Research, Development, and Technology

Turner-Fairbank Highway Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

Optimization of Traffic Data

Collection for Specific Pavement

Design Applications

Publication No. FHWA-HRT-05-079 MAY 2006
FOREWORD 

The objective of this study is to establish the minimum traffic data collection effort required for 

pavement design applications satisfying a maximum acceptable error under a prescribed 

confidence level. The approach consists of simulating the traffic data input to the 2002 National 

Cooperative Highway Research Program (NCHRP) 1-37A design guide for 17 distinct traffic 

data collection scenarios using extended-coverage, weigh-in-motion (WIM) data from the LongTerm Pavement Performance database. They include a combination of site-specific, regional, and 

national WIM, automated vehicle classification, and automated traffic recorder data of various 

lengths of coverage. Regional data were obtained using clustering techniques. Pavement life was 

estimated using mean traffic input and low-percentile input to the NCHRP 1-37A design guide 

for three levels of confidence: 75 percent, 85 percent, and 95 percent. For each confidence level, 

ranges in pavement life prediction errors were estimated. A three-dimensional plot was 

produced, indicating the maximum error by confidence level for each of the traffic data 

collection scenarios analyzed. This plot can be used to establish the minimum required traffic 

data collection effort, given the acceptable error and the desirable level of confidence. 

Gary L. Henderson. 

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 its 

contents or use thereof. This report does not constitute a standard, specification, or regulation. 

The U.S. Government does not endorse products or manufacturers. Trade and manufacturers’ 

names appear in this report only because they are considered essential to the object 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-HRT-05-079

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

5. Report Date 

May 2006 

4. Title and Subtitle 

Optimization of Traffic Data Collection for Specific Pavement Design 

Applications 6. Performing Organization 

Code 

7. Author(s) 

A.T. Papagiannakis, M. Bracher, J. Li, and N. Jackson 

8. Performing Organization 

Report No. 123210-8

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

Address 

Nichols Consulting Engineers 

1885 South Arlington Avenue, 

Suite 111 

Reno, NV 89509-3370 

Washington State University 

Department of Civil and 

 Environmental Engineering 

101 Sloan Hall 

Spokane Street 

Pullman, WA 99164-2910

11. Contract or Grant No. 

DTFH61-02-D-00139

13. Type of Report and Period 

Covered 

Final Report 

March 2003 to July 2005 

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 Officer’s Technical Representative (COTR): Larry Wiser, Long-Term Pavement Performance Team 

16. Abstract 

The objective of this study is to establish the minimum traffic data collection effort required for pavement design 

applications satisfying a maximum acceptable error under a prescribed confidence level. The approach consists of 

simulating the traffic data input to the 2002 National Cooperative Highway Research Program (NCHRP) 1-37A 

design guide for 17 distinct traffic data collection scenarios using extended-coverage, weigh-in-motion (WIM) 

data from the Long-Term Pavement Performance database. They include a combination of site-specific, regional, 

and national WIM, automated vehicle classification, and automated traffic recorder data of various lengths of 

coverage. Regional data were obtained using clustering techniques. Pavement life was estimated using mean 

traffic input and low-percentile input to the NCHRP 1-37A design guide for three levels of confidence: 75 

percent, 85 percent, and 95 percent. For each confidence level, ranges in pavement life prediction errors were 

estimated. A three-dimensional plot was produced, indicating the maximum error by confidence level for each of 

the traffic data collection scenarios analyzed. This plot can be used to establish the minimum required traffic data 

collection effort, given the acceptable error and the desirable level of confidence. 

17. Key Words 

Traffic input, NCHRP 1-37A design guide, LTPP, 

clustering, tolerable errors, confidence level.

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 

126 

22. Price 

Form DOT F 1700.7 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. Appropr e iate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
iii 

TABLE OF CONTENTS 

EXECUTIVE SUMMARY ............................................................................................................ 1 

CHAPTER 1. INTRODUCTION ................................................................................................... 5 

OBJECTIVE ............................................................................................................................. 5 

REPORT ORGANIZATION.................................................................................................... 5 

LITERATURE REVIEW ......................................................................................................... 6 

CHAPTER 2. IDENTIFY SCENARIOS AND KNOWLEDGE GAPS ...................................... 21 

METHODOLOGY FOR IDENTIFYING KEY PAVEMENT DESIGN SCENARIOS ....... 21 

KNOWLEDGE GAPS IN THE SENSITIVITY OF THE PAVEMENT DESIGN 

 PROCESS TO TRAFFIC INPUT..................................................................................... 21 

KNOWLEDGE GAPS IN DATA VARIATION FROM DIFFERENT TRAFFIC 

COLLECTION SCENARIOS .......................................................................................... 22 

SUMMARY............................................................................................................................ 22 

CHAPTER 3. DEFINE TRAFFIC DATA COLLECTION REQUIREMENTS FOR EACH 

SELECTED APPLICATION ................................................................................................. 25 

CHAPTER 4. LTPP DATA ANALYSIS ..................................................................................... 29 

LTPP WIM DATA EXTRACTED......................................................................................... 29 

RATIONALE FOR SELECTING SITES FOR THE DETAILED SENSITIVITY 

ANALYSIS....................................................................................................................... 32 

IDENTIFYING GROUPS OF SITES FOR OBTAINING REGIONAL DATA................... 33 

SIMULATING TRAFFIC DATA COLLECTION SCENARIOS......................................... 42 

Scenario 1-0: Site-Specific Continuous WIM Data............................................................. 42 

Scenario 1-1: Site-Specific WIM Data for 1 Month/4 Seasons........................................... 44 

Scenario 1-2: Site-Specific WIM Data for 1 Week/Season................................................. 47 

Scenario 2-0: Continuous Site-Specific AVC Data and Regional WIM Data..................... 47 

Scenario 2-1: Site-Specific AVC Data for 1 Month/Season and Regional WIM Data ....... 47 

Scenario 2-2: Site-Specific AVC Data for 1 Week/Season and Regional WIM Data......... 47 

Scenario 2-3: Site-Specific AVC Data for 1 Week/Year and Regional WIM Data ............ 47 

Scenario 3-0: Continuous Site-Specific ATR Data, Regional AVC Data, and Regional 

WIM Data ...................................................................................................................... 47 

Scenario 3-1: Site-Specific ATR Data for 1 Week/Season, Regional AVC Data, and 

Regional WIM Data....................................................................................................... 48 

Scenario 4-0: Continuous Site-Specific ATR Data, Regional AVC Data, and 

 National WIM Data........................................................................................................ 48 

Scenario 4-1: Site-Specific ATR Data for 1 Week/Season, Regional AVC Data, and 

National WIM Data........................................................................................................ 48 

Scenario 4-2: Site-Specific ATR Data for 1 Week/Year, Regional AVC Data, and 

 National WIM Data........................................................................................................ 48 

Scenario 4-3: Site-Specific ATR Data for 1 Weekday Plus 1 Weekend/Year, Regional 

AVC Data, and National WIM Data.............................................................................. 49 
iv 

Scenarios 4-4 through 4-7: Various-Coverage, Site-Specific ATR Data, National AVC 

Data, and National WIM Data ....................................................................................... 49 

ESTIMATING TRAFFIC INPUT.......................................................................................... 49 

CHAPTER 5. SENSITIVITY ANALYSIS .................................................................................. 53 

EFFECT OF TRAFFIC DATA COLLECTION SCENARIO ............................................... 53 

EFFECT OF AADTT GROWTH RATE ............................................................................... 58 

CHAPTER 6. DEFINE TRAFFIC COLLECTION REQUIREMENTS...................................... 61 

CHAPTER 7. SUMMARY........................................................................................................... 71 

APPENDIX A. CARD-4 AND CARD-7 DESCRIPTIONS ........................................................ 73 

APPENDIX B. CLUSTER ANALYSIS RESULTS .................................................................... 77 

APPENDIX C. STRUCTURAL AND CLIMATIC INPUT ........................................................ 91 

STRUCTURE: JOINTED PORTLAND CEMENT CONCRETE PAVEMENT.................. 91 

STRUCTURE: CONTINUOUSLY REINFORCED CONCRETE PAVEMENT................. 92 

STRUCTURE: ASPHALT CONCRETE PAVEMENT ........................................................ 93 

STRUCTURE: UNBOUND BASE........................................................................................ 94 

CLIMATE............................................................................................................................... 95 

APPENDIX D. PAVEMENT PERFORMANCE RESULTS ...................................................... 97 

REFERENCES ........................................................................................................................... 115 
v 

LIST OF FIGURES 

Figure 1. Schematic of the sensitivity of distress predictions to load spectra input ..................... 26 

Figure 2. LTPP sites with WIM data available for periods longer than 359 days per year ......... 30 

Figure 3. LTPP sites with WIM data available for periods longer than 299 days per year ......... 31 

Figure 4. Flexible pavement site selection by AADTT and structural number............................ 32 

Figure 5. Rigid pavement site selection by AADTT and slab thickness ...................................... 33 

Figure 6. Annual distributions of tandem-axle loads, Washington State LTPP sites................... 39 

Figure 7. Tandem-axle load distributions for the cluster of Washington State LTPP site 6048 .. 40 

Figure 8. Tandem-axle load distributions for the cluster of Washington State LTPP site 1007 .. 40 

Figure 9. Example of NCHRP 1-37A design guide output, site 181028 in Indiana..................... 54 

Figure 10. Summary of mean in life predictions, site 181028 in Indiana, confidence 

50 percent................................................................................................................................ 56 

Figure 11. Summary of the range in predictions, site 181028 in Indiana, confidence 

75 percent................................................................................................................................ 56 

Figure 12. Summary of the range in predictions, site 181028 in Indiana, confidence 

85 percent................................................................................................................................ 57 

Figure 13. Summary of the range in predictions, site 181028 in Indiana, confidence 

95 percent................................................................................................................................ 57 

Figure 14. Summary of the range in predictions, site 181028 in Indiana, confidence 

99.9 percent ............................................................................................................................ 58 

Figure 15. Pavement life prediction comparison between actual annual AADTT growth rate 

and 4 percent annual AADTT growth rate, flexible pavement sites....................................... 60 

Figure 16. Pavement life prediction comparison between actual annual AADTT growth rate 

and 4 percent annual AADTT growth rate, rigid pavement sites ........................................... 60 

Figure 17. Components of the percentage difference between pavement life predictions for 

scenario X and those for scenario 1-0..................................................................................... 61 

Figure 18. Statistics for error component “A” in life predictions (percent), flexible pavement 

sites with AADTT ≤ 800 trucks/day/lane ............................................................................... 63 

Figure 19. Statistics for error component “A” in life predictions (percentage), flexible 

pavement sites with AADTT > 800 trucks/day/lane............................................................... 63 

Figure 20. Statistics for error component “A” in life predictions (percentage), rigid pavement 

sites with AADTT ≤ 1,200 trucks/day/lane ............................................................................ 64 

Figure 21. Statistics for error component “A” in life predictions (percentage), rigid pavement 

sites with AADTT > 1,200 trucks/day/lane ............................................................................ 64 

Figure 22. Estimated range in NCHRP 1-37A design guide pavement life prediction errors 

from mean traffic input ........................................................................................................... 65 

Figure 23. Estimated range in NCHRP 1-37A design guide pavement life prediction errors 

from low-percentile traffic input............................................................................................. 69 

Figure 24. Clusters of LTPP sites by annual tandem-axle load distribution, Washington 

State......................................................................................................................................... 77 

Figure 25. Clusters of LTPP sites by annual tandem-axle load distribution, Vermont ................ 78 

Figure 26. Clusters of LTPP sites by annual tandem-axle load distribution, Mississippi ............ 79 

Figure 27. Clusters of LTPP sites by annual tandem-axle load distribution, Minnesota.............. 80 

Figure 28. Clusters of LTPP sites by annual tandem-axle load distribution, Michigan ............... 81 

Figure 29. Clusters of LTPP sites by annual tandem-axle load distribution, Indiana .................. 82 
vi 

Figure 30. Clusters of LTPP sites by annual tandem-axle load distribution, Connecticut ........... 83 

Figure 31. Clusters of LTPP sites by annual average truck class distribution, Washington 

State......................................................................................................................................... 84 

Figure 32. Clusters of LTPP sites by annual average truck class distribution, Vermont ............. 85 

Figure 33. Clusters of LTPP sites by annual average truck class distribution, Mississippi ......... 86 

Figure 34. Clusters of LTPP sites by annual average truck class distribution, Minnesota........... 87 

Figure 35. Clusters of LTPP sites by annual average truck class distribution, Michigan ............ 88 

Figure 36. Clusters of LTPP sites by annual average truck class distribution, Indiana................ 89 

Figure 37. Clusters of LTPP sites by annual average truck class distribution, Connecticut ........ 90 
vii 

LIST OF TABLES 

Table 1. Range in life prediction percentage errors from mean traffic input ................................. 2 

Table 2. Range in combined life prediction errors from low-percentile traffic input..................... 3 

Table 3. Accuracy of AADT predictions as a function of factoring procedure.............................. 8 

Table 4. Traffic input levels in the NCHRP 1-37A design guide................................................. 12 

Table 5. Detailed description of the NCHRP 1-37A design guide traffic data input levels......... 13 

Table 6. NCHRP 1-37A design guide flow of calculations in assembling axle-load spectra ...... 15 

Table 7. Suggested levels of reliability for roads of various classes ............................................ 21 

Table 8. Selected traffic data collection scenarios........................................................................ 25 

Table 9. Relationship between two-sided probability of survival/failure and standard normal 

deviate in pavement life predictions ....................................................................................... 27 

Table 10. Definition of variables extracted from the CTDB ........................................................ 31 

Table 11. Background information on the flexible LTPP sites selected....................................... 34 

Table 12. Background information on the rigid LTPP sites selected ........................................... 35 

Table 13. Identification codes for roadway functional classes as defined by LTPP database 

field FUNCTIONAL_CLASS in table INV_ID..................................................................... 35 

Table 14. Euclidean distance matrix: Annual distributions of tandem-axle loads, Washington 

State LTPP sites ...................................................................................................................... 37 

Table 15. Summary of clustering strategies and associated Euclidean distance: Annual 

distributions of tandem-axle loads, Washington State LTPP sites ......................................... 38 

Table 16. LTPP sites used for obtaining regional vehicle classification data .............................. 41 

Table 17. LTPP sites used for obtaining regional axle-load data ................................................. 41 

Table 18. Example of computing MAFs from regional data........................................................ 44 

Table 19. Monthly versus annual vehicle class distribution, AVC cluster, Washington State 

site 6048.................................................................................................................................. 45 

Table 20. Number of single axles per vehicle, annual Washington State data............................. 46 

