly benefit from developing a knowledge base or a catalog 

documenting typical or expected values and ranges of traffic variables, particularly axle 

load spectra for individual vehicle classes. 

Step 4—Development of LTPP Traffic Feedback and Resolution Package 

To meet the objectives of the Phase 2 study, it was necessary to work with all LTPP sites and 

with all participating agencies. The LTPP traffic database contains 890 unique traffic sites 

located in 62 agencies. Reflecting the objectives of the Phase 2 study, the objectives of the 

LTPP Traffic Feedback and Resolution Package were: 

• To report back to the participating agencies the quantity and quality of the traffic data 

available for all LTPP traffic sites in their respective jurisdictions, using easy-tounderstand graphic displays. Although the participating agencies had received traffic 

feedback reports in the past, the previous reports were limited in scope and did not 

contain all relevant traffic data for all historical and monitoring years, nor did they 

include long-term trends in traffic volumes and loads. 
18 

• To present traffic data in the format that would: 

− Facilitate the assessment of the quality and quantity of traffic data by the 

participating agencies, RCOs, and the project team. 

− Facilitate the understanding of the projection process, and the assessment of the 

initial traffic projections by the participating agencies and RCOs. 

To efficiently assess traffic data and carry out traffic projections for all 890 LTPP sites, and to 

communicate the traffic projection results to participating agencies effectively, we developed a 

standardized package—the LTPP Traffic Feedback and Resolution Package (also referred to here 

as “the package”). The package combines graphic traffic data displays, information on how to 

interpret the data displays, and questionnaires addressing overall and site-specific traffic data 

issues. A separate package was prepared for each participating agency. 

The rest of this section describes the content of the package and the involvement of the 

representatives of RCOs and others in its development and validation. 

Content of the LTPP Traffic Feedback and Resolution Package 

The package consisted of five items: 

• Introductory letter. 

• Outline of the LTPP traffic projection procedure. 

• Initial overall feedback and resolution report. 

• Initial site-specific feedback and resolution report. 

• Site-specific reports for each LTPP site within the agency. 

These five items are briefly described below. The package was quite bulky, particularly for 

agencies with many LTPP sites. 

Introductory Letter

The introductory letter was prepared and signed by a representative of the RCO and was 

addressed to the State or Province LTPP contact engineer or other official. Typically, the letter 

outlined the purpose of the package and asked that a person within the agency who was familiar 

with the collection of traffic data for LTPP sites review the package and respond to the issues 

raised. Some letters also included additional information and comments regarding the traffic 

data availability for specific sites (e.g., if data submitted previously by the participating agencies 

were not yet included in the package). 

Outline of the LTPP Traffic Projection Procedure

The outline of the LTPP traffic projection procedure was a 10-page report that explained the 

traffic projection procedure used to estimate traffic loads in straightforward language. The 
19 

objective of the outline was to enable the reviewer to understand the relative importance of the 

different issues, potential discrepancies, and questions posed in the package. The outlined 

discussed: 

• Objectives of the traffic projection procedure. 

• Definition of key technical terms used in the outline. 

• The need to estimate traffic loads in terms of axle load spectra. The outline also included 

a typical example of axle load spectra in graphic and tabular forms. 

• Reasons for the involvement of participating agencies in the traffic estimation process. 

• LTPP traffic projection procedure. 

Initial Overall Feedback and Resolution Report

The initial overall feedback and resolution report was typically a 4- or 5-page report that 

contained a summary of traffic data assessment and traffic projection issues concerning more 

than one LTPP site. The report was called “initial” because it was concerned with the first traffic 

projections carried out for LTPP sites. The initial overall feedback and resolution report served 

two purposes: 

• To summarize issues identified during the initial traffic data assessment and projection 

effort that required input from agency representatives. 

• To seek additional traffic data and information from the agency representatives to 

improve traffic projections. 

The issues and questions were grouped under the following headings: 

• Overall review. 

• Missing data. 

• Location of sections. 

• Traffic volumes. 

• Vehicle classification—operation of AVC equipment. 

• Axle weights—operation of WIM scales. 

Overall review—The overall review included a table listing all LTPP sites for the agency and 

their corresponding LTPP experiment numbers and the initial projection confidence codes. The 

initial projection confidence codes were used to characterize the level of confidence associated 

with initial traffic projections. The assignment of the projection codes is described later 

(“Assignment of Initial Traffic Projection Codes” in step 5). 

Missing data—This section provided a comprehensive listing of traffic data that were missing 

from the IMS database. 

Location of sections—Particular attention was paid to the nearby sites located on the same 

highway, particularly if the sites were also located in the same direction of travel. The objective 

was to ascertain the existence of expected relationships between truck volumes and axle loads on 
20 

related sites. Any potential discrepancies were brought to the attention of the agency 

representatives. 

An example of the data assessment carried out under the heading of “Location of sections” is 

provided in table 3. As expected, nearby sites on the same highway in the same direction have 

similar truck volumes, truck percentages, and truck growth rates. The exception appears to be 

the two northbound sites on U.S. Route 93 in Arizona that have quite different truck percentages 

(15 versus 27) and quite different recent truck growth rates (1.0 versus 10.0 percent). 

Traffic volumes—The objective of this part of the report was to emphasize the importance of 

trends in annual historical and monitoring truck volumes for the development of the traffic load 

projections. Sections with unexpected variation in annual truck volumes were identified and 

reviewers were asked to address truck volume discrepancies on a section-specific basis. 

Vehicle classification—operation of AVC equipment—Typically, the main concern with vehicle 

classification was the number of vehicles that were not properly classified by the traffic 

monitoring equipment. These vehicles are identified in the IMS as Class 14 vehicles. Some 

agencies reported that the percentage of Class 14 vehicles in the total truck flow was, for the 

majority of sites and years, above 10 percent. Some agencies did not report any Class 14 

vehicles. Questions posed to the agencies regarding vehicle classification were regarding: (a) 

distribution of Class 14 vehicles into “legitimate” vehicle classes; (b) reporting of vehicles that 

were not properly classified by AVC equipment; and (c) type of procedures used to ensure that 

the vehicles are properly classified. 

Axle weights—operation of WIM scales—This segment of the overall report contained two 

parts. 

The first part identified the LTPP sites with questionable axle load data and inquired about the 

procedures used to calibrate WIM scales and to review axle load data prior to their submission to 

RCOs. Questionable axle load distributions, for at least some sections and some monitoring 

years, were reported by all agencies that reported monitoring axle loads. Typically, the 

following questions were asked: What type of procedure is used to ensure that WIM scales are 

calibrated? Is WIM calibration done routinely? Do you have resources to review traffic data 

prior to their submission to LTPP? If so, what procedures do you use? 

The second provided the results of analysis carried out to ascertain basic traffic loading patterns 

for all LTPP sites within the agency. The objective was to provide a summary of axle load 

characteristics and to identify sites that do not fit overall patterns. An example of such a 

summary is shown in tables 4 and 5. 
21 

Table 3. Arizona LTPP sites near other sites on the same highway and in the same direction. 

Location, 

Hwy No. and 

Direction 

Nearby 

Sites 

1998 

AADT 

Truck 

Volume 

1998 

Truck 

Percentage 

Recent 

Truck 

Growth, % 

Year or Years for 

which Axle Load Data 

are Available, 

Comments 

0100 900 15 1.0 93 N 1036 800 27 10.0 

1997, axle load spectra 

are identical 

1001 2620 31 7.4 1995 and 1996 

1003 3400 52 7.0 1994 

1006 3160 45 6.7 1995 

1007 3530 44 7.4 1995, 1996, 1997 

10 W 

7614 3490 35 9.0 1996 

1015 620 7 7.0 None 

1016 470 8 6.0 1994 

1018 450 8 6.0 1993 19 S 

6054 570 8 2.0 1993 

1017 540 10 4.0 1997 19 N 6060 590 13 7.0 1996, 1997, 1999 

1002 2210 49 8.0 1997, 1999 

1021 2240 50 11.0 None 

1022 2240 50 11.0 None 

1025 2130 47 7.0 1993, 1994 

40 W 

1062 2190 58 9.0 1993 

1024 2080 46 7.0 1993 40 E 1065 2140 54 9.0 1993 

The results in tables 4 and 5 indicate a similarity of traffic load characteristics with a few 

exceptions. For example, all TFs in table 4 are in the range of 0.9 to 1.3, except the TF of 0.6 for 

Mississippi site 1001. Consequently, the axle load data on this site need to be assessed to 

ascertain the reason for lower-than-expected axle loads. 

Initial Site-Specific Feedback and Resolution Report

The purpose of this report was to: 

• Describe the standardized displays of traffic data and the initial traffic projection results 

for individual LTPP sites. 

• Provide guidelines for the assessment of traffic data and for the interpretation and 

evaluation of traffic projection results. 
22 

Table 4. Traffic loading parameters for Mississippi LTPP sites with axle weight projections. 

Flexible Pavement Rigid Pavement 

Highway 

Functional 

Class 

Site 

No. 

(Prefix 

of 28) 

1998 

Truck 

% 

Average 

Unloaded/Loaded 

Peaks of Tandem 

Axle Load Spectra 

(kips)** 

Average 

Truck 

Factor 

(ESALs/

Truck) 

1998 

Truck

% 

Average 

Unloaded/Loaded 

Peaks of Tandem 

Axle Load Spectra 

(kips)** 

Average 

Truck 

Factor 

(ESALs/

Truck) 

Urban 1016 9 9/31 1.0 – – – 

Principal 

Arterial 5805* – – – 15 11/27 1.1 

0500* n/a 11/33 1.3 – – – 

1001 9 9/29 0.6 – – – 

1802 22 11/31 0.9 – – – 

2807 12 11/33 1.1 – – – 

3018 – – – 24 9/29 1.2 

3019 – – – 24 9/29 1.2 

3081 31 11/32 1.1 – – – 

3082 n/a 13/33 1.2 – – – 

3087 8.5 11/36 1.3 – – – 

3089 12 11/33 1.1 – – – 

3091 13 11/33 1.3 – – – 

3093* 20 11/31 0.9 – – – 

3094* 20 11/31 0.9 – – – 

3097* – – – 14 11/27 1.7 

3099* – – – 41 –/31 1.7 

5006 – – – 28 11/29 1.1 

5025 – – – 16 11/33 1.9 

5803 – – – 36 11/33 1.8 

7012* – – – 29 –/31 1.8 

Rural 

Principal 

Arterial 

9030* 32 13/31 1.0 – – – 

*Interstate; **1 kip = 4450 newtons 

Table 5. Traffic loading parameters for Mississippi sites without axle weight projections. 

Highway 

Functional Class 

Site No. 

(Prefix of 28) 

1998 Projected AADT 

Truck Volume 

1998 

Truck 

% 

1998 Annual 

Growth Rate % 

Historical TF 

(ESALs/

Truck) 

3083 25 8 1.5 0.6–1.5 

3085 25 8 1.5 0.6–1.5 

3090 40 7 2.0 0.3–1.0 

Rural Principal 

Arterial 

4024 130 4 3.0 0.5–1.4 
23 

An example of the Initial Site-Specific Feedback and Resolution Report, prepared for the 

representatives of participating agencies assigned the task to review the initial traffic projections, 

is provided in figure 2. Specifically, figure 2 includes the entire site-specific feedback and 

resolution report prepared for the Mississippi Department of Transportation (DOT); the reports 

prepared for other agencies were similar. 

The report in figure 2 refers to 26 site-specific reports (one for each of the 26 Mississippi LTPP 

sites that have traffic data in the IMS database). One of the 26 site-specific reports for 

Mississippi, report for site 285805 is presented in figures 3 through 10. To assist the reader in 

making the connection between the reports, figure 2 contains references to figures 3 through 10. 

The first data sheet of the site-specific report (figure 3) was on blue paper and is referred to as a 

“Blue Sheet.” 

Site-Specific Reports

The site-specific report for site 285805 (figures 3 through 10) consists of eight data sheets: 

• Feedback and Data Resolution Sheet (figure 3). 

• Site Map (figure 4). 

• Annual Traffic Projection Sheet (figure 5). 

• Projected AADT Truck Volumes (figure 6). 

• Annual Vehicle Class Distribution (figure 7). 

• Annual Load Spectra (figure 8). 

• Average Annual Load Spectrum (figure 9). 

• Projected Annual ESALs (figure 10).

The procedures used to develop site-specific reports are described in the Phase 1 report, and the 

content and purpose of the report are described in figure 2.[1] Subsequent sections will provide a 

description of techniques used to develop the initial projections shown in figures 6 and 10, and to 

assign projection confidence codes. 

The Blue Sheet in figure 3 is shown as having been completed by the Mississippi Department of 

Transportation (MDOT) to avoid the need to show separate examples of uncompleted and 

completed Blue Sheets. The completion of Blue Sheets by participating agencies will be 

discussed in step 7 (Review of LTPP Traffic Feedback and Resolution Packages by Participating 

Agencies). 
24 

INITIAL SITE-SPECIFIC FEEDBACK AND RESOLUTION REPORT FOR 

MISSISSIPPI 

November 2000

ERES LTPP DATS Traffic Analysis Team 

Baltimore-Washington DC Area Office 

Phone: ********** 

Fax: ********** 

e-mail: ********** 

INTRODUCTION

This report is about site-specific traffic projection issues concerning individual LTPP sites. The 

traffic projection issues that are applicable to more than one site are addressed in the Initial Overall 

Feedback and Resolution Report for Mississippi. The report is called “initial” because it comments 

on the first, or initial, traffic projections carried out for Mississippi sites. 

The purpose of this report is twofold: 

• To describe the standardized display of the results obtained during the initial traffic projection 

process for individual LTPP sites. 

• To provide guidelines for the interpretation and evaluation of results. 

Attached to this report are site-specific reports prepared for the 26 Mississippi LTPP sites that 

contain traffic monitoring data in the LTPP traffic database. Each report starts with a blue 

“Feedback and Data Resolution Sheet.” Please review each report according to the guidelines 

provided herein.

In order for the reviewer to understand the relative importance of the different issues, potential 

discrepancies, and questions posed in this report, it is recommended that the reviewer become 

familiar with the overall traffic projection methodology. For this reason, an outline of the traffic 

projection methodology, entitled, An Outline of the LTPP Traffic Projection Procedure, is attached 

to this mailing. 

How To Communicate with Us

Please use the blue Feedback and Data Resolution Sheet in front of the packages prepared for the 

individual sites. Handwritten notes are certainly sufficient. The sheets contain a number of 

questions and comments on a variety of issues, and seek input on these issues from the 

representatives of Mississippi DOT. In some situations, it may be appropriate to respond, in addition 

to using the blue sheet, by other means including telephone and e-mail. 

Thank you very much for your help. 

Figure 2. Initial site-specific report for Mississippi (page 1). 
25 

INSTRUCTIONS FOR THE REVIEWER

In order to facilitate the review of the projected traffic data, and the identification of site-specific issues 

(that need to be brought to the attention of the reviewer), a standardized format was developed to report 

and present traffic data (historical, monitoring, and projected) for the individual LTPP sites. Traffic data 

for each LTPP site that has WIM scale monitoring data are presented using a set of eight sheets. The 

eight-sheet set constitutes a standard package for the display and presentation of the projected traffic data 

and for their review by the representatives of State highway administrations (SHAs). The set contains the 

following sheets. 

• Feedback and Data Resolution Sheet (figure 3). 

• Site Map (figure 4). 

• Annual Traffic Projection Sheet (figure 5). 

• Projected AADT Truck Volumes (figure 6). 

• Annual Vehicle Class Distribution (figure 7). 

• Annual Load Spectra (figure 8). 

• Average Annual Load Spectrum (figure 9). 

• Projected Annual ESALs (figure 10). 

The following briefly describes the purpose of each sheet and highlights typical features and concerns that 

usually require the review and assessment by SHA representatives. 

Feedback and Data Resolution Sheet (Blue Sheet), figure 3

The purpose of this sheet is to summarize all major site-specific features that may influence traffic 

projection. This is the principal communication tool between the traffic team and the SHA reviewer. 

It is expected that the reviewer will complete the blue sheet for each LTPP site under review. Additional 

comments and suggestions are encouraged. Please use additional sheets or the reverse side if needed.

Site Map, figure 4

The objective is to clearly identify the location of the site in question, as well as the location of nearby 

sites (which may serve as surrogate source of traffic data). 

The location of each site should be verified. 

Annual Traffic Projection Sheet, figure 5

The annual traffic projection sheet is used to summarize trends in historical and monitoring traffic data 

(AADT volumes, AADT truck volumes, average ESALs per day, and TFs). 

It is expected that traffic volumes and ESALs will exhibit an increasing trend.

TFs (ESALs per truck) should be at a level or perhaps increasing (to reflect the increased cost-efficiency 

of the trucking industry).

Figure 2 continued. Initial site-specific report for Mississippi (page 2). 
26 

Projected AADT Truck Volumes, figure 6 

This sheet shows historical and monitoring truck volumes and the suggested projection model—typically 

a smooth line or a curve—used to estimate truck volumes for all in-service years. This model is also used 

to project (estimate) axle load spectra. If needed, the model can exactly duplicate the reported historical 

data trends. 

Are additional data available from other State sources? 

Can a better model be developed and used? If so, please sketch the new model on the sheet. 

Annual Vehicle Class Distribution, figure 7 

The top half of the sheet shows actual axle counts for different truck classes. The bottom half of the sheet 

shows the distribution of trucks as a percentage of the total truck count. All available years are plotted to 

spot outliers and questionable results. 

The unexpected truck distributions should be identified. This activity contributes to the judicious 

selection of the base spectra. Usually, for the same site, annual vehicle class distributions should be 

similar from year to year. It is expected that the number of Class 9 vehicles (5-axle tractor-semi-trailers) 

will predominate on rural interstates while, on the other hand, the number of 2-axle single-unit trucks 

will become significant on urban and semi-urban roads. 

Annual Load Spectra, figure 8 

Annual axle load spectra are plotted for all available years (the years with monitoring WIM scale data). 

The annual spectra are used to select/calculate a typical “base” spectrum. The base spectrum is then used 

to estimate axle load spectra (particularly for years without monitoring WIM data). In some instances, 

two base spectra are used for the projection: the first spectrum and the last spectrum. 

If the site has reliable annual spectra for many years, two base annual spectra may be used for the 

projection. For example, the first base spectrum is used to represent traffic during the years before the 

installation of a WIM scale, and during the initial operation of the scale. The second spectrum is used to 

represent traffic levels for the most recent years, with and without WIM scale data. Both the first and the 

last spectra can be the averages of several annual spectra. 

At most sites, and there are exceptions, most tandem axle loads are caused by Class 9 trucks (5-axle 

tractor-semi-trailers). Consequently, the distribution of tandem axles (shown in the middle of the sheet) 

usually has two peaks. The first peak corresponds to unloaded tandems, and the second peak to the fully 

loaded tandems. These two peaks are usually at 10 to 11 kips and 31 to 33 kips, respectively. (Federal 

regulations limit tandem axle weight to 34,000 lb.) 

It is important to identify all incorrect and suspicious spectra, and to recommend which spectra should be 

used (averaged) to obtain the base spectrum. Usually, annual load spectra for the same site should be 

similar. It may be difficult, without inside knowledge, to judge the validity of spectra if there are just a 

few spectra available to define the expected pattern. 

Figure 2 continued. Initial site-specific report for Mississippi (page 3). 
27 

Average Annual Load Spectrum, figure 9 

The average annual load spectrum is obtained by calculating the average of the annual spectra, 

presented in figure 8, that are considered to be valid. It is also the base spectrum used for the 

projection. 

Projected Annual ESALs, figure 10 

This sheet provides a summary of equivalent single axle loads (ESALs) projected for all in-service 

years. ESALs are calculated using the site-specific pavement structure (type and thickness). It is 

recognized that the purpose of traffic collection and analysis is to obtain axle load spectra (and not 

ESALs). ESALs are used mainly for comparison and QA purposes. 

Are the projected annual ESALs and the cumulative ESALs reasonable? Only pavement 

professionals can typically answer this question.

Figure 2 continued. Initial site-specific report for Mississippi (page 4). 
28 

Figure 3. Feedback and Data Resolution Sheet for site 285805. 
29 

Figure 4. Site map for site 285805. 
30 

285805 Annual Traffic Projection Sheet

Availability of Monitoring Data 

Data Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 Total 

Days – – 266 361 351 363 160 – – 1,501

AVC Month – – 9 12 12 12 – – – 45

Days – – 177 333 351 361 161 – – 1,383

WIM Month – – 6 11 12 12 – – – 41

Figure 5. Annual Traffic Projection Sheet for Site 285805. 
31 

AADT Truck Volumes Projected Growth 

Year Historical Monitoring Projected Percentage Factor 

1975 970 – 885 – 0.45 

1976 1060 – 923 4.2 0.47 

1977 1160 – 962 4.2 0.49 

1978 1310 – 1003 4.3 0.51 

1979 1300 – 1046 4.3 0.53 

1980 1330 – 1091 4.3 0.55 

1981 1440 – 1139 4.4 0.58 

1982 1480 – 1189 4.4 0.60 

1983 1530 – 1241 4.4 0.63 

1984 1660 – 1296 4.4 0.66 

1985 1720 – 1354 4.5 0.68 

1986 1790 – 1415 4.5 0.72 

1987 1830 – 1479 4.5 0.75 

1988 2070 – 1545 4.5 0.78 

1989 2190 – 1616 4.5 0.82 

1990 1544 – 1690 4.6 0.85 

1991 1524 – 1767 4.6 0.89 

1992 – 1789 1848 4.6 0.93 

1993 – 1937 1934 4.6 0.98 

1994 – 2079 2023 4.6 1.02 

1995 – 2105 2118 4.7 1.07 

1996 – 2307 2217 4.7 1.12 

1997 – – 2320 4.7 1.17 

1998 – – 2429 4.7 1.23 

Figure 6. Projected AADT volumes for site 285805. 
32 

285805 Annual Vehicle Class Distribution 

Figure 7. Annual vehicle class distribution for site 285805. 

0

100000

200000

300000

400000

500000

600000

4 5 6 7 8 9 10 11 12 13 14

Vehicle Class

Annual Vehicle Counts

0

10

20

30

40

50

60

70

80

4 5 6 7 8 9 10 11 12 13 14

Vehicle Class

Percent of Vehicle Counts

1992 1993 1994 1995 1996
33 

285805 Annual Load Spectra 

Figure 8. Annual load spectra for site 285805. 

Triple Axle

0

200

400

600

800

1000

1200

1400

0

5000

100001500020000250003000035000400004500050000550006000065000700007500080000850009000095000

100000

Axle Loads, lb

Axle Counts

1992 1993 1994 1995 1996

Single Axle

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

0

2000400060008000

10000120001400016000180002000022000240002600028000300003200034000360003800040000

Axle Loads, lb

Axle Counts

Tandem Axle

0

20000

40000

60000

80000

100000

120000

0

5000

100001500020000250003000035000400004500050000550006000065000700007500080000

Axle Loads, lb

Axle Counts

1 lb. = 2.202 kg
34 

285805 Average Annual Load Spectrum

Figure 9. Average annual load spectrum for site 285805. 

Single Axle

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

0

2000400060008000

1000012000140001600018000200002200024000260002800030000

Axle Loads, lb

Axle Counts

 Tandem Axle

0

20000

40000

60000

80000

100000

120000

0

40008000

12000160002000024000280003200036000400004400048000520005600060000

Axle Loads, lb

Axle Counts

Triple Axle

0

200

400

600

800

1000

1200

0

6000

12000180002400030000360004200048000540006000066000

Axle Loads, lb

Axle Counts

1 lb. = 2.202 kg
35 

285805 Projected Annual ESALs (Initial)

Annual ESALs 

Year Historical Monitoring Projected 

1975 330000 – 359237 

1976 371000 – 374473 

1977 414000 – 390386 

1978 479000 – 407112 

1979 486000 – 424689 

1980 509000 – 443146 

1981 565000 – 462405 

1982 595000 – 482762 

1983 631000 – 504029 

1984 702000 – 526419 

1985 748000 – 549829 

1986 799000 – 574507 

1987 840000 – 600428 

1988 978000 – 627632 

1989 1063000 – 656134 

1990 837000 – 686160 

1991 827000 – 717567 

1992 – 618395 750656 

1993 – 744923 785370 

1994 – 833212 822124 

1995 – 1017005 860362 

1996 – 1256965 900519 

1997 – – 942662 

1998 – – 986994 

Cumulative – – 14835602 

Figure 10. Projected annual ESALs for site 285805. 
36 

Evaluation and Testing of LTPP Traffic Feedback and Resolution Package 

The package described in the preceding section was the final version of the one that was 

distributed to the majority of the participating agencies. It was developed and refined over the 

course of several months through an interactive process involving reviews by interested parties 

and by representatives of several participating agencies. The two main review activities were: 

• Review of the package by representatives of RCOs and by other interested parties. 

• Review and enhancement of the package by pilot studies. 

In addition, throughout the course of Phase 2 work, several small changes and enhancements to 

the package were instituted. 

Review by Representatives of RCOs and Other Interested Parties

The important role of the RCOs in the projection process was recognized from the beginning of 

the study. Representatives of RCOs reviewed the first version of the LTPP Traffic Feedback and 

Resolution Package and subsequently reviewed agency-specific packages for all participating 

agencies in their respective regions. 

The first package also was submitted to all members of the LTPP ETG on Traffic Data 

Collection and Analysis for review and comments, and a later version was presented during an 

ETG meeting in the spring of 2000. Reviews of an early version of the Package were also 

obtained from the representatives of the Technical Support Services Contractor and from the 

Minnesota Department of Transportation (Mn/DOT). 

All reviewers provided valuable comments and suggestions on how to make the package more 

effective and user-friendly. 

Review and Enhancement of the Package by Pilot Studies

During Phase 1 traffic projection work, it became apparent that it was necessary to involve 

representatives from the participating agencies in the traffic projection process because many 

data problems cannot be resolved without their involvement and help. The primary contact 

between the participating agencies and the LTPP program has been through RCOs. RCOs 

provide general technical support to the agencies regarding traffic data collection and analysis 

issues and are responsible for initial traffic data quality checks and for processing traffic data 

collected by the agencies. Consequently, the involvement of both the participating agencies and 

RCOs in traffic data assessment and projection is important. Pilot studies brought together the 

representatives of the participating agencies, RCOs, the project team, and others. 

The main feature of the pilot studies was a 1-day meeting that was held at the participating 

agencies and attended by representatives of the participating agency (typically including an 

LTPP contact engineer, personnel responsible for traffic data management, field traffic data 

collection personnel, and others), RCOs (personnel responsible for traffic data), FHWA 
37 

(representing the LTPP program, and FHWA Division Office), and two members of the project 

team. 

The purpose of the pilot studies was to review and enhance the process of traffic data assessment 

and projection of traffic loads, including the content of the LTPP Traffic Feedback and 

Resolution Package, and to discuss specific issues concerning the agencies’ LTPP sites. The 

pilot studies also provided an opportunity for the representatives of the RCOs to become better 

acquainted with the package and traffic data assessment and projection issues. For this reason, 

one pilot study took place in each of the four LTPP regions: 

Agency Meeting Place LTPP Region 

California DOT Sacramento, CA Western Region 

Florida DOT Tallahassee, FL Southern Region 

New Jersey DOT Trenton, NJ North Atlantic Region 

Indiana DOT Indianapolis, IN North Central Region 

During the course of the meeting, attendees discussed the LTPP package prepared for the 

agency. Agency representatives provided comments on specific LTPP sections following the 

format of the Feedback and Data Resolution Sheet (figure 3). 

The pilot studies resulted in several improvements to the package, and in better communication 

among all interested parties, particularly between the representatives of the RCOs and the project 

team. 

Step 5—Preparation of LTPP Traffic Feedback and Resolution Packages for All 

Participating Agencies 

Once the package was developed through the review and consultative process outlined 

previously, a procedure was established to produce a package for each participating agency and 

to send it to RCOs for review. A substantial part of the package was produced as a customized 

printout of data stored in the IMS database. The assessment of data quality, the selection of data 

for projection, and the development of the projection models were done on a section-by-section 

basis using engineering and analytical judgment. The following activities were carried out to 

develop traffic projections: 

• Assessment of traffic data. 

• Projection of truck volumes. 

• Development of base annual spectra. 

• Assignment of initial projection confidence codes. 

• Computation of annual axle load spectra. 

These activities are described in the following sections under separate headings; however, the 

first four activities were intertwined. 

The following principles applied to all traffic data assessment and projection work. 
38 

• Close attention was paid to whether the data and traffic projections encountered in the 

course of the work were reasonable. For example, when working with truck volumes, the 

corresponding highway classification, number of lanes, and AADT volumes reported for 

different years on the same site or on similar sites were noted to identify potential 

idiosyncrasies. 

• All activities requiring engineering and analytical judgment were carried out by at least 

two members of the project team. Typically, one project team member carried out the 

task and the second member independently reviewed the outcome of the task. Any 

differences were discussed to reach a consensus. 

• The initial projections were done with the understanding that they will be reviewed by the 

participating agencies. It was considered more constructive and beneficial for the 

projection if the project team were proactive and developed the initial traffic projection 

whenever possible, rather then to ask agencies first for more data or for directions. 

Consequently, for example, only meager data and engineering judgment were sometimes 

used to propose the truck volume projection model. 

• The traffic data assessment and projection work was done for all sites belonging to an 

agency at the same time. This approach enabled the project team to cross-compare trends 

in data to identify data discrepancies and to develop solutions and for their resolution. 

Assessment of Traffic Data 

A general assessment of data quantity and quality for all LTPP sites within the agency was 

carried out as part of the preparation of the initial overall feedback and resolution report. 

Because traffic data for all LTPP sites within an agency were assessed at the same time, the 

process benefited from cross-comparison of trends observed on all sites. The site-specific 

assessment of data quantity and quality followed similar themes as those used to develop the 

initial overall feedback and resolution report: evaluation of missing data, location of sections, 

traffic volumes, vehicle classification (operation of AVC equipment), and axle weights 

(operation of WIM scales), and was based on the site-specific reports. 

Observations regarding traffic data that were considered to be of interest to the participating 

agencies, or questions that the project team members had for the representatives of the 

participating agencies, were included in part 6 of the Blue Sheets (figure 3). 

In some respects, the assessment of traffic data carried out as part of this study resembled the 

traffic data QA process recommended in the LTPP Traffic QC User’s Guide.

[12] However, there 

were fundamental differences between the quality control (QC) recommended by the guide and 

the traffic data assessment process carried out as part of the traffic projection process. The 

fundamental differences were in the timing and the outcome of the two activities, and in the 

length of the time period for which the traffic data were assessed. 

The QC should be carried out a few days or weeks after traffic data are collected so that an 

appropriate corrective action (such as equipment calibration) can take place in a timely manner. 
39 

Traffic data assessment carried out in this study took place many years after the data were 

collected. The QC process may result in the removal of nonsensical data, but no data were 

removed from the database as part of this study. However, nonsensical data were identified and 

were not used for traffic projections. Finally, the previous QC process evaluated traffic data for 

only relatively short time periods, such as day, week, month, or quarter, without examining longterm trends. Traffic data assessment done in this study evaluated only annual data, but evaluated 

trends in annual data for all in-service years. 

The type of traffic data assessment done in this study is not a replacement for the appropriate QC 

process. The assessment process used was necessitated by the quality of available traffic data 

and the need to provide the initial traffic projection for the development and calibration of the 

2002 Pavement Design Guide.

[7] Traffic data used in this study still need to be evaluated using a 

basic QA process. 