Table 21. Number of tandem axles per vehicle, annual Washington State data........................... 46 

Table 22. Summary of the source of traffic data input to the NCHRP 1-37A design guide for 

the selected scenarios.............................................................................................................. 50 

Table 23. Number of possible traffic sampling combinations by scenario................................... 51 

Table 24. Failure criteria for each pavement type ........................................................................ 53 

Table 25. Scenario 1-0: Life prediction and critical distress, flexible pavement sites ................. 55 

Table 26. Scenario 1-0: Life prediction and critical distress, rigid pavement sites...................... 55 

Table 27. Summary of computed AADTT growth rates and corresponding scenario 1-0 

pavement lives, flexible pavement sites.................................................................................. 59 

Table 28. Summary of computed AADTT growth rates and corresponding scenario 1-0 

pavement lives, rigid pavement sites ...................................................................................... 59 

Table 29. Statistics and ranges for the percentage life prediction errors from mean traffic 

input (i.e., quantity “A”), n=17............................................................................................... 65 

Table 30. Statistics for percentage additional error in life predictions from lowest percentile 

traffic input (i.e., quantity “B”)............................................................................................... 67 

Table 31. Range in mean “B” errors............................................................................................. 68 

Table 32. Overall range in pavement life prediction errors (“A” plus “B” components) by 

probability of exceeding them ................................................................................................ 68 

Table 33. Card-4: Vehicle classification record ........................................................................... 73 
viii 

Table 34. Card-7: Truck weight record......................................................................................... 74 

Table 35. PC strength properties for level 2 input........................................................................ 92 

Table 36. Layer types and thicknesses for all sites....................................................................... 93 

Table 37. Assumed layer moduli .................................................................................................. 94 

Table 38. Site locations used for interpolation of weather station data........................................ 96 

Table 39. Life prediction estimates by scenario and traffic input percentile level, 

section 181028 ........................................................................................................................ 97 

Table 40. Life prediction estimates by scenario and traffic input percentile level, 

section 261010 ........................................................................................................................ 98 

Table 41. Life prediction estimates by scenario and traffic input percentile level, 

section 282807 ........................................................................................................................ 99 

Table 42. Life prediction estimates by scenario and traffic input percentile level, 

section 536048 ...................................................................................................................... 100 

Table 43. Life prediction estimates by scenario and traffic input percentile level, 

section 185518 ...................................................................................................................... 101 

Table 44. Life prediction estimates by scenario and traffic input percentile level, 

section 275076 ...................................................................................................................... 102 

Table 45. Life prediction estimates by scenario and traffic input percentile level, 

section 094008 ...................................................................................................................... 103 

Table 46. Life prediction estimates by scenario and traffic input percentile level, 

section 501682 ...................................................................................................................... 104 

Table 47. Life prediction estimates by scenario and traffic input percentile level, 

section 186012 ...................................................................................................................... 105 

Table 48. Life prediction estimates by scenario and traffic input percentile level, 

section 261004 ...................................................................................................................... 106 

Table 49. Life prediction estimates by scenario and traffic input percentile level, 

section 261012 ...................................................................................................................... 107 

Table 50. Life prediction estimates by scenario and traffic input percentile level, 

section 261013 ...................................................................................................................... 108 

Table 51. Life prediction estimates by scenario and traffic input percentile level, 

section 271019 ...................................................................................................................... 109 

Table 52. Life prediction estimates by scenario and traffic input percentile level, 

section 283081 ...................................................................................................................... 110 

Table 53. Life prediction estimates by scenario and traffic input percentile level, 

section 185022 ...................................................................................................................... 111 

Table 54. Life prediction estimates by scenario and traffic input percentile level, 

section 265363 ...................................................................................................................... 112 

Table 55. Life prediction estimates by scenario and traffic input percentile level, 

section 533813 ...................................................................................................................... 113 
ix 

LIST OF ACRONYMS AND ABBREVIATIONS 

AADT Average Annual Daily Traffic 

AADTT Average Annual Daily Truck Traffic 

AADW Annual Average Day of Week 

AASHTO American Association of State Highway and Transportation Officials 

AC Asphalt Concrete 

AEPV Average ESALs per Vehicle 

ATR Automated Traffic Recorder 

AVC Automated Vehicle Classification 

CMAWD Combined Month and Average Weekday 

CMDW Combined Month and Day of Week 

COTR Contracting Officer’s Technical Representative 

CRC Continuously Reinforced Concrete 

CRCP Continuously Reinforced Concrete Pavement 

CTDB Central Traffic Database 

CWAWD Combined Week and Average Weekday 

DAR Daily Adjustment Ratio 

DOT Department of Transportation 

DOW Day of Week 

DTR Daily Traffic Ratio 

ESAL Equivalent Single-Axle Load 

FHWA Federal Highway Administration 

GPS General Pavement Study 

GVW Gross Vehicle Weight 

ICM Integrated Climatic Model 

IRI International Roughness Index 

JPCP Jointed Plain Concrete Pavement 

LTPP Long-Term Pavement Performance 

M Monthly Adjustment Factor 

MADT Monthly Average Daily Traffic 

MADW Monthly Average Day of Week 

MAF Monthly Adjustment Factor 

MAR Monthly Adjustment Ratio 

MDW Month and Day of Week 

MDWTR Monthly Day of Week Traffic Ratio 

MTR Monthly Traffic Ratio 

N National 

NCHRP National Cooperative Highway Research Program 

PCC Portland Cement Concrete 

PDG Pavement Design Guide 

PSI Present Serviceability Index 

QC Quality Control 

R Regional 

RFP Request for Proposal 

RH Relative Humidity 
x 

SAS Statistical Analysis System®

SD Specific Day 

SD Standard Deviation 

SDNN Specific Day With Noon-to-Noon Factors 

SN Structural Number 

SS Site Specific 

SWDW Separate Week and Day of Week 

TMG Traffic Monitoring Guide 

TTC Truck Traffic Class 

TWRG Truck Weight Road Group 

VC Vehicle Class 

VMT Vehicle-Miles Traveled 

VOL Daily Vehicle Volume Count 

WIM Weigh-in-Motion 
1 

EXECUTIVE SUMMARY 

This study presents a comprehensive approach for establishing the minimum traffic data 

collection effort required for pavement design applications satisfying a maximum acceptable 

error under a prescribed confidence level. This approach consists of simulating the traffic data 

input to the new National Cooperative Highway Research Program (NCHRP) 1-37A design 

guide for 17 distinct traffic data collection scenarios using extended-coverage, weigh-in-motion 

(WIM) data from the Long-Term Pavement Performance (LTPP) database. This simulation 

involves data typically collected by other technologies, such as automated vehicle classification 

(AVC) and automated traffic recorders (ATR). These scenarios are described in table 8 in the 

report. 

Extended coverage was defined as 299 or more days per year of level E WIM data, (i.e., data that 

has passed the quality control (QC) checks conducted by State departments of transportation 

(DOTs) and the LTPP regional support contractor offices). Analysis of LTPP Standard Data 

Release 16.0 revealed a total of 178 general pavement studies (GPS) satisfying this requirement. 

For all of these sites, central traffic database (CTDB) data were extracted in the form of daily 

summaries (level 3). From these sites, a total of 30 sections (15 flexible and 15 rigid) were 

selected for NCHRP 1-37A design guide pavement performance estimation. The selection was 

based on the widest possible distribution of average annual daily truck traffic (AADTT) volumes 

and structural thicknesses. 

A number of the traffic data collection scenarios simulated involved continuous site-specific data 

coverage for axle loads, classification, or counts, while others involved discontinuous sitespecific data coverage (1 month per season, 1 week per season, and so on). Data elements, which 

were assumed to be unavailable at a site for simulation purposes, were estimated from regional 

data. Regional vehicle classification and axle-load data were obtained from the remaining LTPP 

sites identified using clustering techniques. Scenarios involving national data used the default 

traffic input in the NCHRP 1-37A design guide. For each of the traffic data collection scenarios 

involving discontinuous coverage of site-specific data, statistics for each traffic data element 

were computed by considering all possible time-coverage combinations. This allowed the 

establishment of low percentiles for each input to simulate underestimation of the actual traffic 

volumes/loads at a site. This was considered to be critical since it would result in thinner 

pavement designs that failed prematurely. Four confidence levels were selected: 75 percent, 

85 percent, 95 percent, and 99.9 percent. Traffic input for the continuous-coverage traffic data 

collection scenarios involved no variation because of the sampling scheme used. All scenarios 

were simulated using a 4 percent annual growth in AADTT. Additional analysis was conducted 

to compute the annual growth rate in AADTT and its effect on pavement life predictions. 

The NCHRP 1-37A design guide pavement life predictions for each scenario were analyzed to 

compute percentage errors in pavement life predictions with respect to the life predictions 

obtained under continuous site-specific WIM data (scenario 1-0). Reasonable life predictions 

were obtained for 17 of the 30 sections analyzed; the remainder experienced either premature 

failures or no failure at all. Two pavement life prediction error components were identified: 

• “A” is the estimated error from the traffic input of a continuous scenario or from the mean 

traffic input of a discontinuous time-coverage scenario. 
2 

• “B” is the additional error possible in discontinuous-coverage scenarios by inputting the 

lowest percentile input for all traffic input estimates simultaneously. 

Computing statistics for error component “A” for all 17 sections revealed that its mean is 

negligible for all of the scenarios analyzed. Its standard deviation allowed establishment of a 

range of errors by confidence level (table 1 below and figure 22 in the report).

Table 1. Range in life prediction percentage errors from mean traffic input. 

Statistics for error component “B” were processed to yield the mean error and the standard 

deviation in the mean error by traffic data collection scenario. This allowed computation of the 

range in mean error resulting from specifying the lowest percentile for all traffic inputs 

simultaneously. While this is quite conservative, it addresses the question of reliability, so that 

the designer is guaranteed that given a level of confidence, a particular error level will not be 

exceeded. Overall error was computed by adding the range in error from component “A” to the 

range in mean error from component “B.” The results were plotted in a three-dimensional plot, 

indicating the maximum error by confidence level for each of the traffic data collection scenarios 

analyzed (table 2 below and figure 23 in the report). Figure 23 can be used to establish the 

minimum required traffic data collection effort, given the acceptable error and the desirable level 

of confidence.

Error Range by Probability of Exceeding Them

Scenario 25 percent 15 percent 5 percent 

1-1 5.32 8.26 13.42 

1-2 6.87 10.66 17.33 

2-0 10.74 16.65 27.08 

2-1 10.28 15.94 25.91 

2-2 10.72 16.62 27.03 

2-3 10.94 16.97 27.59 

3-0 25.29 39.22 63.78 

3-1 25.00 38.77 63.05 

4-0 27.08 41.99 68.29 

4-1 26.38 40.90 66.51 

4-2 25.11 38.94 63.32 

4-3 28.48 44.17 71.83 

4-4 30.17 46.78 76.08 

4-5 30.38 47.10 76.60 

4-6 30.12 46.71 75.96 

4-7 32.49 50.39 81.94 
3 

Table 2. Range in combined life prediction errors from low-percentile traffic input. 

Overall Range in Errors by 

Probability 

of Exceeding Them 

Scenario

25 

percent

15 

percent 5 percent

1-1 20.89 27.45 41.57 

1-2 28.59 35.91 55.81 

2-0 10.74 16.65 27.08 

2-1 34.70 42.65 58.96 

2-2 23.79 36.55 44.31 

2-3 37.24 51.79 89.88 

3-0 25.29 39.22 63.78 

3-1 30.07 45.78 74.02 

4-0 27.08 41.99 68.29 

4-1 32.14 47.47 76.75 

4-2 47.22 72.55 105.66 

4-3 63.66 92.44 151.79 

4-4 30.17 46.78 76.08 

4-5 35.36 54.36 86.77 

4-6 70.17 112.32 174.65 

4-7 83.84 139.25 206.75 

5 

CHAPTER 1. INTRODUCTION 

OBJECTIVE 

Traffic loads are an essential input to the pavement analysis and design process. In the past, the 

effect of traffic was aggregated into equivalent single-axle loads (ESALs) and input into 

regression-based pavement performance equations. The NCHRP 1-37A design guide(1) 

characterizes traffic in terms of axle numbers by type and their load frequency distribution 

(i.e., axle-load spectra). This is a significant improvement over past methods because it allows a 

mechanistic pavement design approach. It involves computing the pavement structural responses 

to load (i.e., stresses and strains), translating them into damage, and accumulating the damage 

into distress and reduced pavement performance over time. 

Traffic data collection is carried out by a combination of data acquisition technologies, including 

WIM systems, AVC, and ATR. Typically, traffic data unavailable at a pavement design location 

are borrowed from other data collection sites that exhibit similar traffic loading and classification 

properties. 

The data coverage of traffic data acquisition systems can vary widely from continuously 

operating to simple 48-hour (h) (or less) data coverage. Even for continuously operating data 

acquisition systems; however, data coverage may be limited by system malfunctions. These are 

detected by performing a number of data QC checks. This technology has evolved significantly 

in response to the needs of the LTPP program.(2) It is typically based on the repeatability of 

certain traffic patterns (e.g., the distribution of the gross vehicle weight of five-axle semitrailer 

trucks is used for WIM load data QC). Data that fail to pass these QC tests are considered 

suspect and should be excluded from the data coverage of these systems. 

Hence, there is wide variation in traffic data availability and time coverage between pavement 

design sites. The challenge at hand is to determine the combination of traffic data acquisition 

technology and the time coverage required for particular pavement design situations. This issue 

needs to be addressed in light of the sensitivity of the pavement design and performance analysis 

to the level of traffic data input. 

The objective of this study is to resolve this problem. A comprehensive approach is used for 

establishing the relationship between traffic data collection efforts (e.g., combination of traffic 

data acquisition technologies and length of time coverage) and the variability in the predicted 

pavement life using the NCHRP 1-37A design guide. Extended-coverage WIM data are used 

from the LTPP database to simulate these traffic data collection scenarios. 

REPORT ORGANIZATION 

The report is organized in sections that address each of the tasks identified in the request for 

proposals (RFPs): 

• Task 1. Literature review. 

• Task 2. Identification of traffic data collection scenarios and knowledge gaps. 

• Task 5. Definition of traffic data collection requirements. 

• Task 6. LTPP data analyzed. 
6 

• Task 4. Sensitivity analysis of the NCHRP 1-37A design guide to traffic input. 

• Task 7. Recommendations on the minimum traffic data collection effort required, given a 

desired reliability level. 

Note that task 3 as described in the RFP involved submission of an interim report. Those 

findings were incorporated throughout this final report. 

LITERATURE REVIEW 

The literature review focuses on two main areas: 

• Methodologies used for obtaining traffic data input to pavement design and its variability as a 

function of the type of data available and its time coverage. 

• Sensitivity of the pavement design process to the variability in traffic-load input. 

In carrying out this review, emphasis was placed on the methodologies used for estimating 

traffic-load data as described in the 2001 Traffic Monitoring Guide (TMG)(3) and the recently 

completed NCHRP 1-39 study,(4) as well as the handling of traffic data input to the NCHRP 1-

37A design guide.(5-7) The following paragraphs offer a summary of the literature reviewed. 

Early work by Ritchie and Hallenbeck(8) described the relationship between sampling effort in 

terms of the number of weekdays of continuous ATR data available and the accuracy in 

estimating the average annual daily traffic (AADT). Using the central limit theorem produced the 

expression in equation 1 for the difference interval d between the true and the estimated AADT: 

(1) 

Where: 

d = Difference interval between the true and the estimated AADT. 

Za / 2 = Standard normal deviate at a confidence level (1-a). 

Sp = Standard deviation in the population of daily traffic volumes. 

n = Number of weekdays averaged to estimate the AADT. 

Accordingly, the accuracy in predicting AADT increases with the number of days used in 

establishing the mean. Seasonal factors for each month, denoted by β, were derived using two 

alterative methods. First, a zero-intercept, regression-based method shown in equation 2 was 

used: 

(2) 

Where: 

AADT = Average annual daily traffic. 

VOL = Daily vehicle volume count obtained by averaging the counts for 3 weekdays 

 (e.g., Tuesday through Thursday). 

β = Seasonal factors for each month. 

ε = Error term. 

n

S

d Z p = a / 2

AADT = β VOL + ε
7 

Second, a simple ratio-based method shown in equation 3 was used: 

(3) 

Where: 

AADT= Average annual daily traffic. 

VOL = Daily vehicle volume count obtained by averaging the counts for 3 weekdays 

 (e.g., Tuesday through Thursday). 

β = Seasonal factors for each month. 

u = Error term. 

It was rationalized that the second method avoided the problem of heteroscedasticity (a condition 

where the variance in the regression error ε depends on the magnitude of the independent 

variable VOL); therefore, it was deemed preferable for the first method, and later it was adopted 

by the American Association of State Highway and Transportation Officials (AASHTO).(9)

Statistics for these monthly ratios were calculated for groups of roads in Washington State 

organized by geographic region and highway functional class. 

The AASHTO Joint Task Force on Traffic Monitoring Standards proposed the following method 

for estimating AADT from short-term daily traffic volume (i.e., ATR) counts: 

• Compute the average day of week (DOW) for each month (for example, an average Monday 

or Tuesday). 

• Compute an annual average value for that DOW. 

• Compute an average of the seven DOWs to arrive at the AADT. 

This method is expressed mathematically as in equation 4. 

(4) 

Where: 

AADT = Average annual daily traffic. 

VOLijk = Daily traffic volume for day k of DOW i and month j. 

i = DOW ranging from 1 to 7 (i.e., Monday through Sunday). 

j = Month of the year ranging from 1 to 12 (i.e., January through December). 

n = Number of data days from a particular DOW used in computing the average of that 

 DOW in a particular month (maximum of five). 

k = Data day used in computation. 

This approach limits the bias that would result from simply averaging traffic volumes for the 

days of the year available. In implementing this approach, holidays and the days that precede and 

follow them should be excluded. The AASHTO procedure is the one recommended by the 2001 

TMG. Accordingly, monthly adjustment factors (MAF or M) are calculated as in equation 5. 

(5) 

u

VOL

AADT = β +

∑ ∑ ∑ = = =

⎥

⎦

⎤ ⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛ =

7

1 1

12

1

1

12

1

7

1

i

ijk

n

j k

VOL

n

AADT

VOL

AADT MAF = M =
8 

Where: 

MAF = Monthly adjustment factor. 

M = Monthly adjustment factor. 

AADT = Average annual daily traffic. 

VOL = Average daily volume count, computed by one of the two alternative methods 

 using either the simple averaging approach or the AASHTO approach. 

Finally, AASHTO recommended an averaging procedure for estimating missing traffic volume 

data. For example, if the traffic volume for a Wednesday is missing, it can be estimated as equal 

to the average of the available traffic volumes for the other Wednesdays in a particular month. 

Similarly, estimating missing vehicle classification data would involve averaging the volume 

counts by class or groups of similar classes for the same days in the month. Furthermore, missing 

WIM data can be estimated from the vehicle classification data obtained this way and the 

frequency distribution of axle loads by axle configuration available for the same days of the 

month. 

A Federal Highway Administration (FHWA)-funded study used continuous ATR, AVC, and 

WIM data from traffic monitoring sites to compute: 

• AADT. 

• Vehicle miles traveled (VMT). 

• AADT by vehicle class. 

• VMT by vehicle class. 

• ESALs.

The study examines the sensitivity of the computed statistics to various simulated sampling 

schemes and factoring procedures.(10) Seven factoring procedures were described for computing 

AADT from ATR (vehicle count) data, which are listed in table 3 in order of increasing accuracy 

and complexity.(10)

Table 3. Accuracy of AADT predictions as a function of factoring procedure.(10)

No. Factoring Procedure Involves 

Mean 

Absolute 

Error 

(percentage)

Average 

Percentage 

Error 

P (e > 0.2) 

(percentage)

0 Unfactored – 12.4 −0.6 18.2 

1 Separate month and 

DOW (MDW) 

Set of 12 monthly 

factors and another 

set of 7 DOW 

factors (total of 19) 

7.5 −0.5 6.2 

2 Combined month and 

average weekday 

(CMAWD) 

Set of average 

weekday and 

average weekend 

factors for each 

month (total of 24) 

7.6 +0.4 5.9 
9 

Table 3. Accuracy of AADT predictions as a function of factoring procedure (continued). 

No. Factoring Procedure Involves 

Mean 

Absolute 

Error 

(percentage)

Average 

Percentage 

Error 

P (e > 0.2) 

(percentage)

3 Separate week and 

DOW (SWDW) 

Set of 52 weekly 

factors and another 

set of 7 DOW 

factors (total of 59) 

7.5 −0.9 6.0 

4 Combined month and 

DOW (CMDW) 

Set of 7 DOW 

factors for each 

month (total of 84) 

7.4 −0.2 5.8 

5 Combined week and 

average weekday 

(CWAWD) 

Set of average 

weekday and 

weekend factors for 

each week of year 

(total of 104) 

7.3 +0.5 5.1 

6 Specific day (SD) Set of day factors for 

each day, (midnightto-midnight) of the 

year (total of 365) 

7.1 +0.2 5.1 

7 Specific day with 

noon-to-noon factors 

(SDNN) 

Similar to the one 

above, except counts 

are noon-to-noon 

7.0 +0.3 4.8 

This study recommended that procedure 4 (the CMDW method highlighted in table 3) is a good 

compromise between accuracy and complexity. This is the same method recommended by the 

2001 TMG.(3) Accordingly, equation 6 shows the combined monthly and DOW factor for month 

i and DOW j at ATR station l, denoted by CMDWFijl. 

(6) 

Where: 

CMDWFijl = Combined month and day of week factor for month i

 and DOW j at station l. 

AADT l = Average annual daily traffic at station l. 

MADWijl = Average traffic volume for month i and DOW j at station l. 

In applying this procedure, it is recommended to exclude weekdays close to holidays (e.g., the 

Friday after Thanksgiving), although these days should be included in computing the AADT. If 

instead of vehicle counts, conventional axle counts are available, additional axle factoring would 

be necessary to convert axle counts to vehicle counts. 

The traffic patterns established from continuously operating ATR sites can be used to compute 

AADT from short-term volume counts at other comparable sites.(10) Comparable sites are 

established on the basis of roadway functional class. Short-term counts should be taken over at 

ijl

l

ijl MADW

AADT

CMDWF =
10 

least a 24-h period and preferably over multiple 24-h periods, although the improvement in 

predicting AADT from 24 to 48 weekday-h samples was marginal, producing a reduction in 

absolute error of 1 percent. The procedures described for factoring ATR data to obtain AADT(10)

also applies for factoring AVC data to obtain the AADTT by truck class. The essential difference 

is that the counts are per vehicle class rather than for all classes collectively. A subsequent study 

examined the effect of the traffic data collection effort and methodology used in obtaining the 

traffic input necessary for forecasting cumulative ESALs and the resulting difference in 

pavement life predictions and life-cycle pavement costs.(11) 

The 2001 TMG recommends collecting traffic volume data through a combination of a limited 

number of continuously operating reference ATRs and a larger number of shorter duration 

coverage ATRs.(3) Coverage ATRs should record data over at least 24 h and preferably more 

than 48 h using systems that summarize the data hourly. These short-duration counts require 

adjustments to reduce the effects of temporal bias. Adjustment factors are developed for 

particular months and DOWs by analyzing data from continuously operating reference ATR 

stations. Data from these stations are combined into groups of similar characteristics, either 

subjectively (e.g., in terms of geographic location or roadway functional class) or preferably 

through statistical clustering techniques. Appendix 2-b of the 2001 TMG gives an example of 

clustering in identifying ATR sites with similar MAFs using the Statistical Analysis System 

(SAS®) statistical package.(3,12)

AVC counts are collected following principles similar to those used for collecting ATR counts. 

The main difference is that seasonal traffic volume adjustment factors (monthly and daily) are 

developed for three or four broad vehicle classes (passenger cars, single-unit trucks, single-trailer 

trucks, and multitrailer trucks) rather than for all vehicles collectively. This is one of the major 

differences of the 2001 TMG compared to earlier TMG versions (1992 and 1995), and it was 

introduced to account for the seasonal variation in traffic volume patterns of various classes. 

These seasonal factors are developed by analyzing data from continuously operating reference 

AVC stations representing the traffic conditions of the selected roadway groups. These groups 

can be established subjectively (e.g., based on roadway functional class) or through clustering 

techniques, although no particular example for doing so is given in the TMG. Shorter duration 

AVC counts are to cover, at a minimum, 48 consecutive hours, with a recommended monitoring 

cycle of 6 years. It is suggested that an improvement of between 3 and 5 percent in the accuracy 

of predicting annual average traffic volumes can be achieved by increasing the duration of 

classification counts from 24 to 48 h.(13) Low-volume roads exhibited an even higher increase in 

accuracy because of the higher variation in daily traffic counts.(10) The only exception to the 48-h 

data collection recommendation is made for urban areas, where traffic congestion imposes 

variable vehicle speeds. In such situations, it is allowable to collect vehicle classification data 

over shorter periods of time (e.g., 15 minutes (min)) during which traffic is detected to be 

moving at a constant speed. The AADTT for vehicle class c (AADTTc) is computed using 

equation 7, an expression similar to the one for AADT in equation 4. 

(7) 

Where: 

AADTTc = Average annual daily truck traffic for truck class c. 

i = DOW ranging from 1 to 7 (i.e., Monday through Sunday). 

∑ ∑ ∑ = = =

⎥

⎦

⎤ ⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛ =

7

1 1

12

1

1

12

1

7

1

i

ijkc

n

j k

c AADTT

n

AADTT
11 

j = Month of the year ranging from 1 to 12 (January through December). 

n = Number of times data from a particular DOW is available for computing the 

 average in a given month (i.e., 1, 2, 3, 4, or 5). 

AADTTijkc = Average annual daily truck traffic volume for vehicle class c, day k of DOW 

 i, and month j. 

k = Data day used in computation. 

Consequently, adjustment factors are developed from continuously operating AVC sites for a 

particular vehicle class c, DOW i, and month j to AADTT for that vehicle class at location l. 

They are extensions of equation 6, which by dropping the subscript l for the sake of simplicity, is 

expressed as equation 8. 

(8) 

Where: 

CMDWTFijc = Combined month-DOW factor for truck class c, DOW i, and month j. 

AADTTc = Average annual daily truck traffic for truck class c. 

MADWTijc = Daily average traffic count by month by DOW for truck class c, DOW i, 

 and month j.

The 2001 TMG(3) gives a slightly different expression, shown in equation 9, for the difference 

interval d between the true and the estimated AADT and the one used by Ritchie and 

Hallenbeck.(8)

(9) 

Where: 

d = Difference interval between the true and the estimated AADT. 

1−a / 2, n−1 t = Standard deviate of the Student’s t-distribution at a confidence level (1-a) for 

 n - 1 degrees of freedom. 

S = Coefficient of variation in the daily traffic volumes. 

n = Number of days averaged to estimate the AADT. 

The reason for using the Student’s t-distribution instead of the normal distribution is that the 

coefficient of variation in the daily traffic volume population is not really known from the 

relatively small number of days sampled. 

The 2001 TMG defines truck load data collection as the means of obtaining the distribution of 

axle loads by axle configuration and vehicle class for selected roadway groups.(3) This 

information can be obtained only with WIM systems. Establishing roadway groups with 

comparable axle-load distribution patterns is essential in maximizing the benefit of the limited 

number of WIM sites typically available in a jurisdiction. These roadway groups need not be 

identical to the roadway groups identified with reference to the vehicle classification data 

obtained from AVC sites. They can be established subjectively (e.g., based on roadway 

ijc

c

ijc MADWT

AADTT

CMDWTF =

n

S d t = 1−a / 2, n−1
12 

functional class and predominant commodity being carried) or through clustering techniques, 

although no particular example for doing so is given in the TMG. In establishing the number of 

WIM sites n required per roadway group, the expression in equation 10 is used. 

(10) 

Where: 

n = Number of times data from a particular DOW is available for computing the 

 average in a given month. 

t = Value of the Student’s t-distribution for the selected level of confidence α. 

S = Standard deviation established from a sample of a traffic quantity 

(e.g., gross 

 vehicle weight (GVW) or ESAL for FHWA class 9 vehicles). 

D = Desired accuracy range in this traffic quantity. 

Based on this approach and by specifying values of D for GVW and ESAL for class 9 vehicles of 

0.19 and 0.13, respectively, a minimum required number of six WIM sites per roadway group is 

estimated (at 95 percent confidence). It is emphasized that it is more important to have accurate 

rather than continuous WIM data, although it is preferable to have at least one of the six WIM 

sites in each roadway group operating continuously. This allows establishment of daily, weekly, 

and seasonal patterns in the traffic-load data for the particular roadway group. Where continuous 

operation is not possible, WIM systems should operate for at least a period of 7 continuous days 

to capture daily variations. 