Projection of Truck Volumes 

To estimate traffic loads for all in-service years, it was necessary to estimate the AADT volumes 

for all years the pavement was in service. The available historical and monitoring truck volume 

data had to be “backcasted” (to years before the sites became part of the LTPP program), 

interpolated (for the interim monitoring years without monitoring data), and forecasted (for years 

after the data are no longer collected), as shown schematically in figure 11. 

The AADT volumes for all in-service years were estimated using a projection model. The 

AADT projection model was also used to obtain annual projection factors required for the 

calculation of the annual axle load spectrum (see equation 1). 

Figure 11. Projection of truck volumes using historical and monitoring data. 

Figure 6 provides an example of the AADT truck projection model for site 285805. Twelve 

other examples of the projection model were provided in the Phase 1 report.[1] The table in 

Interpolation

Backcasting

Forecasting

1990 1995 2000 

Year

1985

Monitoring data

1980 

AADT Truck Volume

Historical data
40 

figure 6 lists historical, monitoring, and projected AADT volumes from 1975 when the pavement 

was open to traffic to 1998. Also listed are the projected growth percentage and the projected 

growth factor. The projected growth percentage, indicating the historical rate of growth in truck 

volumes, was provided to facilitate the review of the projection models by the participating 

agencies. The projected growth factor, also referred to as “Annual Projection Factor” in equation 

1, is a multiplier that was applied to the base annual spectrum to obtain projected annual axle 

load spectra for each year the section was in service. Projected growth factors were used to scale 

overall truck volumes up or down compared to base conditions and to account for traffic growth 

for different years. 

The development of the AADT truck projection model followed the procedures developed in 

Phase 1 and documented in reference 1. Briefly, the available historical and monitoring annual 

truck volumes were plotted separately for each site and analyzed to determine their statistical 

characteristics. Plots of annual volumes often revealed considerable variation. Typically, simple 

regression models would fit the data best. However, least square regressions were not carried 

out because the technique cannot accommodate many considerations that were used to develop 

the projection model, and would not provide more meaningful results. 

Some considerations used to develop the AADT truck projection models are summarized here. 

• The monitoring AADT truck volumes, based on measured data, should be more reliable 

than historical volumes based on estimated data. In situations where historical and 

monitoring truck volumes did not match, the projection model placed more emphasis on 

the monitoring data and followed the monitoring data more closely than the historical 

data. The emphasis on the measured truck volumes is shown in the projection model in 

figure 6. 

• Monitoring annual truck volumes based on measurements carried out during a substantial 

portion of the year should be more reliable than monitoring annual volumes based on 

short-duration measurements. The number of days and months during which AVC and 

WIM data were collected was known for all monitoring years up to 1998 and was listed 

on the bottom of Annual Traffic Projection Sheet (figure 5). 

• The development of the projection model considered not only the trends in AADT truck 

volumes, but also the trends in AADT vehicle volumes, truck factors (TFs), and ESALs 

summarized in the Annual Traffic Projection Sheet (figure 5). 

• Consistency of the historical estimates and the relationship between the historical and 

monitoring volumes for all sites within the agency. For example, if the historical truck 

volume estimates matched the subsequent monitoring volumes well for the majority of 

the sites within the agency, the historical volumes would be given greater weight for the 

sites where the match between the historical and monitoring volumes was poor. 

• To initiate the traffic projection process and to provide a concrete example of the initial 

model for consideration and comments by the participating agencies, the AADT truck 

projection models were developed for all LTPP sites with at least some truck volume 
41 

information. For example, if the AADT truck volume was available for only one 

monitoring year in 1998 and the section was opened to traffic in 1991, we would still 

provide truck volume estimates for all years between 1991 and 1998. The volume 

reported for 1998 would be assessed for reasonableness by considering other available 

data, such as AADT volumes, traffic volumes on similar sites in the same agency, the 

number of lanes, and highway classification. The projected growth in truck volumes 

between 1991 and 1998 would be based on the growth rate at similar sites in the same 

jurisdiction. Because this information was unavailable for a few sites, we used a 5 

percent historical growth rate in truck traffic for interstates and major highways and a 2 

percent rate for other highways. These rates were established by examining vehicle 

travel statistics available at FHWA’s Web site, www.fhwa.dot.gov/ohinstat.htm. 

Subsequent analysis of LTPP data (presented in table 13, later in this report) confirmed 

that the selected growth rates are reasonable. It needs to be emphasized that the growth 

rate estimates were made to initiate the projection process and to submit a concrete 

projection for the review and comment by the participating agencies. Agencies were 

asked to verify the proposed growth rates using local information. 

• Specific attention was paid to the influence of unclassified vehicles reported as Class 14 

vehicles. The number of Class 14 vehicles strongly influenced total truck volumes on 

many sites in several agencies. After analysis of trends in truck volumes, Class 14 

vehicles were usually attributed to passenger cars. Example of such analysis for three 

Minnesota LTPP sites is shown in figure 12. The treatment of Class 14 vehicles used in 

this study should be considered to be an interim measure. For some agencies and sites, 

consideration should be given to re-processing raw traffic data to distribute the Class 14 

vehicles among the 13 vehicle classes. 

Development of Base Annual Spectra 

The objective of the development of base annual spectra was to obtain the annual axle load 

distribution or distributions that best reflect the axle loads on the site. The development of base 

annual spectra was the most challenging part of the entire projection process. The shape of the 

base annual spectrum (i.e., the normalized base annual spectrum) remained the same for all 

years. To obtain annual axle load spectra for all in-service years, the base annual spectrum was 

multiplied by a scale factor (annual projection factor) to reflect the historical changes in truck 

volumes (equation 1). 

The annual axle load spectra showed a large variation in the amount and quality of monitoring 

axle load data. For example, some spectra had unexpected shapes with very few loaded axles, 

others had a large proportion of apparent axle overloads. Annual spectra also varied 

considerably from year to year. Reasons for the variation in and unusual shape of the spectra 

include: 

• Length of the sampling period—The number of days per year during which the WIM scales 

were operating varied from site to site and year to year. The length of the WIM datacollection period for monitoring years up to 1998 was reported on the bottom part of the 

Annual Traffic Projection Summary Sheet (figure 5). The shortest period necessary for 
42 

obtaining annual axle loads spectra was 24 consecutive hours. A spectrum based on several 

months of data may be quite different from one based on only a few days of data. 
43 

Section 271019

0

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1992 1993 1994 1995 1996 1997

Year

AADT Volume

Section 279075

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Year

AADT Volume

Section 271016

0

200

400

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1400

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1991 1992 1993 1994 1995 1996 1997

Year

AADT Volume

Vehicle Class 14 Truck 

Figure 12. Comparison of AADT volumes for Class 14 vehicles with AADT volumes for all 

trucks. 
44 

• Equipment errors—These errors, caused by equipment limitations, can result in bias and 

cannot be remedied without changes to the equipment. It is possible that some occurrences 

with the large numbers of Class 14 vehicles were due to equipment errors. 

• Calibration errors—These errors are caused by inaccuracies resulting from the way the 

physical response of the equipment to the passing vehicles is transformed into units of 

weight. After the initial calibration, the scales may have been allowed to drift. Thus, the 

axle load spectrum based on the first year of operation may be different from the spectra 

obtained for the subsequent years. Automatic calibration based on the weight of steering 

axles of Class 9 vehicles is not always reliable. 

• Changes in traffic patterns—Annual axle load spectra for sites with low truck volumes may 

be significantly influenced by changes in the location and operation of nearby large truck 

traffic generators (e.g., by the construction of a large subdivision or by opening of new 

industry). 

• Natural variation—Traffic loads naturally vary from year to year due to economic and other 

factors. 

Selection of the base annual spectra was carried out by examining all available monitoring data 

for a given site, including all available truck class and axle load distributions. Graphic displays 

of traffic data, contained in the site-specific reports, were used for this purpose. The assessment 

considered the shape of the spectra (e.g., presence and location of peaks in the tandem axle 

spectra) and the differences and trends among the available spectra. 

The main technique used to assess axle load spectra was the expectation that many large trucks 

operate either fully loaded or empty. The loaded peak is usually below the maximum allowable 

load because shippers may not know the exact weight of the empty trucks that will transport 

shipments. Consequently, they prepare or partition shipments with a margin of safety (so that 

the shipment and the weight of the truck will not exceed the allowable axle weight and gross 

vehicle weight limits). For example, if the allowable axle load on tandem axles is 15,436 kg 

(34,000 lb), the majority of fully loaded trucks should have tandem axle loads in the range of 

14,074 to 14,982 kg (31,000 to 33,000 lb). Typical values for unloaded and fully loaded axles, 

together with Federal axle load regulations, are summarized in table 6.[13] 
45 

Table 6. Characteristic values of axle load spectra. 

Expected Guideline Value, lb Axle Type 

Federal Regulation for 

Maximum Allowable 

Axle Weight, lb Unloaded Loadeda

Class 5 20,000 3,000–4,000 16,000–18,000 

Steering Axlesb Single n/a 10,000–12,000 10,000–12,000 

Load Axlesc

 20,000 3,000–4,000 16,000–18,000 

Tandem 34,000 7,000–12,000 31,000–33,000 

Triple 42,000d 8,000–14,000 35,000–40,000 a

 Loaded to achieve maximum allowable weight. 

b

 For vehicle classes 7 to 13 

c

 Single payload carrying axles for all vehicle classes 

d

 Depends on Bridge Gross Weight Formula 

1 lb = 2.202 kg 

As outlined in the Phase 1 report, there are many exceptions to the Federal vehicle weight 

regulations on the State and Provincial levels.[1] The enforcement of vehicle weight regulations 

also plays an important part. The values provided in table 6, therefore, are only typical guideline 

values. 

The basic procedures used to obtain the base annual spectrum included: 

• Computing the mean of all available annual axle load spectra. 

• Computing the mean of selected annual axle load spectra. 

• Selecting one or two annual axle load spectra. 

• Rejecting all available annual axle load spectra. 

An additional computational provision was made for sites with many years of reliable annual 

axle load spectra. It is possible to utilize two base annual spectra for the traffic load projection 

on one site. The first base spectrum can be used to represent traffic loads during the years before 

the installation of a WIM scale and during the initial operation of the scale; the second spectrum 

can be used to represent traffic loads for the most recent years with and without WIM scale data. 

Both the first and the second base annual spectra can be the averages of several annual spectra. 

However, this provision was not used for any LTPP site because its use was not warranted by 

quality of available axle load data. 

Mean of all Available Annual Axle Load Spectra

A mean of all available annual axle load spectra was used if the mean spectrum was considered 

to be the best representation of traffic loads for the given site. An example of annual axle load 

spectra that were averaged to obtain the base annual spectrum is shown in figure 13 (for 

California site 063042, located on Interstate 5 south of Sacramento, CA). The base annual 

spectrum was obtained as a mean value of the annual spectra for 1990 to 1998, inclusively. 
46 

Mean of Selected Annual Axle Load Spectra

A mean of selected annual axle load spectra was used if some of the available annual spectra 

were considered to be outliers (for example, because of the large percentage of very excessive 

loads, or because the expected loaded and unloaded peaks were not present) and could not be 

used to determine the base annual spectrum. The base spectrum was calculated as the mean of 

the remaining spectra. An example of establishing the base annual spectrum as a mean of 

selected annual spectra is shown in figure 14 (for site 185518 on a rural interstate in Indiana). 

The base annual spectrum for the site in figure 14 was calculated as the mean of annual axle load 

spectra for three years (1991, 1992, and 1995). The truck class distribution for the site was 

examined and was considered to be stable throughout the monitoring years. Consequently, the 

annual spectra were expected to be similar for all monitoring years. 
47 

Specific reasons that several of the annual axle load spectra shown in figure 14 were not used for 

calculating the base annual spectrum were: 

• 1993 spectrum: The spectrum is flat and contains a relatively large number of overloaded 

tandem axles; the spectrum for single axle loads has no peak. 

• 1994 spectrum: The spectrum is on the margin of being acceptable, and it is possible that 

other analysts would include this spectrum in the calculation of the base annual spectrum. 

However, it includes a relatively large number of tandem axle overloads (tandem axle 

weights exceeding 15,436 kg (34,000 lb)). Adding the 1994 spectra—to the already 

selected spectra for 1991, 1992, and 1995—would have only a small influence on the 

resulting base annual spectrum and on the cumulative axle loads. 

• 1996, 1997, and 1998 spectra: The peak for single axles is below 4,540 kg (10,000 lb), 

and there are no peaks for tandem axles corresponding to the loaded and unloaded axles. 

Another example of establishing the base annual spectrum as a mean of selected spectra is 

provided in figure 8, showing five annual axle load spectra for Mississippi site 285805. Initially, 

the base spectrum was calculated as the mean of 1992, 1993, 1994, and 1995 spectra. The 1996 

spectrum was not used for the initial projection because it was considered to be an outlier. Its 

peak for the loaded tandem axles was about 2,270 kg (5,000 lb) higher than the peaks of the 

other four annual spectra (14,528 kg (32,000 lb) compared to 12,228 kg (27,000 lb)). However, 

in response to a subsequent review carried out by a representative of the Mississippi DOT, the 

1996 annual spectrum was included in the calculation of the reviewed base annual spectrum. 

The utilization of review comments received by the participating agencies is discussed in step 8 

(“Implementation of Review Comments Received from Participating Agencies”). 

Selection of One or Two Annual Axle Load Spectra

Many sites had only one or two annual axle load spectra that were considered suitable for the 

development of base annual spectrum. An example of such a situation is provided in figure 15 

for site 124057, located on a rural Interstate 75 east of Tampa, FL. From the eight annual spectra 

available for this site, only axle load spectra for 1991 and 1992 were used for the projection. The 

reasons for the rejection of the remaining spectra (for years 1993 through 1998) were similar to 

those given for the rejection of the spectra in figure 14. 

The 1991 and 1992 spectra have similar shapes but have different magnitudes: the 1992 

spectrum is based on a much smaller number of trucks than the 1991 spectrum. Because the 

projection process utilizes the normalized spectra, the difference in magnitude does not influence 

the calculation of annual base spectra. It should be pointed out that the allowable tandem axle 

load in Florida is 19,976 kg (44,000 lb)even though the maximum allowable gross vehicle 

weight is still 36,320 kg (80,000 lb). Consequently, the 1991 and 1992 spectra (given in figure 

15) used for the development of base annual spectra do not contain many overloads. 

Rejection of All Available Annual Axle Load Spectra

For many sites, all available monitoring annual axle load spectra were judged to be inappropriate 

for the development of base annual spectra, and thus for the projection of traffic loads. The main 
48 

consideration in not using the available monitoring data was the possibility that their use would 

result in a larger error in the projected traffic loads than the use of surrogate data such as siterelated, regional, or generic axle load spectra. See figure 16 for an example of a site for which 

all available spectra were rejected. 
49 

063042 Annual Load Spectra 

Figure 13. Use of the mean of all annual axle load spectra to obtain base annual spectrum. 

1 lb = 2.202 kg
50 

185518 Annual Load Spectra 

Figure 14. Use of the mean of 1991, 1992, and 1995 annual axle load spectra to obtain base 

annual spectrum. 

1 lb = 2.202 kg
51 

124057 Annual Load Spectra 

Figure 15. Use of the mean of 1991 and 1992 annual axle load spectra to obtain base annual 

spectrum. 

1 lb = 2.202 kg
52 

473104 Annual Load Spectra 

Figure 16. Rejection of all available annual axle load spectra. 

Triple Axle

0

5

10

15

20

25

0

5000

100001500020000250003000035000400004500050000550006000065000700007500080000850009000095000

100000

Axle Loads, lb

Axle Counts

1992 1993 1994 1995 1996

Single Axle

0

500

1000

1500

2000

2500

3000

0

2000400060008000

10000120001400016000180002000022000240002600028000300003200034000360003800040000

Axle Loads, lb

Axle Counts

Tandem Axle

0

20

40

60

80

100

120

140

160

180

200

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100001500020000250003000035000400004500050000550006000065000700007500080000

Axle Loads, lb

Axle Counts

1 lb = 2.202 kg
53 

Figure 16 shows five annual axle load spectra for site 473104 in Tennessee located on a rural 

major collector highway. The 1995 and 1996 spectra contain an unreasonable number of 

overloaded tandem axles. The main problem with the 1992, 1993, 1994, and 1995 spectra is 

probably the small number of observations (weighted trucks) used to develop the spectra. 

Additional information on the indicators used to decide whether individual axle load spectra 

were suitable for the projection of traffic loads is discussed next. 

Assignment of Initial Projection Confidence Codes 

The purpose of developing projection confidence codes was to characterize the uncertainty 

associated with traffic load projections. Such characterization is useful for the development of 

pavement performance models and for the development of pavement design procedures that 

incorporate reliability concepts. 

The level of confidence in the initial traffic projections has been expressed in the form of initial 

traffic projection codes that have been assigned to all LTPP sites. The following three codes 

were used to characterize the level of confidence associated with initial traffic projection results: 

• IA—Initial acceptable projection results. 

• IQ—Initial questionable projection results. 

• IN—Initial not available projection results. 

The traffic projections were classified according to the confidence codes to provide guidance to 

the pavement analyst regarding overall expected accuracy of traffic projections. However, the 

actual accuracy of traffic projections is unknown because the actual traffic loads that went over 

the sites during the time the pavement was in service is not known. To provide guidance to the 

pavement analyst, the following approximate interpretation of the initial projection confidence 

codes has been provided: 

• IA—Cumulative ESAL estimates are probably within ±50 percent of the actual 

cumulative ESAL values. 

• IQ—Cumulative ESAL estimates are probably off by more than ±50 percent from the 

actual cumulative ESAL values, but are probably better than the estimates based entirely 

on surrogate (regional or generic) data. Cumulative ESAL estimates are probably within 

±100 percent of actual ESAL values. 

• IN—Axle load estimates could not be provided at this time. 

The initial traffic projection codes were assigned subjectively using engineering judgment and 

considering: 

• Site-specific historical and monitoring traffic data. 

• Agencywide historical and monitoring traffic data. 

• Other data such as highway location, functional class, and total number of lanes. 
54 

Only three traffic projection codes were used because of uncertainties inherent in the traffic 

projection process caused by the amount and quality of traffic data. Traffic data available for the 

projection of traffic loads received only a cursory QC and QA review, and include good, 

marginal, and erroneous data. 

The initial projection of traffic loads and the assignment of the projection codes were carried out 

for all sites within the agency at the same time. The projection process utilized agencywide 

trends and similarities in historical and monitoring data. This was achieved, for example, by 

comparing traffic loads reported for nearby sites (as shown in table 3) and similar sites, or by 

comparing the match between historical and monitoring truck volumes, or between historical and 

monitoring TFs, for all sites. When weighing the information provided by historical and 

monitoring data, the extent, consistency, and quality of the agency’s historical monitoring data 

were taken into account. 

An agency’s consistency and reliability in providing historical traffic load estimates, particularly 

estimates that were in good agreement with the subsequent monitoring data, imparted additional 

confidence in the traffic projections. For many sites, the number of historical years exceeded the 

number of monitoring years, so the reliability of historical traffic estimates played an important 

role in assigning projection confidence codes. For example, figure 5 shows that for site 285805 

there were 17 historical years (1975 to 1991) and 7 monitoring years (1992 to 1998, even though 

1997 and 1998 had no monitoring data). The reliability of cumulative traffic load estimates 

(from 1975 to 1998) at this site depends also on the reliability of historical data. 

The level of confidence was linked to the accuracy of traffic projections even though the 

accuracy of traffic projections cannot be determined and will remain unknown. It is believed 

that the comparison of the projected traffic loads with the actual expected traffic loads is more 

meaningful than the comparisons of the projected traffic loads with a relative reference, such as 

the estimated traffic loads based on monitoring data, because the relative reference also may be 

subject to error. 

The level of confidence was related to the cumulative ESALs. Cumulative rather than annual 

ESALs were used to avoid the influence of annual variation in traffic loads that can occur by 

chance alone, or can be caused by other reasons, such as special events. 

The assignment of the initial projection codes using judgment was a complex task that used 

subjective interpretation of all relevant information provided by historical and monitoring data. 

To minimize the subjectivity involved in assigning the confidence codes for the initial traffic 

projections, researchers ensured that: 

• The assignment of codes was based on guidelines. 

• At least two project team members were involved in assigning codes for each section. 

The experience with pavement condition rating (which is also done subjectively using 

guidelines) indicates that the use of multiple raters has a positive influence on the 

variability of pavement condition rating.[14]
55 

The guidelines for assigning projection confidence codes described in the following section are 

organized under the main headings of “Guidelines for Assigning IA, IN, and IQ Codes,” 

respectively, and under the subheadings of “Location, Truck Volumes, Truck Classification, and 

Axle Weights.” However, the assignment of the projection confidence codes considered 

simultaneously all traffic data characteristics and was done simultaneously with the traffic data 

assessment and traffic projection activities. The guidelines reflect the knowledge acquired 

during the course of this study and information obtained from the feedback on the initial traffic 

projections received from the participating agencies. The feedback information received from 

the participating agencies is discussed in step 7 (“Review of LTPP...Packages by Participating 

Agencies”). 

Guidelines for Assigning IA Codes

The IA code was assigned to LTPP sections with site-specific traffic volume and axle weight 

data where it was judged that the cumulative ESALs were probably within ±50 percent of the 

actual cumulative ESALs. Typical requirements for assigning IA codes included the following 

conditions. 

Location—There was an agreement between the description of the site location and the position 

of the site when plotted on a highway map. Ambiguity regarding the site location did not play a 

decisive role in assigning the projection codes for any of the LTPP sites because any ambiguity 

was addressed during the traffic data assessment process. 

There was a logical agreement between traffic characteristics (e.g., truck volumes, TF, and truck 

growth) on nearby sites. For example, truck volumes on nearby sites were similar and tended to 

increase with the proximity to large cities. An example comparing traffic characteristics for 

nearby sites is provided in table 3. 

Truck Volumes—There were no large discrepancies between the historical and monitoring 

trucks volumes. The truck volume projection model—the model estimating the total annual 

number of trucks for each year since the highway was opened to traffic—fit the annual historical 

and monitoring truck volumes within a typical range of about ±50 percent. Outliers were 

permitted where it was felt that the historical or monitoring data were probably in error. An 

example of a truck volume projection model that contains an outlier is shown in figure 17. 

Truck Classification—the distribution of trucks into the 13 FHWA vehicle classes appeared to be 

reasonable considering: 

• The functional class of the highway. For example, it was expected that about 50 to 80 

percent of all trucks on rural interstates would consist of five-axle single trailer trucks 

(vehicle Class 9). 

• Monitoring truck class distribution obtained for different years. It was expected that the 

truck class distribution for the major truck classes such as Class 5 and Class 9 vehicles 

(table 1) would not vary by more than about ±25 percent from year to year (particularly 

on highways with daily truck volumes exceeding about 300 trucks). 
56 

• Relatively small number of unclassified vehicles. Typically, the number of unclassified 

vehicles did not exceed about 15 percent of all vehicles. 

AADT Truck Volumes Projected Growth 

Year Historical Monitoring Projected Percentage Factor 

1986 529 – 735 – 0.29 

1987 716 – 808 10.0 0.32 

1988 780 – 889 10.0 0.35 

1989 1348 – 978 10.0 0.38 

1990 1162 – 1076 10.0 0.42 

1991 – 3812 1183 10.0 0.46 

1992 – 1302 1302 10.0 0.51 

1993 – 1417 1367 5.0 0.53 

1994 1234 1813 1435 5.0 0.56 

1995 – 792 1507 5.0 0.59 

1996 – 1061 1582 5.0 0.62 

1997 – 770 1661 5.0 0.65 

1998 – 889 1744 5.0 0.68 

Figure 17. Projected AADT truck volumes for site 124057. 
57 

Axle Weights—The distribution of axle weights appeared to be reasonable considering: 

• There was a basic correspondence between the truck class distribution and the truck axle 

load spectra. For example, for the sites with many two-axle trucks, the predominant 

component of the axle load spectra was expected to be single axles. 

• There was at least one year for which axle load spectra were considered to be appropriate. 

Typical characteristics of acceptable axle load spectra are summarized in table 6, and 

included: 

- Single axles had a predominant peak at about 4,540 to 5,448 kg (10,000 to 12,000 

lb), and a secondary peak (when warranted by the truck classification data 

indicating the presence of vehicles with single axles carrying payload) at about 

7,264 to 8,172 kg (16,000 to 18,000 lb). 

- Tandem axles had the loaded peak at about 14,074 to 14,982 kg (31,000 to 33,000 

lb) for agencies with the allowable axle weight for tandem axles of 15,436 kg 

(34,000 lb). The unloaded peak was about 3,178 to 5,448 kg (7,000 to 12,000 lb). 

- Triple axles had the peak at about 18,160 kg (40,000 lb), and their number was 

commensurable with the occurrence of six-and-more axle trucks. Based on the 

Bridge Gross Weight Formula, for the triple axle spacing exceeding 2.44 m (8 ft), 

the allowable axle weight is 19,068 kg (42,000 lb).[13]

• If the axle load spectra were available for more than 2 years, the requirements for the peak 

axle weights to reach the specific ranges were relaxed because the base annual spectra 

were calculated as an average of all annual spectra that were considered acceptable. 

• For agencies that provided apparently good estimates of historical truck volumes and TFs, 

the monitoring TFs were typically within ±50 percent of the historical TFs. 

For a few sites, the quality of axle weight data was also evaluated by examining the distribution 

of gross vehicle weights (GVW) for Class 9 vehicles (5-axle single trailer trucks). The logic 

underlying the QC process utilizing the GVW distribution is that many Class 9 trucks operate 

either unloaded or loaded, and if loaded their GVW is close to the maximum allowable GVW of 

36,320 kg (80,000 lb). Thus, a typical distribution of the GVW of Class 9 vehicles is expected to 

have two peaks, the first peak, associated with unloaded vehicles, at about 12,712 to 16,344 kg 

(28,000 to 36,000 lb), and the second peak, associated with loaded vehicles, at 31,780 to 35,412 

kg (70,000 to 78,000 lb). 

The reasons for not using GVW of Class 9 vehicles more extensively included: 

• For sites where Class 9 vehicles predominate (for example on rural interstates and on 

other major rural highways), the plots of single and tandem axle load distributions are 

governed by the presence of the Class 9 vehicles already (and convey similar information 

as the GVW plots). 

• For sites where Class 9 vehicles do not dominate (for example on some urban highways 

and minor rural roads), the knowledge of the GVW distribution of Class 9 vehicles is not 

critical for assessing the quality of axle weight data. 

• The weights of the individual vehicles are not stored in the IMS database and the 

production of the GVW plots would require considerable additional analytical effort. 
58 

The use of the GWV distribution of Class 9 vehicles is recommended in the LTPP Traffic QC 

User’s Guide as the basic QA process for the operation of WIM scales.[12]

Regardless of the use of the GVW of Class 9 vehicles, there are fundamental differences between 

(a) the QA process using the GVW of Class 9 vehicles recommended by the user’s guide and (b) 

the traffic data assessment process used to carry out traffic projection and the assignment of the 

projection confidence codes. The fundamental differences are in the timing, outcome, and length 

of the time period for which the data were assessed as outlined previously. It appears that the 

many axle load data stored in the IMS have not been subjected to the QC process recommended 

by the LTPP Traffic QC User’s Guide. 

Example of Assigning IA Code—The following example summarizes some considerations 

involved in assigning IA codes. Many considerations also apply to traffic data assessment and 

traffic projection activities done in unison with the assignment of projection confidence codes. 

The appearance of annual axle load spectra shown in figure 13 was one reason the IA code was 

assigned to the traffic projections carried out for site 063042. The nine annual axle load spectra 

for this site (for 1990 to 1998, inclusive) show a consistent downward trend, illustrated by the 

plot of average ESALs per truck (figure 18). 

According to figure 18, the average ESALs per truck gradually declined between 1990 and 1998 

from 1.8 to about 1.3 per truck. However, because there has been a significant increase in truck 

volume at the same time, the total annual number of ESALs has remained relatively constant. 

The reason the decline appears to be real (not caused by a WIM scale calibration drift) is based 

on the observation that axle weights that are not sensitive to payload have remained relatively 

unchanged, whereas the weight of loaded axles has gradually declined. For example, the second 

peak of the single axle load spectra (top chart in figure 13) can be attributed principally to the 

steering axle of heavy trucks. The load on the steering axle of these trucks is relatively 

insensitive to the load carried and has remained substantially unchanged over recent years. 

Similarly, the first peak for tandem axles (middle chart in figure 13), which corresponds to 

unloaded tandem axles, has also remained unchanged. On the other hand, the second peak for 

tandem axles, corresponding to loaded payload-carrying tandem axles, gradually declined. 

It is possible that the historical decline in axle weights reflected in the decline in ESALs per 

truck is the result of deregulation process of the motor carrier industry and the emergence of lowweight high-value freight.[15]
59 

063042 Annual Traffic Projection Sheet 

Availability of Monitoring Data 

Data Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 Total 

Days 0 153 127 79 78 161 170 163 135 931 

AVC Month 12 8 10 11 – – – – – 41 

Days 34 185 66 85 85 164 174 165 140 958 

WIM Month 5 12 10 11 – – – – – 38 

Figure 18. Annual Traffic Projection Sheet for site 063042. 
60 

Additional considerations that lead to the assignment of the IA code included: 

• Three well-defined peaks for annual load spectra for single axles (first part of figure 13). 

The first peak at about 1,816 kg (4,000 lb) corresponds to Class 5 vehicles (2-axle single 

unit trucks), the second peak at about 4,994 kg (11,000 lb) to steering axles of Class 9 

vehicles, and the third peak corresponds to load axles of Class 9 vehicle consisting of a 3-

axle single unit truck pulling a 2-axle trailer (a truck type common on some highways in 

California). 

• Clearly defined and logical trend in historical and monitoring AADT truck volumes (first 

part of figure 18). 

• Correspondence between historical and monitoring TFs (second part of figure 18). 

• Relatively small number of triple axles that had been gradually increasing over the years.

Guidelines for Assigning IN Codes 

The IN code means that the axle load projections were not carried out at this time because of lack 

of site-specific axle load data. For some sites, the IN code was assigned because there were no 

site-specific axle load data. For other sites, there were axle load data, but the data were 

questionable to the degree that it appeared probable that better traffic load estimates could be 

provided by using surrogate (regional or generic) axle load data rather than by using the 

available site-specific axle load data. However, for all sites with historical or monitoring truck 

volume data, but without axle weight data, annual truck volume projections were still carried out 

for all in-service years. The exceptions were four Specific Pavement Section (SPS)-8 sites 

(environmental sites with little or no truck traffic). 

Because both truck volumes and axle load data were used for the projection of traffic loads, a site 

could be assigned the IN code due to inadequacies in: (a) truck volume data alone; (b) axle load 

data alone; or (c) the combination of truck volume and axle load data. However, the situations 

where the IN code was assigned primarily because of inadequacies in truck volume data were 

rare. The typical reason for assigning the IN code was the combination of truck volume and axle 

load data inadequacies, with the inadequacies in axle load data predominating. 

The guidelines for assigning IN code do not enumerate all possible combinations of truck 

volume, truck distribution, and axle load data inadequacies. The principal consideration in 

assigning the IN code was the judgment whether the traffic projection using the available sitespecific data would be likely to provide better results than could be obtained using surrogate 

traffic data. 