NCHRP 1-37A is the main study for the development of a new pavement design guide. (See 

references 1, 5, 6, and 7.) The mechanistic pavement damage computations in the NCHRP 1-37A 

design guide require traffic-load spectra, defined as the number of axle passes by load level and 

axle configuration. In practice, this axle-load spectra information is synthesized by combining 

data from WIM, AVC, and ATR systems, including either the specific pavement site or other 

regional/representative traffic data collection sites. Table 4 (of this report) outlines the actual 

combination of the technology/data used in establishing the load spectra defines four levels of 

traffic input, as described in appendix AA in the final report.(1) 

Table 4. Traffic input levels in the NCHRP 1-37A design guide. 

Traffic Input Levels

Data Element/Input Variables 1 2 3 4 

WIM data: Site/segment specific X – – – 

WIM data: Regional representative – X X – 

WIM data: National representative weight (LTPP) – – – X 

AVC data: Site/segment specific X X – – 

AVC data: Regional representative – – X – 

AVC data: National representative classification (LTPP) – – – X 

ATR data: Site specific – – X X 

It should be noted that the NCHRP 1-37A design guide makes no explicit recommendations on 

the length of data coverage for these data sets that would produce “reliable” estimates of the 

2

2

2

/ 2 D

S

n t = a
13 

required input elements. It should also be noted that these traffic input levels are not rationally 

related to the input levels identified by the NCHRP 1-37A design guide for other groups of input 

(e.g., layer properties and environmental data). A more detailed description of the traffic input 

levels and the technology required for obtaining them is given in table 5. 

Table 5. Detailed description of the NCHRP 1-37A design guide traffic data input levels. 

NCHRP 1-37A 

Design Guide 

Traffic 

Input Levels

Description 

1 Requires site-specific vehicle classification and axle-load data. The 

traffic data measured at the site includes counts, classification, and 

weighing by lane and direction over a sufficiently long period of time to 

reliably establish patterns in these traffic inputs. It is possible only with 

an onsite WIM installation, and it is recommended for use in designing 

most high-volume highways. 

2 Requires site-specific vehicle classification data, but it relies on 

representative (e.g., regional) axle weight data by vehicle class and axle 

configuration. The regional axle-load data are to be obtained from WIM 

installations on roadways that exhibit similar traffic-load patterns as the 

site in question. It is possible with an onsite AVC installation and 

sufficient WIM data from installations that have similar traffic-load 

patterns. Recommended for roadways of lesser importance. 

3 Requires site-specific traffic volume counts and percentage truck data. It 

relies on representative (e.g., regional) vehicle classification and axle 

weight data. These regional data are to be obtained from AVC and/or 

WIM installations from sites that exhibit similar traffic distributions and 

load patterns as the site in question. It is possible with an onsite ATR 

installation and onsite truck percentage counts. The latter can be either 

automated (e.g., vehicle length based algorithm) or manual. 

Recommended for roadways of even lesser importance. 

4 Similar to level 3 input, with the only difference being the lack of 

regional classification and load data. This approach resorts to default 

(i.e., national average) vehicle classification and axle-load distributions. 

Suggested as the minimum possible traffic input level for roadways of 

very low importance. 

The axle-load spectra information in the NCHRP 1-37A design guide is synthesized from input 

arranged in four main modules: 

1. Traffic Volume: 

• Average annual two-directional, multilane daily truck traffic (i.e., FHWA classes 

4 through 13). 

• Number of lanes in the design direction. 

• Percentage of trucks in the design direction. 
14 

• Percentage of trucks in the design lane. 

• Operational speed. 

Note that the first of these input components can be updated annually through a specified linear 

or compound growth rate (see next input module), while the remaining four are treated as 

constant throughout the pavement design life. 

2. Traffic Volume Adjustment Factors: 

• Monthly adjustments factors (MAFs as defined by equation 5) for each month per truck class 

(FHWA classes 4 through 13) with a default of 1.00. 

• Truck class distribution, defined in terms of the percentage of the traffic volume by vehicle 

class (4 through 13). 

• Hourly frequency distribution. 

• Traffic growth factors, either the same for all classes or per individual vehicle class. 

Note that all of these factors are treated as constant throughout the pavement design life. 

3. Axle-Load Distribution Factors: 

• Load frequency distribution (i.e., percent axles by load level) by axle configuration, by 

month, and by truck class. 

Note again that these factors are treated as constant throughout the pavement design life. 

4. General Traffic Input: 

• Number of axles by axle configuration and truck class. 

• Axle/tire configuration, spacing, and tire inflation pressure. 

• Wheelbase data. 

Note that this input is also treated as constant throughout the pavement design life. A summary 

of the traffic input components, the size of the associated data tables, and the flow of calculations 

in the NCHRP 1-37A design guide software is given in table 6. The resulting number of axles by 

load level, axle configuration, and month is further disaggregated by the distribution of truck 

traffic volume through the typical day. It should be noted that no differentiation is made in traffic 

volumes by the DOW within each month. For flexible pavements, the NCHRP 1-37A design 

guide software considers the following distresses: 

• Fatigue cracking (bottom-up alligator and top-down longitudinal). 

• Plastic deformation as a result of nonrecoverable strain in all pavement and subgrade layers. 

• Roughness (international roughness index (IRI)). 
15 

Table 6. NCHRP 1-37A design guide flow of calculations in assembling axle-load spectra. 

Traffic 

Input 

Component 

Main Data Element 

Input 

Array 

Size 

Calculation and Result 

1 AADTT in the design lane 1 – 

2 Distribution of trucks by class 

(FHWA 4 through 13) 1 by 10 

1 by 2 = annual average 

daily number of trucks by 

class 

3 MAFs by truck class 12 by 10 

1 by 2 by 3 = adjusted 

average daily number of 

trucks by class, by month 

4 

Number of axles by axle configuration 

(single, tandem, triple, quad), by truck 

class 

4 by 10 

1 by 2 by 3 by 4 = 

average number of axles 

by axle configuration, by 

month 

5 

Load-frequency distribution 

(percentage) by axle configuration, by 

month, by truck class 

4 by 12 by 

10 by 41 

1 by 2 by 3 by 4 by 5 = 

number of axles by load 

range, by axle 

configuration, by month 

The NCHRP 1-37A design guide considers two types of portland cement concrete (PCC) 

pavement structures: jointed plain concrete pavement (JPCP) and continuously reinforced 

concrete pavement (CRCP). JPCP can be either doweled or nondoweled. The following distress 

mechanisms are considered: 

• Fatigue transverse cracking, both bottom-up and top-down (JPCP). 

• Joint faulting (JPCP). 

• Punchouts (CRCP). 

• Roughness (JPCP and CRCP). 

Cracking-related damage is accumulated for both flexible and rigid pavements using Miner’s 

hypothesis. This consists of summing the damage ratios calculated by dividing the actual number 

of strain cycles by the number of cycles that would cause fatigue failure at this strain level. 

(11) 

Where: 

Damage = Damage (percentage) associated with particular distress mechanism. 

nijk = Actual number of pavement response cycles from axle configuration i, load 

 level j over month k. 

Nijk = Number of pavement response cycles that cause failure from axle 

 configuration i, load level j over month k. 

i = Axle configuration. 

= ∑∑∑.. 100

i jk ijk

ijk

N

n

Damage
16 

j = Load level. 

k = Month. 

Plastic deformation of flexible pavements and faulting damage of rigid pavements are simply 

additive. More information on the actual damage functions used for each distress mechanism is 

given in the final report for the NCHRP 1-37A design guide.(1)

NCHRP study 1-39(4) developed a methodology for processing the output of a combination of 

AVC and WIM systems in a jurisdiction to synthesize the axle-load spectra input to the NCHRP 

1-37A design guide for a particular pavement design site.(4) This methodology relies on factoring 

the available traffic data at that site using the temporal axle-load and vehicle classification 

distribution patterns from similar sites in the jurisdiction (e.g., State) as prescribed by the 2001 

TMG.(3) The type of technology (AVC and WIM) and the length of coverage involved at these 

traffic data collection sites define the level of traffic input. This methodology is implemented in a 

software package called TrafLoad. The input of TrafLoad is in terms of the standardized output 

of AVC and WIM systems, as the hourly summary C-records or 4-cards and the individual 

vehicle W-records or 7-cards, respectively. The format of the standard cards is given in 

appendix A. These data are assumed to have passed independent QC tests before inputting into 

TrafLoad. In addition, the user needs to input the following information: 

• Vehicle classification scheme in the jurisdiction (the 13 FHWA classes or others). 

• Any aggregation of these vehicle classes. 

• Grouping of traffic data sites in the jurisdiction with respect to vehicle classification 

distributions (e.g., the 17 truck traffic classes (TTC) distinguished in the NCHRP 1-37A 

design guide). 

• Grouping of traffic data sites with respect to axle-load distributions (truck weight road 

groups (TWRGs) based on indicators of pavement loading or functional class. 

• Seasonal load spectra by either month or month and DOW. 

The seasonal load spectra is used in factoring incomplete sets of load spectra, as explained later. 

It should be noted that some of this input, especially the site grouping and the seasonal load 

spectra computations, may require considerable preprocessing of the available WIM and AVC 

data before running TrafLoad.

TrafLoad distinguishes several levels of traffic input, depending on the load and classification 

data available at a particular pavement design site/lane. In terms of WIM data availability, there 

are three pavement design levels: 

• Level 1. Site-specific, high-quality WIM data over periods of time sufficient to estimate 

monthly or monthly DOW load spectra at the site/design lane (12 sets or 12 × 7 = 84 sets). In 

this case, “sufficient” implies a minimum length of continuous coverage of high-quality 

WIM data for at least each of the seven DOW in each month, which is, in effect, continuous 

WIM data coverage over a year. These data are calculated externally by the user and supplied 

as input to TrafLoad. Where partial sets of WIM data are available (e.g., missing DOW or 

months), TrafLoad estimates them through factoring, as explained later. 
17 

• Level 2. No site-specific WIM data are available; however, the site can be clearly assigned to 

a TWRG for which level 1 WIM data are available. 

• Level 3. No site-specific WIM data are available, and the site cannot be clearly assigned to a 

TWRG. In such cases, jurisdiction-wide averages of load spectra need to be used. 

It should be noted that since levels 2 and 3 lack site-specific WIM data, their assignment to one 

of the TWRGs is, by necessity, subjective. 

For complete year-long level 1 WIM data, TrafLoad produces all of the necessary input to the 

NCHRP 1-37A design guide. For incomplete level 1 WIM data, TrafLoad uses DOW and 

monthly factor ratios based on complete level 1 WIM sites belonging to the same TWRG. This is 

done in terms of the pavement damage affected by month and DOW as indexed by the average 

ESALs per vehicle (AEPV). As shown in equation 12, the daily adjustment ratio (DAR) for a 

particular DOW d is computed as the average over the number of months available m of the ratio 

of the AEPV for that missing DOW divided by the monthly AEPV: 

(12) 

Where: 

DARipd = Daily adjustment ratio for WIM TWRC group i, pavement type p, 

 and DOW d.

Averagem = Average for month m. 

AEPVimpd = Average ESAL per vehicle for WIM TWRG group i, month m, pavement 

 type p, and day d. 

i = WIM TWRG group. 

p = Pavement type (i.e., flexible versus rigid). 

d = DOW. 

m = Month. 

These ratios allow estimation of the number of vehicles by class for missing DOWs, accounting 

for the relative pavement damage affected in these DOWs. The monthly adjustment ratios 

(MARs) for a missing month m′ is computed from the available months m using equation 13. 

(13) 

Where: 

MARipm = Monthly adjustment ratios, WIM TWRG, pavement type, and month. 

Averagem = Average month. 

AEPVimpd = Average ESAL per vehicle for WIM TWRG group i, month m, pavement 

 type p, and DOW d. 

AEPVip = Average ESAL per vehicle for WIM TWRG group i, pavement type p. 

1/ 4

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ =

imp

impd

ipd m AEPV

AEPV

DAR Average

1/ 4

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ =

ip

ipm

ipm m AEPV

AEPV

MAR Average
18 

This allows estimation of the number of vehicles for missing months, accounting for the relative 

pavement damage affected in these months. Finally, load spectra adjustment ratios are computed 

by load range using equation 14. 

(14) 

Where: 

Aijmkd = Load spectrum value corresponding to load range k, vehicle class i, axle 

 type j, month m, and day d. 

MADWimd = Monthly average DOW traffic volumes for WIM TWRG group i, month m,

 and day d. 

Aijmk = Load spectrum value corresponding to load range k, vehicle class i, axle 

 type j, and month m. 

d = Data day used in computation. 

In terms of AVC data availability, TrafLoad distinguishes the following levels: 

• Level 1. Continuous AVC data are available for at least 1 week for each of 12 months per 

year. This level is further subdivided into 1A and 1B for site-specific AVC data and adjacent 

site/same route AVC data, respectively. 

• Level 2A. Sites for which continuous AVC counts are available over a period of at least 

48 weekday hours. 

• Level 2B. Sites where continuous manual vehicle classification counts are available over a 

period of at least 6 weekday hours. 

• Level 3A. Sites where only site-specific vehicle count data are available (no vehicle 

classification data are available). 

• Level 3B. Other. 

TrafLoad processes the AVC data from level 1A sites to establish monthly, daily, and hourly 

trends in vehicle classification counts. This is done in the following sequence: 

1. For each vehicle class i and lane l, the average hourly vehicle count AADTil is computed 

by month and DOW (total of 12×7×24 = 2016 average hourly counts per vehicle class). 

 

2. Average DOW volumes are computed by vehicle class by month MADWil, by summing 

the hourly volumes within each DOW. 

3. Annual average DOW AADWil is computed by averaging the MADWil values for 12 

consecutive months. 

4. Annual average daily traffic for vehicle class i and lane l AADTil is computed by 

averaging the seven AADWil values computed above. 

ijmkd

d

d

imd

imd

ijmk A

MADW

MADW A ∑ ∑ =
19 

This information serves two functions: (1) It contributes input to the NCHRP 1-37A design guide 

for analyzing the particular pavement site, and (2) it provides traffic distribution trends for 

factoring data from similar sites with lesser AVC information (e.g., AVC sites 1B, 2, and 3). 

Factoring in TrafLoad is carried out by dividing the short-term count by a traffic ratio. This is a 

departure from the standard practice that involves multiplying the short-term count by a traffic 

factor as suggested by AASHTO and the 2001 TMG (i.e., table 3 and equations 6 and 10). The 

difference between these two apparently equivalent factoring approaches arises when averaging 

factors versus averaging ratios from a group of sites. The rationale for selecting ratios is that the 

target value (e.g., AADTT) is in the denominator, and therefore, averaging ratios from a group of 

AVC sites with the same AADTT would yield the intuitive value of 1.00.(4)

As explained next, this study follows the NCHRP 1-37A design guide approach in identifying 

four traffic data collection input levels by a combination of the traffic data collection 

technologies involved for a particular site (WIM, AVC, or ATR). It identifies a number of traffic 

data collection scenarios by extending these four levels identified in table 4 by specifying the 

length of the site-specific data coverage. Furthermore, this study uses clustering techniques for 

identifying regional vehicle classification groups and regional axle-load distribution groups. 

These yield the second and fifth traffic input components to the NCHRP 1-37A design guide, 

which are in frequency distribution format, as described in table 6; therefore, it is not necessary 

to establish regional traffic data sets in the conventional TRWG sense, nor is it necessary to use 

the rather outmoded ESAL concept for doing so. 

21 

CHAPTER 2. IDENTIFY SCENARIOS AND KNOWLEDGE GAPS 

METHODOLOGY FOR IDENTIFYING KEY PAVEMENT DESIGN SCENARIOS 

Pavement design requirements are a function of the importance of a roadway facility. It is 

traditionally defined in terms of functional classification (Interstate, U.S. highway, State 

highway, or secondary road), which to a large extent reflects the traffic volumes and axle loads 

that need to be accommodated. Importance, in turn, defines the acceptable reliability in the 

pavement design of a facility and, hence, dictates the required quality of input data for both 

materials and traffic. Reliability is defined as the probability that a pavement section will not fail 

before the end of the analysis period is reached. Table 7 gives the pavement design reliability 

levels established by the 1993 edition of the AASHTO design guide, which provides a guideline 

for establishing reliability levels in the current study. (14) If the variation in the NCHRP 1-37A 

design guide output is known, appropriate levels of reliability of input can be selected. For traffic 

data input, this reliability will define the type of monitoring equipment and the length of data 

coverage required. This is the methodology that will be followed in establishing the traffic data 

collection scenarios required for specific pavement design applications. 

Table 7. Suggested levels of reliability for roads of various classes. 

Functional Class Urban 

(percentage) 

Rural 

(percentage) 

Interstate and other freeways 85–99.9 80–99.9 

Principal arterials 80–99 75–95 

Collectors 80–95 75–95 

Local 50–80 50–80 

Results based on a survey of the AASHTO Pavement Design Task Force.

As described later, the traffic data collection scenarios will be identified by expanding the four 

levels of traffic input defined by the NCHRP 1-37A design guide (table 4) to account for varying 

time lengths of coverage in the site-specific traffic data. 

KNOWLEDGE GAPS IN THE SENSITIVITY OF THE PAVEMENT DESIGN 

PROCESS TO TRAFFIC INPUT 

The majority of literature in this area treats traffic input in terms of cumulative ESALs and 

concerns the performance-based design process of the 1993 AASHTO design guide.(14) This 

process treated uncertainty in predicting performance as the present serviceability index (PSI) by 

artificially increasing the estimated number of ESALs. This was done by adding to the logarithm 

of the estimated ESALs, the product of the standard normal deviate corresponding to the desired 

reliability multiplied by the standard error in predicting PSI. This increased significantly the 

number of ESALs input to the empirical performance equations (e.g., for 85 percent confidence 

and a standard error in predicting PSI of 0.5, the logarithm of ESALs was increased by 

1.037 × 0.5 = 0.5185, which arithmetically is a factor of 3.3). Although this is not directly 

applicable to the design philosophy of the new NCHRP 1-37A design guide, it does reflect the 

significant uncertainties in quantifying traffic loading. 
22 

There are few exceptions in the literature where axle-load spectra are used directly in damage 

calculations, such as the rigid pavement design procedure developed by the Portland Cement 

Association.(15) This procedure uses axle-load spectra and computes the resulting slab fatigue 

damage and joint erosion through a Miner’s hypothesis-type accumulation algorithm. Experience 

with this method shows that: 

• A large number of light axle-load passes causes negligible damage. 

• A significant percentage of the damage is caused by the few passes of heavy (especially over 

the legal limit) axle loads. 

• Layer thickness indisputably affects damage accumulation. 

The third fact reemphasizes the need for performing any pavement design sensitivity analysis of 

traffic loads by considering the thickness of the layers involved. 

In summary, the knowledge gap in this area is considerable. Little is known about the sensitivity 

of the new NCHRP 1-37A design guide design process to traffic input. For a particular pavement 

type and combination of layer thicknesses, there is a need to study the extent of variation in 

pavement life predictions by distress type, in response to variations in traffic input. 

KNOWLEDGE GAPS IN DATA VARIATION FROM DIFFERENT TRAFFIC 

COLLECTION SCENARIOS 

As summarized in the literature review, considerable work has been done to analyze the effects 

of various traffic data collection scenarios on the accuracy of traffic volume estimates, such as 

AADT and AADTT, as well as cumulative pavement damage, such as ESALs.(2,11-12) The 

common method used in these studies is simulating traffic data collection scenarios from 

continuous traffic records and comparing the traffic estimates to the ground truth, thereby 

establishing accuracy levels. There has been little work, however, on the accuracy in axle-load 

distribution estimates from short-term WIM data and particularly on the ability to capture the 

few high axle loads that cause disproportionately high pavement damage. Hence, there is some 

literature related primarily to the first four traffic input components to the NCHRP 1-37A design 

guide (table 6), but little is available on the fifth traffic input component, the distribution of axle 

loads. As described later in “LTPP Data Analysis” (chapter 4), extended-coverage WIM data 

from LTPP sites will be used to simulate the effects of various sampling scenarios on the traffic 

data input components. 

SUMMARY 

Two main knowledge gaps were identified in selecting a traffic data collection effort for 

particular NCHRP 1-37A design guide applications: 

• Extent of variation in traffic data input with respect to the type of traffic data monitoring 

equipment and length of coverage available. 

• Sensitivity of the NCHRP 1-37A design guide output to variations in traffic data input. 
23 

These knowledge gaps will be filled by analyzing data from the LTPP database and conducting a 

sensitivity analysis of the new NCHRP 1-37A design guide software output with respect to 

traffic input. 

25 

CHAPTER 3. DEFINE TRAFFIC DATA COLLECTION REQUIREMENTS 

FOR EACH SELECTED APPLICATION 

Traffic data collection requirements were based on the four levels of traffic monitoring 

technology defined in table 4 of this report based on Appendix AA of the final NCHRP 1-37A 

report.(1) Distinct scenarios were developed by defining the length of data coverage of the sitespecific data. Seventeen traffic-sampling scenarios were selected, as shown in table 8, where SS

denotes site-specific, R denotes regional, and N denotes national data. The third column of this 

table, highlighted by bold letters, specifies the time coverage of the site-specific data. The fourth 

column provides identification codes for these scenarios, consisting of two numbers separated by 

a dash; the first number indicates the NCHRP 1-37A design guide traffic input level, and the 

second number identifies the length of data coverage (e.g., scenario 2-1 signifies site-specific 

AVC data with a coverage of 1 month in each of 4 seasons plus regional WIM data). In selecting 

these scenarios, the following two main criteria were adhered to: 

• WIM and AVC systems are typically fixed, and therefore, are likely to operate over longer 

periods of time than ATRs. 

• Jurisdictions with neither AVC nor WIM data are unlikely to have extended time coverage of 

site-specific ATR counts. 

Table 8. Selected traffic data collection scenarios. 

NCHRP 1-37A 

Design Guide 

Traffic Input 

Level 

Traffic Data 

Source 

Time Coverage of SiteSpecific Data Over 1-Year 

Period 

Scenario 

ID 

1 WIM Data = SS Continuous 1-0 

AVC Data = R 1 month/4 seasons 1-1 

 1 week/4 seasons 1-2 

2 WIM Data = R Continuous 2-0 

AVC Data = SS 1 month/4 seasons 2-1 

 1 week/4 seasons 2-2 

 1 week 2-3 

3 WIM Data = R Continuous 3-0 

AVC Data = R 1 month/4 seasons 3-1 

ATR Data = SS – 

4 WIM Data = N Continuous 4-0 

 AVC Data = R 1 week/4 seasons 4-1 

 ATR Data = SS 1 week 4-2 

1 weekday plus 1 weekend day 4-3 

 WIM Data = N Continuous 4-4 

 AVC Data = N 1 week/4 seasons 4-5 

 ATR Data = SS 1 week 4-6 

 1 weekday plus 1 weekend day 4-7 

SS = Site-specific, R = Regional, N = National (NCHRP 1-37A design guide defaults) 
26 

range in PDG 

mean+Z predictions a

.

S = b 

 

b a 

load bin 

range from 

sampling 

scenario 

frequency 

PDG 

distress i 

max tolerable 

truth from 

100% 

sample

True Life

mean-Za

.

S = a 

 

Most 

Conservative 

As described later, representative regional (R) traffic input can be obtained by averaging data 

from sites with similar traffic characteristics. There are a variety of methods for establishing 

similarities in traffic characteristics, ranging from the purely subjective (e.g., same roadway 

functional class) to the fairly mathematical (e.g., clustering techniques).(3,17) The clustering 

approach was selected for identifying similarities between the LTPP sites selected for the 

detailed NCHRP 1-37A design guide analysis and the remaining extended-coverage LTPP sites. 

Similarities were established on the basis of vehicle classification distributions and tandem-axle 

load distributions. The average traffic element for each cluster defined the regional data input for 

each site. In simulating these scenarios, the default values provided in the NCHRP 1-37A design 

guide software were assumed to represent the national (N) traffic input. For each of these traffic 

data collection scenarios, the range in the traffic input elements to the NCHRP 1-37A design 

guide (table 3) are computed and the resulting range in pavement performance predictions by 

distress are estimated. This process is shown schematically in figure 1, where S symbolizes the 

standard deviation in a particular traffic data input element, in this case, load frequency, and Za is 

the standard normal deviate (i.e., values for Za will be selected in accordance to the desired 

reliability level as per table 9). For each sampling scenario and reliability level selected, the 

range in pavement life predictions by distress mechanism is obtained using the software for the 

NCHRP 1-37A design guide. 

Figure 1. Schematic of the sensitivity of distress predictions to load spectra input. 

This type of sensitivity analysis will be conducted for both flexible and rigid pavements under 

realistic structural conditions. This involves site-specific layer thicknesses as described in the 

LTPP database and environmental conditions as simulated by the NCHRP 1-37A design guide 

software. Layer moduli were specified in the same fashion for all sites simulated in order to keep 

the variation in nontraffic-related properties to a minimum. Incidentally, another study is needed 

to establish the sensitivity of the NCHRP 1-37A design guide design process to variations in 

layer properties, considering the traffic input as a constant. This would ensure that groups of 

input with comparable accuracy would be used in pavement design. 
27 

Table 9. Relationship between two-sided probability of survival/failure 

and standard normal deviate in pavement life predictions. 

Percentage of Probability 

of Survival/Failure 

Standard Normal 

Deviate Za

50/50 0.00 

75/25 1.15 

85/15 1.44 

95/5 1.96 

99.9/0.1 3.18 

29 

CHAPTER 4. LTPP DATA ANALYSIS 

The data necessary for filling the two knowledge gaps identified earlier were extracted from the 

LTPP database: 

• Extended-coverage WIM data to allow simulation of the selected traffic data collection 

scenarios identified in table 8. 

• Detailed structural data for the pavement sites selected to ensure realistic simulation of their 

performance under the selected traffic input scenarios. 

The following subsections present: 

• Data extraction from extended-coverage WIM sites in the LTPP database. 

• Rationale for selecting several of these sites for the sensitivity analysis, while using the 

remainder to obtain the regional data sets necessary for factoring short-term, site-specific 

data in simulating the selected scenarios. 

• Analysis conducted for establishing the regional traffic data sets. 

• Methodology used for simulating each of the traffic data collection scenarios. 

LTPP WIM DATA EXTRACTED 

The main criterion for selecting data from the LTPP database was the extent of WIM data 

coverage in terms of the total number of data days per year. A search of the LTPP database(16) 

was performed based on this criterion. Initially, a filter of 359 days per year or greater was 

selected (i.e., 2 percent of days per year missing). This resulted in a total of 58 sites, some 

involving multiple data years. To increase the number of sites available for analysis, a lower 

threshold filter was used involving WIM coverage of 299 days per year or greater (i.e., 

20 percent of days per year missing). This resulted in a total of 178 sites, some involving 

multiple data years. The number of LTPP sites meeting these two criteria versus the number of 

data years available are plotted in figures 2 and 3, respectively. Figure 2, for example, suggests 

that 46 sites have more than 359 days per year of WIM data for 1 year; 6 sites do so for 2 years, 

and so on. Multiple years of data for the same site are advantageous because they allow for the 

establishment of traffic growth patterns. The data quality for these sites was deemed to be level E 

(i.e., the data had passed the quality control conducted by the State DOTs and the LTPP regional 

support contractor offices). To further ensure data quality, the LTPP quality assurance reports 

pertaining to these 178 sites were examined. They revealed no particular problems with any of 

them. These quality assurance reports were not appended here, but they are available on request. 

The highest resolution of traffic data necessary for simulating the scenarios in table 8 is daily 

summaries, which are not contained in Data Release; therefore, data had to be retrieved from the 

CTDB. It contains traffic data at five levels of resolution: 

• Level 1. Annual load/count summary records by axle (uploaded to the information 

management system database to become part of the periodic Data Release). 

• Level 2. Annual loads by vehicle class and annual load spectra by truck type. 

• Level 3. Daily summary traffic records. 
30 

• Level 4. Submitted traffic loading records (i.e., raw individual card-4 and card-7 data). 

• Level 5. Additional traffic loading information. 

Given the highest resolution of daily data desired for simulating the 17 traffic scenarios, level 3 

WIM data were extracted from the CTDB for the 178 WIM sites for the data years identified. 

The data fields extracted are described in table 10. The data was in Microsoft® Access format. It 

contained the daily number of axle passes by truck class, axle type, and load bin as it combined 

axle weight and vehicle classification information. 

46

6 3 2 1 0

0

20

40

60

80

1 year 2 years 3 years 4 years 5 years 6 years

Number Of Data Years 

Number of Sites With WIM > 359/yr

Figure 2. LTPP sites with WIM data available 

for periods longer than 359 days per year.(16)
31 

76

45

27

22

6

2

0

20

40

60

80

1 year 2 years 3 years 4 years 5 years 6 years

Number Of Data Years

Number of Sites With WIM > 299/yr

Figure 3. LTPP sites with WIM data available 

for periods longer than 299 days per year.(16)

Table 10. Definition of variables extracted from the CTDB. 