Truck Volume—Typical problems encountered included large differences between historical and 

monitoring truck volumes and/or large variation in monitoring truck volumes, making the 

estimates of annual truck volumes unreliable. 
61 

Truck Classification—The truck distribution could exhibit any combination of the following 

problems: 

• Unexpected truck distribution considering the highway functional type. For example, a 

higher percentage of four-or-less-axle single trailer trucks (class 8) than of five-axle 

single trailer trucks (class 9) on a rural interstate. 

• Highly variable annual truck distributions, with no single annual distribution that could 

be identified as being correct or expected. 

• A large number of unclassified vehicles, sometimes exceeding 50 percent. 

Axle Weights—Conditions that characterized axle load spectra that were considered to be 

inadequate, and that were not used for the projection, included: 

• Disjointed truck class distribution and axle load distribution data. For example, axle load 

spectra for single and tandem axles would appear as flat lines while the truck class 

distribution would indicate that the predominant truck type was Class 9. 

• A large variation in annual axle load spectra, but without a year for which the annual axle 

load spectrum could be considered appropriate. For example, for a 4-lane highway, an 

annual tandem axle load spectrum would have only 10 percent of axles weighing more 

than 6,810 kg (15,000 lb) one year, whereas the next year 50 percent of tandem axles 

would be more than 15,463 kg (34,000 lb). 

• Monitoring TFs appeared to be unreasonable judging by (a) historical TFs and (b) TFs 

obtained on similar sites. For example, a monitoring TF for an interstate would be less 

than 0.2, while the corresponding historical TF would be 1.2 (reported by an agency that 

provided historical TFs that have been, in general, in good agreement with monitoring 

TFs), and the monitoring TFs obtained for similar sites (for the same agency and the 

same pavement type) were in the range of 0.8 to 1.3. 

Example of Assigning IN Code—The shape of the annual axle load spectra presented in figure 

16 was the main reason the IN code was assigned to site 473104 and no axle load projections 

were done for this site. The annual axle load spectra shown in figure 16 exhibit considerable 

variation. For example, the 1992 TF was about 0.1, while the 1996 factor was about 4.0. Truck 

volume projections were still carried out and are shown in figure 19. 

Guidelines for Assigning IQ Codes 

The IQ code was assigned to sites with traffic data characteristics falling between IA and IN 

characteristics. In other words, the projected traffic loads were probably better than the estimates 

based entirely on surrogate axle load data but not as good as the IA results. Cumulative ESAL 

estimates were probably typically within ±100 percent of the actual ESALs. 

Example of Assigning IQ Code—The assignment of IQ codes is illustrated using examples for 

sites 285805 (figure 8) and 124057 (figure 15). 

The main reason for assigning the IQ code to site 285805 was inconsistencies in annual axle load 

spectra. The peak of single axle load spectra for all years was below 4,540 kg (10,000 lb) (figure 
62 

8), even though about 65 percent of all trucks were Class 9 trucks (figure 7). It was unclear 

whether the 1996 tandem axle load spectra were better than the spectra for the remaining years 

(1992 to 1995). The annual truck traffic projection model followed the trend set by the 

monitoring truck volumes (figure 6). 

The shape of the annual axle load spectra for site 124057, presented in figure 15, was not the 

main reason the IQ code was assigned to this site. The 1991 and 1992 spectra appear to be 

reasonable even though their peaks for single axle loads at about 3,632 kg (8,000 lb) were lower 

than expected. The main reason was the uncertainty regarding the projection of truck volumes 

(see figure 17). According to figure 17, the initial truck volume projection was based on 

historical data and on estimated monitoring data only. The monitoring truck volumes show a 

decline between 1992 and 1998. 

Concluding Remarks 

The projection confidence codes are subjectively assigned indicators of the reliability of traffic 

load estimates. The codes may change if more data become available, or a different 

interpretation of the data is made. The initial codes may also change after the initial traffic 

projections are reviewed by the agencies. For example, the initial IQ code for site 285805 was 

changed to IA based on the review of the initial projection provided by a representative of the 

Mississippi DOT. The review confirmed the legitimacy of 1996 axle load spectra (figure 8) and 

the appropriateness of the initial truck volume projection (figure 6). 

Computation of Annual Axle Load Spectra

The computation of annual axle load spectra was carried out using a procedure outlined by 

equation 1. In addition, for QC purposes, annual ESALs and cumulative ESALs were also 

calculated using projected axle load spectra and compared with historical and monitoring 

ESALs, as shown in figure 10. 

Step 6—Review of LTPP Traffic Feedback and Resolution Packages by RCOs 

The collection of traffic data on LTPP sites has been the responsibility of the participating 

agencies. However, throughout the LTPP program, RCOs have been responsible for many 

activities concerning traffic data, such as: 

• Communicating the LTPP traffic data collection requirements to the participating 

agencies. 

• Providing technical assistance to the agencies regarding traffic data collection and traffic 

data handling and reporting. 

• Providing QA for traffic data supplied by the participating agencies. 

• Inputting and storing traffic data in the regional traffic database. 
63 

AADT Truck Volumes Projected Growth 

Year Historical Monitoring Projected Percentage Factor 

1986 5 – 6 – 0.91 

1987 10 – 6 5.1 0.96 

1988 6 – 7 5.3 1.01 

1989 6 – 7 5.6 1.07 

1990 7 – 8 5.8 1.13 

1991 8 – 8 6.0 1.20 

1992 9 2 9 6.3 1.27 

1993 – 9 9 6.5 1.36 

1994 – 9 10 6.7 1.45 

1995 – 6 10 6.9 1.55 

1996 – 12 11 7.1 1.66 

1997 – – 12 7.3 1.78 

1998 OUT OF STUDY: 02/07/1997 

Figure 19. Projected AADT truck volumes (initial) for site 473104. 

Because RCOs are responsible for communicating with participating agencies and have detailed 

knowledge of local traffic data and issues acquired through long-standing cooperation with local 

agencies, the LTPP Traffic Feedback and Resolution Packages were first sent to RCOs for 

review and comments. Representatives of RCOs reviewed the packages, often providing written 

comments to the project team, and chose one of the following alternatives: 

• Sending the package to the agency with a standard introductory letter that asked the 

agency to review the package and respond to questions. The introductory letter became 

part of the package as outlined in the section describing the content of the package. 

About 60 percent of the packages were sent this way. 

• Sending the package to the agency using an introductory letter that provided additional 

information to the agency regarding traffic data. Typically, this information was about 

traffic data that were not included in site-specific reports (because the data had not yet 

reached the IMS database). Some RCOs also included handwritten notes on the packages. 
64 

• Asking the project team for explanation or for modifications in the package before 

sending it to the agencies. Typically, modifications requested by RCOs concerned the 

initial overall feedback and resolution report. About 15 percent of the packages were 

modified by the project team after initial submission to the RCOs. Once the explanation 

and modifications were provided to the satisfaction of the RCOs, the RCOs sent the 

packages to the participating agencies for review. 

Step 7—Review of LTPP Traffic Feedback and Resolution Packages by Participating 

Agencies 

It was recognized early in Phase 1 that the involvement of the participating agencies in the traffic 

data assessment and traffic load projection process would be essential for meeting the project 

objectives. Specific issues requiring local agency involvement included trends in annual 

historical and monitoring truck volumes, changes in local traffic patterns due to changes in 

highway network, influence of predominant single-commodity traffic, existence of local truck 

weight and size regulations and truck operating permits, and the availability of local QA/QC 

information. 

All LTPP Traffic Feedback and Resolution Packages were sent to the participating agencies by 

the end of March 2001. By the end of May 2001, the project team received responses from about 

40 percent of participating agencies. These responses varied considerably. Some reviews may 

not have been as useful as was originally anticipated, while others were very useful, insightful, 

and even included additional supplemental traffic data. 

Representatives of the participating agencies were asked specifically to respond to two items of 

the LTPP Traffic Feedback and Resolution Package: the initial overall feedback and resolution 

report and the Blue Sheet (figure 3) that was part of the initial site-specific report. 

The initial overall feedback and resolution report contained general questions regarding the 

procedures used to classify vehicles and to measure axle loads; responses varied considerably. 

Regarding vehicle classification, some agencies assigned unclassified vehicles proportionally to 

all vehicle classes unless there was a clear indication that a Class 14 vehicle belonged to a 

particular class. Other agencies simply reported the existence of Class 14 vehicles. Regarding 

WIM calibration procedures, some agencies attempt to carry out periodic verification of the 

accuracy of WIM scales, while others had not calibrated their scales beyond the initial 

calibration. Some agencies reviewed traffic data before their submission to RCOs and some did 

not. 

The Blue Sheet was the main means of communication between the participating agencies and 

the project team; it contains seven parts (figure 3): 

• The first part solicits additional information on truck volumes. 

• The second part concerns the existence of single commodity traffic on the LTPP site. 

With very few exceptions, the LTPP sites were not exposed to single or predominant 

commodity traffic. 
65 

• The third part enquires about the existence of additional traffic data, other than the one 

submitted to LTPP, that may be useful for traffic data assessment and projection of 

traffic loads. This question was probably not specific enough because most respondents 

answered that no additional data existed. However, most agencies have extensive traffic 

databases that may yield useful information. 

• The fourth part asked the agencies to comment on four specific traffic data 

characteristics. These characteristics were selected to provide an overall assessment of 

traffic loads and to motivate the reviewer to compare the reported LTPP data with data 

from other sources. The question regarding the 1998 truck percentage was included to 

obtain verification of truck volumes based on AADT volumes. 

• The fifth part was used to solicit an agency’s response to specific observations and 

questions. For some sites, the responses to the observations and questions were used to 

change the initial projection confidence codes. 

• The sixth part asked the reviewer to provide an opinion regarding the accuracy of 

historical and monitoring traffic data. The respondent for site 285805 (figure 3) felt that 

the accuracy of both the historical and monitoring traffic data were similar (good). 

• The seventh part solicited the reviewer’s opinion for improving the quality of traffic data. 

Typical responses included the installation of new traffic data collection equipment and 

the calibration of WIM scales. 

Step 8—Implementation of Review Comments Received from Participating Agencies 

This last activity consisted of three tasks: 

• Responding to agencies. 

• Preparation of revised projections. 

• Placing traffic data into computed parameter tables. 

Responding to Agencies 

A response letter was prepared for each agency that provided a review of the LTPP Traffic 

Feedback and Resolution Package. The response letter included a listing of specific changes, if 

any, that were made to the initial projections based on the agency’s recommendations. 

Preparation of Revised Projections 

The preparation of revised projections also included the assignment of revised projection 

confidence codes. The revised projection confidence codes were assigned using the same 

guidelines as those used for the assignment of the initial projection confidence codes, but also 

utilized information supplied by the participating agencies. 
66 

Based on the comments received from the agencies, about 10 percent of the initial projections 

were revised and about 15 percent of the initial projection confidence codes were changed. 

Typically, the change was from “questionable” to “acceptable” code. 

Placing Projected Traffic Data into Computed Parameter Tables 

The projected traffic data that were placed into the computed parameter tables (these tables will 

become a part of the IMS database) included traffic projections with both acceptable and 

questionable projection confidence codes, whether or not they had been reviewed by the 

participating agencies. The projection confidence code and the review status information were 

included in the database. The description of computed parameter tables used to store the 

projected traffic data is provided in chapter 4. 
67 

CHAPTER 3. RESULTS OF DATA ASSESSMENT AND TRAFFIC PROJECTION 

This chapter describes the results of traffic data assessment and traffic projection activities 

carried out using the procedures presented in chapter 2. The following are provided: 

• Projected axle load spectra for all in-service years. The main product of the study is 

traffic axle load projections that were developed for all in-service years for 558 of the 

890 LTPP traffic sites processed during the study. The axle load projections were 

expressed as annual axle load spectra for single, tandem, and triple axles, and were 

placed into the IMS computed parameter tables. Storage of the projected axle load 

spectra in the computed parameter tables and computational procedures used to generate 

the spectra are described in chapter 4. 

• Summary results. Summary results of the traffic data assessment and projection process 

are described in this chapter. The objective of presenting the summary results is to 

provide information on the overall scope and quality of LTPP traffic data, to summarize 

the availability of traffic projections for different categories of sites, and to describe 

historical trends regarding the amount and quality of monitoring traffic data. 

• Summary results for individual sites. Summary results for each of the 890 LTPP traffic 

sites processed during the study are provided in appendix A. Appendix A contains a 

table that provides, for each of the 890 LTPP traffic sites, site-specific information on the 

availability of traffic data, traffic projection results, and traffic characteristics. 

Availability of Traffic Projections 

The traffic data assessment process distinguished between the traffic data that were included in 

the IMS database and the traffic data that were of sufficient quality to be used for the projection. 

Figure 20 compares the distribution of the 890 sites according to: (a) data presence in the 

database; (b) data availability for the projection; and (c) data quality. For the total of 890 LTPP 

sites, 695 (78 percent) had some monitoring axle weight, vehicle class, and truck volume data in 

the database. However, of the 695 sites, only 543 (61 percent of the total of 890 sites) had data 

of sufficient quality to be used for the projection of axle loads. Of these 543 sites, only 194 sites 

(21.8 percent of the total 890 sites) had traffic load projections that were assigned an acceptable 

projection confidence code. 

For 650 General Pavement Section (GPS) sites, 650 sites had monitoring axle weight data in the 

database, and 511 sites had data suitable for the projection. Of the 99 SPS sites, 45 had 

monitoring axle weight, vehicle class, and truck volume data in the database; 32 had data of 

sufficient quality to be used for the projection of axle loads; and only 10 sites (of the 99 total 

SPS sites) were judged to have an acceptable projection confidence code. 

For 15 LTPP sites without axle load data in the database, axle load projections were carried out 

using data from adjacent sites. Thus, altogether, axle load projections were carried out for 558 

sites: the 543 sites with their own data of sufficient quality for projections plus the 15 sites for 

which data from adjacent sites were used. 
68 

Data Presence 

a. Data presence total sites. b. Data presence GPS sites. c. Data presence SPS sites. 

Data Usability for Projection 

d. Data usability total sites. e. Data usability GPS sites. f. Data usability SPS sites. 

Data Quality for Projection

g. Data quality total sites. h. Data quality GPS sites. i. Data quality SPS sites. 

Figure 20. Summary of LTPP sites sorted by data presence and data usability for projection. 
69 

The availability of traffic projections for the LTPP sites is also summarized in table 7. Of the 

890 sites, 194 (21.8 percent) were assigned an acceptable projection confidence code and 364 

(40.9 percent) were assigned a questionable projection confidence. For 269 sites (30.2 percent), 

only the projection of annual truck volumes was provided, and for 63 sites (7.1 percent), no 

traffic projections were carried out because of lack of data. 

Table 7. Availability of traffic projections. 

All Sites All Sites GPS SPS Projection 

Result No. of 

Sites % No. of Sites % No. of Sites % No. of Sites % 

Acceptable 194 21.8 184 23.3 10 10.1 

Questionable 364 40.9 

558 62.7 

342 43.2 22 22.2 

Truck Volume 269 30.2 255 32.2 14 14.1 

None 63 7.1 

332 37.3 

10 1.3 53 53.5 

TOTAL 890 100 890 100 791 100 99 100 

Projection Results: 

Acceptable = projection confidence code was “Initial Acceptable” or “Reviewed Acceptable”; 

Questionable = projection confidence code was “Initial Questionable” or “Reviewed Questionable”; 

Truck Volume = only the projection of annual truck volumes was carried out; 

None = no traffic projection was carried out. 

Considering that only about 23 percent of all GPS sites and about 10 percent of all SPS sites had 

acceptable axle load estimates, greater attention should be paid to the quality of traffic data. 

About 37 percent (332 sites) of all sites had insufficient site-specific truck class and/or axle 

weight distribution data to carry out axle load projections. To obtain axle load estimates for 

these 332 sites, new traffic monitoring data must be collected or surrogate data must be used. 

Chapter 5 outlines the proposed LTPP PLG that would facilitate the projection of traffic loads 

for these sites. 

The projection results provided here are for LTPP sites with unique traffic identification 

numbers. The total number of all LTPP sections is larger than the number of the LTPP sites

because one site may provide traffic data for several sections. 

Table 8 shows the availability of traffic projections by the experiment type and the projection 

confidence code. For the GPS experiment types with more the 10 sites, the percentage of sites 

without axle load projections (projection confidence code N) ranges from 16.1 percent for GPS7B to 40.8 percent for GPS-6A. As already indicated in figure 20, the percentage of sites 

without traffic projections is higher for SPS experiments than for GPS experiments. 
70 

Table 8. Availability of traffic projections by experiment type and projection confidence code. 

Projection Confidence Code 

Experiment A Q N 

Type 

Total 

Number 

of Sites Number 

of Sites 

Percent 

of Sites 

Number 

of Sites

Percent 

of Sites 

Number 

of Sites 

Percent 

of Sites 

GPS-1 161 29 18.0 69 42.9 63 39.1 

GPS-2 90 11 12.2 43 47.8 36 40.0 

GPS-3 129 42 32.6 46 35.7 41 31.8 

GPS-4 53 14 26.4 29 54.7 10 18.9 

GPS-5 72 21 29.2 31 43.1 20 27.8 

GPS-6A 49 8 16.3 21 42.9 20 40.8 

GPS-6B 83 12 14.5 38 45.8 33 39.8 

GPS-6C 14 5 35.7 5 35.7 4 28.6 

GPS-6D 4 1 25.0 1 25.0 2 50.0 

GPS-6S 43 12 27.9 17 39.5 14 32.6 

GPS-7A 22 7 31.8 11 50.0 4 18.2 

GPS-7B 31 11 35.5 15 48.4 5 16.1 

GPS-7C 1 1 100.0 – – – – 

GPS-7D 2 – – 2 100.0 – – 

GPS-7S 11 4 36.4 4 36.4 3 27.3 

GPS-9 26 6 23.1 10 38.5 10 38.5 

SPS-1 18 2 11.1 5 27.8 11 61.1 

SPS-2 13 2 15.4 5 38.5 6 46.2 

SPS-4 2 – – – – 2 100.0 

SPS-5 12 2 16.7 4 33.3 6 50.0 

SPS-6 12 3 25.0 5 41.7 4 33.3 

SPS-7 4 1 25.0 2 50.0 1 25.0 

SPS-8 16 – – – – 16 100.0 

SPS-9 22 – – 1 4.5 21 95.5 

TOTAL 890 194 21.8 364 40.9 332 37.3 

The availability of traffic projections for LTPP sites according to the highway functional 

classification is presented in table 9. Of the 890 total sites, 635 (71.3 percent) are located on 

rural interstates or on rural principal arterial highways. Typically, highway functional classes 

that are expected to have large truck volumes have the higher percentage of sites with the 

acceptable projection confidence code. For example, 34.8 percent of all sites on rural interstates 

had an acceptable projection confidence code, while only 6.9 percent of the sites on rural minor 

arterial highways were in the acceptable category. This trend is likely caused by larger samples 

of trucks weighted on major highways, and by the inclination of the participating agencies to 

install traffic monitoring equipment on major highways rather than on local highways. 

The presence of different types of traffic data for sites located in the four LTPP regions is 

summarized in table 10. More than 70 percent of sites for all LTPP regions had monitoring axle 

weight, truck class, and truck volume data. The availability of traffic projections in the four 
71 

regions is shown in table 11. The highest percentage of sites with acceptable axle load 

projections was in the Western region (29.4 percent); the lowest percentage (12.1 percent) was in 

the Southern region. The percentage of sites without traffic load projection ranged from about 

30 in the North Central region to 45 in the Southern region. 

Table 9. Availability of traffic projections by highway functional class and projection 

confidence codes. 

Projection Confidence Code 

FHWA Highway A Q N 

Functional Class 

Total 

Number of 

Sites Number 

of Sites 

Percent 

of Sites 

Number 

of Sites 

Percent 

of Sites 

Number 

of Sites 

Percent 

of Sites 

Rural Principal 

Arterial—Interstate 282 98 34.8 104 36.9 80 28.4 

Rural Principal 

Arterial—Other 353 62 17.6 157 44.5 134 38.0 

Rural Minor Arterial 72 5 6.9 43 59.7 24 33.3 

Rural Major 

Collector 19 2 10.5 8 42.1 9 47.4 

Rural Minor 

Collector 3 – – 1 33.3 2 66.7 

Rural Local Collector 8 – – – – 8 100.0 

Urban Principal 

Arterial—Interstate 56 13 23.2 23 41.1 20 35.7 

Urban Principal 

Arterial—Other 

Freeways or 

Expressways 

24 6 25.0 8 33.3 10 41.7 

Urban Other 

Principal Arterial 42 7 16.7 19 45.2 16 38.1 

Urban Minor Arterial 4 1 25.0 1 25.0 2 50.0 

Urban Collector 2 – – – – 2 100.0 

Unclassified 25 – – – – 25 100.0 

TOTAL 890 194 21.8 364 40.9 332 37.3 
72 

Table 10. Presence of traffic data by LTPP region. 

Sites with Traffic Data 

Weight, Class, 

and Truck 

Volumes 

LTPP 

Region 

Total 

Number 

of Sites 

Number % 

Only Class 

and Truck 

Volumes 

Only 

Historical 

Truck 

Volumes 

Sites with No 

Traffic Data

North Atlantic 149 117 78.5 2 21 9 

North Central 247 207 83.8 6 12 22 

Southern 290 230 79.3 30 8 22 

Western 204 141 69.1 11 46 6 

TOTAL 890 695 78.1 49 87 59 

Table 11. Availability of traffic projections by LTPP region and projection confidence codes. 

Projection Confidence Code 

LTPP A Q N 

Region 

Total 

Number 

of Sites Number 

of Sites 

Percent 

of Sites 

Number 

of Sites

Percent 

of Sites 

Number 

of Sites 

Percent 

of Sites 

North Atlantic 149 39 26.2 57 38.3 53 35.6 

North Central 247 60 24.3 114 46.2 73 29.6 

Southern 290 35 12.1 125 43.1 130 44.8 

Western 204 60 29.4 68 33.3 76 37.3 

TOTAL 890 194 21.8 364 40.9 332 37.3 

The availability of traffic projections for all participating agencies, in terms of projection 

confidence codes, is summarized in table 12. For agencies with 10 or more LTPP sites, the 

percentage of sites with the acceptable projection confidence code ranged from 1.1 percent in 

Texas to 79.3 percent in Arizona. 
73 

Table 12. Availability of traffic projections by participating agencies and projection 

confidence codes. 

Projection Confidence Code 

A Q N State/Province 

Total 

Number of 

Sites Number of 

Sites 

Percent

of Sites

Number of 

Sites 

Percent 

of Sites 

Number of 

Sites 

Percent

of Sites

Alabama 19 3 15.8 10 52.6 6 31.6 

Alaska 6 – – 6 100.0 – – 

Arizona 29 23 79.3 5 17.2 1 3.4 

Arkansas 19 7 36.8 7 36.8 5 26.3 

California 38 9 23.7 12 31.6 17 44.7 

Colorado 20 1 5.0 8 40.0 11 55.0 

Connecticut 5 1 20.0 2 40.0 2 40.0 

Delaware 7 – – – – 7 100.0 

District of 

Columbia 

1 – – – – 1 100.0 

Florida 33 – – 21 63.6 12 36.4 

Georgia 24 – – 1 4.2 23 95.8 

Hawaii 4 – – – – 4 100.0 

Idaho 13 3 23.1 10 76.9 – – 

Illinois 19 10 52.6 7 36.8 2 10.5 

Indiana 18 6 33.3 9 50.0 3 16.7 

Iowa 16 1 6.3 7 43.8 8 50.0 

Kansas 19 – – 10 52.6 9 47.4 

Kentucky 7 2 28.6 4 57.1 1 14.3 

Louisiana 4 – – 2 50.0 2 50.0 

Maine 8 – – – – 8 100.0 

Maryland 6 1 16.7 – – 5 83.3 

Massachusetts 3 – – 1 33.3 2 66.7 

Michigan 17 2 11.8 13 76.5 2 11.8 

Minnesota 36 21 58.3 8 22.2 7 19.4 

Mississippi 28 14 50.0 8 28.6 6 21.4 

Missouri 27 1 3.7 20 74.1 6 22.2 

Montana 10 – – – – 10 100.0 

Nebraska 17 10 58.8 6 35.3 1 5.9 

Nevada 11 6 54.5 4 36.4 1 9.1 

New Hampshire 1 – – – – 1 100.0 

New Jersey 10 3 30.0 4 40.0 3 30.0 

New Mexico 16 2 12.5 7 43.8 7 43.8 

New York 7 1 14.3 3 42.9 3 42.9 

North Carolina 27 5 18.5 17 63.0 5 18.5 

North Dakota 5 – – 3 60.0 2 40.0 

Ohio 14 3 21.4 6 42.9 5 35.7 
74 

Table 12. Availability of traffic projections by participating agencies and projection 

confidence codes (continued). 

Projection Confidence Code 

A Q N State/Province

Total 

Number of 

Sites Number 

Of Sites 

Percent 

of Sites

Number 

Of Sites 

Percent 

of Sites 

Number 

of Sites

Percent 

of Sites

Oklahoma 23 7 30.4 8 34.8 8 34.8 

Oregon 12 7 58.3 4 33.3 1 8.3 

Pennsylvania 21 11 52.4 9 42.9 1 4.8 

Rhode Island 1 – – 1 100.0 – – 

South Carolina 9 – – 7 77.8 2 22.2 

South Dakota 16 1 6.3 2 12.5 13 81.3 

Tennessee 16 1 6.3 10 62.5 5 31.3 

Texas 95 1 1.1 44 46.3 50 52.6 

Utah 15 – – – – 15 100.0 

Vermont 5 3 60.0 2 40.0 – – 

Virginia 13 6 46.2 5 38.5 2 15.4 

Washington 21 9 42.9 9 42.9 3 14.3 

West Virginia 5 3 60.0 – – 2 40.0 

Wisconsin 22 3 13.6 11 50.0 8 36.4 

Wyoming 14 2 14.3 10 71.4 2 14.3 

Puerto Rico 4 – – – – 4 100.0 

Alberta 7 – – – – 7 100.0 

British Columbia 4 – – – – 4 100.0 

Manitoba 6 – – 4 66.7 2 33.3 

New Brunswick 4 – – 3 75.0 1 25.0 

Newfoundland 3 – – – – 3 100.0 

Nova Scotia 1 1 100.0 – – – – 

Ontario 7 1 14.3 3 42.9 3 42.9 

Prince Edward 

Island 

3 – – 2 66.7 1 33.3 

Quebec 11 3 27.3 5 45.5 3 27.3 

Saskatchewan 8 – – 4 50.0 4 50.0 

TOTAL 890 194 21.8 364 40.9 332 37.3 

The geographical distribution of the LTPP sites with the acceptable projection confidence code is 

shown in figure 21. In addition, the sites in figure 21 were identified as “Urban” or “Rural,” 

depending on their highway functional class. Because there were no sites with the acceptable 

projection confidence code in Alaska, Hawaii, Puerto Rico, and most of the Canadian provinces, 

these jurisdictions were not included in the figure 21. 
Figure 21. Geographical distribution of LTPP sites with acceptable projection confidence cod

75
76 

Scope of the LTPP Traffic Data 

Table 13 provides an overview of the traffic characteristics for the 558 LTPP sites with the 

acceptable and questionable projection confidence codes. 

The minimum AADT truck volume ranges from 30 trucks per day for a site located on rural 

minor collector highways to 6310 trucks per day for a site located on an urban interstate. The 

mean annual growth of truck volumes during the period of 1994 to 1998, disregarding one site 

on rural minor collector highway, ranged from 6.5 percent for urban freeways and expressways 

to 3.0 percent for rural minor arterial highways. 

The TFs provided in table 13 show considerable variation within different highway functional 

classes, particularly for interstates and principal arterial highways. A part of the variation in TFs 

can be attributed to the influence of the site-specific pavement type and pavement thickness used 

in the ESAL calculation. 

The vehicle class distribution for LTPP sites with acceptable and questionable projection 

confidence codes for different highway functional classes is shown in table 14 and in figures 22 

and 23. (Vehicle classes were defined in table 1.) As expected, Class 9 vehicles (5-axle single 

trailer trucks) dominate vehicle class distribution on interstates and other major highways. On 

rural major collector and rural minor arterial highways, the dominant truck class is 5 (2-axle 

single unit trucks). 

Of the 10 vehicle classes representing buses and trucks, classes 5, 6, 8, and 9 typically account 

for more than 90 percent of all vehicles. Vehicles in the remaining classes (4, 7, 10, 11, 12, and 

13) are typically less than 3 percent, on average. 

Table 14 also shows the mean and standard deviation (SD) for unclassified vehicles (Vehicle 

Class 14). The mean number of unclassified vehicles has not exceeded 7 percent for any 

functional class. However, the percentage of unclassified vehicles on some individual sites and 

for some years was considerably higher. Also, data presented in table 14 are only for sites with 

acceptable and questionable projection confidence codes. 

Vehicle classification data presented in table 14 and in figures 22 and 23 are useful for assessing 

whether the truck distribution data are reasonable and for estimating truck class distributions if 

site-specific data are not available. 
77 

Table 13. Traffic characteristics of LTPP sites with acceptable and questionable 

projection confidence codes.

AADT Truck Volumes (1998 or Last Projection Year) 

FHWA Highway Functional Class Number of 

Sites Mean 

Standard 

Deviation 

(SD) 

Min. Max. 

Rural Principal Arterial—Interstate 202 1921 1027 160 5340 

Rural Principal Arterial—Other 219 558 467 50 4320 

Rural Minor Arterial 48 338 274 30 1100 

Rural Major Collector 10 178 151 50 540 

Rural Minor Collector 1 90 n/a 90 90 

Urban Principal Arterial—Interstate 36 1821 1155 390 6310 

Urban Principal Arterial—Other 

Freeways or Expressways 14 925 407 270 1490 

Urban Other Principal Arterial 26 775 654 138 2920 

Urban Minor Arterial 2 225 n/a 190 260 

Annual Growth Rate in Truck Volumes (1994 to 1998) 

FHWA Highway Functional Class Number of 

Sites Mean SD Min. Max. 

Rural Principal Arterial—Interstate 202 4.6 3.8 -17.2 17.0 

Rural Principal Arterial—Other 219 3.9 3.9 -10.8 21.2 

Rural Minor Arterial 48 3.0 4.2 -13.8 14.1 

Rural Major Collector 10 3.8 3.2 0.0 11.4 

Rural Minor Collector 1 0.0 n/a 0.0 0.0 

Urban Principal Arterial—Interstate 36 4.6 3.2 0.0 12.0 

Urban Principal Arterial—Other 

Freeways or Expressways 14 6.4 5.3 1.2 22.0 

Urban Other Principal Arterial 26 3.3 4.4 -7.5 11.4 

Urban Minor Arterial 2 3.5 n/a 2.0 5.1 

Truck Factor 

FHWA Highway Functional Class Number of 

Sites Mean SD Min. Max. 

Rural Principal Arterial—Interstate 202 1.3 0.5 0.4 3.0 

Rural Principal Arterial—Other 219 0.9 0.5 0.2 4.8 

Rural Minor Arterial 48 0.7 0.3 0.3 1.7 

Rural Major Collector 10 0.8 0.4 0.3 1.5 

Rural Minor Collector 1 0.8 n/a 0.8 0.8 

Urban Principal Arterial—Interstate 36 1.1 0.4 0.5 2.1 

Urban Principal Arterial—Other 

Freeways or Expressways 14 0.9 0.4 0.4 1.6 

Urban Other Principal Arterial 26 0.9 0.4 0.2 2.5 

Urban Minor Arterial 2 0.5 n/a 0.5 0.6 
78 

Table 14. Truck class distribution by highway functional classes for LTPP sites with 

acceptable and questionable projection confidence codes. 

Vehicle Class Highway Functional Class Number 

of Sites 

Statistic 4 5 6 7 8 9 10 11 12 13 14

Mean 1.7 12.3 3.7 0.4 7.8 60.3 2.4 4.6 1.1 2.9 1.9 Rural Principal Arterial—Interstate 202 SD 1.6 8.7 3.4 0.7 4.8 17.0 4.4 3.7 1.6 5.1 5.9

Mean 2.6 21.9 7.5 0.8 9.3 45.2 2.8 1.8 0.5 2.0 5.4 Rural Principal Arterial—Other 219 SD 3.4 12.3 5.6 1.3 6.0 17.9 4.3 4.5 0.8 4.0 11.8

Mean 3.2 36.7 15.0 3.4 7.5 25.8 2.6 0.5 0.3 6.7 0.1 Rural Major Collector 48 SD 4.1 19.3 8.9 3.5 4.4 13.1 2.6 0.7 0.6 19.8 0.6

Mean 3.2 35.2 9.2 1.0 10.6 29.5 2.5 1.3 1.1 2.8 4.7 Rural Minor Arterial 10 SD 3.8 21.5 5.9 1.0 8.3 16.7 3.2 2.8 6.6 5.1 8.1

Rural Minor Collector 1 Mean 2.0 22.2 7.2 0.0 13.9 54.2 0.5 0.0 0.0 0.0 0.0

Mean 3.2 19.6 10.3 1.3 11.3 44.6 2.3 2.1 0.8 1.3 3.2 Urban Principal Arterial—Interstate 36 SD 2.6 12.2 8.1 1.7 10.0 16.8 2.6 1.9 1.8 2.8 9.4

Urban Principal Arterial—Other Mean 4.5 23.8 8.3 3.2 7.9 41.6 1.5 2.5 0.3 0.4 3.1

Freeways or Expressways 14 SD 7.3 9.7 3.3 5.0 4.0 17.3 1.9 2.5 0.6 1.7 6.7

Mean 3.0 26.5 9.6 1.3 11.0 35.2 3.0 1.8 0.5 2.4 4.8 Urban Other Principal Arterial 26 SD 2.6 15.1 5.5 1.5 8.0 15.5 3.4 2.0 0.8 3.4 10.9

Urban Minor Arterial 2 Mean 4.9 31.3 9.6 0.1 9.0 23.4 6.3 0.5 1.1 6.9 6.9
79 

Note: Not included are rural minor collector and rural local collector because of insufficient data. 

Figure 22. Mean vehicle class distribution for LTPP sites with acceptable and questionable 

projection confidence codes located on rural highways. 

Note: Not included is urban minor arterial because of insufficient data. 

Figure 23. Mean vehicle class distribution for LTPP sites with acceptable and questionable 

projection confidence codes located on urban highways. 
80 

Historical Trends in LTPP Traffic Data Quantity and Quality 

A historical perspective on the availability of monitoring axle load data is provided in table 15 

and figure 24. Table 15 shows the number of sites for which axle load distributions are stored in 

the IMS database for specific years, and the corresponding number of sites with axle load 

distributions that were used for the projection. For example, in 1993, there were 446 sites in the 

IMS database that had (monitoring) annual axle load spectra. Of these 446 sites, 94 (21.1 

percent) have axle load spectra that could be used for projections yielding the acceptable 

projection confidenece code and 137 (30.7 percent) have axle load spectra yielding traffic 

projections with the Questionable projection confidence code. In total, 231 sites (51.8 percent) 

had axle load spectra yielding acceptable or questionable traffic projections. 

Data given in table 15 are plotted in figure 24 to illustrate the overall historical trend in the 

available axle load spectra. After the peak of 446 sites was reached in 1993, the number of sites 

with existing axle load spectra declined to 287 sites in 1998. These numbers refer to the 

existence of the spectra only. The number of spectra that could be used for the projection of axle 

loads declined from the peak of 231 sections in 1993 to 150 sections in 1998. In other words, in 

1998, of the 890 sites, only 150 (16.8 percent) had axle load spectra that could be used for 

projection of axle loads yielding acceptable or questionable projections. Only 51 sites in 1998 

(5.7 percent of all sites) had axle loads yielding acceptable axle load projections. 

The ratio between axle load spectra available in the database and the axle load spectra that were 

used for the projection has been relatively constant over the years and was in the range of about

52 to 56 percent. For example, in 1991, 201 sites had axle load spectra in the database; of these, 

109 sites (54.2 percent) were used for the projection of axle loads. The corresponding 

percentage in 1998 was 52.3. In other words, just over 50 percent of annual axle load spectra 

obtained by WIM scale measurements could be used in the projection. That this percentage is 

not improving with time indicates the need to improve the quality of traffic data. 

According to figure 20, 543 of 890 sites (61 percent) had axle load spectra yielding acceptable or 

questionable projections. This percentage is higher than the percentages reported in table 15. 

The higher percentages reported in table 7 resulted from combining several annual axle load 

spectra to obtain traffic projections for one site. 

For the sites with acceptable and questionable projection confidence codes, the number of years 

with annual axle load data per site varied from 1 to 8. On average, about 4.5 years of annual data 

were available for each site, and about 2.8 years of annual data were used for the computation of 

the base annual spectra. 

The steady decline in the amount of monitoring axle load data shown in figure 24 highlights the 

need for renewed data collection efforts. It also highlights the importance of traffic modeling 

and projections, and the need for the proposed LTPP PLG for estimating traffic loads on LTPP 

sites without site-specific truck class and axle load distributions. 
81 

Table 15. Availability of annual axle weight data and their use for projection.

Number of Sites 

Annual Axle Load Spectra 1990 1991 1992 1993 1994 1995 1996 1997 1998 Total

Available in IMS 58 201 314 446 410 412 383 370 287 2881

Conf. Code A 9 33 76 94 109 106 88 82 51 648

Used for Projection Conf. Code Q 17 76 93 137 120 124 112 121 99 899

Total 26 109 169 231 229 230 200 203 150 1547

Total Percent Used for Projection 44.8 54.2 53.8 51.8 55.9 55.8 52.2 54.9 52.3 53.7

Figure 24. Trends in the number of monitoring axle load spectra. 

Data Analysis/Operations Feedback Reports 

Questionable or missing data identified during the study were reported to FHWA using data 

analysis/operations feedback reports. The feedback reports are not concerned with engineering 

validity of the data, but only with issues connected with data storage and operations. Table 16 

provides a list of data analysis/operations items identified in the course of the study and provides 

recommendations to resolve problems. 

0

50

100

150

200

250

300

350

400

450

500

1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Year

Number of Sites with Axle Load Data 

Available in IMS

Used for projection
82 

Table 16. Summary of data analysis/operations feedback reports prepared during the study. 

Report 

Number 

Subject Recommended Action 

ERES_BW_82 "Zero" data in table 

TRF_MONITOR_AXLE_DISTRIB 

(1) Use "null" data type for future data processing 

when no axle count data are available; (2) review 

records that are already in the IMS table 

TRF_MONITOR_AXLE_DISTRIB and eliminate 

years where all axle count records are set to "zero" or 

change data type from "zero" to "null." 

ERES_BW_99 Multiple monitoring traffic data entries for 

the same year 

The identified records with multiple entries should be 

deleted. Only records with the highest modification 

numbers should be left in IMS data tables. 

ERES_BW_100 Missing data in the 

TRF_MONITOR_BASIC_INFO table 

The identified records should be reviewed and either 

added to the TRF_MONITOR_BASIC_INFO table or 

excluded from the TRF_MONITOR_AXLE_DISTRIB 

and TRF_MONITOR_VEHICLE_DISTRIB tables. 

ERES_BW_101 All-zero traffic data entries in the 

TRF_MONITOR_* tables 

The identified records should be reviewed and either 

added to the TRF_MONITOR_BASIC_INFO table or 

excluded from the TRF_MONITOR_AXLE_DISTRIB 

and TRF_MONITOR_VEHICLE_DISTRIB tables. 

ERES_BW_102 Missing records in 

TRF_EST_ANL_TOT_LTPP_LN table 

RCOs should review the identified records and the 

TRF_EST_ANL_TOT_LTPP_LN table should be 

updated. 

ERES_BW_103 Missing records in TRF_BASIC_INFO 

table 

The identified GPS sites and SPS experiments should 

be reviewed by RCOs and the TRF_BASIC_INFO 

table should be updated. 

ERES_BW_107 Sites with questionable pavement types in 

1998 in table 

TRF_MONITOR_BASIC_INFO 

The identified records should be reviewed and the IMS 

table TRF_MONITOR_BASIC_INFO should be 

updated. 

ERES_BW_108 Sites with questionable class distribution in 

TRF_MONITOR_VEHICLE_DISTRIB 

table 

The identified records should be reviewed and updated 

or deleted from the IMS table 

TRF_MONITOR_VEHICLE_DISTRIB. 

ERES_BW_109 Sites with extremely low axle load counts 

in IMS table 

TRF_MONITOR_AXLE_DISTRIB 

The identified records should be reviewed and updated 

or deleted from the IMS table 

TRF_MONITOR_VEHICLE_DISTRIB. 
83 

CHAPTER 4. COMPUTED PARAMETER TABLES 

FOR PROJECTED TRAFFIC DATA 

This chapter describes computational procedures used to generate projected annual axle load 

spectra, explains how the projected data are stored in the computed parameter tables in the IMS 

Traffic Module, and describes relationships between computed traffic parameters and other data 

elements in the IMS database. The computational procedure described here applies only to sites 

with site-specific annual axle load data. 

Description of Computed Parameter Tables 

The projection of traffic loads for all in-service years for the LTPP traffic sites resulted in the 

five computed parameter tables. These tables contain both the projected annual axle load spectra 

and the intermediate variables. The intermediate variables carry important information 

documenting how the projected axle load spectra were calculated. The following list represents 

a set of the projected traffic computed parameter tables. Each table is named twice: the 

descriptive name is first, and the proposed IMS name follows in bold type. 

Main Table: 

• Projected Annual Axle Load Spectra (TRF_PRJ_YR_AXLE_DISTRIB). 

Intermediate Tables: 

• Normalized Base Annual Axle Load Spectra (TRF_PRJ_BAS_ANL_PCT_AXLE). 

• Base Annual Axle Load Summary (TRF_PRJ_BAS_ANL_AXLE_SUM). 

• Annual Projection Factors (TRF_PRJ_YR_MULTIPLIER). 

• Projection Summary Table (TRF_PRJ_MASTER). 

Figure 25 presents an overview of the IMS Traffic Module that includes historical, monitoring, 

and projected traffic data. The relationship between the projected and other traffic tables stored 

in IMS is shown in figure 26. Figure 26 also provides a flowchart used for the calculation of 

variables stored in the computed parameter tables. The main computed parameter table is 

highlighted by heavy borderlines. Below the main table, in the oval shape, is the provision for 

calculating “cumulative axle loads.” The cumulative axle load spectra are not available in the 

IMS database; they can be obtained by adding up annual axle load spectra for any combination 

of in-service years. 
84 

Figure 25. Overview of the IMS Traffic Module showing the proposed addition 

of projected traffic data. 

Generation of Projected Annual Axle Load Spectra 

This section describes the computational procedure used to generate projected annual axle load 

spectra for all in-service years, and the relationships among the variables in the computed 

parameter tables. The description builds on the outline of the traffic projection procedure 

provided in chapter 2. Since only traffic projections for Category 1 and 2 sites were carried out 

during the study, the description herein is based on methodology developed for Category 1 and 2 

projections (see p. 8 of this report for descriptions of the categories). 

Computation of the projected annual axle load spectra for all in-service years and preparation of 

the computed parameter tables involves the following major steps: 

1. Computation of normalized base annual axle load spectra (table 

TRF_PRJ_BAS_ANL_PCT_AXLE). 

2. Computation of base annual axle load summary (table 

TRF_PRJ_BAS_ANL_AXLE_SUM). 

3. Computation of annual projection factors (table TRF_PRJ_YR_MULTIPLIER). 

4. Computation of projected annual axle load spectra (table 

TRF_PRJ_YR_AXLE_DISTRIB). 

5. Reporting projection summary (table TRF_PRJ_MASTER). 

These steps are described in more detail in the following sections. 

Projected Data 

Projected Annual Axle 

Load Spectra 

Intermediate 

Data Elements 

IMS Traffic

Axle Load Spectra 

Volume by Class

Monitoring Data

ESAL 

Total Volume 

Major Data Elements: 

Historical Data

ESAL 

Total Volume 

Major Data Elements: 
85 

Figure 26. Flowchart used for calculating computed parameter tables. 

Computation of Normalized Base Annual Axle Load Spectra 

Normalized base annual axle load spectra for single, tandem, triple, and quad axle groups are 

given in table TRF_PRJ_BAS_ANL_PCT_AXLE. The normalized base annual axle load 

spectrum is the base annual load spectrum with the axle load counts for a given weight range 

(and axle type) expressed as percentages of the total axle counts for the given axle type. This 

spectrum is computed for the same set of weight ranges that are used in table 

TRF_MONITOR_AXLE_DISTRIB. Normalized spectrum provides a characteristic shape of 

axle weight distribution for each LTPP traffic site. 

To develop normalized base annual spectrum, available monitoring annual spectra from the IMS 

table TRF_MONITOR_AXLE_DISTRIB were critically assessed and base annual spectrum 

was computed by averaging annual axle load data for selected years as described in chapter 2. 

Then, the computed base annual spectrum was normalized with respect to the total annual axle 

TRF_PRJ_YR_AXLE_DISTRIB 

TRF_PRJ_BAS_ANL_AXLE_SUM 

TRF_PRJ_YR_MULTIPLIER 

TRF_EST_ANL_TOT_GPS_LN TRF_MONITOR_BASIC INFO 

TRF_MONITOR_VEHICLE_DIST TRF_MONITOR_AXLE_DISTRIB 

Cumulative 

Axle Loads 

by Year 

IMS Traffic Module

TRF_PRJ_BAS_ANL_PCT_AXLE 

TRF_PRJ_MASTER 

Historical and Monitoring

Projected Data 
86 

load counts for each axle type. The computational procedure for obtaining normalized base 

annual axle load spectrum is shown in figure 27. 

Figure 27. Flowchart for computation of the normalized base annual load spectra. 

Computation of Base Annual Axle Load Summary 

The base total annual numbers of axles for single, tandem, and triple axle groups contained in the 

base annual spectrum are given in table TRF_PRJ_BAS_ANL_AXLE_SUM (base annual axle 

load summary). The base total annual number of axles is associated with normalized base 

number of axles from TRF_PRJ_BAS_ANL_PCT_AXLE and is used to calculate projected 

annual axle load spectra. The base total annual number of axles provides information about 

overall truck volume (in terms of total axle counts) that is representative for each LTPP traffic 

site. 

The total base annual number of axles was computed (see figure 28) by summing axles from the 

computed base annual spectrum across all weight ranges. This summation was done separately 

for each axle type. 

TTRF_PRJ_BAS_ANL_PCT_AXLE 

TRF_MONITOR_BASIC_INFO TRF_MONITOR_AXLE_DISTRIB 

IMS Traffic Module 

Retrieve supporting traffic data and 

other information. 

Extract available annual load spectra for 

all monitoring years.

Select annual axle load spectra. Calculate base annual spectrum by averaging the selected 

annual axle load spectra. 

Normalize axle counts in base spectrum with respect to the total number of axles for each 

axle type. 
87 

Figure 28. Flowchart for computation of the base annual axle load summary. 

Computation of Annual Projection Factors 

Annual projection factors, calculated for each in-service year for LTPP sites, are given in table 

TRF_PRJ_YR_MULTIPLIER. These factors define an annual truck traffic growth pattern and 

adjust the base total annual axle counts up or down to fit the selected projection model. Annual 

projection factors are used to compute projected annual axle load spectra. 

The annual projection factors are selected based on critical review of the available historical and 

monitoring traffic data for all available years from the following IMS tables: 

• TRF_MONITOR_BASIC INFO. 

• TRF_EST_ANL_TOT_GPS_LN. 

• TRF_MONITOR_AXLE_DISTRIB. 

• TRF_MONITOR_VEHICLE_DIST. 

The computational procedure for the annual projection factors is shown in figure 29. 

TRF_MONITOR_BASIC_INFO TRF_MONITOR_AXLE_DISTRIB 

IMS Traffic Module 

Select annual axle load spectra. Calculate base annual spectrum 

by averaging the selected annual axle load spectra.

Obtain base total annual number of axles for each axle type. 

TRF_PRJ_BAS_ANL_AXLE_SUM 
88 

Figure 29. Flowchart for computation of the annual projection factors. 

Computation of Projected Annual Axle Load Spectra 

Projected annual axle load spectra (for single, tandem, and triple axle groups) are the main 

product of the LTPP traffic projection process. These results are presented in the table 

TRF_PRJ_YR_AXLE_DISTRIB. Projected annual load spectra for each year are computed 

(see figure 30) by multiplying the base annual load spectrum with annual traffic projection 

factors from table TRF_PRJ_YR_MULTIPLIER. The base annual load spectrum is computed 

by multiplying the normalized base annual axle load spectrum for single, tandem, and triple axle 

groups from the table TRF_PRJ_BAS_ANL_PCT_AXLE with the total base annual number of 

axles for single, tandem, and triple axle groups from the table 

TRF_PRJ_BAS_ANL_AXLE_SUM. The projected load spectra are reported in terms of 

annual axle counts for the same set of load ranges used in table 

TRF_MONITOR_AXLE_DISTRIB. 

Extract site-specific historical 

AADT volume and ESAL 

information. 

TRF_MONITOR_AXLE_DISTRIB 

TRF_MONITOR_VEHICLE_DIST 

IMS Traffic Module

Extract site-specific monitoring 

AADT volume and ESAL 

information. 

Analyze monitoring and historical traffic data and develop traffic projection model. 

TRF_PRJ_YR_MULTIPLIER 

Compute annual traffic projection factors for each in-service year. 

Is empty? 

Estimate AADT volume and 

ESAL based on site-specific 

monitoring vehicle class and axle 

count data. 

TRF_EST_ANL_TOT_GPS_LN TRF_MONITOR_BASIC_INFO 

No 

Yes 
89 

Figure 30. Flowchart for computation of projected annual axle load spectra. 

Reporting Projection Summary 

The summaries of projection results (including applicable traffic projection interval; information 

about traffic projection category used for each LTPP traffic site; the assigned traffic projection 

codes; and any specific traffic characteristic for each LTPP site) are stored in the table 

TRF_PRJ_MASTER.

To assign projection confidence codes and for QA purposes, the projected annual axle load 

spectra were used to calculate ESALs. Calculated ESALs were then compared with the available 

historical ESAL trends. Also, TFs were computed and compared with historical TFs and with 

typical TFs based on FHWA functional highway classification. Analysts assigned initial 

projection confidence codes to the results using the guidelines described in chapter 2. After the 

traffic projection results were reviewed by the participating highway agencies, the changes were 

made to the initial projections, and reviewed projection confidence codes were assigned to each 

site. 

TRF_PRJ_YR_AXLE_DISTRIB 

TRF_PRJ_BAS_ANL_PCT_AXLE 

(Normalized number of axles) 

TRF_PRJ_YR_MULTIPLIER 

(Annual projection factors for 

each year y since site opening to 

traffic to 1998 or “out-of-study” date). 

Obtain annual axle load spectrum for each year by multiplying base number of axles in each 

weight category by annual projection factors. 

Projected number of axles for year y = Base number of axles * Annual projection factor for year y

TRF_PRJ_BAS_ANL_AXLE_SUM 

(Total number of axles) 

Base number of axles = Normalized number of axles * Total number of axles 

91

CHAPTER 5. PROTOTYPE LTPP PAVEMENT LOADING GUIDE 

This chapter describes the purpose, design parameters, and functionality of the proposed PLG. It 

contains a blueprint for the development of the PLG and examples of using the PLG to obtain 

traffic load projections for LTPP sites without site-specific truck class or axle load data. 

The need for the PLG was identified during the Phase I work and was reinforced by the traffic 

prediction work carried out in Phase 2. The process of estimating, selecting, or assessing truck 

class and axle weight distributions for LTPP sites needs to be supported by a reference database 

summarizing characteristic truck class and axle load distribution data. This reference database 

should contain not only traffic characteristics encountered on the LTPP sites, but also typical 

benchmark characteristics for truck class and axle load distributions. The PLG would fulfill: 

• The need to estimate missing truck class and axle load distributions for Category 3 and 4 

sites. 

• The need to assess the quality of monitoring traffic data used for traffic projection. 

The Need to Estimate Missing Traffic Data 

The traffic data assessment and traffic projection work summarized in this report processed all 

890 LTPP sites. Of the 890 sites, axle load projections were carried out for 558 sites. Of the 558 

sites with axle load projections, the projections for 194 sites were assigned an acceptable 

projection confidence code and 364 sites were assigned a questionable projection confidence 

code. No traffic load estimates were provided for 332 LTPP sites for which the required sitespecific traffic data (truck class distributions or axle loads) were not available. Yet many of the 

332 sites without the site-specific data contain a wealth of information regarding pavement 

materials, environment, and pavement performance. For example, pavement materials were 

subjected to a battery of laboratory tests, and the pavement performance has been evaluated 

using a series of profile, distress, and falling weight deflectometer (FWD) measurements over 

the years. Without the corresponding traffic data, this wealth of information cannot be utilized 

for developing load-related pavement performance models. 

The additional traffic data that need to be estimated depend on the traffic load projection 

category of the site: 

• For Category 3 sites, axle load spectra for the individual truck. 

• For Category 4 sites, truck class distribution and axle load spectra for the individual truck 

classes. 

The estimation of missing truck class and axle weight distributions can have significant 

consequences on the reliability of the resulting traffic load estimates and must be done 

judiciously. 

The estimating process should consider data collected in the vicinity of the site on the same 

highway (site-related data) and data collected on similar highway links in the same area or 

jurisdiction (regional data). In many cases, applicable site-related or regional traffic data may 
92

not be available. To facilitate the task of estimating traffic data for a variety of sites, and to 

make the estimating process consistent, a database containing traffic values for a variety of sites, 

as well as typical benchmark values, is required. Such a database will be developed as part of 

the proposed PLG. 

There is no substitute for site-specific traffic data. However, to utilize the large amount of 

pavement-related data collected on the 332 LTPP sites with missing traffic data for the prediction 

of pavement performance, some traffic data need to be estimated. Research shows that it is 

possible to use estimated traffic data to carry out basic calibration and verification of pavement 

design models.[16] Without traffic estimates, it would not be possible to utilize many LTPP 

sections for the development and verification of load-dependent pavement performance models. 

The Need to Assess the Quality of Monitoring Traffic Data 

As discussed in chapter 2, traffic data stored in the LTPP databases exhibit considerable 

variation in quality. Consequently, the annual truck class and axle load distributions need to be 

examined and verified before they can be used for traffic projection. The assessment and 

verification of truck class and axle weight distributions requires the development of software that 

displays and compares data. 

In addition, it has become a common practice to assess the quality of axle weight data by 

examining the GVW distribution of Class 9 vehicles (5-axle single trailer trucks).[12] However, 

the data necessary to plot the GVW of Class 9 trucks against the frequency of occurrence are not 

stored in the IMS database, and the retrieval of the necessary data from the CTDB and the 

development of appropriate graphical displays require considerable effort and specialized skills. 

Computational software that can facilitate the development of graphical displays showing the 

GVW distribution of Class 9 vehicles exists, but is not readily available and would need to be 

adapted to process annual data. 

For the efficient verification and QC of traffic data used for the projection, it is desirable to 

compare axle load and other data obtained for a specific site with corresponding data obtained 

for similar sites and with typical or expected data. By facilitating the comparison of axle load 

spectra and other traffic data, and by providing typical values and ranges for traffic variables, the 

proposed PLG will facilitate the QA process. 

Scope of Pavement Loading Guide: LTPP PLG or General PLG 

A catalogue containing validated truck class and axle load distributions, and with additional 

capabilities to display and compare data graphically, would be useful not only for the projection 

of LTPP traffic data, but also for estimating traffic loads for general pavement design purposes. 

The need for such tool is highlighted by the emergence of mechanistic-based pavement design 

procedures that require axle load spectra. The use of axle load spectra in pavement design is 

relatively new. Many pavement designers may benefit from information on typical values of 

truck class and axle load distributions that are expected to occur on different classes of highways. 

Consequently, the LTPP PLG has the potential for wider applicability beyond the needs of the 

LTPP program. 
93

The main purpose of the LTPP PLG is to facilitate traffic loading projections for LTPP sites 

without site-specific traffic loading data, and the work summarized in this chapter has been 

directed toward the development of the PLG that would serve this need. Only a limited amount 

of work has been done to prepare groundwork to enhance the LTPP PLG to serve general 

pavement design needs. 

There are many similarities between a PLG serving LTPP’s needs and a PLG serving general 

pavement design needs. Of course, the two versions also have several differing characteristics. 

Characteristic Features of LTPP PLG 

• The LTPP PLG is concerned mainly with backcasting of traffic loads using historical and 

monitoring data. 

• Data input and output functions need to be compatible with the LTPP databases (IMS and 

CTDB). 

• The number of potential active users is limited, and the majority of users are expected to 

have a research background. Consequently, user-friendliness of the software and 

application guidelines may not be of paramount importance. 

• Traffic projections need to be carried out only in terms of axle load spectra. The LTPP 

traffic projection process uses ESALs for QC purposes only. 

Characteristic Features of General PLG 

• The general PLG is concerned with forecasting of future traffic loads. 

• Data input functions need to be flexible to accommodate user data; data output functions 

and formats should match the input format requirements of common pavement design 

methods (e.g., 1993 AASHTO guide[17] or the not-yet-finalized National Cooperative 

Highway Research Program (NCHRP) 2002 guide[7]. 

• The number of potential users is large, and they may have quite diverse backgrounds. 

User-friendly software and application guidelines are important. 

• The majority of pavement designers currently need axle loads in ESALs. Axle load 

spectra will be required by the NCHRP 2002 guide and by other mechanistic-based 

pavement design methods. 

This chapter concentrates on describing the development of the LTPP PLG to facilitate the 

projection of traffic loads on LTPP sites. However, the concepts for the development of the 

LTPP PLG described in this chapter also apply to the development of a general PLG. 
94

Objectives of the PLG and Purpose of This Chapter 

It is necessary to make a distinction between the objectives of the PLG and the purpose of this 

chapter. The objectives of the PLG refer to the functionality and potential use of the proposed 

PLG, its software and guidelines, and include the following: 

• Provide tools for projecting traffic loads for LTPP sites without site-specific truck class 

and axle loads distribution data. 

• Facilitate the assessment and QA of LTPP traffic data. 

• Facilitate understanding of traffic load characteristics, such as truck distributions and axle 

load spectra, and provide educational and training opportunities. 

• Provide groundwork for a PLG that can be used for forecasting traffic loads for pavement 

design. 

• Promote the use of LTPP traffic data for other applications. 

This chapter describes the functionality of the proposed PLG and the methodology for its 

development. Some specific objectives of this chapter are to: 

• Develop design criteria and functional features required to meet the objectives of the 

PLG. 

• Describe data, information, and data analysis needed to develop the engineering 

underpinnings for the PLG, such as typical characteristics of axle load spectra for 

different highway functional classes. 

• Describe the use of the PLG to obtain LTPP traffic projections for sites without sitespecific data using practical examples. 

• Illustrate the operation of the PLG software using prototype demonstration software. 

Working with axle load spectra involves working with large data sets that are best handled by a 

computer. To illustrate some features of the proposed PLG, particularly how axle load spectra 

can be compared, combined, or selected using graphical displays, prototype PLG software was 

developed. The operation of the software is described briefly in this chapter. 

 

The prototype demonstration software contained several functions of the proposed PLG but 

lacked many of the analytical and engineering underpinnings that still need to be developed. The 

main purpose of the prototype software was to illustrate the overall concept and functionality of 

the proposed PLG. 

Role of PLG in Traffic Projection 

Traffic data collected in the field are typically in the form of samples that are uneven in duration 

and quality. The samples of collected traffic data are used to calculate cumulative traffic loads 

through an analytical process involving factoring and traffic modeling referred to as traffic 

projection. The LTPP traffic projection is done in two steps. First, the monitoring data collected 

during a given year are projected (or factored up) to obtain annual monitoring data (this factoring 

step is required because traffic monitoring equipment seldom operates all the time). Second, the 
95

annual monitoring data (available for some of the years) are combined with historical data and 

are projected to all years the pavement was in service. 

Traffic data collection alone is not enough to obtain cumulative traffic loads. The data that were 

not collected in the past will remain missing, and it is not possible (nor it is necessary) to collect 

traffic data all the time. To obtain cumulative traffic loads for all years the pavement has been in 

service, it is necessary to use both the traffic data (historical and monitoring data) and the traffic 

projection procedures. 

The PLG is not a substitute for the site-specific collection of traffic data, which is required to 

obtain reliable estimates of traffic loads and needs to be encouraged and promoted. However, 

traffic data alone, without traffic projection procedures, will not provide the required results, 

either. Both the data collection and the data projections activities have undeniable roles and need 

to be n balance. The role of the PLG is to strengthen and facilitate the traffic data projection. 

The use of PLG, together with guidelines for selecting missing data, can be instrumental in 

alleviating some uncertainty resulting from the unavoidable need to factor up and estimate traffic 

data. 

Conceptual Outline of PLG 

This section outlines how the concept of estimating ESALs using TFs can be modified to apply 

to estimating axle load spectra. 

The AASHTO Guide for Design of Pavement Structures 1993, the most common pavement 

design method in North America, uses ESALs to characterize traffic loads.[17] Consequently, 

many pavement professionals are familiar with the concept of estimating ESALs for pavement 

design. In the following, the procedure of estimating traffic loads using ESALs is described to 

demonstrate its similarity with the procedure, utilized by the PLG, of estimating traffic loads 

using axle load spectra. 

Estimating ESALs typically starts with the division of the average annual daily traffic volume 

into a car volume and a truck volume. Truck volume is then subdivided into several classes, and 

each truck class is assigned a representative TF defined as the number of ESALs per truck. For 

example, the computerized version of the 1993 AASHTO guide (the DARWin® 3.1 pavement 

design software) encourages the user to classify vehicles into 13 vehicle classes and to provide 

TFs for each vehicle class. 

To obtain the daily number of ESALs for a base year, it is necessary to sum the product of the 

daily truck volumes in different classes and their corresponding TFs. To obtain the annual 

number of ESALs, the average daily number of ESALs is multiplied by 365. Mathematically, 

the process of calculating ESALs can be expressed by equation 3. 

 

 (3)
96

Where: 

ESALs year = Total annual number of ESALs for a base year. 

i = Truck class number. 

n = Number of truck classes. 

ADTVi = Average daily volume of truck class i. 

TFi = Truck factor for truck class i. 

 365 = Constant to convert daily traffic to annual traffic. 

To obtain the number of ESALs for an entire pavement design period, the annual number of 

ESALs estimated for a base year is factored to account for traffic that will occur during the entire 

design pavement service period. 