Variable Name Definition 

STATE_CODE State/province ID 

SHRP_ID Test section LTPP identifier 

LANE_TRF Lane identifier, where 1 is the lane nearest the right-hand shoulder 

DIR_TRF Traffic direction (1, 2, 3, 4 indicate east, west, north, south, respectively) 

VEH_CLASS FHWA vehicle classes 1 through 13; 14 indicates “other,” 15 indicates 

“unknown” 

AXLE_GROUP Axle configuration (1, 2, 3, 4 indicate single, tandem, tridem, quad axles, 

respectively) 

YEAR Year the data were collected 

MONTH Month the data were collected 

DAY Day of week 

RECORD_STATUS QC code from A through E 

AX_CT_01 

to 

AX_CT_40 

Number of axle passes by load bin. Depending on axle type, these bins are: 

• Singles: AX_CT_01 is 0 to 4.44 kilonewtons (kN) (0 to 9,892 pounds 

force (lbf) 

• Tandems: AX_CT_01 is 0 to 8.89 kN (0 to 1999 lbf); subsequent bins 

are in increments of 907 kilograms (kg) (2,000 pounds (lb)). 

• Triples/Quads: AX_CT_01 is 0 to 13.34 kN (0 to 2999 lbf); subsequent 

bins are in increments of 13.35 kN (3001 lbf). 
32 

RATIONALE FOR SELECTING SITES FOR THE DETAILED SENSITIVITY 

ANALYSIS 

A number of these extended WIM data coverage LTPP sites were selected for the detailed 

sensitivity analysis of the NCHRP 1-37A design guide with respect to the traffic input obtained 

from the simulated traffic data collection scenarios (table 8). The remaining sites were used for 

obtaining the regional traffic data sets (i.e., vehicle classification and axle-load distribution 

estimates for the detailed sensitivity analysis sites). 

The following criteria were used for selecting sites for the detailed sensitivity analysis: 

• WIM data coverage of preferably 299 days per year or greater. 

• Availability of WIM data over several years to allow the study of the effect of traffic growth. 

• Distribution of sites over a wide range of truck traffic volumes (i.e., AADTT) and structural 

thicknesses. 

The latter was indexed by the structural number (SN) and the concrete slab thickness for flexible 

and rigid pavement sites, respectively. Figures 4 and 5 show the distribution of AADTT versus 

structural thickness for all of the extended-coverage WIM sites identified from the LTPP 

database for flexible and rigid pavements, respectively. For each pavement type, two AADTT 

intervals were identified. 

For flexible pavements, two truck traffic volumes were defined: 

• Fewer than or equal to 800 trucks/day/lane. 

• More than 800 trucks/day/lane. 

0

500

1,000

1,500

2,000

1 3 5 7 9 11

Structural Number

AADTT (Trucks per day)

= WIM > 299 = WIM > 299 Analyzed = WIM > 359 = WIM > 359 Analyzed

Figure 4. Flexible pavement site selection by AADTT and structural number. 
33 

Figure 5. Rigid pavement site selection by AADTT and slab thickness. 

For rigid pavements, two truck traffic volumes were defined: 

• Fewer than or equal to 1,200 trucks/day/lane. 

• More than 1,200 trucks/day/lane. 

The final selection of sites for the detailed sensitivity analysis was carried out by identifying 5 to 

10 sites in each AADTT interval. For each interval, the sites selected covered a range in 

AADTT, while satisfying the other two criteria listed earlier (i.e., highest possible WIM data 

coverage over multiple years). For rigid pavement selection, additional consideration was given 

to structural configuration (roughly half of the pavement sections selected was jointed and the 

other half was continuously reinforced). Background information on the sections selected for the 

detailed sensitivity analysis of the NCHRP 1-37A design guide is given in tables 11 and 12. 

These tables identify the years of extended WIM data coverage (i.e., 299 days per year or 

greater), the AADTT for the year selected for the detailed sensitivity analysis, and the structural 

thickness/configuration of the sites. The data for the remaining years were used to establish the 

truck traffic growth rate for these sites. 

IDENTIFYING GROUPS OF SITES FOR OBTAINING REGIONAL DATA 

As shown in table 8, the numerous traffic data collection scenarios needed for simulation involve 

representative regional traffic data; therefore, it was necessary to establish a formal process for 

developing representative regional traffic data for the detailed sensitivity analysis sites identified 

above (tables 11 and 12). This grouping needs to be carried out separately for establishing 

vehicle classification information and axle-load information (the second and fifth input 

0

750

1,500

2,250

3,000

3,750

15 20 25 30

Slab Thickness, D (centimeters) (cm)

AADTT (Trucks per day)

= WIM > 299 = WIM > 299 Analyzed = WIM > 359 = WIM > 359 Analyzed
34 

components of the NCHRP 1-37A design guide, respectively, as described in table 6). It could be 

done subjectively using roadway functional class criteria, such as the ones shown in table 13. It 

is clearly better to do so using objective criteria, such as clustering techniques. As described in 

the literature review, clustering was introduced in the 2001 TMG (appendix 2-b as the preferred 

technique for identifying sites with similar seasonal traffic volume distribution patterns.)(3)

Clustering is used in this study to identify sites with similar vehicle classification distributions 

and axle-load distributions. As mentioned earlier, the vehicle classification and axle-load 

distributions in the NCHRP 1-37A design guide are input in the form of frequency distributions 

(percentage). As a result, there is no need to establish regional sites in terms of similar pavement 

loading, as is done for conventional TWRGs, nor it is necessary to use the rather outmoded 

ESAL concept for doing so (this is likely to influence future editions of the TMG). 

In terms of load distribution, regional clusters were identified with respect to tandem axles only 

because they are the most common in the traffic stream. It should be noted that a number of 

alternatives were considered, including the use of raw load distribution for all four-axle types 

and the load distribution of all four axle types weighed by their relative frequency in the traffic 

stream. Using the distribution of the tandem axles was only favored for its simplicity. 

Furthermore, in developing regional traffic data, clustering was done by State. Although there is 

no fundamental reason for partitioning the nationwide data, it better simulates the practice of 

individual DOTs that work primarily with their own data. A detailed description of the clustering 

technique can be found in statistical texts.(16) A brief overview of the method is given below and 

explained through an example involving the LTPP WIM sites in Washington State. 

Table 11. Background information on the flexible LTPP sites selected. 

Site State 

Structural 

Number in 

millimeters 

(mm) 

(inches) 

Data years1

Data 

Days2 AADTT2

 AADTT Level

091803 CT 114 (4.5) 1994,95 359 165 

261004 MI 43 (1.7) 1992,94,95,96,97,98 348 229 

271019 MN 76 (3.0) 1992,94,95,96 313 268 

282807 MS 140 (5.5) 1995,96 321 457 

531007 WA 66 (2.6) 1993,94,95 365 177 

182008 IN 158 (6.2) 1992,93,97,98 349 709 

182009 IN 234 (9.2) 1998 356 655 

261010 MI 122 (4.8) 1994,95,98 362 647 

536048 WA 107 (4.2) 1994 365 783 

AADTT ≤ 800 

261012 MI 135 (5.3) 1994,95,98 355 977 

181028 IN 178 (7.0) 1997,98 319 1535 

186012 IN 231 (9.1) 1992,97,98 324 1473 

261013 MI 150 (5.9) 1994,98 334 1395 

283081 MS 122 (4.8) 1993 356 1120 

283093 MS 102 (4.0) 1995 341 1920 

AADTT > 800 

1

Data year used in traffic data collection scenario simulation is bolded. 2

AADTT volumes and data days are for the year bolded. 
35 

Table 12. Background information on the rigid LTPP sites selected. 

Site State 

Slab in 

mm 

(inches)) 

Configuration Data Years1 Data 

Days2 AADTT2 AADTT Level 

094020 CT 230 (9.0) JRCP 1994 308 546 

263069 MI 230 (9.0) JRCP 1994,95,97 319 577 

284024 MS 200 (8.0) JRCP 1995 360 99 

501682 VT 200 (8.0) JRCP 1992,94,95,97 363 419 

533813 WA 198 (7.8) JRCP 1992,93,94 365 548 

185022 IN 230 (9.0) CRCP 1997 313 1164 

AADTT 

≤ 1200 

094008 CT 230 (9.0) JRCP 1994 364 1496 

265363 MI 230 (9.0) CRCP 1993,94,95,97 355 1247 

274055 MN 225 (8.9) JRCP 1994,97 300 1381 

275076 MN 230 (9.0) CRCP 1997 344 1438 

095001 CT 200 (8.0) CRCP 1995 323 1590 

185518 IN 230 (9.0) CRCP 1994,97,98 365 3746 

264015 MI 230 (9.0) JRCP 1994,96,97,98 341 1807 

285006 MS 200 (8.0) CRCP 1993,94,95,97 361 1559 

285805 MS 200 (8.0) CRCP 1993,94,95 361 2024 

AADTT 

> 1200 

1

Data year used in traffic data collection scenario simulation is bolded. 

2

Volumes and data days are for the year bolded. 

Table 13. Identification codes for roadway functional classes as defined by LTPP database 

field FUNCTIONAL_CLASS in table INV_ID. 

ID Roadway Functional Class 

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 or Expressways 

14 Urban Other Principal Arterial 

16 Urban Minor Arterial 

17 Urban Collector 

19 Urban Local 

Clustering is a mathematical approach for establishing similarities between different objects. 

Objects are described by their attributes. For this particular example, the objects are the LTPP 

WIM sites identified in Washington State (the 17 that met the study criteria) and the attributes 

are the distribution of the load of tandem axles (40 load bins from 8.90 to 355.9 kN (2 to 

80 thousand pounds force (kips)). In this particular example, the attributes need not be 

normalized because they are all frequencies adding up to 100 percent. The next step is to 
36 

compute a dissimilarity coefficient matrix. For this purpose, the so-called Euclidean distance e is 

used, which is defined as the distance between attributes for each pair of objects. If there were 

only two attributes, i and j, and they were plotted in a Cartesian coordinate system, the Euclidean 

distance eij would be the linear distance between the two objects defined on this plot by their 

coordinates. For more than two attributes, a similar definition would apply, the difference being 

that this would be a multidimensional plot (40 dimensional in this example). The Euclidean 

matrix for the annual distribution of tandem-axle loads in Washington State LTPP sites is shown 

in table 14. A value of the coefficient eij close to 0.0 suggests a similarity between the pair of 

objects, while higher eij values suggest a significant difference between the pair of objects. The 

next step is to construct what is referred to as a clustering tree, where pairs of similar objects are 

successively grouped together and compared with the remaining objects in order of increasing eij. 

The clustering tree for this example is presented in table 15 and plotted graphically in figure 6. 

This clustering method is referred to as Ward’s Minimum Variance Method. All of these 

calculations were carried out using an add-on function to Microsoft Excel found in the 

statistiXL® library.(17) Figure 6 allows identification of groups of WIM sites in Washington State 

with similar distributions of tandem-axle loads, given a selected value of the Euclidean distance, 

and therefore, a level of acceptable dissimilarity. Three clusters were identified, assuming an eij

value of 0.07. 

For the two WIM sites to be analyzed (6048 and 1007 as indicated by arrows), the selected 

groups for obtaining regional WIM data are identified by the two uppermost squares in figure 6. 

Figures 7 and 8 show the frequency distributions of tandem-axle loads for these two groups and 

illustrate the distinct difference in the patterns between the two groups of WIM sites identified. 

For the LTPP sites selected for traffic scenario simulation, tables representing clustering trees by 

State are presented in appendix B. This includes clusters with respect to the annual average 

tandem-axle load distributions and clusters with respect to the annual average truck classification 

and distributions (i.e., FHWA classes 4 through 13). The actual LTPP sites finally selected for 

obtaining regional AVC and WIM data are summarized in tables 16 and 17, respectively. The 

highlighted sites in these two tables are the ones used in the detailed sensitivity analysis of the 

NCHRP 1-37A design guide, while data from the other sites are used to estimate regional vehicle 

classification and axle-load distributions. As an example, the regional vehicle classification data 

for site 182008 were estimated as the average of the vehicle classification distributions for sites 

181037, 183031, and 184042. For sites that exhibit no similarities with others (e.g., site 091803), 

the statewide average was assumed to be representative of the regional data. 
37 

Table 14. Euclidean distance matrix: Annual distributions 

of tandem-axle loads, Washington State LTPP sites, 

Washington State LTPP Sites Analyzed 

 1002 1005 1006 1007 1008 1801 3011 3013 3014 3019 3812 3813 6020 604

1002 1005 0.016 1006 0.003 0.018 1007 0.005 0.011 0.013 1008 0.009 0.007 0.016 0.002 1801 0.021 0.025 0.018 0.030 0.034 3011 0.010 0.012 0.014 0.009 0.011 0.012 3013 0.016 0.031 0.012 0.033 0.039 0.006 0.017 3014 0.017 0.029 0.026 0.012 0.016 0.028 0.008 0.033 3019 0.024 0.008 0.033 0.015 0.011 0.034 0.015 0.044 0.030 3812 0.011 0.006 0.010 0.017 0.016 0.016 0.013 0.015 0.034 0.016 3813 0.004 0.011 0.005 0.010 0.013 0.009 0.004 0.007 0.016 0.020 0.006 6020 0.011 0.019 0.006 0.020 0.023 0.006 0.010 0.005 0.024 0.034 0.011 0.004 6048 0.003 0.010 0.004 0.006 0.008 0.011 0.004 0.012 0.013 0.019 0.008 0.002 0.005 6056 0.018 0.036 0.013 0.037 0.044 0.010 0.022 0.001 0.040 0.052 0.017 0.010 0.007 0.017322 0.025 0.039 0.017 0.044 0.050 0.006 0.024 0.002 0.043 0.055 0.020 0.013 0.008 0.017409 0.012 0.009 0.014 0.015 0.016 0.007 0.005 0.012 0.022 0.012 0.005 0.004 0.010 0.00
38 

Table 15. Summary of clustering strategies and associated Euclidean distance: 

Annual distributions of tandem-axle loads, Washington State LTPP sites. 

Cluster First Item Second Item 

Euclidean 

Distance 

1 6056 3013 0.001 

2 1008 1007 0.002 

3 6048 3813 0.002 

4 Cluster 1 7322 0.003 

5 1006 1002 0.005 

6 Cluster 3 3011 0.007 

7 7409 3812 0.005 

8 6020 1801 0.006 

9 3019 1005 0.008 

10 Cluster 7 Cluster 6 0.022 

11 Cluster 8 Cluster 4 0.028 

12 Cluster 10 Cluster 5 0.036 

13 Cluster 9 Cluster 2 0.045 

14 Cluster 12 3014 0.059 

15 Cluster 14 Cluster 13 0.078 

16 Cluster 15 Cluster 11 0.132 
39 

3013

6056

1007

1008

3813

6048

7322

1002

1006

3011

3812

7409

1801

6020

1005

3019

3014

0.00 0.15

Figure 6. Annual distributions of tandem-axle loads, 

Washington State LTPP sites. 