For LTPP traffic projection purposes, the projection is not done in terms of ESALs but in terms 

of axle load spectra. Thus, instead of the representative TFs for different truck classes used in 

equation 3, we need to use the representative axle load spectra for different truck classes. The 

overall concept of classifying trucks into classes, assigning a representative load-related factor to 

each truck class (i.e., axle load spectra or TFs), and combining the result remains the same. 

Using the axle load spectra instead of TFs, equation 3 can be written as follows. 

 (4) 

Where: 

ALS year = (Annual) combined axle load spectra (for single, tandem, triple axles) for a 

base year. 

i = Truck class number.

n = Number of truck classes. 

ADTVi = Average daily volume of truck class i. 

RTSi = Representative axle load spectrum for truck class i. 

 365 = Constant to convert daily traffic to annual traffic. 

To obtain annual axle load spectra for the entire pavement design period, the annual axle load 

spectra estimated for the base year are factored to account for traffic that will occur during the 

entire pavement design period. 

Equation 4 is an abbreviated expression intended to demonstrate the similarity between the use 

of ESALs and TFs on the one hand, and the use of axle load spectra on the other. The variable 

ALSyear represents three separate arrays for single, tandem, and triple axle spectra. (There is no 

array for quadruple axles\ spectrum because the LTPP traffic data do not contain any data for 

quadruple axles.) RTSi represents three arrays for each truck class i, as shown in equation 5. 

(5) 
97

The Si, Di, and Ti are arrays for single, tandem, and triple axle spectra, respectively. They are 

defined for each truck class i according to equations 6, 7, and 8. 

(6) 

(7) 

(8) 

Where: 

Si = Adjusted normalized axle load spectrum for single axles (an array containing 

the number of single axles belonging to each of the pre-defined load categories 

for single axles). 

si = Array containing normalized single axle load spectra for vehicles class i. 

ai = Single-axle coefficient (number of single axles per vehicle) for vehicle class i. 

Di, Ti = Adjusted normalized axle load spectra for tandem and triple axles, respectively. 

di, ti = Arrays containing normalized tandem and triple axle load spectra, respectively. 

bi, ci = Tandem and triple axle coefficients, respectively. 

The adjusted normalized load spectra used in arrays Si, Di, and Ti are expressed as the product of 

normalized spectra and axle-per-class coefficients. The normalized spectra contain proportions 

of axle loads that occur within designated load ranges. The axle-per-class coefficients are 

required to obtain actual axle load spectra from the normalized spectra. The calculation 

procedure embodied in equations 3 through 8 corresponds to the calculation procedure developed 

for Category 3 and 4 sites in Phase 1.[1]

The use of axle load spectra as the basic traffic characteristics for pavement design is relatively 

new and presents several challenges: 

• Axle load spectra are large. They consist of many values representing axle load 

distributions for single, tandem, triple, and quadruple axles for different vehicle classes. 

Because of the voluminous nature of axle load spectra, their manipulation requires the 

development of computerized procedures. 

• There is little information available on the characteristics of axle load spectra. A novice 

user of axle load spectra would have difficulties judging whether the spectra reported for 

different vehicle classes or for the total traffic flow are reasonable. Guidelines on the 

typical spectra for different vehicle classes on different highway classes are required for 

QA purposes and for promoting confidence in the use of axle load spectra. The 

guidelines may include information on the location of typical peaks and valleys of the 

axle load distribution, and on the temporal and spatial variability of spectra. 

• The easiest way to compare spectra rapidly and to evaluate their reasonableness and 

interpret their meaning is by using graphical displays of spectra and summary 

characteristics such as ESALs. This also requires the development of computerized 

procedures. 
98

The PLG is intended to overcome these challenges to computation and data comparison. The 

PLG will also contain a catalogue of typical benchmark values and characteristics of axle load 

spectra, as well as software to display, compare, and combine axle load spectra, and to calculate 

annual cumulative axle load spectra. 

Functionality of PLG 

This section contains a detailed description of the main functions and features of the proposed 

PLG. The functionality of the PLG and its design parameters have been formulated to meet the 

objectives of the PLG presented previously. Briefly, the main purpose of the PLG is to facilitate 

traffic loading projections for LTPP sites without site-specific traffic data. To meet the 

objectives of the PLG, the PLG will support the following main functions: 

• Database management, including: 

− Data storage. 

− Selection, sorting, and retrieval. 

− Importing and exporting data. 

• Data comparison and assessment. 

• Development of pavement loading estimates for LTPP sites in Categories 3 and 4. 

• Development of combined axle load spectra and cumulative traffic estimates. 

An overview of the PLG functions is presented in figure 31. 
99

Figure 31. Overview of PLG functions. 

Data Storage 

The LTPP databases (IMS and CTDB) contain all LTPP traffic data submitted by participating 

agencies that have passed basic data QA checks. During the course of this project, large amounts 

of traffic data stored in the IMS were identified by the project team and by the participating 

agencies as being of dubious quality. Consequently, not all IMS and CTDB data should be part 

of the PLG, as the PLG data will be used for the estimation of traffic loads at other LTPP sites 

and need to be of high quality. All traffic data stored in the LTPP databases should be verified 

using a comprehensive QA process that would identify and remove dubious and nonsensical 

data. In the absence of such QA process, the only data that should be included in the PLG 

database at present (that is, if the development of the PLG were to commence prior to the 

PLG FUNCTIONS PLG OPERATION 

Database Management • Data storage. 

• Selection, sorting, and retrieval. 

• Importing and exporting data. 

Data Comparison and 

Assessment 

• Selection of sites using different filters. 

• Display of selected data. 

- Truck distribution. 

- Axle load spectra. 

- Site location map. 

• Data comparison. 

- LTPP and other data. 

- Generic data.

Development of Pavement 

Loading Estimates 

• Selection of truck class and axle load 

distributions for individual vehicle 

classes using: 

- Direct input. 

- Data from other sites. 

- Generic data. 

Development of Axle Load 

Spectra and Cumulative 

Traffic Estimates 

• Base annual axle load spectra. 

• Projected axle load spectra for all years. 

• Cumulative axle load spectra. 

• Annual and cumulative ESALs. 
100

proposed comprehensive traffic data QA process) are those that were used to produce traffic load 

projections assigned the acceptable projection confidence code. The inclusion of only verified or 

“acceptable” data in the PLG is one of the main distinctions between the PLG and the DataPave 

software.[18]

The data stored in the PLG will be of two origins: 

• LTPP data obtained or derived from IMS and CTDB databases. 

• User-supplied data. 

Regardless of the origin, the data stored in the proposed PLG will be of four types: supporting 

data, monitoring traffic data, projected traffic data, and generic or typical traffic data. 

Variables that will be stored in the PLG to support its functionality are described below and 

summarized in table 17. 

Supporting Data

Supporting data are non-traffic data required for the identification and retrieval of traffic data. 

Supporting data stored in the PLG will include: 

• Site identification data. 

• Data describing the physical properties of the site: pavement structure, number of lanes, 

direction of travel, etc. 

• Highway functional class. 

Monitoring Traffic Data

Monitoring traffic data are data derived from AVC and WIM measurements, and are typically 

annual data reported for a specific year. Monitoring traffic data stored in the PLG will include: 

• Annual truck volume distribution into the 10 classes. 

• Annual axle load spectra for all trucks combined (normalized spectra plus axle counts). 

• Annual axle load spectra for individual truck classes (normalized spectra plus axle 

counts). 

• Annual TFs for all trucks combined. 

• Annual TFs for individual truck classes. 

• Annual axles-per-truck coefficients for single, tandem, and triple axle types, and for 

individual truck classes. 

• Truck percentage. 

• Annual AADT truck volumes. 
101

Table 17. List of main variables stored in PLG. 

FHWA FHWA Vehicle Classes 4 through 13 

Highway 

Functional 

Type 

Truck Traffic 

Classification 

(TTC) of 

Highways 

Class 4 Classes 5, 6, 7–13 

Classes 4 through 13 

Combined 

TTC 1 

• Axle load spectra for 

single and tandem axles 

(normalized). 

• Axle per vehicle class 

coefficients.

• Load spectra 

coefficients.

• ESAL/truck.

Same for other vehicle 

classes (including triple 

and quad axle groups, if 

appropriate) 

• Truck volume 

distribution 

(normalized). 

• Annual load spectra for 

single and tandem axles 

(normalized). 

• AADT volume. 

• Truck percentage. 

• Load spectra 

coefficients. 

• ESAL/truck.

Rural 

Interstates 

TTC 2 

through 

TTC 17

Repeated for other TTC categories 

Repeated for other highway functional types 

Urban 

Collector 

TTC 1 

through 

TTC 17

[As Above] [As Above] [As Above] 

Projected Traffic Data

Projected traffic data are data derived from monitoring data. Projected traffic data stored in the 

PLG will include: 

• Base annual truck distribution. 

• Base annual axle load spectra for all trucks (normalized spectra plus axle counts). 

• Base annual axle load spectra for individual trucks (normalized spectra plus axle counts). 

• Base annual axles-per-truck coefficients for single, tandem, and triple axle types and for 

individual truck classes. 

• TF for base axle load spectra for all trucks. 

• TF for base axle load spectra for individual trucks. 

• Truck growth rate, typical for the past 5 years. 
102

Generic Traffic Data

Generic traffic data are typical or default traffic data. Unlike monitoring and projected traffic 

data that are tied to the specific LTPP and other traffic sites, generic data will be provided only 

for highway functional classes. For the purposes of the PLG, the 11 highway functional classes 

(6 rural and 5 urban) currently used by the LTPP may need to be further subdivided. 

Generic traffic data items stored in the PLG will be similar to the projected traffic data items 

listed above. However, generic traffic data will not be “base” data but “typical” data. 

Notes on Data Storage

The following notes apply to monitoring, projected, and generic traffic data stored in the 

proposed PLG: 

• To facilitate comparisons and mathematical operations, all truck class distributions and 

axle load spectra will be stored as normalized distributions or spectra. Normalized axle 

load spectra will be accompanied by axle-per-class coefficients. 

• TFs should be calculated using the AASHTO load equivalency factors.[17]

• Data on monthly variation in traffic loads may also be stored in the PLG. However, 

substantial research effort will be required to identify monthly variation in traffic loads 

using LTPP traffic data. 

Selection, Sorting, and Retrieval 

In many respects, the selection, sorting, and retrieval functions of the PLG will be similar to 

corresponding functions in DataPave.[18] In view of the popularity of DataPave software, and of 

similar needs to select, sort, and retrieve LTPP data, the user interface of the PLG software and 

DataPave will be similar. This will make it easier for users to become proficient in using either 

software package. 

Basic site and data selection, sorting, and display functions will include the following modules: 

• Section Selection Module. Selection of any number of LTPP traffic sites from the PLG 

database, based on user-selected filtering and sorting criteria such as participating 

agency, highway functional class, pavement type, and experiment type. 

• Select by Map Module. Selection of any number of LTPP traffic sites from the database 

using an interactive map option. 

• View by Map Module. Selection of traffic data from the PLG database using interactive 

selection of sites from a map display combined with additional filtering/sorting criteria. 

• Presentation Module. Presentation of detailed traffic data (e.g., annual axle load spectra 

for the individual vehicle types) for single sites in tabular and graphical formats. 

• Data Extraction and Retrieval Module. Retrieval of traffic data from the PLG database 

using a variety of filtering and sorting criteria. 
103

Importing and Exporting Data 

To supplement the resident PLG data with the user-supplied data, the user will have the option to 

import additional data into the PLG. User-supplied data may be particularly useful if sitespecific LTPP data are missing. For example, some highway agencies have reported monitoring 

axle weight data that were collected on the LTPP sites during time periods shorter than 24 

consecutive hours. Such data have not been included in the LTPP database and, consequently, 

will not be transferred from the LTPP to the PLG. Yet the use of this type of site-specific data is 

probably preferable to the use of site-related or regional data. 

The storage of user-supplied data may be temporary or permanent. The permanent storage of 

user-supplied data has the advantage of customizing the PLG with local data that can be used in 

the future. The PLG database will distinguish between the LTPP and user-supplied data. 

Appropriate functions will be developed to export data stored in the PLG, as well as traffic 

projections developed through the PLG, for subsequent use. 

Data Comparison and Assessment 

The comparison and assessment function facilitates cross-comparison and evaluation of axle load 

spectra, and other traffic data. It builds on the PLG’s data-storage, selection, sorting, and 

retrieval functions. Whereas the retrieval function will typically display data for one section at 

the time, the comparison and assessment function will display data for several sections 

simultaneously. The comparison and assessment function is unique to the PLG, and will 

enhance the traffic data assessment and projection by helping the user to: 

• Compare the measured or projected site-specific truck class and axle load distributions 

with expected or typical distributions or spectra. 

• Select surrogate truck class or axle load distributions for sites without the site-specific 

distributions (Category 3 and 4 sites). 

• Obtain a better understanding of the spatial and temporal variability of traffic data 

characterized by truck class and axle load distributions. 

The comparison and assessment function should include the following capabilities: 

• The option to provide graphical and tabular displays of several data sets at the same time. 

The typical data sets to be displayed will include monitoring and projected truck class 

and axle load distributions. 

• The ability to display multiple data sets of different origins (LTPP, generic, and usersupplied) and of different types (monitoring, projected, and generic). For example, one 

could compare axle load spectra for vehicle Class 9 measured on an LTPP site in 1994 

with the generic axle load spectra for Class 9 vehicles that are characteristic of rural 

interstates. 

• The option to display statistical measures such as means, ranges, and standard deviations 

for selected traffic data sets. 
104

• The option of displaying not only multiple data sets, but also multiple data types. For 

example, in addition to displaying axle load spectra in a graphical format, the screen 

would also display, at the same time, the corresponding TFs. 

Many features of the data-selection, sorting, retrieval, and comparison and assessment functions 

were implemented in the prototype PLG demonstration software. 

Development of Pavement Loading Estimates for LTPP Sites in Categories 3 and 4 

The PLG will contain guidelines for developing missing truck class distributions (required for 

Category 4 sites) and axle load spectra for individual truck classes (required for Category 3 and 4 

sites), and a mechanism for calculating base annual and cumulative axle load spectra. 

Developing Truck Class Distribution

The user will have several options to develop truck class distributions required for Category 4 

sites: direct input of truck class distributions, use of truck class distributions for the selected 

LTPP sites, and use of generic truck class distributions. 

Direct Input of Truck Class Distribution—For some LTPP sites, additional truck class 

distribution data (i.e., data not included in the LTPP traffic database) may be available. Also, in 

some situations, it may be preferable to modify truck distributions developed by analyzing truck 

distributions on similar sites or suggested by the generic truck class distribution data. Table 18 

provides an example of truck class distribution and contains a provision to accommodate an 

additional truck type designated as “special.” 

Table 18. Example of truck class distribution. 

FHWA Vehicle Class Number 

4 5 6 7 8 9 10 11 12 13 Special 

Fraction of Commercial Vehicles 

0.01 0.11 0.06 0.00 0.10 0.65 0.01 0.03 0.02 0.01 0.00 

Truck Class Distributions for Selected Sites—The estimation of a missing truck class distribution 

would typically utilize site-related data or data on similar sites in the same agency. The user will 

be able to employ the comparison and assessment function to display the data for the selected 

sites graphically, and to calculate the means of the selected truck class distributions. 
105

Generic Truck Class Distribution—The use of generic truck class distributions is an option if 

there are no suitable surrogate truck class distribution data from other (nearby or regional) sites. 

Generic truck class distributions will be provided for all highway functional classes. 

Development of Axle Load Spectra for Individual Truck Classes

The user will have three options to develop axle load spectra for the individual truck classes 

required for Category 3 and 4 sites: direct input of axle load spectra, use of axle load spectra for 

selected sites, and use of generic axle load spectra. The direct input of axle load spectra and the 

use of axle load spectra for selected sites would use similar procedures to select and modify 

surrogate data as outlined for the selection of truck class distributions. Development of the axle 

load spectra will be also facilitated by the comparison and assessment function of the PLG. 

It is important to allow the user to make adjustments to the truck class and axle load distributions 

using data from other sites or using generic data. Users may have various bits of information 

about the composition of traffic stream that could improve the estimates (for example, the 

proportion of short and long trucks, or the number of buses using an urban highway). 

Development of Combined Axle Load Spectra and Cumulative Traffic Load Estimates 

The calculation of the projected traffic data, such as the annual base spectra or cumulative axle 

load estimates, can be accomplished by using the existing LTPP traffic projection procedures. 

However, before exporting the projected traffic data from the PLG for subsequent use, the user 

may want to see the overall results of the traffic projection process expressed in terms of ESALs, 

and have the option of carrying out sensitivity analysis by quantifying the consequences of 

selecting different truck class and axle load distributions. 

In addition, the knowledge of the overall traffic projection results (for example the average TF or 

the annual number of ESALs) will provide valuable feedback to the process of selecting truck 

class and axle load distributions, and provide additional assurance to the analyst that the traffic 

projection results are sound. Consequently, the option to calculate the projected traffic data 

within the PLG is required and should be implemented. 

Development of Generic Truck Characteristics and Data-Selection Guidelines 

Extensive additional statistical and engineering analyses are needed to develop generic truck 

characteristics (in terms of truck class and axle load distributions), and guidelines for the use of 

the generic and other non-site specific truck characteristics in the projection procedure. These 

analyses will use LTPP and other traffic data. 

The main features of this effort may include the development of: a new highway classification 

system; generic truck class distributions and generic axle load spectra for individual truck 

classes; and guidelines for judicious selection of missing truck class and axle load distributions 

for Category 3 and 4 sites. 
106

Highway Classification System 

Generic traffic data will be defined for typical highway functional classes. At present, the LTPP 

sites are classified into the six rural highway functional classes and five urban functional classes 

listed below: 

• Rural Principal Arterial—Interstate. 

• Rural Principal Arterial—Other. 

• Rural Minor Arterial. 

• Rural Major Collector. 

• Rural Minor Collector. 

• Rural Local Collector. 

• Urban Principal Arterial—Interstate. 

• Urban Principal Arterial—Other Freeways or Expressways. 

• Urban Other Principal Arterial. 

• Urban Minor Arterial. 

• Urban Collector. 

These classes are broad and have a universal applicability. However, they may not be specific 

enough for the projection of traffic loads. The development of LTPP functional classes for the 

projection of traffic should be based on the assessment of commonalities in truck class and axle 

load distributions among LTPP sites. Consideration should also be given to the development of 

regional highway functional classes. 

The number of functional classes for which the generic traffic data will need to be developed 

may be different from the existing LTPP functional highway classes. For example, the 2002 

Pavement Design Guide is expected to use 17 distinct highway classification groups, called 

Truck Traffic Classifications (TTC): 

• TTC 1: Major Single-Trailer Truck Route (Type I). 

• TTC 2: Major Single-Trailer Truck Route (Type II). 

• TTC 3: Major Single- and Multi-Trailer Truck Route (Type I). 

• TTC 4: Major Single-Trailer Truck Route (Type III). 

• TTC 5: Major Single- and Multi-Trailer Truck Route (Type II). 

• TTC 6: Intermediate Light and Single-Trailer Truck Route (I). 

• TTC 7: Major Mixed Trailer Truck Route (Type I). 

• TTC 8: Major Multi-Trailer Truck Route (Type I). 

• TTC 9: Intermediate Light and Single-Trailer Truck Route (II). 

• TTC 10: Major Mixed Trailer Truck Route (Type II). 

• TTC 11: Major Multi-Trailer Truck Route (Type II). 

• TTC 12: Intermediate Light and Single-Trailer Truck Route (III). 

• TTC 13: Major Mixed Trailer Truck Route (Type III). 

• TTC 14: Major Light Truck Route (Type I). 
107

• TTC 15: Major Light Truck Route (Type II). 

• TTC 16: Major Light and Multi-Trailer Truck Route. 

• TTC 17: Major Bus Route. 

Generic Truck Class and Axle Load Distributions 

The development of generic truck class distributions should start with the evaluation of the truck 

class distributions on all LTPP sections to identify commonalities in the distribution for various 

highway functional categories. 

The development of generic axle load spectra may draw on the parallel between TFs (number of 

ESALs per truck) and the axle load spectra. The relationship between TFs and axle load spectra 

for individual truck classes was discussed in the section titled “Conceptual outline of PLG.” 

There is a body of knowledge dealing with truck axle loads in terms of TFs. Several highway 

agencies have evaluated the temporal and spatial variation of TFs, and many highway agencies 

use generic and other TFs in the pavement design process.[19,20] The variation in TFs is to some 

degree indicative of the variation in the corresponding axle load spectra. However, our current 

understanding of the expected axle load spectra on different class highways is limited and will 

need to be developed by an in-depth analysis of LTPP and other traffic data. 

The understanding of the variation of truck class and axle load distributions will affect the 

division of the highway network into highway functional classes. Consequently, the 

enhancement of the existing LTPP highway functional classes, or the development of new 

classes, and the development of generic truck class and axle load distributions will need to be 

interactive. 

Guidelines for Data Selection 

Guidelines for selecting surrogate truck class and axle load distributions for Category 3 and 4 

sites will help a user who is estimating missing data. The guidelines will use new information on 

the characteristics of truck flows (expected or generic truck class and axle load distributions on 

characteristic highway links) and the PLG comparison and assessment functions. 

Prototype Demonstration Software 

To cope with the large amount of traffic data, the proposed PLG will function as a stand-alone 

software product operating as a relational database with many additional built-in computational 

and reporting features. Prototype software was developed to illustrate the operation of the PLG. 

The development of final PLG software will require, in addition to the programming effort, 

considerable engineering and analytical effort to develop generic truck class and axle load 

distributions and provide guidelines for their use. For this reason, the prototype PLG software 

demonstrated only data-management, comparison, and assessment functions. 
108

Example Use of PLG 

This section contains two examples of estimating annual axle loads for all in-service years for 

LTPP sites using the proposed PLG. One is for Category 3 sites that lack axle load data, the 

other for Category 4 sites that lack axle load data and truck class distribution data. These 

examples: 

• Describe the traffic projection procedure for sites lacking site-specific data (Category 3 

and 4 sites) using realistic examples. 

• Illustrate how the proposed PLG can facilitate and improve the projection process and the 

functionality of the PLG. 

• Estimate the consequences of using surrogate traffic data instead of site-specific data. 

These two examples use the LTPP traffic projection procedure outlined in chapter 2 of this report 

(and documented in the Phase 1 report), and they assume the existence of the proposed PLG.[1] 

To estimate the consequences of using surrogate data, the sites selected for the examples actually 

had site-specific monitoring truck class and axle load distribution data; however, it was assumed 

that the site-specific data were missing and had to be estimated using surrogate data. Thus, it 

was possible to compare traffic loads estimated using surrogate data with traffic loads based on 

site-specific monitoring data. The comparison was done in terms of the annual axle load spectra 

and the cumulative number of ESALs. 

Use of PLG for Category 3 Sites 

The example of traffic projection for Category 3 sites was based on California LTPP site 068150, 

located on a two-lane rural minor arterial highway east of Los Angeles, CA. It was assumed that 

the site had truck class distribution data (annual number of trucks that belong to different vehicle 

classes) but lacked axle load data. The task was to develop (select) surrogate axle load spectra 

for individual truck classes and combine them with the known (site-specific) number and type of 

trucks to calculate annual axle load spectra. 

The PLG database can be queried to facilitate the selection of axle load data. This can be done 

by searching the database for similar sites in California (for example, all sites on rural minor or 

principal arterial highways) or in other jurisdictions. The search of the prototype PLG database 

identified two similar California sites with monitoring axle load data (site 062040 located on a 

two-lane rural principal arterial highway east of San Francisco, CA, and site 066044 located on a 

four-lane principal arterial highway south of Eureka, CA). The truck class distributions on the 

three California sites are compared in figure 32, which was produced by the prototype PLG 

software; it indicates that the truck distributions on the three sites are similar. While the 

similarity of truck class distributions does not mean the similarity in axle loads, it does provide 

assurance that the sites serve similar traffic flows and are on highways with similar functional 

classification. 
109

Figure 32. Comparison of truck class distributions for sites 062040, 066044, and 068150. 

The required surrogate axle load data are needed as axle load distributions (spectra) for 

individual truck types. The prototype PLG database was used to compare axle load spectra on 

nearby sites. An example of the spectra comparison for sites 062040 and 066044 is provided in 

figures 33 and 34 for single and tandem axles for Class 9 vehicles (5-axle single-trailer trucks), 

respectively. Also shown in figures 33 and 34 is the surrogate (computed) axle load spectrum 

for site 068150 obtained as the mean spectrum for sites 062040 and 066044. It should be noted 

that figures 33 and 34 show the example of axle load spectra for only 1 of the 10 truck types for 

which the surrogate spectra are required. 

The process of assessing available data and selecting surrogate data would benefit from the 

comparison of the selected spectra (shown in figures 33 and 34) with generic or typical spectra. 

However, the generic spectra are not available in the prototype PLG. 
110

Figure 33. Comparison of single axle load distributions for vehicle Class 9 for sites 

062040 and 066044 with computed mean distribution. 

Figure 34. Comparison of tandem axle load distributions for vehicle Class 9 for sites 

062040 and 066044 with computed mean distribution. 
111

The annual axle load spectrum for site 068150 was obtained by combining the surrogate axle 

load spectra for the individual truck classes (expressed as normalized spectra) and the sitespecific (classified) annual truck volumes. The resulting estimated annual spectrum for site 

068150 is presented in figure 35. The spectrum was obtained by calculations done outside the 

prototype PLG. Such calculation should be done by the PLG, and the PLG can then be used to 

compare the calculated annual spectra using surrogate data with annual spectra obtained for 

nearby sites or with typical spectra. It should be noted that the site-specific spectrum for site 

068150 would not exist for Category 3 sites. This is the spectrum that is to be estimated using 

surrogate data. It has been included in figure 35 for comparison purposes. 

The comparison of annual axle load spectra for site 068150 in figure 35 indicates that the actual 

monitoring spectrum and the estimated surrogate spectrum are quite similar. For example, the 

three peaks of the single axle load distribution are duplicated quite well. The annual axle load 

spectrum for site 068150 in figure 35 is for all vehicle classes combined and represents, using the 

LTPP projection terminology, the base annual spectrum, or surrogate base annual spectrum. 

To quantify the consequences of using the surrogate base annual spectrum, the surrogate base 

annual spectrum was used to calculate the number of cumulative ESALs. The annual base 

spectrum was combined with the projected growth factor (established previously as part of traffic 

load projection for site 068150) to obtain annual axle load spectra, and then the annual spectra 

were expressed in terms of ESALs. The result of this calculation is presented in figure 36, which 

also shows the corresponding results obtained for monitoring site-specific axle load spectra 

(labelled Site-Specific Category 2). Figure 36 include a “Projected Annual ESALs” sheet (which 

was part of site-specific reports described in chapter 2) for site 068150 adapted to also display 

ESALs estimated using surrogate data. 

As already indicated by the similarity of the base annual spectra for Class 9 vehicles (figures 33 

and 34), ESALs are also similar. The cumulative number of ESALs using surrogate spectra was 

2.06 million, while the corresponding number of ESALs for site-specific spectra was 2.2 million 

(figure 36). 

Use of PLG for Category 4 Sites 

Arizona site 041017, located in the northbound direction on a four-lane rural interstate south of 

Tucson, AZ, was used to illustrate the use of the PLG to obtain traffic projection for Category 4 

sites. Even though site-specific axle load data were available for this site, it was assumed that 

these data were not available and that both truck class and axle load distributions had to be 

estimated. It was assumed that the only available information about truck loads on this site were 

AADT truck volumes (assumed to be 540 in 1998). 

The site selected to obtain surrogate data for the subject site was another Arizona site, 041007, 

located in the westbound direction of a rural interstate west of Phoenix, AZ. The 1998 annual 

AADT truck volume on this site, 3525, was approximately 7 times larger than the corresponding 

truck volume on the subject site. Thus, the two sites are located on different interstates and carry 

different truck volumes. While it is possible to use data for other sites, or to use a combination 

of data obtained for several sites, this example demonstrates the use of a single site. 
112

Figure 35. Comparison of site-specific and surrogate base annual spectra for site 068150. 

Single Axle

0

20000

40000

60000

80000

100000

120000

140000

0

2000400060008000

1000012000140001600018000200002200024000260002800030000

Axle Loads, lb

Axle Counts

 Tandem Axle

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

0

40008000

12000160002000024000280003200036000400004400048000520005600060000

Axle Loads, lb

Axle Counts

Triple Axle

0

100

200

300

400

500

600

700

800

900

0

6000

12000180002400030000360004200048000540006000066000

Axle Loads, lb

Axle Counts

Site-Specific (for 068150) Surrogate (from mean of 066044 and 062040)

1 lb. = 2.202 kg
113

Figure 36. Comparison of projected, historical, and monitoring annual ESALs for site 068150. 

The truck class distribution for the two sites is compared in figure 37. The data for the subject 

site shown in figure 37 would not exist for a Category 4 site and are included for comparison 

purposes only. To determine whether the selected surrogate truck distribution is typical, the PLG 

will contain not only truck class distributions for other Arizona sites, but also generic or typical 

truck class distributions for typical highway functional classes. 

 Annual ESALs 

Projected Year Historical Monitoring Site-Specific Spectra 

Category 2 

Surrogate Spectra 

Category 3 

1984 – – 108,789 102,577 

1985 63,000 – 113,118 106,714 

1986 67,000 – 117,675 111,000 

1987 67,000 – 122,388 115,443 

1988 96,000 – 127,274 120,124 

1989 81,000 – 132,356 124,930 

1990 83,000 – 137,657 129,895 

1991 83,000 – 143,154 135,134 

1992 171,000 – 148,921 140,544 

1993 473,000 – 154,887 146,211 

1994 389,000 – 161,068 152,068 

1995 – 176,297 167,491 158,168 

1996 – 177,884 174,487 164,528 

1997 – 169,527 181,354 171,104 

1998 – – 188,601 177,975 

Cumulative ESALs 2,179,220 2,056,415 
114

Figure 37. Comparison of truck class distributions for sites 041007 and 041017. 

The base annual spectrum for site 041017 was obtained by combining the site-specific annual 

truck volumes with surrogate truck classification and axle load spectra (for individual vehicle 

types) obtained from site 041007. The resulting surrogate base annual axle load spectrum is 

compared with the spectrum obtained using site-specific monitoring axle load data in figure 38. 

The projected annual spectra for all in-service years are compared, in terms of ESALs, in figure 

39. Figure 39 is a “Projected Annual ESALs” sheet for site 041017 also adapted to display 

ESALs estimated using surrogate data. The results in figures 38 and 39 show a very good 

agreement between traffic loads estimated using surrogate data and site-specific data. For 

example, the number of ESALs estimated using site-specific axle load data was 2.1 million, 

while the number of ESALs estimated using surrogate data was 2.9 million. 

Example Summary 

The two examples provided in this section indicate that reasonable traffic loading estimates can 

be obtained by judiciously selecting surrogate data (in the absence of site-specific truck class and 

axle load data). The relatively close agreement between the site-specific and surrogate 

projections may leave a false impression that the site-specific axle load data are not important 

because they can be estimated using surrogate data. While the judicious selection of surrogate 

data is important, and the proposed PLG can facilitate and guide the selection, surrogate data can 

never replace site-specific data. Also, the greater the amount of site-specific data, the easier it is 

to develop appropriate surrogate data. 
115

Figure 38. Comparison of site-specific and surrogate base annual spectra for 041017. 

1 lb = 2.202 kg
116

Annual ESAL 

Projected Year Historical Monitoring Site-Specific Spectra 

Category 2 

Surrogate Spectra 

Category 4 

1976 948,000 – 58,254 80,142 

1977 1,089,000 – 60,593 83,384 

1978 806,000 – 63,011 86,725 

1979 792,000 – 65,514 90,192 

1980 849,000 – 68,209 93,802 

1981 821,000 – 70,925 97,594 

1982 792,000 – 73,740 101,483 

1983 821,000 – 76,717 105,564 

1984 877,000 – 79,785 109,804 

1985 962,000 – 82,964 114,213 

1986 1,075,000 – 86,276 118,801 

1987 1,160,000 – 89,785 123,561 

1988 1,556,000 – 93,361 128,539 

1989 1,556,000 – 97,104 133,668 

1990 1,273,000 – 100,986 139,034 

1991 230,000 – 105,026 144,581 

1992 240,000 – 109,220 150,414 

1993 – 102,230 113,585 156,450 

1994 150,000 – 118,142 162,729 

1995 150,000 – 122,857 169,267 

1996 150,000 – 127,755 176,025 

1997 – 132,850 133,030 183,091 

1998 – – 138,279 190,454 

Cumulative 2,135,118 2,939,517 

Figure 39. Comparison of projected, historical, and monitoring annual ESALs for site 041017.
117

CHAPTER 6. SUMMARY AND RECOMMENDATIONS 

Summary

A principal objective of the LTPP program is to quantify the relationship between pavement 

performance and traffic loads. Consequently, traffic data collection and analysis, required to 

obtain traffic loads, is the key activity within the LTPP program. 

In 1998, the FHWA sponsored a study to estimate traffic loads on LTPP sites. Phase 2, 

described in this report, included the assessment of the overall quality of traffic data for all 890 

LTPP traffic sites, the distribution of summary traffic data reports to all participating agencies 

describing what traffic data are available for the sites in their jurisdictions, and soliciting their 

input regarding traffic projections and the projection of axle loads for all LTPP sites with 

adequate traffic data. 

Axle load projections were developed for all in-service years for 558 LTPP traffic sites that had 

adequate traffic monitoring data in the IMS database. The axle load projections were expressed 

as annual axle load spectra for single, tandem, and triple axles, and were placed into IMS 

computed parameter tables. 

The main traffic data assessment and traffic projection activities carried out during the course of 

the Phase 2 study were: 

• Data assessment and traffic projection carried out for all individual LTPP traffic sites. 

• Preparation of the LTPP Traffic Feedback and Resolution Packages for all participating 

agencies; these summarized traffic data assessment and traffic projection results. 

• Review of LTPP Traffic Feedback and Resolution Packages by RCOs. 

• Review of LTPP Traffic Feedback and Resolution Packages by participating agencies. 

• Implementation of review comments received from participating agencies. 

Because of the large variation in the quality of traffic data and the uncertainty associated with 

traffic load projections developed for individual sites, traffic projections were assigned 

projection confidence codes to characterize the level of confidence associated with projected 

traffic loads: 

• A: Acceptable projection results. Cumulative ESAL estimates are probably within ±50 

percent of the actual cumulative ESAL values. 

• Q: Questionable projection results. Cumulative ESAL estimates are probably within 

±100 percent of actual cumulative ESAL values. 

• N: Not available projection results. Axle load estimates could not be provided at the 

time. 

Of the 890 LTPP traffic sites, 194 (21.8 percent) were assigned the acceptable projection 

confidence code and 364 (40.9 percent) were assigned the questionable projection confidence 

code. For 269 sites (30.2 percent), only the projection of AADT truck volumes was provided 

because of the unavailability of adequate site-specific axle load data. Many of these 269 sites 
118

had site-specific axle load data in the database, but the data were considered inadequate to be 

used for the projection of axle loads. No traffic projections were carried out for 63 sites because 

of lack of traffic data. The projection results for all 890 sites are summarized in appendix A. 

The projection results were provided for the LTPP sites with unique traffic identification 

numbers. The total number of all LTPP sections is larger than the number of the LTPP sites 

because one traffic site may provide traffic data for several sections. 

No axle load projections could be developed for 332 LTPP sites because of inadequate or 

missing data. Nonetheless, the 332 sites, representing 37 percent of all LTPP sites, contain a 

wealth of information regarding pavement materials, environment, and pavement performance. 

Without the required traffic data, this information cannot be utilized for the development of loadrelated pavement performance models. In order to provide traffic projections for these sites, 

truck classification and axle load distributions must be estimated in lieu of missing site-specific 

data. Any such estimates must be done judiciously. However, no guidelines exist for the 

estimation of truck class and axle load distributions, and the knowledge of typical or 

characteristic distributions is widely dispersed. 

To overcome the difficulty of estimating the missing traffic data, it was proposed to develop the 

LTPP PLG. This report contains a description of the purpose, design parameters, and 

functionality of the PLG, a blueprint for the development of the PLG, and two examples of using 

the PLG to obtain traffic load projections for LTPP sites without site-specific truck class or axle 

load data. 

Conclusions 

• Traffic data collection in the field is essential for obtaining reliable traffic load data but 

not sufficient to obtain traffic loads for all years the pavement was in service because it is 

not possible to collect past traffic data or to collect traffic data all the time. To obtain 

pavement loads for all years the LTPP sites were in service, it is necessary to use the 

combination of traffic data (both historical and monitoring) and mathematical modeling 

referred to as traffic projection. 

• The projection procedure used to estimate traffic loads reflects the quantity and quality of 

available historical and monitoring data. However, the projection procedure cannot fully 

overcome limitations caused by the presence of questionable data in the database and the 

use of data that has been factored to annual values. Recommendations to overcome the 

limitations caused by data quality concerns and factoring of data to annual values are 

presented in the next section. 

• Considering that only about 23 percent of all GPS sites, and about 10 percent of all SPS 

sites, have acceptable axle load estimates, greater attention should be paid to the quality 

of traffic data. About 37 percent of all sites (332 sites) had insufficient or missing sitespecific truck class or axle weight distribution data to carry out axle load projections. To 

obtain traffic load estimates for those 37 percent, replacement traffic data will need to be 

used. 
119

• The LTPP traffic database provides good representation of traffic flows on major 

highways. Of the 558 sites with traffic projections assigned the acceptable or 

questionable projection confidence codes, 471 sites (84.4 percent) are located on rural or 

urban interstates and principal arterial highways. The minimum annual average daily 

truck volume on LTPP sites ranged from 30 trucks per day for a site located on rural 

minor collector highway to 6310 trucks per day on a site located on an urban interstate. 

• The mean annual growth in truck volumes between 1994 and 1998 ranged from 6.5 

percent for urban freeways and expressways to 3.0 percent for rural minor arterial 

highways. The mean annual growth in truck volumes on rural interstates was 4.6 percent. 

• Since 1993, there has been a steady decline in the amount of monitoring axle load data 

available for traffic projection. The number of annual axle load spectra that could be 

used for the projection declined from 231 sections in 1993 to 150 sections in 1998. In 

other words, in 1998, of the 890 sites, only 150 (16.8 percent) had axle load spectra that 

could be used for projection of axle loads that yielded acceptable or questionable 

confidence codes. Only 51 sites in 1998 (5.7 percent of all sites) had axle loads yielding 

acceptable axle load projections. The decline in the amount of acceptable traffic data 

highlights the need for a renewed data-collection effort. It also highlights the importance 

of traffic modeling to extend limited sampling data and to compensate for the lack of 

data, and the need for the proposed LTPP PLG. 

Recommendations 

This section describes recommended future analytical, modeling, and traffic data-management 

activities needed to improve traffic load estimates for LTPP sites. There is an obvious need to 

collect more traffic data and, particularly, to collect high-quality traffic data; however, the 

activities recommended in this section are concerned only with traffic data analysis and 

management and not with data collection. The recommendations are divided into: 

• Short-term activities required to carry out traffic projection, initiated in Phase 2, to 

completion. 

• Long-term activities that go beyond the scope and methodology of Phase 2 work and are 

needed for better utilization of the existing traffic data. 

Short-Term Activities 

Responding to Participating Agencies

At the conclusion of Phase 2, approximately 60 percent of the participating agencies had not yet 

completed the review of the initial traffic projections. When the agencies’ reviews of the initial 

traffic projections are completed, the reviews should be clarified and discussed with the 

participating agencies, and utilized to develop reviewed projections. 
120

Developing the LTPP PLG

The LTPP PLG is essential for the estimation of pavement loads for the LTPP sites that do not 

have site-specific traffic monitoring data. Of 890 LTPP sites, 332 are without traffic load 

projections. To provide traffic load projections for these sites, axle load spectra and/or truck 

classification volumes must be estimated in lieu of missing data. The proposed PLG will provide 

a knowledge base, guidelines, and computational software to facilitate the estimation of traffic 

loads for the LTPP sites without site-specific traffic data. It will also be a very useful product 

emerging from the LTPP program for estimating traffic loads for general pavement design 

purposes. For these reasons, it is recommended to proceed with the development of the proposed 

PLG on a priority basis. 

Completing Traffic Projection for all LTPP Sites

After the development of the LTPP PLG, the initial traffic projection for the remaining 332 

LTPP sites without traffic load projections should be carried out, sent to the participating 

agencies for review, and included in the database. The completion of the initial traffic 

projections for all LTPP sites will enable pavement performance analyses that require the 

knowledge of traffic loads for the additional 334 LTPP sites. Potentially, the number of 

additional LTPP sections that could be used in the load-dependent performance modeling could 

be even greater because more than one LTPP section could use traffic data from a single LTPP 

traffic site. 

Traffic projections carried out in Phase 2 were done for the period from the time the pavement 

was open to traffic to 1998. The projections should be updated to incorporate additional traffic 

data when the data become available. 

Long-Term Activities 

The recommended long-term activities include: 

• Development of an action plan. 

• QA of traffic data. 

• Use of monthly traffic data. 

• Regional traffic modeling. 

Development of an Action Plan

The action plan should identify all issues facing LTPP traffic data collection and analysis and 

recommend specific actions for their resolution. Several components of such an action plan are 

recommended here. 
121

Quality Assurance of Traffic Data

The previous traffic data QA process has resulted in traffic data that cannot be used without 

reservations. As documented in chapter 3, about 50 percent of all annual axle load spectra stored 

in the IMS database were judged unacceptable for estimating traffic loads. 

The scope and effectiveness of the original LTPP traffic data QA process faced several 

limitations: 

• The previous LTPP traffic data QA reports were carried out for periods equal to, or less 

than, 1 year. Consequently, it was not possible to assess long-term trends in historical 

and monitoring traffic data, such as annual differences in monitoring truck volumes (e.g., 

to compare AADT truck volumes for two consecutive years) and axle load spectra. 

• Comparisons between historical and monitoring traffic data were not carried out, and no 

long-term assessment of trends in traffic data (spanning the entire time the pavement 

sections were in service) was performed. 

• When the original traffic data QA work started, monitoring traffic data was available for 

only a few years, and often the amount of data was insufficient to establish trends and to 

obtain an understanding of what type of data are expected on a particular site. This 

paucity of data has made it difficult to screen data and reject suspect data confidently. 

• Vehicles that could not be properly classified by the monitoring equipment (usually 

referred to as Class 14 vehicles) were typically not included in the traffic data QA 

process. 

These barriers have been removed through: the development of the computerized procedure to 

display and summarize long-term trends in traffic data developed under this project; better 

understanding of trends and variation in traffic data; and the availability of additional traffic data. 

The anticipated development of the LTPP PLG will also contribute to the body of knowledge 

that can be used to assess and verify traffic data. 

In summary, there is a fundamental need to carry out a basic traffic data QA process to better use 

traffic data that have been collected in the field. The traffic data QA process is necessary to 

provide reliable traffic load estimates. The collection of traffic data in the field is very expensive 

considering the cost of purchasing, installing, and maintaining AVC or WIM system equipment, 

and data collection and processing. The QA process will help agencies obtain a return on this 

investment and enhance the LTPP data. 

The following activities may be required to carry out the basic traffic data QA process and to 

remove nonsensical data from the database: 

• Reprocessing of some of the raw traffic data (for example, for agencies that have reported 

a large percentage of Class 14 vehicles). 

• Improvements in the software used to produce LTPP traffic data QA reports so the graphs 

displaying traffic data are more user-friendly and understandable to representatives of 

participating agencies. 
122

• Preparation of new LTPP traffic data QA reports (or re-examination of the old reports) 

and identification of erroneous data. 

• Purging erroneous data. 

Use of Monthly Traffic Data

If an annual axle load spectrum were judged to be unacceptable, all axle loads constituting the 

annual spectrum were rejected and not used in the projection. Yet annual axle loads may consist 

of an amalgam of both valid and questionable axle load data collected at different times of the 

year. Rejecting the entire annual spectrum from the projection process could have serious 

consequences for sites for which only a few monitoring annual axle load spectra are available. 

Using shorter time periods, such as a month, enables a better utilization of available traffic data 

because axle load spectra are accepted or rejected as monthly, not annual, chunks. 

Consequently, a portion of the annual axle load data that would have been rejected as part of the 

annual data could be utilized as monthly data. Monthly axle load spectra are not available and 

need to be calculated from the raw binary files. Because of the large computational 

requirements, the monthly axle load spectra can be calculated only through CTDB data 

managers. 

The use of annual data for the projection of traffic loads has been appropriate as the starting 

point. However, to overcome the disadvantages of using annual data, it is recommended to 

develop software to calculate monthly traffic data and, in particular, monthly axle load spectra, 

and to use the monthly axle load spectra for the projection of axle loads. The availability of 

monthly data will also provide a measure of seasonal variation in traffic loads. 

Regional Traffic Modeling

Regional traffic modeling may be the best and most cost effective way to extend limited LTPP 

data and supplement them with data that are not part of the LTPP database. This situation 

specifically applies to agencies such as Florida and Texas that have a large corporate traffic 

database containing extensive WIM-type data but relatively little LTPP data. The feasibility of 

this approach (utilizing both LTPP and non-LTPP data for LTPP traffic projection) should be 

investigated using a pilot study in one or two agencies. 

The proposed PLG, with its database management, display, and comparison features, will also 

facilitate the utilization of regional traffic data in lieu of missing LTPP data. 
123

APPENDIX A. SUMMARY OF RESULTS 

This appendix provides summary of traffic projection results for all 890 sites with unique traffic ID 

numbers that are currently in the LTPP program and are included in the IMS database. The summary 

results are given in table 21. Traffic and other data were not available for several sites; a hyphen in a cell 

in table 21 indicates the unavailability of the data. The listing contains the following 14 variables: 

1. Agency: Postal abbreviation for the State or Province of the participating agency. 

2.

3. SHRP ID: Site identification number assigned by LTPP program (SHRP stands for 

Strategic Highway Research Program). 

4. Traffic ID: Unique ID obtained by the combination of STATE_CODE (a 2-digit code 

assigned to each participating agency) and SHRP ID. 

5. GPS/SPS: Code indicating whether the section is a GPS (G) or SPS (S) section. 

6. Exp. No.: Code indicating to which LTPP experiment (exp.) the pavement section is 

assigned. 

7. Highway Functional Class: Code indicating functional classification of the highway site. 

Table 19. Codes in the IMS database. 

Highway 

Functional Class Description

1 Rural Principal Arterial—Interstate 

2 Rural Principal Arterial—Other 

6 Rural Minor Arterial 

7 Rural Major Collector 

8 Rural Minor Collector 

9 Rural Local Collector 

11 Urban Principal Arterial—Interstate 

12 Urban Principal Arterial—Other Freeways and Expressways 

14 Urban Other Principal Arterial 

16 Urban Minor Arterial 

17 Urban Collector 

19 Urban Local 
124

8. Projection Cat.: 

Table 20. Code indicating projection category (2 to 5). 

Projection Category Description 

2 Sites with truck class and axle weight distribution data. 

3 Sites with truck class distribution data but without site-specific 

axle weight data. 

4 Sites with truck volume data but without site-specific truck 

class and axle weight distribution data. 

5 Sites without traffic data. 

Note: Projection Category 1 is reserved for the sites with truck class and axle weight 

distribution data that can be used for the projection of monthly variation in traffic loads. 

The adequacy of data to project monthly variation in axle loads was not assessed. 

9. Projection Code: Projection confidence code: 

• A—Acceptable. 

• Q—Questionable. 

• N—Not available. 

10. Projection Status: Code indicating projection status: 

• I—Initial. 

• R—Reviewed. 

11. Pav. Type: Code indicating pavement type: 

• F—Flexible. 

• R—Rigid. 

12. Last Pr. Year: Last year for which traffic projections (pr.) are provided in the 

last three columns of table 21. 

13. Truck Volume: Projected average annual daily truck volume (in the LTPP traffic 

lane) in the last year (item 11, above) for which the projection is 

provided (trucks per day rounded to the nearest 10). 

14. Annual Growth Rate: Projected average annual growth rate (for the average daily truck 

volume) for the last 5 years for which the projection is provided. 

15. ESALs: Projected total annual ESALs (in the LTPP traffic lane) for the last 

year for which the projection is provided (rounded to the nearest 

10). 
125

Table 21. Summary of traffic projection results. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