Euclidean Distance, eij
40 

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

9 36 62 89 116 142 169 196 222 249 276 302 329 356

load (kN)

frequency

6048

3011

3812

3813

7409

 1 kN=225 lbf

Figure 7. Tandem-axle load distributions for the cluster 

of Washington State LTPP site 6048. 

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

9 36 62 89 116 142 169 196 222 249 276 302 329 356

load (kN)

frequency 

1007

1005

1008

3019

 1 kN=225 lbf

Figure 8. Tandem-axle load distributions for the cluster 

of Washington State LTPP site 1007. 
41 

Table 16. LTPP sites used for obtaining regional vehicle classification data. 

State 

Code Vehicle Classification Cluster Sites 

9 4008 4020 5001 – – – – – – – – – 

9 1803 – – – – – – – – – – – 

18 1028 5518 6012 9020 – – – – – – – – 

18 4042 3031 2008 1037 – – – – – – – – 

18 5538 5528 2009 5022 5043 3030 – – – – – – 

26 1010 1001 1004 – – – – – – – – – 

26 9030 9029 5363 – – – – – – – – – 

26 7072 4015 3069 1012 1013 – – – – – – – 

27 4055 1023 1028 1085 3003 4033 4040 4054 5076 6251 7090 9075 

27 1016 1019 1087 3013 4037 4050 – – – – – – 

28 2807 1001 1016 3087 3089 4024 5025 – – – – – 

28 5006 3081 9030 7012 3094 3093 3019 3018 3099 5805 3091 1802 

28 3085 3083 3090 – – – – – – – – – 

50 1682 1002 1681 1683 – – – – – – – – 

50 1004 – – – – – – – – – – – 

53 1007 1005 1801 3014 3019 7409 – – – – – – 

53 7322 6056 3013 3813 3011 1002 – – – – – – 

53 6048 1006 1008 3812 6020 – – – – – – – 

Note: The 30 sites used for the detailed sensitivity analysis are shaded. 

Table 17. LTPP sites used for obtaining regional axle-load data. 

State 

Code Tandem-Axle Load Cluster Sites 

9 4008 1803 4020 5001 – – – – – – – – – – 

18 1028 2009 3030 3031 4042 5022 5043 5518 9020 – – – – – 

18 6012 1037 2008 – – – – – – – – – – –

18 5538 5528 – – – – – – – – – – – – 

26 1010 1001 1004 1004 1012 1013 3069 4015 5363 7072 9029 9030 – – 

27 4055 1023 1028 4054 5076 – – – – – – – – – 

27 6251 4050 4040 4037 4033 1019 1016 9075 1085 7090 1087 3003 – – 

27 3013 – – – – – – – – – – – – – 

28 2807 1001 1016 1802 3018 3019 3083 3085 3087 3089 3090 3091 4024 5025 

28 9030 3099 3094 3093 5805 5006 3081 7012 – – – – – – 

50 1682 1002 1004 1681 1683 – – – – – – – – – 

53 1007 1005 1008 3019 – – – – – – – – – – 

53 6020 1801 6056 3013 7322 – – – – – – – – – 

53 6048 1002 1006 3011 3014 3812 3813 7409 – – – – – – 

Note: The 30 sites used for the detailed sensitivity analysis are shaded. 
42 

SIMULATING TRAFFIC DATA COLLECTION SCENARIOS 

As described in the literature review, obtaining traffic input to the NCHRP 1-37A design guide 

from short-term traffic samples involves considerable calculations in factoring the site-specific 

data available using representative regional or national vehicle distribution and axle-load 

distributions. TrafLoad(13) could be used to carry out these calculations; however, it accepts as 

input raw data (e.g., card-4 and card-7) and therefore, was not directly applicable to the daily 

summary data format used in this study. More important, TrafLoad could not be used to analyze 

all of the possible combinations of data used in simulating short-term scenarios from extendedcoverage WIM data (1 month/season of data involves 34

 = 81 combinations of months, as 

described later). Therefore, it was decided to develop customized software for computing the 

traffic data input to the NCHRP 1-37A design guide. The software developed is written in 

Microsoft Visual Basic®. It reads daily traffic data summaries from the Microsoft Access 

database extracted from the CTDB and computes the traffic input elements to the NCHRP 1-37A 

design guide following the procedures described in the 2001 TMG.(3) Furthermore, it adopts the 

traffic ratio approach in factoring short-term counts, as described by NCHRP 1-39.(13)

Accordingly, equations 15, 16, and 17 for factor ratios are used. (The subscripts i for vehicle 

class and l for direction were dropped for brevity.) 

(15) 

(16) 

(17) 

Where: 

MDWTR = Monthly DOW traffic ratios. 

MADW = Monthly average day of week. 

MTR = Monthly traffic ratios. 

MADT = Monthly average daily traffic. 

DTR = Daily traffic ratios. 

AADW = Annual average day of week. 

For each of the traffic data collection scenarios, the software computes the mean and the standard 

deviation (SD) for each of the NCHRP 1-37A design guide traffic data input elements outlined in 

table 6. The methodology used for doing so follows. 

Scenario 1-0: Site-Specific Continuous WIM Data 

This scenario represents the most complete traffic data set for generating input to the NCHRP 

1-37A design guide, and for this reason, it is defined as the truth in traffic data. For the 30 sites 

analyzed, WIM data coverage ranged from more than 299 days per year to more than 359 days 

AADT

MADW MDWTR =

AADT

MADT MTR =

AADT

AADW DTR =
43 

per year. Following is an explanation of how the five traffic data input components to the 

NCHRP 1-37A design guide (refer to table 6) were computed: 

• Component 5 of the NCHRP 1-37A Design Guide Input (Axle-Load Distributions): 

− Browse the daily summary data table to obtain the number of days per DOW (from 

Sunday through Saturday) for each month that has traffic records. 

− For each month and DOW, sum the axle passes per truck class for each axle type and 

each load bin. 

− Divide each sum by the number of days of data computed above to obtain the average 

number of daily axle passes per bin, per axle type, per truck class for each DOW and 

month. 

− Average the number of daily axle passes per bin for the seven DOWs to obtain the 

monthly average number of axle passes by axle type, load bin, and truck class for each 

month. 

− Translate the number of passes per bin into load distributions (percent) by axle type, 

truck class, and month. 

• Component 4 of the NCHRP 1-37A Design Guide Input (Number of Axles per Truck): 

− Compute the average daily number of axles by axle type and truck class, regardless of 

load bin, over the 12-month period. 

− Compute the average daily number of trucks by class. 

− Divide the two values computed above to obtain the average number of axles by truck 

class and axle type. 

• Components 1, 2, and 3 of the NCHRP 1-37A Design Guide Input (AADTT, Truck Class 

Distribution, and MAFs): 

− For each month and DOW, sum the number of trucks by class. 

− Divide each sum by the number of days of data computed above to obtain the average 

number of daily vehicle passes by truck class per DOW and month. 

− Average the number of trucks by class for the seven DOWs to obtain the monthly 

average number of trucks by class per month. 

− Average the number of trucks for the 12 months to obtain AADTT by truck class. 

− Translate these average values into frequencies (percentage). 

− Add the number of trucks for all classes to obtain AADTT. 

− Compute MAFs by truck class using the data above and equation 5. 

The procedure described above accommodates WIM traffic data sets with missing data days. For 

some of the WIM sites that have the largest number of missing days (i.e., 299 days of WIM data 

per year or more), additional assumptions had to be made: 

• Where entire months of data are missing, data are assumed to have values equal to the 

average of the data for the months available. 

• Where entire DOWs are missing for a particular month, data are assumed to have values 

equal to the average of the data for the available DOWs for the same month. 
44 

Scenario 1-1: Site-Specific WIM Data for 1 Month/4 Seasons 

This scenario involves WIM data that cover 1 month in each of 4 seasons. It is simulated from 

the continuous WIM data set of the 30 sites selected and is carried out by computing all of the 

necessary traffic input to the NCHRP 1-37A design guide from random combinations of sets of 4 

months, each from a different season (a maximum of 81 combinations is possible). Only months 

with more than 25 days of data were considered for this analysis. The challenge in simulating 

this scenario is that the traffic volume by truck class is not known for all months of the year. All 

that is known for the site is the volume for 4 months of the year. The following paragraphs 

describe the methodology used in obtaining each of the five traffic data components input to the 

NCHRP 1-37A design guide (table 6). 

Component 3 of the NCHRP 1-37A Design Guide Input (MAFs): 

There are a number of alternative algorithms for computing traffic volumes and, as a result, 

MAFs by vehicle classification for the months considered missing. The one selected for this 

study uses the average regional MAF values for all truck classes to estimate truck volumes by 

class for the missing months. This algorithm is explained in the following example, and it is 

demonstrated in table 18. 

Table 18. Example of computing MAFs from regional data. 

Month 

R MAF(all 

truck 

classes) 

Measured 

VOL by 

Class 

Estimating VOL 

by Class 

VOL by 

Class 

Estimated 

SS MAF by 

Class 

January 0.8 900 – 900 0.85 

February 0.9 – 8,489x0.9/8.06 = 948 0.90 

March 1.09 – 8,489x1.09/8.06 = 1,148 1.09 

April 1.05 1,100 – 1,100 1.04 

May 1.12 – 8,489x1.12/8.06 = 1,180 1.12 

June 1.15 – 8,489x1.15/8.06 = 1,211 1.15 

July 1.1 1,200 – 1,200 1.14 

August 1 – 8,489x1/8.06 = 1,053 1.00 

September 1 – 8,489x1/8.06 = 1,053 1.00 

October 0.99 950 – 950 0.90 

November 0.95 – 8,489x0.95/8.06 = 1,001 0.95 

December 0.85 – 8,489x0.85/8.06 = 895 0.85 

Sum of four MAFs 3.94 Sum = 4,150 – AADTT= 

1,053 

Sum =12.00 

12 (sum of 4 MAFs) 8.06 8,489 

Consider that for a given truck class, daily traffic volumes (VOL) are available only for January, 

April, July, and October (they add up to a volume of 4,150 vehicles). Given the average regional 

MAF values above, compute the sum of them for the available months (i.e., 3.94). This suggests 

that the sum of the regional MAF values for the 8 missing months is 8.06 (=12−3.94), which 

gives a total volume of 8,489 (= 4,150×8.06/3.94) for these months. This, in turn, allows 

estimation of the traffic volume of the missing months (e.g., February volume is computed as 

8,489×0.9/8.06 and so on). Note that this approach preserves the traffic volume for the available 

months. 
45 

The group of sites used for obtaining the regional MAF data was identified as the agencyspecific cluster that exhibited a similar truck classification pattern as the site under consideration 

(truck classification clusters are presented in appendix B and summarized in table 16). This was 

deemed to be reasonable compromise between using agencywide average MAF data for all truck 

classes and MAF cluster data for individual truck classes. Furthermore, it was practical to 

implement since the monthly vehicle classification distributions are relatively stable (table 19), 

and thus identifying a cluster from 4 months of traffic data is realistic. 

Table 19. Monthly versus annual vehicle class distribution, 

AVC cluster, Washington State site 6048. 

 Vehicle Class 

Month 4 5 6 7 8 9 10 11 12 13 

Jan. 2.3 34.0 5.6 0.1 7.8 36.4 3.9 1.2 1.7 7.1

Feb. 2.3 34.2 6.1 0.2 7.9 35.5 3.8 1.2 1.8 7.2

Mar. 1.2 37.3 5.8 0.2 5.4 36.1 3.8 1.4 1.7 7.2

Apr. 1.4 41.7 5.9 0.3 5.8 31.6 3.6 1.1 1.6 7.0

May 1.5 43.4 6.3 0.3 6.4 29.8 3.4 1.1 1.5 6.4

June 1.5 42.6 6.2 0.2 6.9 29.2 3.8 1.1 1.4 7.0

July 1.6 49.4 5.8 0.3 7.4 24.4 3.2 0.6 1.2 6.2

Aug. 1.4 49.5 5.7 0.5 8.6 22.0 3.2 0.5 1.3 7.2

Sept. 1.9 45.2 5.8 0.2 9.5 25.0 2.9 0.6 1.3 7.8

Oct. 1.5 45.9 6.2 0.3 7.0 27.4 3.0 0.6 1.2 6.9

Nov. 1.6 46.3 6.3 0.4 4.9 29.0 3.5 0.6 1.3 6.3

Dec. 1.6 46.0 5.4 0.4 4.3 30.3 3.6 0.6 1.3 6.5

Mean 1.7 43.0 5.9 0.3 6.8 29.7 3.5 0.9 1.4 6.9

Components 1, 2, and 5 of the NCHRP 1-37A Design Guide Input (AADTT, 

Truck Class, and Axle-Load Distribution): 

Having established the volumes by truck class for the missing months, the algorithm used for 

obtaining traffic data input components 1, 2, and 5 was identical to that for scenario 1-0. 

Component 4 of the NCHRP 1-37A Design Guide Input (Number of Axles per 

Truck): 

The number of axles by axle configuration and truck class was assumed to be constant and equal 

to each statewide average for the sites analyzed. This assumption is justified considering that the 

number of axles for the most common truck classes (classes 5 and 9) is relatively constant. 

Tables 20 and 21 show the number of single and tandem axles per vehicle for the Washington 

State sites analyzed. It can be seen that the number of single and tandem axles for vehicle classes 

5 and 9 varies only slightly between sites. This is not the case for vehicle classes 7 and 11; 

however, they account for less than 4 percent of the total truck volumes. Another reason for this 

assumption was that the number of axles per vehicle type (i.e., 4 by 10 matrix) had to be input 

manually to the NCHRP 1-37A design guide software, and therefore, assuming it to be constant 

for each agency, significantly reduced the data input effort. 
46 

Table 20. Number of single axles per vehicle, annual Washington State data. 