AL 0100 010100 S 1 2 5 N I – – – – – 

AL 0600 010600 S 6 – 5 N I – – – – – 

AL 1001 011001 G 6B 2 2 A I F 1998 680 1.60 268,740 

AL 1011 011011 G 2 6 3 N I F 1998 190 0.49 – 

AL 1019 011019 G 6S 2 2 Q I F 1998 440 0.77 194,420 

AL 1021 011021 G 2 6 2 Q I F 1998 390 4.12 197,910 

AL 3028 013028 G 3 11 2 Q I R 1998 1,550 8.86 668,160 

AL 3998 013998 G 7C 1 2 A I R 1994 2,350 3.81 1,088,600 

AL 4007 014007 G 4 11 2 Q I R 1998 960 4.22 743,190 

AL 4073 014073 G 2 2 2 Q I F 1998 500 6.52 159,830 

AL 4084 014084 G 4 11 2 Q I R 1998 3,170 4.00 2,271,870 

AL 4125 014125 G 2 14 2 Q I F 1998 750 2.00 358,400 

AL 4126 014126 G 1 1 2 Q I F 1998 3,030 3.00 876,890 

AL 4127 014127 G 6B 2 3 N I F 1998 310 0.42 – 

AL 4129 014129 G 6B 2 2 Q I F 1997 460 0.00 137,070 

AL 4155 014155 G 6B 2 3 N I F 1998 770 6.00 – 

AL 5008 015008 G 5 1 2 Q I R 1998 3,120 6.14 2,226,460 

AL 6012 016012 G 6A 1 2 A I F 1998 2,780 3.90 1,482,520 

AL 6019 016019 G 6D 1 3 N I – 1998 2,320 5.57 – 

AK 1001 021001 G 1 2 2 Q I F 1998 200 0.68 26,360 

AK 1002 021002 G 6B 2 2 Q I F 1998 200 0.68 26,960 

AK 1004 021004 G 6B 14 2 Q I F 1998 360 3.77 86,040 

AK 1008 021008 G 6A 6 2 Q I F 1998 120 2.00 38,440 

AK 6010 026010 G 6A 1 2 Q I F 1996 320 1.87 58,140 

AK 9035 029035 G 6B 2 2 Q I F 1998 100 6.00 33,300 

AZ 0100 040100 S 1 2 2 A R F 1998 900 9.27 272,650 

AZ 0200 040200 S 2 1 2 A R R 1998 3,290 6.00 1,706,650 

AZ 0500 040500 S 5 1 2 A R F 1998 720 3.00 283,870 

AZ 0600 040600 S 6 1 2 A R R 1998 2,620 5.00 1,917,230 

AZ 1001 041001 G 1 1 2 A R F 1996 2,620 7.17 913,190 

AZ 1002 041002 G 1 1 2 A R F 1998 2,200 7.77 841,830 

AZ 1003 041003 G 6S 1 3 A R F 1998 3,400 7.05 1,231,890 

AZ 1006 041006 G 6S 1 2 A R F 1998 3,160 6.62 1,216,480 

AZ 1007 041007 G 6S 1 2 A R F 1998 3,530 -11.25 1,308,390 

AZ 1015 041015 G 1 1 3 A R F 1998 620 7.42 253,850 
126

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

AZ 1016 041016 G 1 1 2 A R F 1998 540 7.37 219,680 

AZ 1017 041017 G 1 1 2 Q R F 1998 540 4.00 138,280 

AZ 1018 041018 G 1 1 3 A R F 1998 520 5.23 219,040 

AZ 1021 041021 G 1 1 3 A R F 1998 2,240 11.25 1,400,330 

AZ 1022 041022 G 1 1 3 A R F 1998 2,240 11.25 1,348,000 

AZ 1024 041024 G 1 1 2 A R F 1998 2,080 6.70 1,156,220 

AZ 1025 041025 G 1 1 2 A R F 1998 2,130 7.35 1,305,460 

AZ 1034 041034 G 1 6 2 Q R F 1998 460 3.81 73,190 

AZ 1036 041036 G 1 2 3 A R F 1998 800 12.15 252,190 

AZ 1037 041037 G 1 2 3 N R F 1998 300 7.49 – 

AZ 1062 041062 G 6S 1 2 A R F 1998 2,190 8.75 1,494,820 

AZ 1065 041065 G 6S 1 2 A R F 1998 2,140 9.28 762,300 

AZ 6053 046053 G 6A 1 2 A R F 1997 2,080 4.00 1,003,140 

AZ 6054 046054 G 6A 1 2 Q R F 1998 570 1.63 164,270 

AZ 6055 046055 G 6A 2 2 Q R F 1998 790 1.40 167,870 

AZ 6060 046060 G 6A 1 2 A R F 1998 590 6.68 223,870 

AZ 7079 047079 G 5 12 2 A R R 1998 660 13.10 193,680 

AZ 7613 047613 G 3 14 2 Q R R 1998 2,000 9.86 502,380 

AZ 7614 047614 G 3 1 2 A R R 1998 1,700 4.58 777,360 

AR 0100 050100 S 1 2 2 Q I F 1998 820 5.29 217,740 

AR 0200 050200 S 2 1 3 N I R 1998 3,490 0.00 – 

AR 0800 050800 S 8 17 5 N I – – – – – 

AR 0900 050900 S 9 – 5 N I – – – – – 

AR 2042 052042 G 6S 2 2 A I F 1998 180 0.00 45,280 

AR 3011 053011 G 3 2 2 A I R 1998 690 4.18 303,190 

AR 3048 053048 G 2 2 2 Q I F 1998 110 5.00 31,440 

AR 3058 053058 G 2 12 2 Q I F 1998 1,120 22.00 320,510 

AR 3059 053059 G 4 11 2 Q I R 1998 660 6.00 260,080 

AR 3071 053071 G 2 2 2 A I F 1998 1,770 8.12 478,440 

AR 3073 053073 G 4 11 4 N I – 1998 6,570 8.00 – 

AR 3074 053074 G 4 1 2 Q I R 1998 3,210 0.00 1,921,590 

AR 4019 054019 G 4 12 2 A I R 1998 1,220 8.00 551,030 

AR 4021 054021 G 4 14 2 A I R 1998 990 -7.54 390,970 

AR 4023 054023 G 4 2 2 A I R 1998 1,320 6.00 442,060 

AR 4046 054046 G 4 2 2 Q I R 1998 790 6.78 357,540 
127

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

AR 5803 055803 G 5 11 2 A I R 1998 720 10.85 122,810 

AR 5805 055805 G 5 11 2 Q I R 1998 1,870 7.21 450,270 

AR A600 05A600 S 6 2 5 N I – – – – – 

CA 0500 060500 S 5 1 2 Q I F 1998 2,040 6.50 821,640 

CA 1253 061253 G 1 2 2 Q I F 1998 90 2.56 9,700 

CA 2002 062002 G 2 2 4 N I – 1998 610 0.80 – 

CA 2004 062004 G 2 1 4 N I – 1998 4,500 10.00 – 

CA 2038 062038 G 2 2 4 N I – 1998 400 0.50 – 

CA 2040 062040 G 2 2 2 A I F 1998 840 -10.78 201,420 

CA 2041 062041 G 6B 2 4 N I – 1998 840 6.35 – 

CA 2051 062051 G 6B 12 3 N I F 1998 1,080 4.00 – 

CA 2053 062053 G 2 1 2 Q I F 1998 520 5.00 71,220 

CA 2647 062647 G 2 2 2 Q I F 1998 530 2.88 200,330 

CA 3005 063005 G 3 1 2 A I R 1998 2,920 4.61 1,711,740 

CA 3010 063010 G 3 11 4 N I – 1998 3,600 7.50 – 

CA 3013 063013 G 3 1 4 N I – 1998 2,600 7.00 – 

CA 3017 063017 G 3 12 4 N I – 1998 890 3.07 – 

CA 3019 063019 G 3 1 4 N I – 1998 4,500 6.73 – 

CA 3021 063021 G 3 1 2 A I R 1998 700 -16.11 180,780 

CA 3024 063024 G 3 1 3 N I R 1998 2,290 7.00 – 

CA 3030 063030 G 3 1 2 A I R 1998 2,850 4.16 1,468,130 

CA 3042 063042 G 3 1 2 A I R 1998 3,990 3.50 2,034,110 

CA 6044 066044 G 6A 2 2 A I F 1998 390 2.00 42,580 

CA 7452 067452 G 2 6 2 Q I F 1998 230 3.42 21,600 

CA 7454 067454 G 1 6 2 Q I F 1998 110 2.00 22,830 

CA 7455 067455 G 5 1 2 A I R 1998 2,260 7.58 809,980 

CA 7456 067456 G 3 1 4 N I – 1998 1,850 7.00 – 

CA 7491 067491 G 6S 1 4 N I – 1998 1,360 4.58 – 

CA 7493 067493 G 3 1 4 N I – 1998 2,800 6.43 – 

CA 8149 068149 G 6S 1 4 N I – 1998 1,770 7.31 – 

CA 8150 068150 G 2 6 2 Q I F 1998 890 -0.78 188,600 

CA 8151 068151 G 2 1 2 A I F 1998 2,220 6.68 937,730 

CA 8153 068153 G 6B 6 2 Q I F 1998 250 1.38 27,240 

CA 8156 068156 G 1 6 2 Q I F 1998 250 2.30 94,070 

CA 8201 068201 G 2 6 2 Q I F 1998 230 3.00 36,330 
128

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

CA 8202 068202 G 2 2 2 Q I F 1998 570 3.34 93,550 

CA 8534 068534 G 6B 1 2 Q I F 1998 730 5.24 128,700 

CA 8535 068535 G 6B 1 2 A I F 1998 960 4.00 196,790 

CA 9048 069048 G 9 1 4 N I – 1998 1,100 5.80 – 

CA 9049 069049 G 9 12 4 N I – 1998 3,080 6.13 – 

CA 9107 069107 G 9 1 4 N I – 1998 1,460 3.48 – 

CO 0200 080200 S 2 1 2 Q I R 1998 960 0.00 346,050 

CO 0500 080500 S 5 1 2 Q I F 1998 1,150 5.00 536,060 

CO 0800 080800 S 8 9 5 N I – – – – – 

CO 1029 081029 G 6B 2 2 Q I F 1998 100 1.00 14,480 

CO 1047 081047 G 6B 7 2 Q I F 1998 150 0.00 38,210 

CO 1053 081053 G 1 2 3 N I F 1998 450 2.94 – 

CO 1057 081057 G 1 14 3 N I F 1998 310 3.00 – 

CO 2008 082008 G 2 2 3 N I F 1994 260 0.00 – 

CO 3032 083032 G 3 1 2 A I R 1998 820 5.34 381,890 

CO 6002 086002 G 6C 1 2 Q I F 1998 1,530 3.88 264,700 

CO 6013 086013 G 6A 14 3 N I F 1998 370 12.00 – 

CO 7035 087035 G 7S 1 3 N I R 1998 1,640 3.73 – 

CO 7036 087036 G 7A 1 4 N I – 1997 1,580 5.32 – 

CO 7776 087776 G 3 1 3 N I R 1998 1,700 4.93 – 

CO 7780 087780 G 6B 2 3 N I F 1998 340 0.51 – 

CO 7781 087781 G 6B 2 3 N I F 1998 260 0.00 – 

CO 7783 087783 G 6A 1 2 Q I F 1998 770 3.00 305,640 

CO 9019 089019 G 9 1 2 Q I R 1998 2,610 5.76 667,800 

CO 9020 089020 G 9 1 2 Q I R 1998 2,810 6.15 882,360 

CO A400 08A400 S 4 – 5 N I – – – – – 

CT 0900 090900 S 9 – 5 N R – – – – – 

CT 1803 091803 G 1 2 3 N R F 1998 210 7.00 – 

CT 4008 094008 G 4 1 2 Q R R 1998 2,700 5.23 1,444,240 

CT 4020 094020 G 7B 2 2 A R R 1998 800 2.84 119,860 

CT 5001 095001 G 7B 1 2 Q R R 1998 3,780 4.73 1,734,800 

DE 0100 100100 S 1 2 5 N R – – – – – 

DE 0200 100200 S 2 2 5 N R – – – – – 

DE 1201 101201 G 4 2 4 N R – 1998 720 6.50 – 

DE 1450 101450 G 6C 2 4 N R – 1998 1,200 1.48 – 
129

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

DE 4002 104002 G 7B 2 4 N R – 1998 480 11.23 – 

DE 5004 105004 G 5 1 4 N R – 1996 4,730 16.23 – 

DE 5005 105005 G 7B 2 4 N R – 1998 620 5.06 – 

DC 1400 111400 G 6B 11 4 N I – 1996 1,110 5.00 – 

FL 0100 120100 S 1 2 3 N I F 1998 800 5.00 – 

FL 0500 120500 S 5 2 5 N I – – – – – 

FL 0900 120900 S 9 – 5 N I – – – – – 

FL 1030 121030 G 1 2 3 N I F 1998 400 10.00 – 

FL 1060 121060 G 1 12 3 N I F 1998 160 10.00 – 

FL 1370 121370 G 1 6 2 Q I F 1998 110 3.00 53,380 

FL 3804 123804 G 3 1 2 Q I R 1998 1,430 8.50 1,156,350 

FL 3811 123811 G 3 1 2 Q I R 1998 1,620 6.00 784,160 

FL 3995 123995 G 1 11 2 Q I F 1998 1,960 7.00 713,630 

FL 3996 123996 G 1 2 2 Q I F 1998 220 2.00 69,400 

FL 3997 123997 G 6B 2 3 N I F 1998 510 5.17 – 

FL 4000 124000 G 3 2 2 Q I R 1998 560 2.00 257,460 

FL 4057 124057 G 3 1 2 Q I R 1998 1,740 5.00 956,430 

FL 4059 124059 G 3 2 2 Q I R 1998 130 6.00 40,200 

FL 4096 124096 G 2 6 2 Q I F 1998 100 -13.83 24,980 

FL 4097 124097 G 2 1 2 Q I F 1998 1,630 6.00 467,340 

FL 4099 124099 G 1 14 2 Q I F 1998 250 2.00 144,540 

FL 4100 124100 G 2 14 2 Q I F 1998 140 3.00 30,180 

FL 4101 124101 G 6B 12 3 N I F 1998 1,150 4.00 – 

FL 4102 124102 G 1 12 5 N I – – – – – 

FL 4103 124103 G 1 12 3 N I F 1998 620 10.00 – 

FL 4105 124105 G 1 14 2 Q I F 1998 670 3.19 259,830 

FL 4106 124106 G 1 1 2 Q I F 1998 1,390 7.00 534,520 

FL 4107 124107 G 1 2 2 Q I F 1998 200 6.00 119,960 

FL 4108 124108 G 2 6 2 Q I F 1998 320 3.51 67,320 

FL 4109 124109 G 3 2 3 N I R 1998 130 4.00 – 

FL 4135 124135 G 6B 2 2 Q I F 1998 1,050 2.00 456,530 

FL 4136 124136 G 6B 2 2 Q I F 1998 1,050 2.00 450,900 

FL 4137 124137 G 6B 2 2 Q I F 1998 1,050 2.00 428,700 

FL 4138 124138 G 3 2 2 Q I R 1998 560 2.00 274,360 

FL 4153 124153 G 1 6 5 N I – – – – – 
130

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

FL 4154 124154 G 1 6 3 N I F 1998 22,040 171.58 – 

FL 9054 129054 G 1 6 2 Q I F 1998 430 2.00 174,670 

GA 0500 130500 S 5 1 5 N I – – – – – 

GA 1001 131001 G 1 2 3 N I F 1998 910 5.09 – 

GA 1004 131004 G 1 6 3 N I F 1998 500 8.56 – 

GA 1005 131005 G 1 7 4 N I 1998 620 5.39 – 

GA 1031 131031 G 6S 2 3 N I F 1998 480 11.30 – 

GA 3007 133007 G 3 2 3 N I R 1998 640 14.88 – 

GA 3011 133011 G 3 1 3 N I R 1998 1,420 12.56 – 

GA 3015 133015 G 3 1 3 N I R 1998 1,350 12.50 – 

GA 3016 133016 G 3 1 4 N I – 1998 3,120 6.14 – 

GA 3017 133017 G 3 1 3 N I – 1998 4,710 9.16 – 

GA 3018 133018 G 3 1 3 N I R 1998 4,710 9.16 – 

GA 3019 133019 G 3 2 3 N I R 1998 1,110 0.00 – 

GA 3020 133020 G 3 2 2 Q I R 1998 300 0.00 95,000 

GA 4092 134092 G 2 2 3 N I – 1998 380 5.08 – 

GA 4093 134093 G 2 2 3 N I – 1998 380 5.08 – 

GA 4096 134096 G 2 8 3 N I – 1998 40 0.00 – 

GA 4111 134111 G 1 7 3 N I F 1998 270 0.00 – 

GA 4112 134112 G 6S 1 3 N I F 1998 3,600 4.70 – 

GA 4113 134113 G 6S 1 3 N I F 1998 3,600 4.70 – 

GA 4118 134118 G 9 1 3 N I R 1998 3,970 3.72 – 

GA 4119 134119 G 2 1 3 N I F 1998 4,760 1.89 – 

GA 4420 134420 G 6B 2 3 N I F 1998 570 3.00 – 

GA 5023 135023 G 5 1 3 N I R 1998 4,050 6.00 – 

GA 7028 137028 G 7S 1 3 N I R 1998 4,150 5.28 – 

HI 1003 151003 G 1 14 3 N I F 1998 190 6.61 – 

HI 1006 151006 G 1 14 3 N I F 1998 220 3.90 – 

HI 1008 151008 G 1 14 3 N I F 1998 270 2.35 – 

HI 7080 157080 G 1 2 4 N I – 1998 320 8.42 – 

ID 1001 161001 G 1 2 2 Q R F 1998 580 3.92 174,380 

ID 1005 161005 G 1 2 2 Q R F 1998 120 2.45 25,080 

ID 1007 161007 G 6B 2 2 Q R F 1998 110 0.00 32,400 

ID 1009 161009 G 1 1 2 A R F 1998 1,040 7.00 497,320 

ID 1010 161010 G 1 1 2 A R F 1997 610 5.80 257,190 
131

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

ID 1020 161020 G 1 2 2 Q R F 1998 170 0.00 55,190 

ID 1021 161021 G 1 2 2 Q R F 1998 470 1.50 153,040 

ID 3017 163017 G 3 1 2 Q R R 1998 960 3.89 640,120 

ID 3023 163023 G 3 1 2 A R R 1998 1,380 0.00 816,360 

ID 5025 165025 G 5 1 2 Q R R 1998 600 5.85 210,110 

ID 6027 166027 G 6A 2 2 Q R F 1997 400 0.00 282,890 

ID 9032 169032 G 1 2 2 Q R F 1998 190 0.00 28,080 

ID 9034 169034 G 1 2 2 Q R F 1998 250 0.00 111,820 

IL 0600 170600 S 6 1 2 Q R R 1998 1,670 -5.39 1,080,600 

IL 1002 171002 G 1 2 2 A R F 1998 400 3.50 101,910 

IL 1003 171003 G 1 2 2 A R F 1998 340 4.07 66,310 

IL 4074 174074 G 4 2 2 Q R R 1998 450 4.00 180,370 

IL 4082 174082 G 4 2 2 A R R 1998 180 4.00 39,880 

IL 5020 175020 G 5 2 2 A R R 1998 220 4.00 51,840 

IL 5151 175151 G 7B 1 2 A R R 1998 2,630 2.50 1,671,520 

IL 5217 175217 G 7B 1 2 Q R R 1998 1,950 3.92 558,900 

IL 5423 175423 G 7A 1 2 A R R 1998 1,830 2.50 769,070 

IL 5453 175453 G 7A 1 2 Q R R 1998 4,820 5.47 3,075,630 

IL 5843 175843 G 5 1 2 A R R 1998 3,040 3.60 1,697,030 

IL 5849 175849 G 5 1 2 A R R 1998 1,350 2.00 721,120 

IL 5854 175854 G 5 2 2 Q R R 1998 570 5.74 120,880 

IL 5869 175869 G 5 12 2 Q R R 1998 690 5.39 156,730 

IL 5908 175908 G 5 2 2 A R R 1998 340 2.00 88,260 

IL 6050 176050 G 6A 7 3 N R F 1998 50 1.00 – 

IL 7937 177937 G 7D 6 2 Q R R 1998 120 6.00 32,000 

IL 9267 179267 G 5 1 2 A R R 1998 2,520 2.50 1,602,540 

IL 9327 179327 G 7B 1 3 N R R 1998 1,820 4.00 – 

IN 1028 181028 G 6S 1 2 A R F 1998 1,980 7.00 602,030 

IN 1037 181037 G 6B 6 2 Q R F 1998 470 6.53 106,780 

IN 2008 182008 G 2 2 2 A R F 1998 1,330 2.50 358,920 

IN 2009 182009 G 2 6 2 Q R F 1998 980 3.99 181,860 

IN 3002 183002 G 3 2 2 A R R 1998 830 3.03 474,930 

IN 3003 183003 G 7B 2 2 Q R R 1998 1,360 2.11 623,630 

IN 3030 183030 G 3 7 3 N R R 1998 650 5.45 – 

IN 3031 183031 G 3 6 2 A R R 1998 830 2.03 236,450 
132

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

IN 4021 184021 G 4 14 2 Q R R 1998 930 4.00 235,240 

IN 4042 184042 G 4 6 2 A R R 1998 830 4.12 226,700 

IN 5022 185022 G 7B 11 2 Q R R 1998 6,310 4.79 2,470,400 

IN 5043 185043 G 5 2 3 N R R 1998 180 2.86 – 

IN 5518 185518 G 7B 1 2 A R R 1998 4,500 4.70 1,940,110 

IN 5528 185528 G 7B 6 2 Q R R 1998 550 2.00 134,150 

IN 5538 185538 G 7B 6 2 Q R R 1998 470 2.30 104,280 

IN 6012 186012 G 6S 1 2 Q R F 1998 2,170 7.22 793,950 

IN 9020 189020 G 9 1 2 Q R R 1998 3,670 7.99 1,136,320 

IN A900 18A900 S 9 – 5 N I – – – – – 

IA 0100 190100 S 1 2 2 Q I F 1998 170 0.00 34,060 

IA 0200 190200 S 2 12 3 N I R 1998 550 13.27 – 

IA 0600 190600 S 6 1 2 Q I R 1998 1,890 3.16 670,760 

IA 0700 190700 S 7 1 2 A I R 1998 1,960 2.69 977,240 

IA 1044 191044 G 1 2 2 Q I F 1998 290 0.96 107,570 

IA 3006 193006 G 3 2 2 Q I R 1998 380 2.43 145,710 

IA 3009 193009 G 3 11 3 N I R 1998 1,100 7.00 – 

IA 3028 193028 G 3 2 2 Q I R 1998 940 2.58 345,010 

IA 3033 193033 G 3 2 3 N I R 1998 760 7.09 – 

IA 3055 193055 G 3 2 2 Q I R 1998 610 6.75 194,270 

IA 5042 195042 G 5 1 3 N I R 1998 1,550 3.63 – 

IA 5046 195046 G 5 1 3 N I R 1998 1,550 3.63 – 

IA 6049 196049 G 6A 1 3 N I F 1998 3,000 2.41 – 

IA 6150 196150 G 2 7 3 N I F 1998 60 0.00 – 

IA 9116 199116 G 7B 1 2 Q I R 1998 1,410 4.92 509,100 

IA 9126 199126 G 7B 1 3 N I R 1998 2,470 4.71 – 

KS 0100 200100 S 1 2 2 Q I F 1998 480 0.00 197,440 

KS 0200 200200 S 2 1 2 Q I R 1998 1,200 6.00 1,069,450 

KS 1005 201005 G 1 6 3 N I F 1998 180 0.82 – 

KS 1006 201006 G 6A 2 2 Q I F 1998 200 0.00 19,400 

KS 1009 201009 G 1 2 2 Q I F 1998 170 0.80 23,610 

KS 1010 201010 G 1 2 3 N I F 1997 310 2.76 – 

KS 3013 203013 G 3 11 3 N I R 1998 820 10.00 – 

KS 3015 203015 G 3 2 2 Q I R 1998 760 4.00 244,690 

KS 3060 203060 G 3 11 3 N I R 1998 500 10.57 – 
133

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

KS 4016 204016 G 4 2 3 N I R 1998 270 3.04 – 

KS 4052 204052 G 4 2 2 Q I R 1998 390 5.11 120,540 

KS 4053 204053 G 4 11 3 N I R 1998 820 1.24 – 

KS 4054 204054 G 4 1 2 Q I R 1998 1,150 3.00 880,380 

KS 4063 204063 G 4 1 2 Q I R 1998 820 10.00 352,880 

KS 4067 204067 G 7D 14 2 Q I R 1998 600 2.90 224,620 

KS 6026 206026 G 6D 2 3 N I F 1998 410 5.00 – 

KS 7073 207073 G 7S 1 3 N I R 1994 1,030 3.06 – 

KS 7085 207085 G 7S 2 2 Q I R 1997 350 3.00 57,750 

KS 9037 209037 G 9 12 3 N I R 1994 450 4.11 – 

KY 1010 211010 G 1 7 2 Q I F 1994 60 4.49 16,420 

KY 1014 211014 G 1 2 2 Q I F 1998 400 5.00 181,180 

KY 1034 211034 G 6S 2 2 Q I F 1998 590 12.96 114,820 

KY 3016 213016 G 3 1 2 A I R 1998 3,410 2.35 1,507,820 

KY 4025 214025 G 4 1 2 A I R 1995 2,050 4.27 930,340 

KY 6040 216040 G 6A 12 2 Q I F 1993 1,000 4.78 563,140 

KY 6043 216043 G 6A 2 3 N I F 1998 360 1.09 – 

LA 0100 220100 S 1 2 5 N I – – – – – 

LA 0700 220700 S 7 1 5 N I – – – – – 

LA 3056 223056 G 2 1 2 Q I F 1998 630 4.01 262,170 

LA 4001 224001 G 4 1 2 Q I R 1998 1,800 4.01 849,480 

ME 1001 231001 G 6S 1 4 N I – 1998 650 3.30 – 

ME 1009 231009 G 6B 2 4 N I – 1998 420 2.39 – 

ME 1012 231012 G 1 1 4 N I – 1998 1,550 2.41 – 

ME 1026 231026 G 6B 2 4 N I – 1998 260 2.26 – 

ME 1028 231028 G 6B 2 4 N I – 1998 360 2.46 – 

ME 3013 233013 G 3 1 4 N I – 1998 1,350 7.26 – 

ME 3014 233014 G 3 1 4 N I – 1998 1,340 7.26 – 

ME 7023 237023 G 7A 1 4 N I – 1998 1,740 2.45 – 

MD 0500 240500 S 5 2 2 A R F 1998 830 0.00 224,760 

MD 1632 241632 G 2 2 4 N R – 1998 620 7.14 – 

MD 1634 241634 G 6C 2 4 N R – 1998 170 0.00 – 

MD 2401 242401 G 2 2 4 N R – 1998 880 8.45 – 

MD 2805 242805 G 6C 1 4 N R – 1998 1,500 0.00 – 

MD 5807 245807 G 5 11 4 N R – 1998 640 6.64 – 
134

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

MA 1002 251002 G 1 1 2 Q I F 1998 490 1.66 74,770 

MA 1003 251003 G 1 2 3 N I F 1998 200 4.08 – 

MA 1004 251004 G 1 1 3 N I F 1998 1,040 5.23 – 

MI 0100 260100 S 1 2 2 Q R F 1998 100 0.00 12,290 

MI 0200 260200 S 2 2 2 Q R R 1998 4,320 15.50 2,040,510 

MI 0600 260600 S 6 2 2 Q R R 1998 890 2.44 330,140 

MI 0900 260900 S 9 – 5 N R – – – – – 

MI 1001 261001 G 1 6 2 Q R F 1998 160 14.09 22,760 

MI 1004 261004 G 1 14 2 Q R F 1998 150 0.00 41,130 

MI 1010 261010 G 1 6 2 Q R F 1998 340 13.36 67,960 

MI 1012 261012 G 1 2 2 Q R F 1998 850 7.77 166,360 

MI 1013 261013 G 1 2 3 Q R F 1998 1,040 8.00 202,250 

MI 3068 263068 G 3 2 2 A R R 1993 320 8.49 92,570 

MI 3069 263069 G 3 2 2 A R R 1998 560 8.69 151,450 

MI 4015 264015 G 4 1 2 Q R R 1998 1,640 10.49 606,580 

MI 5363 265363 G 5 1 2 Q R R 1998 1,940 8.53 1,010,520 

MI 6016 266016 G 6A 1 2 Q R F 1998 350 0.00 78,320 

MI 7072 267072 G 7A 1 2 Q R R 1998 1,840 11.21 417,080 

MI 9029 269029 G 9 1 3 N R R 1998 1,910 2.00 – 

MI 9030 269030 G 9 2 2 Q R R 1998 2,270 2.32 976,200 

MN 0700 270700 S 7 1 2 Q R R 1998 1,200 4.16 358,990 

MN 1003 271003 G 1 1 2 A R F 1998 2,370 6.02 603,260 

MN 1004 271004 G 1 1 2 A R F 1998 2,370 6.02 663,090 

MN 1016 271016 G 1 2 2 A R F 1998 180 4.84 36,240 

MN 1017 271017 G 1 1 5 N R – – – – – 

MN 1018 271018 G 1 2 2 A R F 1998 300 3.66 79,580 

MN 1019 271019 G 1 2 2 Q R F 1998 300 4.50 64,700 

MN 1020 271020 G 1 1 5 N R – – – – – 

MN 1023 271023 G 1 2 2 A R F 1998 400 4.15 142,250 

MN 1028 271028 G 1 2 2 A R F 1998 350 -2.06 86,950 

MN 1029 271029 G 1 6 2 Q R F 1998 210 0.55 50,660 

MN 1085 271085 G 1 6 2 A R F 1998 100 1.00 19,080 

MN 1087 271087 G 1 14 2 A R F 1998 340 1.27 24,960 

MN 2018 272018 G 1 1 5 N R – – – – – 

MN 2023 272023 G 2 1 5 N R – – – – – 
135

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

MN 3003 273003 G 3 2 2 A R R 1998 200 2.02 74,230 

MN 3005 273005 G 3 1 3 A R R 1998 2,370 6.02 939,660 

MN 3007 273007 G 3 1 2 A R R 1998 2,370 6.02 1,051,670 

MN 3009 273009 G 3 1 2 A R R 1998 2,370 6.02 1,051,660 

MN 3010 273010 G 3 1 2 A R R 1998 2,370 6.02 1,080,010 

MN 3012 273012 G 3 1 2 A R R 1998 2,370 6.02 1,080,020 

MN 3013 273013 G 3 12 2 A R R 1998 380 1.21 54,910 

MN 4033 274033 G 4 11 2 Q R R 1998 700 4.12 171,640 

MN 4034 274034 G 4 11 4 N R – 1998 1,320 4.15 – 

MN 4037 274037 G 4 11 2 Q R R 1998 500 3.59 127,800 

MN 4040 274040 G 4 2 2 Q R R 1998 430 1.50 109,020 

MN 4050 274050 G 4 2 2 Q R R 1998 160 -5.63 39,950 

MN 4054 274054 G 4 1 2 A R R 1998 1,410 7.00 909,290 

MN 4055 274055 G 4 1 2 A R R 1998 2,190 5.50 1,273,080 

MN 4082 274082 G 4 2 3 N R R 1998 450 2.00 – 

MN 5076 275076 G 7B 11 2 Q R R 1998 1,500 4.56 412,460 

MN 6064 276064 G 6S 1 5 N R – – – – – 

MN 6251 276251 G 1 2 2 A R F 1998 600 6.50 86,030 

MN 6300 276300 G 9 1 2 A R R 1998 550 3.59 153,980 

MN 7090 277090 G 7A 2 2 A R R 1998 900 1.50 349,860 

MN 9075 279075 G 9 2 2 A R R 1998 130 0.00 42,870 

MS 0500 280500 S 5 1 2 Q R F 1998 1,550 5.00 726,370 

MS 0800 280800 S 8 2 5 N R – – – – – 

MS 0900 280900 S 9 – 5 N R – – – – – 

MS 1001 281001 G 6S 2 2 Q R F 1998 380 5.23 86,400 

MS 1016 281016 G 2 14 2 A R F 1997 240 9.49 91,960 

MS 1802 281802 G 2 2 2 A R F 1998 240 0.44 77,840 

MS 2807 282807 G 6S 2 2 A R F 1998 360 3.00 140,450 

MS 3018 283018 G 3 2 2 A R R 1998 650 9.62 282,410 

MS 3019 283019 G 3 2 2 A R R 1998 660 9.61 285,180 

MS 3081 283081 G 6S 2 2 A R F 1998 1,860 11.12 714,120 

MS 3082 283082 G 2 2 2 Q R F 1998 460 7.67 204,110 

MS 3083 283083 G 2 2 3 N R F 1998 20 1.66 – 

MS 3085 283085 G 2 2 3 N R F 1998 20 1.66 – 

MS 3087 283087 G 6C 2 2 Q R F 1998 170 0.00 80,580 
136

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

MS 3089 283089 G 2 2 2 A R F 1998 360 3.00 140,440 

MS 3090 283090 G 2 2 3 N R F 1998 40 1.84 – 

MS 3091 283091 G 6S 2 2 Q R F 1998 290 1.00 137,040 

MS 3093 283093 G 6B 1 2 A R F 1998 2,400 4.88 818,290 

MS 3094 283094 G 6B 1 2 A R F 1998 2,400 4.88 818,470 

MS 3097 283097 G 7A 1 2 Q R R 1994 1,390 2.60 847,660 

MS 3099 283099 G 7B 1 2 A R R 1998 2,260 6.65 1,422,470 

MS 4024 284024 G 4 14 3 N R R 1998 120 3.01 – 

MS 5006 285006 G 5 2 2 A R R 1998 1,950 7.80 765,280 

MS 5025 285025 G 5 2 2 A R R 1998 380 4.40 261,050 

MS 5803 285803 G 5 2 2 Q R R 1998 1,830 5.83 1,205,250 

MS 5805 285805 G 5 11 2 A R R 1998 2,430 4.67 986,990 

MS 7012 287012 G 9 1 2 Q R R 1998 2,620 6.00 1,725,340 

MS 9030 289030 G 9 1 2 A R R 1998 2,880 5.75 1,072,640 

MO 0500 290500 S 5 – 5 N I – – – – – 

MO 0600 290600 S 6 2 2 Q I R 1998 2,030 4.06 1,169,210 

MO 0700 290700 S 7 14 2 Q I R 1998 740 -7.22 261,450 

MO 0800 290800 S 8 9 5 N I – – – – – 

MO 0900 290900 S 9 – 5 N I – – – – – 

MO 1002 291002 G 1 7 2 Q I F 1998 100 11.41 15,800 

MO 1005 291005 G 1 2 2 Q I F 1998 500 3.58 84,410 

MO 1008 291008 G 1 6 3 N I F 1998 250 2.94 – 

MO 1010 291010 G 1 1 2 Q I F 1998 2,800 6.24 1,046,880 

MO 4036 294036 G 4 11 2 Q I R 1998 1,610 9.90 1,064,270 

MO 4069 294069 G 7B 11 2 Q I R 1998 1,220 7.34 585,200 

MO 5000 295000 G 4 1 2 Q I R 1998 1,470 4.79 419,400 

MO 5047 295047 G 5 12 2 A I R 1998 1,250 5.14 748,870 

MO 5058 295058 G 4 1 3 Q I R 1998 1,470 4.79 420,730 

MO 5081 295081 G 4 1 3 Q I R 1998 1,470 4.79 422,300 

MO 5091 295091 G 4 1 3 Q I R 1998 1,470 4.79 421,030 

MO 5393 295393 G 7B 6 2 Q I R 1998 300 2.79 48,530 

MO 5403 295403 G 6B 6 2 Q I F 1998 510 6.85 245,560 

MO 5413 295413 G 6B 6 2 Q I F 1998 470 6.75 127,400 

MO 5473 295473 G 4 1 2 Q I R 1998 3,660 7.12 2,076,000 

MO 5483 295483 G 7B 2 2 Q I R 1998 470 2.32 174,280 
137

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

MO 5503 295503 G 4 2 2 Q I R 1998 1,200 4.96 437,130 

MO 6067 296067 G 6A 2 2 Q I F 1998 300 -0.53 128,320 

MO 7054 297054 G 7S 1 2 Q I R 1998 3,350 6.32 1,874,510 

MO 7073 297073 G 7A 2 2 Q I R 1998 350 1.46 79,600 

MO A600 29A600 S 6 – 5 N I – – – – – 

MO A800 29A800 S 8 9 5 N I – – – – – 

MT 0100 300100 S 1 1 5 N R – – – – – 

MT 0800 300800 S 8 9 5 N R – – – – – 

MT 0900 300900 S 9 – 5 N R – – – – – 

MT 1001 301001 G 1 2 3 N R F 1998 200 2.43 – 

MT 6004 306004 G 6A 2 3 N R F 1998 40 1.81 – 

MT 7066 307066 G 6B 1 3 N R F 1998 910 4.39 – 

MT 7075 307075 G 6A 1 3 N R F 1998 1,400 4.50 – 

MT 7076 307076 G 6B 1 3 N R F 1998 470 5.40 – 

MT 7088 307088 G 6B 1 3 N R F 1998 960 4.70 – 

MT 8129 308129 G 1 2 3 N R F 1998 160 5.37 – 

NE 0100 310100 S 1 2 2 Q I F 1998 330 0.00 110,120 

NE 0900 310900 S 9 2 5 N I – – – – – 

NE 1030 311030 G 1 2 2 Q I F 1998 100 0.00 28,470 

NE 3018 313018 G 3 1 2 A I R 1998 5,340 4.46 1,397,190 

NE 3023 313023 G 3 1 2 Q I R 1998 2,350 4.40 2,031,850 

NE 3024 313024 G 3 1 2 Q I R 1998 2,450 3.98 1,258,810 

NE 3028 313028 G 3 2 2 A I R 1998 400 10.65 151,220 

NE 3033 313033 G 3 2 2 A I R 1998 330 6.53 155,830 

NE 4019 314019 G 4 11 2 A I R 1998 790 1.50 356,690 

NE 5052 315052 G 5 11 2 A I R 1998 890 2.50 444,710 

NE 6700 316700 G 6B 2 2 A I F 1998 110 0.50 35,920 

NE 6701 316701 G 9 14 2 A I R 1998 210 0.50 76,110 

NE 6702 316702 G 7B 1 2 A I R 1998 1,770 5.00 1,321,520 

NE 7005 317005 G 7S 1 2 A I R 1998 2,380 4.00 1,639,440 

NE 7017 317017 G 7A 2 2 Q I R 1998 150 0.45 78,270 

NE 7040 317040 G 7S 2 2 Q I R 1998 280 8.00 145,730 

NE 7050 317050 G 7S 1 2 A I R 1998 2,750 4.08 1,724,620 

NV 0100 320100 S 1 1 2 A R F 1998 1,430 17.00 595,460 

NV 0200 320200 S 2 1 2 A R R 1998 1,430 17.00 958,890 
138

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

NV 1020 321020 G 1 2 2 A R F 1998 280 7.82 138,120 

NV 1021 321021 G 1 14 2 Q R F 1998 140 4.00 40,070 

NV 1030 321030 G 6B 12 2 Q R F 1997 660 7.47 165,690 

NV 2027 322027 G 2 1 3 A R F 1997 940 5.00 468,610 

NV 3010 323010 G 3 1 2 Q R R 1998 1,260 4.42 772,350 

NV 3013 323013 G 3 1 2 A R R 1998 1,050 4.50 689,480 

NV 7000 327000 G 2 1 2 A R F 1997 940 5.00 478,580 

NV 7084 327084 G 3 1 2 Q R R 1998 2,270 6.44 1,686,480 

NV B400 32B400 S 4 – 5 N R – – – – – 

NH 1001 331001 G 1 1 3 N R F 1998 890 4.48 – 

NJ 0800 340800 S 8 – 5 N I – – – – – 

NJ 1003 341003 G 6S 6 2 Q R F 1998 1,100 3.00 177,500 

NJ 1011 341011 G 1 1 2 A R F 1998 1,330 5.04 368,560 

NJ 1030 341030 G 1 14 3 N R F 1998 480 1.00 – 

NJ 1031 341031 G 6S 12 2 Q R F 1998 1,380 3.00 472,270 

NJ 1033 341033 G 2 2 2 Q R F 1998 320 1.47 45,080 

NJ 1034 341034 G 2 12 2 Q R F 1998 1,480 5.00 282,930 

NJ 1638 341638 G 2 12 2 A R F 1998 1,490 5.00 564,670 

NJ 4042 344042 G 4 11 2 A R 1998 2,610 2.30 1,698,690 

NJ 6057 346057 G 6A 11 3 N R F 1998 1,850 3.33 – 

NM 0100 350100 S 1 1 3 N I F 1998 380 3.96 – 

NM 0500 350500 S 5 1 3 N I F 1998 2,360 4.00 – 

NM 0800 350800 S 8 9 5 N I – – – – – 

NM 0900 350900 S 9 – 5 N I – – – – – 

NM 1002 351002 G 6A 2 2 A I F 1998 940 21.20 274,430 

NM 1003 351003 G 1 2 2 A I F 1998 940 21.20 277,680 

NM 1005 351005 G 1 1 3 N I F 1998 770 2.40 – 

NM 1022 351022 G 1 2 2 Q I F 1998 460 8.34 147,610 

NM 1112 351112 G 1 2 2 Q I F 1998 260 0.63 77,850 

NM 2006 352006 G 2 2 2 Q I F 1998 310 5.05 108,810 

NM 2007 352007 G 6S 2 2 Q I F 1998 460 8.34 149,950 

NM 2118 352118 G 2 1 3 N I F 1998 2,390 6.00 – 

NM 3010 353010 G 3 2 2 Q I R 1998 260 0.00 102,820 

NM 6033 356033 G 6A 1 3 N I F 1998 620 3.09 – 

NM 6035 356035 G 6A 1 2 Q I F 1998 2,080 3.47 978,900 
139

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

NM 6401 356401 G 6A 1 2 Q I F 1998 2,080 3.47 669,890 

NY 0800 360800 S 8 – 3 N I F 1998 10 6.68 – 

NY 1008 361008 G 6B 14 2 Q I F 1998 920 11.38 120,890 

NY 1011 361011 G 6B 1 2 Q I F 1998 700 7.38 157,770 

NY 1643 361643 G 6B 2 2 Q I F 1998 700 0.96 262,050 

NY 1644 361644 G 6S 6 3 N I F 1998 100 5.00 – 

NY 4017 364017 G 4 2 3 N I R 1998 1,360 5.24 – 

NY 4018 364018 G 4 1 2 A I R 1998 1,130 5.05 403,790 

NC 0200 370200 S 2 2 2 Q R R 1998 1,230 7.61 835,310 

NC 0800 370800 S 8 – 5 N R – – – – – 

NC 0900 370900 S 9 – 5 N R – – – – – 

NC 1006 371006 G 6S 11 2 Q R F 1998 2,740 7.00 633,650 

NC 1024 371024 G 6S 6 2 Q R F 1998 240 5.75 24,250 

NC 1028 371028 G 1 2 2 Q R F 1998 220 2.26 55,290 

NC 1030 371030 G 1 2 2 Q R F 1998 260 2.71 74,410 

NC 1040 371040 G 6B 2 2 Q R F 1998 300 3.64 31,610 

NC 1352 371352 G 6S 2 2 Q R F 1998 260 2.00 82,780 

NC 1645 371645 G 2 2 2 Q R F 1998 670 7.21 254,860 

NC 1801 371801 G 6S 11 2 A R F 1998 2,250 5.52 715,720 

NC 1802 371802 G 6B 16 3 N R F 1998 140 1.45 – 

NC 1803 371803 G 6B 2 2 Q R F 1998 580 1.17 41,880 

NC 1814 371814 G 1 2 3 N R F 1998 120 3.96 – 

NC 1817 371817 G 6C 2 2 Q R F 1998 520 19.91 116,410 

NC 1992 371992 G 1 2 2 Q R F 1998 750 10.34 169,760 

NC 2819 372819 G 6C 2 2 A R F 1998 1,090 11.93 182,630 

NC 2824 372824 G 6C 2 2 Q R F 1998 760 5.54 187,530 

NC 2825 372825 G 2 16 3 N R F 1998 140 6.08 – 

NC 3008 373008 G 3 14 2 Q R R 1998 630 3.79 358,720 

NC 3011 373011 G 3 1 2 A R R 1998 2,820 5.75 1,395,750 

NC 3044 373044 G 3 1 2 A R R 1995 2,090 2.00 1,079,280 

NC 3807 373807 G 3 2 2 A R R 1998 1,220 8.52 489,890 

NC 3816 373816 G 3 2 2 Q R R 1998 1,030 1.17 284,950 

NC 5037 375037 G 5 11 2 Q R R 1998 2,160 5.00 951,620 

NC 5826 375826 G 7B 1 2 Q R R 1998 3,150 7.84 1,727,060 

NC 5827 375827 G 5 2 2 Q R R 1998 1,010 6.29 429,660 
140

Table 21. Summary of traffic projection results, continued 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

ND 0200 380200 S 2 1 5 N I – – – – – 

ND 2001 382001 G 2 2 3 N I F 1998 290 3.00 – 

ND 3005 383005 G 3 2 2 Q I R 1998 290 3.83 115,800 

ND 3006 383006 G 3 2 2 Q I R 1998 220 4.46 85,320 

ND 5002 385002 G 5 1 2 Q I R 1998 480 -17.25 118,090 

OH 0100 390100 S 1 2 3 N I F 1998 210 6.00 – 

OH 0200 390200 S 2 2 3 N I R 1998 1,460 6.00 – 

OH 0800 390800 S 8 9 3 N I F 1998 140 6.00 – 

OH 0900 390900 S 9 2 3 N I F 1998 210 6.00 – 

OH 3013 393013 G 7B 6 2 Q I R 1998 160 0.00 68,560 

OH 3801 393801 G 3 14 2 Q I R 1998 620 5.25 556,770 

OH 4018 394018 G 4 11 2 A I R 1998 1,510 12.00 627,350 

OH 4031 394031 G 4 11 2 A I R 1998 2,800 0.36 884,990 

OH 5003 395003 G 5 2 2 Q I R 1998 800 3.16 388,330 

OH 5010 395010 G 7B 1 2 Q I R 1998 570 4.38 242,890 

OH 5569 395569 G 9 12 2 Q I R 1998 540 1.91 137,410 

OH 7021 397021 G 7A 11 2 Q I R 1998 2,660 0.00 1,063,620 

OH 9006 399006 G 9 1 2 A I R 1998 3,780 5.00 1,786,940 

OH 9022 399022 G 9 11 4 N I – 1998 1,500 0.00 – 

OK 0100 400100 S 1 2 5 N I – – – – – 

OK 0500 400500 S 5 2 5 N I – – – – – 

OK 0600 400600 S 6 1 2 A I R 1998 1,700 2.31 749,730 

OK 1015 401015 G 2 6 3 N I – 1998 210 0.00 – 

OK 1017 401017 G 2 2 3 N I F 1996 1,140 3.38 – 

OK 3018 403018 G 3 11 2 A I R 1998 850 3.84 167,610 

OK 4086 404086 G 6B 6 2 Q I F 1998 310 1.17 69,990 

OK 4087 404087 G 6C 2 3 N I F 1998 280 3.07 – 

OK 4088 404088 G 2 2 4 N I – 1995 240 0.00 – 

OK 4154 404154 G 2 6 2 Q I F 1998 300 1.14 141,220 

OK 4155 404155 G 9 2 3 N I R 1998 340 0.00 – 

OK 4157 404157 G 3 2 2 Q I R 1998 1,170 1.13 455,200 

OK 4158 404158 G 5 2 2 Q I R 1998 400 0.00 116,680 

OK 4160 404160 G 3 12 2 A I R 1998 270 3.21 49,980 

OK 4161 404161 G 2 2 2 Q I F 1998 300 5.00 84,570 

OK 4162 404162 G 3 2 2 Q I R 1998 200 1.00 45,520 
141

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

OK 4163 404163 G 2 2 2 Q I F 1998 270 0.00 60,260 

OK 4164 404164 G 6B 6 2 Q I F 1998 240 2.73 77,340 

OK 4165 404165 G 2 2 3 N I F 1998 230 4.00 – 

OK 4166 404166 G 5 2 2 A I R 1998 1,630 7.51 622,320 

OK 5021 405021 G 5 2 2 A I R 1998 680 2.86 232,140 

OK 6010 406010 G 6A 7 2 A I F 1998 300 2.38 111,590 

OK 7024 407024 G 7A 1 2 A I R 1998 2,020 5.58 815,130 

OR 2002 412002 G 6S 2 2 Q R F 1998 680 5.98 191,350 

OR 5005 415005 G 5 1 3 A R R 1998 3,830 4.72 2,509,200 

OR 5006 415006 G 5 1 2 Q R R 1998 1,950 7.28 1,451,930 

OR 5008 415008 G 5 1 2 A R R 1998 1,720 6.36 1,101,920 

OR 5021 415021 G 5 1 2 A R R 1998 3,370 4.00 1,854,410 

OR 5022 415022 G 5 1 2 A R R 1998 3,280 3.66 2,090,380 

OR 6011 416011 G 6S 1 2 A R F 1998 3,630 5.00 1,619,800 

OR 6012 416012 G 6A 1 4 N R – 1991 1,130 4.49 – 

OR 7018 417018 G 7S 1 2 A R R 1998 4,040 3.57 2,550,930 

OR 7019 417019 G 7A 2 2 Q R R 1998 950 2.66 187,620 

OR 7025 417025 G 7A 1 2 A R R 1997 2,600 6.00 1,687,410 

OR 7081 417081 G 5 1 2 Q R R 1998 1,400 10.14 1,003,580 

PA 0600 420600 S 6 1 2 Q R R 1998 2,810 2.00 2,324,180 

PA 1597 421597 G 1 6 2 Q R F 1998 100 0.31 19,030 

PA 1598 421598 G 5 1 2 A R R 1998 4,020 -11.35 1,597,610 

PA 1599 421599 G 1 7 2 A R F 1998 250 0.00 92,880 

PA 1605 421605 G 1 6 2 A R F 1998 1,100 2.95 358,190 

PA 1606 421606 G 4 2 2 A R R 1998 540 3.60 240,870 

PA 1608 421608 G 6A 7 2 Q R F 1998 50 5.29 5,900 

PA 1610 421610 G 7A 1 2 A R R 1998 2,120 5.01 710,520 

PA 1613 421613 G 7B 11 2 A R R 1998 2,580 2.00 997,750 

PA 1614 421614 G 7B 2 2 A R R 1998 970 3.98 384,890 

PA 1617 421617 G 7B 11 4 N R – 1998 3,730 2.00 – 

PA 1618 421618 G 6B 7 2 Q R F 1998 80 3.97 26,220 

PA 1623 421623 G 3 1 2 Q R R 1998 1,060 1.62 511,360 

PA 1627 421627 G 9 1 2 A R R 1998 3,640 2.12 2,647,030 

PA 1690 421690 G 4 1 2 Q R R 1998 980 1.60 463,640 

PA 1691 421691 G 7B 2 2 A R R 1998 450 8.00 143,190 
142

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

PA 3044 423044 G 3 1 2 A R R 1998 3,460 5.39 1,886,830 

PA 5020 425020 G 5 11 2 Q R R 1998 3,510 9.85 1,219,130 

PA 7025 427025 G 7A 2 2 Q R R 1998 500 0.00 194,780 

PA 7037 427037 G 7S 1 2 A R R 1998 3,000 2.91 1,644,700 

PA 9027 429027 G 9 1 2 Q R R 1998 3,480 6.16 2,092,660 

RI 7401 447401 G 7A 2 2 Q R R 1998 870 5.19 1,535,220 

SC 1008 451008 G 1 6 2 Q I F 1998 70 1.00 12,850 

SC 1011 451011 G 1 14 2 Q I F 1998 1,400 2.82 335,990 

SC 1024 451024 G 1 17 3 N I F 1998 20 0.00 – 

SC 1025 451025 G 6B 2 3 N I F 1998 150 7.91 – 

SC 3012 453012 G 3 1 2 Q I R 1998 2,010 3.84 934,880 

SC 5017 455017 G 5 1 2 Q I R 1998 1,470 6.96 637,890 

SC 5034 455034 G 5 1 2 Q I R 1998 1,750 9.00 1,056,970 

SC 5035 455035 G 5 1 2 Q I R 1998 1,840 9.00 879,660 

SC 7019 457019 G 7A 2 2 Q I R 1998 450 4.33 64,760 

SD 0600 460600 S 6 2 2 A I R 1998 280 5.00 104,690 

SD 0800 460800 S 8 7 3 N I F 1998 20 0.00 – 

SD 3009 463009 G 3 1 4 N I – 1998 640 3.94 – 

SD 3010 463010 G 3 1 4 N I – 1998 480 5.42 – 

SD 3012 463012 G 3 1 2 Q I R 1998 670 2.26 361,100 

SD 3013 463013 G 3 1 3 N I R 1998 400 2.59 – 

SD 3052 463052 G 3 1 2 Q I R 1998 160 0.99 63,930 

SD 3053 463053 G 3 6 3 N I R 1998 170 4.56 – 

SD 5020 465020 G 5 1 4 N I – 1998 560 2.31 – 

SD 5025 465025 G 5 1 4 N I – 1998 690 4.00 – 

SD 5040 465040 G 5 1 4 N I – 1998 910 3.00 – 

SD 6600 466600 G 3 2 4 N I – 1998 320 7.00 – 

SD 7049 467049 G 7A 6 4 N I – 1998 190 2.52 – 

SD 9106 469106 G 6B 6 4 N I – 1998 60 6.00 – 

SD 9187 469187 G 1 6 4 N I – 1998 40 2.63 – 

SD 9197 469197 G 6B 6 4 N I – 1998 110 8.93 – 

TN 0600 470600 S 6 – 5 N I – – – – – 

TN 1023 471023 G 6B 1 2 Q I F 1998 2,990 1.99 750,440 

TN 1028 471028 G 2 2 2 Q I F 1998 350 4.26 138,900 

TN 1029 471029 G 6S 2 3 N I F 1998 400 12.30 – 
143

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

TN 2001 472001 G 6B 2 2 Q I F 1998 800 3.34 365,960 

TN 2008 472008 G 6B 2 2 A I F 1998 430 2.50 121,200 

TN 3075 473075 G 1 2 3 N I F 1998 170 6.31 – 

TN 3101 473101 G 6B 2 2 Q I F 1998 100 4.34 40,580 

TN 3104 473104 G 1 7 3 N I F 1997 10 6.91 – 

TN 3108 473108 G 6B 1 2 Q I F 1998 3,010 2.05 715,930 

TN 3109 473109 G 6B 2 2 Q I F 1998 180 2.00 24,000 

TN 3110 473110 G 6B 2 2 Q I F 1998 190 4.12 94,520 

TN 6015 476015 G 6A 1 2 Q I F 1998 3,580 3.79 1,434,860 

TN 6022 476022 G 6A 2 2 Q I F 1993 300 4.03 82,970 

TN 9024 479024 G 6B 2 3 N I F 1998 80 6.00 – 

TN 9025 479025 G 6B 2 2 Q I F 1998 100 4.34 50,260 

TX 0001 480001 G 1 11 2 Q I F 1998 390 5.82 134,880 

TX 0100 480100 S 1 2 5 N I – – – – – 

TX 0800 480800 S 8 9 5 N I F – – – – 

TX 0900 480900 S 9 2 5 N I – – – – – 

TX 1039 481039 G 6B 2 3 N I F 1998 690 4.50 – 

TX 1046 481046 G 6A 1 2 Q I F 1998 2,620 7.00 1,208,890 

TX 1047 481047 G 1 1 2 Q I F 1998 2,410 7.00 1,662,560 

TX 1048 481048 G 2 16 2 Q I F 1998 190 2.00 33,660 

TX 1049 481049 G 2 14 3 N I F 1996 1,250 2.00 – 

TX 1050 481050 G 1 6 3 N I F 1996 240 5.28 – 

TX 1056 481056 G 1 2 3 N I F 1998 150 1.00 – 

TX 1060 481060 G 1 2 2 Q I F 1998 1,040 7.00 242,720 

TX 1061 481061 G 1 2 5 N I – – – – – 

TX 1065 481065 G 1 2 3 N I F 1997 180 2.08 – 

TX 1068 481068 G 1 2 2 Q I F 1998 440 5.00 111,130 

TX 1069 481069 G 2 2 2 Q I F 1998 610 8.00 139,330 

TX 1070 481070 G 2 2 3 Q I F 1998 670 8.00 139,330 

TX 1076 481076 G 1 14 2 Q I F 1998 310 0.00 78,310 

TX 1077 481077 G 1 2 3 N I F 1998 800 3.40 – 

TX 1087 481087 G 1 14 3 N I F 1998 170 2.00 – 

TX 1092 481092 G 1 2 2 Q I F 1998 400 6.00 74,890 

TX 1093 481093 G 6B 1 2 Q I F 1998 1,230 6.00 265,360 

TX 1094 481094 G 6B 2 3 N I F 1998 190 7.00 – 
144

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

TX 1096 481096 G 1 2 2 Q I F 1998 430 4.86 95,520 

TX 1109 481109 G 2 2 2 Q I F 1998 340 5.00 72,080 

TX 1111 481111 G 1 2 3 N I F 1998 330 2.50 – 

TX 1113 481113 G 6B 2 3 N I F 1998 520 4.00 – 

TX 1116 481116 G 6S 2 3 N I F 1998 930 5.00 – 

TX 1119 481119 G 6B 2 3 N I F 1998 310 4.00 – 

TX 1122 481122 G 1 2 2 Q I F 1998 270 6.19 52,970 

TX 1123 481123 G 1 1 2 Q I F 1993 750 6.00 274,850 

TX 1130 481130 G 6B 6 2 Q I F 1998 170 4.50 24,610 

TX 1168 481168 G 1 8 3 N I F 1998 20 10.00 – 

TX 1169 481169 G 1 6 3 N I F 1998 460 3.50 – 

TX 1174 481174 G 1 2 3 N I F 1998 280 1.80 – 

TX 1178 481178 G 1 2 3 N I F 1995 480 10.00 – 

TX 1181 481181 G 1 1 2 Q I F 1998 1,200 5.00 392,790 

TX 1183 481183 G 1 2 3 N I F 1994 410 0.00 – 

TX 2108 482108 G 2 2 2 Q I F 1998 140 0.00 32,930 

TX 2133 482133 G 2 6 2 Q I F 1998 340 10.00 86,450 

TX 2172 482172 G 2 1 2 Q I F 1995 1,560 3.00 381,640 

TX 2176 482176 G 2 7 3 N I F 1998 70 2.00 – 

TX 3003 483003 G 3 14 3 N I R 1998 360 4.00 – 

TX 3010 483010 G 3 14 2 Q I R 1998 750 7.60 153,460 

TX 3559 483559 G 2 2 2 Q I F 1998 220 5.50 40,440 

TX 3569 483569 G 9 1 3 N I R 1998 2,450 3.00 – 

TX 3579 483579 G 6B 6 3 N I F 1998 360 6.00 – 

TX 3589 483589 G 3 2 3 N I R 1998 1,100 3.40 – 

TX 3609 483609 G 1 2 3 N I F 1991 100 0.00 – 

TX 3629 483629 G 7A 1 3 N I R 1996 2,020 6.00 – 

TX 3669 483669 G 2 6 3 N I F 1998 140 4.00 – 

TX 3679 483679 G 2 6 3 N I F 1997 180 4.00 – 

TX 3689 483689 G 2 2 3 N I F 1998 180 0.00 – 

TX 3699 483699 G 4 14 2 Q I R 1998 1,850 6.00 435,380 

TX 3719 483719 G 5 2 3 N I R 1998 680 4.00 – 

TX 3729 483729 G 1 2 2 Q I F 1998 880 2.50 110,190 

TX 3739 483739 G 1 2 3 N I F 1998 650 5.00 – 

TX 3749 483749 G 1 2 3 N I F 1997 210 3.00 – 
145

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

TX 3769 483769 G 1 2 2 Q I F 1998 250 2.86 57,500 

TX 3779 483779 G 5 11 3 N I R 1998 980 6.00 – 

TX 3835 483835 G 1 2 2 Q I F 1998 840 10.00 186,510 

TX 3845 483845 G 9 1 2 Q I R 1998 2,450 5.74 1,374,970 

TX 3855 483855 G 6B 2 3 N I F 1998 480 6.00 – 

TX 3865 483865 G 1 2 2 Q I F 1998 210 4.00 54,830 

TX 3875 483875 G 6B 2 3 N I F 1998 560 4.84 – 

TX 4142 484142 G 4 2 3 N I R 1998 590 5.00 – 

TX 4143 484143 G 4 2 2 Q I R 1998 370 6.00 116,460 

TX 4146 484146 G 4 6 2 Q I R 1998 330 6.00 155,940 

TX 4152 484152 G 4 2 2 Q I R 1998 290 4.00 85,750 

TX 5024 485024 G 5 2 2 Q I R 1998 580 6.50 218,460 

TX 5026 485026 G 5 6 2 Q I R 1998 240 10.50 57,630 

TX 5035 485035 G 5 11 3 N I R 1998 1,130 0.00 – 

TX 5154 485154 G 5 1 2 A I R 1998 2,030 6.00 734,970 

TX 5274 485274 G 5 1 3 N I R 1998 1,510 5.50 – 

TX 5278 485278 G 5 7 3 N I R 1998 190 0.00 – 

TX 5283 485283 G 5 11 3 N I R 1998 1,090 6.00 – 

TX 5284 485284 G 5 11 3 N I R 1998 490 0.00 – 

TX 5287 485287 G 5 11 3 N I R 1998 1,130 6.50 – 

TX 5301 485301 G 5 11 3 N I R 1998 400 4.00 – 

TX 5310 485310 G 5 2 3 N I R 1998 700 8.00 – 

TX 5317 485317 G 5 12 3 N I R 1998 940 4.00 – 

TX 5323 485323 G 5 1 2 Q I R 1998 2,280 7.00 2,152,650 

TX 5328 485328 G 5 2 2 Q I R 1998 1,400 6.00 787,190 

TX 5334 485334 G 5 1 2 Q I R 1998 2,530 7.00 656,970 

TX 5335 485335 G 5 1 2 Q I R 1998 2,570 7.00 1,994,090 

TX 5336 485336 G 5 1 2 Q I R 1998 800 4.00 445,310 

TX 6079 486079 G 6A 1 3 N I F 1998 2,250 6.00 – 

TX 6086 486086 G 6A 1 2 Q I F 1998 840 5.00 192,730 

TX 6160 486160 G 6A 2 3 N I F 1993 410 1.25 – 

TX 6179 486179 G 6A 2 3 N I F 1998 240 1.31 – 

TX 7165 487165 G 7S 11 2 Q I R 1998 530 0.00 277,000 

TX 9005 489005 G 1 8 2 Q I F 1998 90 0.00 27,390 

TX 9167 489167 G 9 1 2 Q I R 1998 2,460 6.00 1,388,120 
146

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

TX 9355 489355 G 9 11 2 Q I R 1998 2,700 3.00 1,023,730 

TX A500 48A500 S 5 2 2 Q I F 1998 640 9.00 124,920 

UT 0800 490800 S 8 6 5 N I – – – – – 

UT 1001 491001 G 1 6 4 N I – 1998 150 2.00 – 

UT 1004 491004 G 6A 6 4 N I – 1998 190 1.84 – 

UT 1005 491005 G 6A 14 4 N I – 1998 430 2.16 – 

UT 1006 491006 G 6A 2 4 N I – 1998 480 2.52 – 

UT 1007 491007 G 6A 2 4 N I – 1995 680 5.02 – 

UT 1008 491008 G 1 2 4 N I – 1998 560 2.81 – 

UT 1017 491017 G 1 6 4 N I – 1998 150 1.00 – 

UT 3010 493010 G 3 1 4 N I – 1998 1,140 3.65 – 

UT 3011 493011 G 3 1 4 N I – 1998 700 2.45 – 

UT 3015 493015 G 3 11 4 N I – 1998 850 10.00 – 

UT 7082 497082 G 3 1 4 N I – 1998 510 6.00 – 

UT 7083 497083 G 3 11 4 N I – 1998 160 4.45 – 

UT 7085 497085 G 3 2 4 N I – 1998 360 8.24 – 

UT 7086 497086 G 3 14 4 N I – 1998 350 4.66 – 

VT 1002 501002 G 1 2 2 Q R F 1998 350 2.50 86,920 

VT 1004 501004 G 1 2 2 Q R F 1998 250 3.33 26,250 

VT 1681 501681 G 6B 2 2 A R F 1998 580 3.50 155,680 

VT 1682 501682 G 7B 2 2 A R R 1998 610 3.50 225,700 

VT 1683 501683 G 6B 2 2 A R F 1998 610 3.50 146,120 

VA 0100 510100 S 1 2 3 N I – 1998 750 5.91 – 

VA 1002 511002 G 6B 6 3 N I F 1998 110 0.00 – 

VA 1023 511023 G 6C 1 2 A I F 1998 2,600 3.69 1,141,360 

VA 1417 511417 G 6D 2 2 Q I F 1998 1,200 3.00 401,040 

VA 1419 511419 G 6D 2 2 A I F 1998 290 0.00 70,930 

VA 1423 511423 G 6B 2 2 A I F 1998 270 2.00 132,710 

VA 1464 511464 G 6S 1 2 A I F 1998 1,810 -1.38 519,010 

VA 2004 512004 G 6C 14 2 A I F 1998 860 8.24 304,590 

VA 2021 512021 G 6C 6 2 A I F 1998 420 5.25 151,400 

VA 2564 512564 G 5 11 2 Q I R 1998 1,660 0.00 663,760 

VA 5008 515008 G 5 11 2 Q I R 1998 800 0.00 150,900 

VA 5009 515009 G 5 7 2 Q I R 1998 540 4.31 300,440 

VA 5010 515010 G 5 1 2 Q I R 1998 1,580 6.36 1,493,440 
147

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

WA 0200 530200 S 2 12 2 Q I R 1998 810 6.00 470,550 

WA 0800 530800 S 8 9 5 N I F – – – – 

WA 1002 531002 G 1 2 2 Q I F 1998 200 2.90 82,920 

WA 1005 531005 G 6B 1 2 A I F 1998 1,000 3.37 399,300 

WA 1006 531006 G 1 2 3 N I F 1998 340 2.66 – 

WA 1007 531007 G 6B 6 2 Q I F 1998 280 3.00 173,420 

WA 1008 531008 G 6B 2 2 A I F 1998 360 1.97 71,490 

WA 1501 531501 G 1 2 4 N I – 1998 60 3.46 – 

WA 1801 531801 G 1 16 2 A I F 1998 260 5.11 52,670 

WA 3011 533011 G 3 11 2 A I R 1998 1,200 3.49 431,930 

WA 3013 533013 G 3 2 2 A I R 1998 360 1.97 72,770 

WA 3014 533014 G 3 2 2 A I R 1998 1,050 3.66 551,740 

WA 3019 533019 G 3 11 2 Q I R 1998 1,330 3.40 641,670 

WA 3812 533812 G 3 11 2 Q I R 1993 1,340 4.16 389,760 

WA 3813 533813 G 3 14 2 A I R 1998 990 6.59 272,320 

WA 6020 536020 G 6A 2 2 Q I F 1998 740 3.07 119,730 

WA 6048 536048 G 6C 2 2 Q I F 1998 1,050 6.00 167,480 

WA 6049 536049 G 6A 14 2 A I F 1998 2,920 4.23 706,580 

WA 6056 536056 G 6S 2 2 Q I F 1998 300 4.62 73,920 

WA 7322 537322 G 6A 2 2 Q I F 1998 240 4.59 26,290 

WA 7409 537409 G 3 1 2 A I R 1998 1,530 4.89 461,410 

WV 1640 541640 G 6B 2 3 N R F 1998 350 6.00 – 

WV 4003 544003 G 4 2 3 N R R 1998 450 4.98 – 

WV 4004 544004 G 7B 1 2 A R R 1998 3,490 -8.82 3,848,320 

WV 5007 545007 G 7B 2 2 A R R 1998 530 2.76 186,720 

WV 7008 547008 G 7A 11 2 A R R 1998 2,810 1.85 1,426,860 

WI 0100 550100 S 1 14 5 N I – – – – – 

WI 0200 550200 S 2 14 5 N I – – – – – 

WI 0800 550800 S 8 14 5 N I – – – – – 

WI 0900 550900 S 9 – 5 N I – – – – – 

WI 3008 553008 G 3 1 2 Q I R 1998 2,680 5.21 1,321,470 

WI 3009 553009 G 3 2 2 Q I R 1998 480 0.00 214,100 

WI 3010 553010 G 3 2 2 Q I R 1998 470 6.93 139,350 

WI 3012 553012 G 3 6 3 N I R 1998 90 0.00 – 

WI 3014 553014 G 3 1 2 A I R 1998 1,480 4.23 530,510 
148

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

WI 3015 553015 G 3 2 2 A I R 1998 660 1.68 337,870 

WI 3016 553016 G 3 2 2 A I R 1998 600 1.65 236,130 

WI 3019 553019 G 3 2 2 Q I R 1998 100 0.00 20,040 

WI 5037 555037 G 5 2 2 Q I R 1998 290 0.00 78,970 

WI 5040 555040 G 5 1 2 Q I R 1998 2,150 5.34 1,257,940 

WI 6351 556351 G 3 2 2 Q I R 1998 620 0.00 325,840 

WI 6352 556352 G 3 2 2 Q I R 1998 620 0.00 322,110 

WI 6353 556353 G 3 2 2 Q I R 1998 620 0.00 328,560 

WI 6354 556354 G 3 2 2 Q I R 1998 620 0.00 325,520 

WI 6355 556355 G 3 2 2 Q I R 1998 620 0.00 307,680 

WI A900 55A900 S 9 – 5 N I – – – – – 

WI B900 55B900 S 9 – 5 N I – – – – – 

WI C900 55C900 S 9 14 5 N I – – – – – 

WY 1007 561007 G 1 6 2 Q I F 1998 80 0.88 10,360 

WY 2015 562015 G 2 1 2 A I F 1998 440 0.83 152,260 

WY 2017 562017 G 2 6 2 Q I F 1998 100 -3.94 38,950 

WY 2018 562018 G 2 2 3 N I F 1994 200 0.00 – 

WY 2019 562019 G 2 6 2 Q I F 1998 210 0.00 56,700 

WY 2020 562020 G 2 1 2 Q I F 1998 460 4.22 138,820 

WY 2037 562037 G 2 6 2 Q I F 1998 100 0.00 32,160 

WY 3027 563027 G 3 11 2 A I R 1998 2,300 4.21 1,529,380 

WY 6029 566029 G 6A 2 2 Q I F 1998 50 -3.59 8,340 

WY 6031 566031 G 6A 2 5 N I – – – – – 

WY 6032 566032 G 6A 7 2 Q I F 1998 150 6.00 18,620 

WY 7772 567772 G 2 6 2 Q I F 1998 60 2.00 10,220 

WY 7773 567773 G 2 6 2 Q I F 1998 30 0.00 6,250 

WY 7775 567775 G 1 7 2 Q I F 1998 100 1.59 48,740 

PR 1003 721003 G 2 2 4 N I – 1997 1,340 3.00 – 

PR 3008 723008 G 3 2 4 N I – 1998 4,070 6.00 – 

PR 4121 724121 G 3 2 4 N I – 1998 990 5.70 – 

PR 4122 724122 G 2 2 4 N I – 1998 990 5.70 – 

Canadian Provinces 

AB 0500 810500 S 5 2 4 N R – 1998 650 9.00 – 

AB 1803 811803 G 1 1 4 N R – 1998 410 3.41 – 

AB 1804 811804 G 6B 2 4 N R – 1998 520 8.20 – 
149

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

AB 1805 811805 G 6B 2 4 N R – 1998 270 4.42 – 

AB 2812 812812 G 2 2 4 N R – 1998 210 8.00 – 

AB 8529 818529 G 2 1 4 N R – 1998 1,400 7.00 – 

AB A900 81A900 S 9 2 4 N R – 1998 320 1.30 – 

BC 1005 821005 G 6S 2 4 N I – 1998 460 9.50 – 

BC 6006 826006 G 6A 2 4 N I – 1998 1,180 3.00 – 

BC 6007 826007 G 6S 1 4 N I – 1998 1,030 3.00 – 

BC 9017 829017 G 2 2 4 N I – 1998 400 8.00 – 

MB 1801 831801 G 1 1 2 Q I F 1998 330 2.65 280,670 

MB 3802 833802 G 3 2 2 Q I R 1998 370 6.93 309,080 

MB 6450 836450 G 6B 1 2 Q I F 1998 310 4.00 235,830 

MB 6451 836451 G 6B 1 3 N I F 1998 310 4.00 – 

MB 6452 836452 G 7B 2 2 Q I R 1998 380 9.86 173,400 

MB 6454 836454 G 2 2 3 N I F 1998 700 10.03 – 

NB 1684 841684 G 6S 2 2 Q I F 1998 270 1.62 68,200 

NB 1802 841802 G 2 2 2 Q I F 1998 340 8.60 315,600 

NB 3803 843803 G 3 2 3 N I R 1996 290 8.71 – 

NB 6804 846804 G 6S 2 2 Q I F 1998 720 2.97 477,280 

NF 1801 851801 G 1 2 3 N I F 1998 500 2.36 – 

NF 1803 851803 G 1 2 3 N I F 1998 200 0.00 – 

NF 1808 851808 G 1 2 3 N I F 1998 140 0.00 – 

NS 6802 866802 G 6A 1 2 A R F 1998 1,250 4.18 430,550 

ON 0900 870900 S 9 – 5 N I – – – – – 

ON 1620 871620 G 6S 1 2 Q I F 1998 630 3.65 362,500 

ON 1622 871622 G 6S 2 2 Q I F 1998 710 3.99 359,190 

ON 1680 871680 G 6S 1 3 N I F 1998 1,430 8.59 – 

ON 1806 871806 G 6S 1 3 N I F 1998 1,430 8.59 – 

ON 2811 872811 G 7A 1 2 Q I F 1998 1,470 4.22 696,600 

ON 2812 872812 G 7A 1 2 A I F 1998 1,750 5.49 859,880 

PE 1645 881645 G 1 1 2 Q R F 1998 250 5.57 94,720 

PE 1646 881646 G 2 1 2 Q R F 1998 220 7.63 72,270 

PE 1647 881647 G 2 2 3 N R F 1998 160 0.86 – 

PQ 0900 890900 S 9 – 5 N R – – – – – 

PQ 1021 891021 G 6C 1 2 A R F 1998 900 3.50 342,820 

PQ 1125 891125 G 6B 1 2 A R F 1998 1,020 4.01 283,880 
150

Table 21. Summary of traffic projection results, continued. 

Projections Traffic Characteristics 

Agency SHRP 

ID 

Traffic 

ID 

GPS/ 

SPS 

Exp. 

No. 

Highway 

Functional 

Class Cat. Code Status

Pav. 

Type 

Last 

Pr. 

Year 

Traffic 

Volume 

Annual 

Growth 

Rate 

(%) 

ESALs 

PQ 1127 891127 G 6B 1 2 Q R F 1998 800 4.00 198,610 

PQ 2011 892011 G 2 2 2 Q R F 1998 150 5.00 47,500 

PQ 3001 893001 G 3 1 2 Q R R 1998 430 3.00 396,790 

PQ 3002 893002 G 3 1 2 Q R R 1998 840 8.80 327,160 

PQ 3015 893015 G 3 1 2 Q R R 1998 800 2.11 708,820 

PQ 3016 893016 G 3 1 3 A R R 1998 910 1.00 549,100 

PQ 9018 899018 G 9 2 4 N R – 1998 390 3.98 – 

PQ A900 89A900 S 9 – 5 N R – – – – – 

SK 0900 900900 S 9 14 2 Q I F 1998 400 0.00 118,190 

SK 1802 901802 G 1 2 5 N I – – – – – 

SK 6400 906400 G 6A 2 3 N I F 1998 80 2.97 – 

SK 6405 906405 G 1 2 2 Q I F 1998 170 3.00 128,840 

SK 6410 906410 G 6B 2 2 Q I F 1998 430 2.05 191,430 

SK 6412 906412 G 6B 2 2 Q I F 1998 430 2.05 188,960 

SK 6420 906420 G 1 2 3 N I F 1998 40 1.60 – 

SK 6801 906801 G 6A 2 3 N I F 1998 80 2.97 – 
151

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14. Rada, G.R., Wu, C.L., Elkins, G.E., Bhandari, R.K., and Bellinger, W.Y. “Update of LTPP 

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FHWA-RD-03-094 Estimating Cumulative Traffic Loads, Volume II: Traffic Data Assessment and Axle Load 

Projection for theSites with Acceptable Axle Weight Data, Final Report for Phase 2
Recycled

Recyclable HRDI-13/03-05(600)E