 Vehicle Class 

Site 4 5 6 7 8 9 10 11 12 13 

1002 1.59 1.99 1.00 0.67 2.40 1.21 1.21 4.52 3.78 2.39 

1005 1.21 2.00 1.00 0.64 2.26 1.15 1.07 4.53 3.83 2.14 

1006 1.36 1.97 1.00 1.08 2.26 1.14 1.10 4.61 3.02 2.37 

1007 1.49 1.99 1.07 1.15 2.26 1.20 1.06 4.73 3.75 2.22 

1008 1.21 2.00 1.00 0.98 2.24 1.28 1.22 4.86 3.45 1.90 

1801 1.59 1.98 1.00 0.98 2.34 1.22 1.03 3.61 3.62 2.53 

3011 1.25 2.00 1.00 0.73 2.38 1.03 1.03 4.33 3.21 1.76 

3013 1.29 2.00 1.00 1.03 2.31 1.22 1.15 4.49 3.47 2.27 

3014 1.65 1.99 1.23 0.94 2.56 1.14 1.07 4.35 3.62 2.29 

3019 1.52 1.99 1.00 0.67 2.38 1.09 1.15 4.34 3.57 2.13 

3812 1.81 2.00 1.00 0.44 2.59 1.10 1.07 4.34 3.65 1.82 

3813 1.82 2.00 1.00 1.00 2.53 1.15 1.01 3.75 3.70 1.91 

6020 1.26 2.00 1.00 1.25 2.24 1.13 1.35 4.66 3.34 2.25 

6048 1.43 2.00 1.00 0.98 2.34 1.14 1.04 4.07 2.80 1.46 

6056 1.41 1.99 1.05 1.24 2.31 1.25 1.12 4.21 3.48 2.06 

7322 1.50 2.00 1.12 0.78 2.32 1.28 1.12 4.70 3.73 2.04 

7409 1.71 2.00 1.00 0.97 2.16 1.09 1.12 3.97 3.58 2.38 

Mean 1.48 1.99 1.03 0.91 2.35 1.17 1.11 4.36 3.51 2.11 

Table 21. Number of tandem axles per vehicle, annual Washington State data. 

 Vehicle Class 

Site 4 5 6 7 8 9 10 11 12 13 

1002 0.73 0.06 1.00 1.00 0.66 1.88 1.06 0.67 1.10 2.21 

1005 0.79 0.02 1.00 1.26 0.77 1.92 1.02 0.27 1.05 2.48 

1006 0.72 0.03 1.00 0.84 0.75 1.92 0.99 0.96 1.21 2.06 

1007 0.92 0.05 0.97 0.84 0.81 1.90 0.92 0.25 1.06 2.13 

1008 0.79 0.01 1.00 1.06 0.76 1.85 1.06 0.27 1.11 2.04 

1801 0.72 0.07 1.00 0.65 0.72 1.89 0.93 0.86 1.09 2.17 

3011 0.76 0.01 1.00 1.28 0.58 1.98 1.06 0.47 1.32 2.45 

3013 0.73 0.00 1.00 1.67 0.69 1.88 1.14 0.47 1.20 2.33 

3014 0.36 0.01 0.89 0.13 0.61 1.93 0.93 0.26 1.11 2.34 

3019 0.54 0.03 1.00 0.83 0.64 1.95 0.96 0.34 1.17 2.01 

3812 0.21 0.00 1.00 1.54 0.42 1.94 0.96 0.39 1.11 2.52 

3813 0.38 0.01 1.00 0.77 0.48 1.92 0.98 0.80 1.06 2.57 

6020 0.75 0.01 1.00 0.90 0.76 1.93 1.34 0.41 1.19 2.36 

6048 0.59 0.00 1.00 0.29 0.66 1.91 1.00 0.42 1.07 1.18 

6056 0.77 0.01 0.98 1.35 0.71 1.87 1.14 0.56 1.23 2.25 

7322 0.72 0.00 0.94 1.68 0.73 1.85 1.17 0.34 1.07 2.26 

7409 0.54 0.01 1.00 0.51 0.84 1.95 1.04 0.63 1.19 2.34 

Mean 0.65 0.02 0.99 0.98 0.68 1.91 1.04 0.49 1.14 2.22 
47 

Scenario 1-2: Site-Specific WIM Data for 1 Week/Season 

This scenario was simulated in a manner similar to the one described under scenario 1-1. The 

difference was that only 1 week per season of WIM data was considered available. For each 

season, a week was selected at random, after excluding the dates involving national holidays and 

those having incomplete data. This simply yielded a higher number of combinations to be 

simulated (i.e., depending on data coverage, up to 20,736 combinations). Each week was 

assumed to be representative of the entire month. The handling of the remaining elements of the 

NCHRP 1-37A design guide input was identical to that described under scenario 1-1. 

Scenario 2-0: Continuous Site-Specific AVC Data and Regional WIM Data 

This scenario used only the vehicle classification information that is available from the 30 WIM 

sites being analyzed. NCHRP 1-37A design guide inputs 1, 2, and 3 were obtained in an identical 

manner as done for scenario 1-0. For input 4, the number of axles by configuration and vehicle 

class, the agencywide average was used for reasons explained earlier. Input 5, which uses the 

load frequency distribution by axle configuration, had to be estimated from regional WIM data. 

In doing so, it was assumed that although there are no site-specific WIM data, there is sufficient 

qualitative information for truck weights for the site to allow classification of it into one of the 

axle-load clusters determined within a particular agency. As a result, input 5 was obtained from 

the average WIM data of the appropriate cluster, rather than from agencywide WIM data. 

Scenario 2-1: Site-Specific AVC Data for 1 Month/Season and Regional WIM Data 

This scenario was simulated in a manner similar to that for scenario 1-1. The difference was that 

traffic data input 5, the load distribution by axle configuration, was obtained from regional WIM 

data as described under scenario 2-0. 

Scenario 2-2: Site-Specific AVC Data for 1 Week/Season and Regional WIM Data 

This scenario was simulated in a manner similar to that for scenario 1-2. The difference was that 

traffic data input 5, the load distribution by axle configuration, was obtained from regional WIM 

data as described under scenario 2-0. 

Scenario 2-3: Site-Specific AVC Data for 1 Week/Year and Regional WIM Data 

This scenario was simulated by assuming that the week of data considered available is 

representative of the month to which it belongs. After excluding those involving national 

holidays and those having incomplete data, weeks were selected at random, and subsequently, in 

traffic data input 3, the MAFs were estimated from the regional vehicle classification cluster 

corresponding to the site in question. Traffic data inputs 1, 2, and 4 were also estimated as per 

scenario 1-1. Finally, traffic data element 5, the load distributions by axle type, were obtained 

from regional WIM data as described under scenario 2-0. 

Scenario 3-0: Continuous Site-Specific ATR Data, Regional AVC Data, and Regional WIM 

Data 

This scenario consists of continuous site-specific vehicle counts for an entire year combined with 

regional AVC and regional WIM data. These vehicle counts include vehicle classes 1 through 3: 

motorcycles, passenger cars, and light four-tire trucks. Although no site-specific vehicle 

classification or load information is available, it was assumed that there exists qualitative 

information to assign the site correctly to one of the AVC clusters and one of the WIM clusters 
48 

developed for the agencies analyzed; therefore, the percentage of trucks at the site (classes 4 

through 13) was assumed to be equal to the average of the percentage of trucks at the sites that 

belong to the actual AVC cluster for this site. This allowed calculation of AADTT according to 

the method described under scenario 1-0. Traffic data input 2 was obtained as the average of the 

vehicle classification distribution for the sites that belong to the actual AVC cluster for the site. 

Similarly, traffic data input 3 was obtained as the average of the MAFs for the sites that belong 

to the actual AVC cluster for the site. Traffic data input 4, the number of axles by type and 

vehicle class, was assumed to be equal to the statewide average for the reasons described under 

scenario 1-1. Traffic data input 5, the load distribution by axle configuration, was obtained as the 

average of the data for the actual WIM cluster to which the site belongs. It should be noted that 

this scenario results in a far lower variation in traffic data input than most of the scenarios 

described earlier because it relies on continuous regional data for the majority of the input. 

Scenario 3-1: Site-Specific ATR Data for 1 Week/Season, Regional AVC Data, and 

Regional WIM Data 

This scenario was simulated in a manner similar to scenario 3-0. The only difference is that 

vehicle volume data are considered known only for 1 month for each of 4 seasons. Traffic data 

input 2, 3, 4, and 5 were obtained in a similar manner to scenario 3-0. Traffic data input 1, the 

AADTT, was computed as described under scenario 1-1. 

Scenario 4-0: Continuous Site-Specific ATR Data, Regional AVC Data, and National WIM 

Data 

This scenario is similar to scenario 3-0. The only difference was that the axle-load information 

from the WIM cluster was replaced with information from national average WIM data. The latter 

was assumed to be equal to the default axle-load distributions embedded into the NCHRP 1-37A 

design guide software. This assumption affected only traffic data input 5, the load distribution by 

axle configuration. 

Scenario 4-1: Site-Specific ATR Data for 1 Week/Season, Regional AVC Data, and 

National WIM Data 

This scenario was simulated in a manner similar to scenario 3-1. The difference was that the 

axle-load information from the WIM cluster was replaced with information from national 

average WIM data. The latter was assumed to be equal to the default axle-load distributions 

embedded into the NCHRP 1-37A design guide software. 

Scenario 4-2: Site-Specific ATR Data for 1 Week/Year, Regional AVC Data, and National 

WIM Data 

This scenario is a variation of scenario 4-1, where only a single week of data is available per 

year. As in scenario 2-3, 1 week was selected at random after excluding those weeks that 

involved national holidays or incomplete traffic data. This week was assumed to be 

representative of the entire year. As in scenario 3-0, regional AVC cluster data were used to 

compute percentage of trucks and average MAF values were used to obtain the traffic volumes 

by month and truck class. National WIM data (the default values in the NCHRP 1-37A design 

guide software) were used for traffic data input 5. 
49 

Scenario 4-3: Site-Specific ATR Data for 1 Weekday Plus 1 Weekend/Year, Regional AVC 

Data, and National WIM Data 

This scenario involves ATR counts from 1 weekday and 1 weekend day. Traffic volumes on 

these days were weighted by 5 and 2, respectively, to compute weekly traffic volumes. All weeks 

that did not involve holidays or missing data were considered at random under this scenario. 

Subsequently, all traffic data input elements were computed as described under scenario 4-2. 

Scenarios 4-4 through 4-7: Various-Coverage, Site-Specific ATR Data, National AVC Data, 

and National WIM Data 

These scenarios are essentially identical to scenarios 4-0, 4-1, 4-2, and 4-3, respectively. The 

only difference is that traffic data inputs 2 and 3 were not computed from the regional AVC data, 

but rather from national data. For the latter, the default vehicle classification values embedded 

into the NCHRP 1-37A design guide were used. In doing so, the default classification 

distribution for truck traffic class (TTC) type 1 was arbitrarily selected and described as a major 

single-trailer truck route (i.e., predominantly class 9 trucks). The default MAF values embedded 

into the NCHRP 1-37A design guide were 1.00 for all months and vehicle classes. For each time 

coverage in site-specific ATR data, the method used for computing each of the traffic data input 

elements to the NCHRP 1-37A design guide was described earlier. 

ESTIMATING TRAFFIC INPUT 

The preceding discussion documents in detail the methodology and assumptions used in 

obtaining each of the five traffic data input elements to the NCHRP 1-37A design guide (table 6) 

for each of the 17 traffic data collection scenarios considered (table 8). A summary of the source 

of data used in computing each traffic data input element to the NCHRP 1-37A design guide is 

given in table 22. 
50 

Table 22. Summary of the source of traffic data input 

to the NCHRP 1-37A design guide for the selected scenarios. 

NCHRP 1-37A Design Guide Input 

Scenario 

AADTT 

Percent Trucks 

by Class 

Vehicle 

Classification 

Distribution 

MAFs No. of Axles per 

Truck 

Load Frequency 

Distribution 

1-0 SS SS SS SS SS SS 

1-1 SS SS SS From VC cluster State average SS 

1-2 SS SS SS From VC cluster State average SS 

2-0 SS SS SS SS State average From WIM cluster

2-1 SS SS SS From VC cluster State average From WIM cluster

2-2 SS SS SS From VC cluster State average From WIM cluster

2-3 SS SS SS From VC cluster State average From WIM cluster

3-0 SS From VC cluster From VC cluster From VC cluster State average From WIM cluster

3-1 SS From VC cluster From VC cluster From VC cluster State average From WIM cluster

4-0 SS From VC cluster From VC cluster From VC cluster State average National average 

4-1 SS From VC cluster From VC cluster From VC cluster State average National average 

4-2 SS From VC cluster From VC cluster From VC cluster State average National average 

4-3 SS From VC cluster From VC cluster From VC cluster State average National average 

4-4 SS National average National average National average National average National average 

4-5 SS National average National average National average National average National average 

4-6 SS National average National average National average National average National average 

4-7 SS National average National average National average National average National average 

Table 23 shows the number of possible time-coverage combinations analyzed for each scenario. 

Obviously, the continuous data coverage scenarios (i.e., scenarios 1-0, 2-0, 3-0, 4-0, and 4-4) 

involve only a single time-coverage combination and, as a result, yield singular estimates of the 

traffic data input elements of the NCHRP 1-37A design guide (table 6). On the other hand, the 

discontinuous scenarios yield one set of traffic data input elements per data coverage 

combination. Statistics for this traffic data input were computed and their range was established 

as a function of the desired level of confidence. 
51 

Table 23. Number of possible traffic sampling combinations by scenario. 

Scenario Time-Coverage 

Combinations 

1-0 1 

1-1 81 

1-2 20,736 

2-0 1 

2-1 81 

2-2 20,736 

2-3 48 

3-0 1 

3-1 81 

4-0 1 

4-1 20,736 

4-2 48 

4-3 480 

4-4 1 

4-5 20,736 

4-6 48 

4-7 480 

For each confidence level, NCHRP 1-37A design guide simulations for the discontinuous timecoverage scenarios were conducted by considering the lowest percentile for all traffic input 

elements simultaneously (i.e., 1, 2, 3, and 5 as identified in table 6). The reason for considering 

traffic underprediction as critical is because it results in pavement designs that are thinner than 

required, which, in turn, would fail prematurely. The reason for specifying the lowest percentile 

of all traffic input simultaneously is because it allows computation of the statistical maximum 

error in pavement life predictions given a confidence level. As a result, it reflects the confidence 

that this level of error will not be exceeded, which, in turn, is the reliability in the pavement 

design process. In performing these NCHRP 1-37A design guide simulations, it was decided to 

keep the traffic growth rate constant for all vehicle classes (4 percent annually) to ensure 

comparable results between sites. The effect of the actual traffic growth rate on pavementperformance predictions for each site was studied separately and is detailed in chapter 5 of this 

report. 

53 

CHAPTER 5. SENSITIVITY ANALYSIS 

EFFECT OF TRAFFIC DATA COLLECTION SCENARIO 

The 30 LTPP sections described in tables 11 and 12 were used in conducting the detailed 

sensitivity analysis of the NCHRP 1-37A design guide related to traffic input. The traffic data 

were simulated according to the scenarios described above. The NCHRP 1-37A design guide 

analysis was conducted using site-specific layer thicknesses and climatic data as summarized in 

appendix C; however, no site-specific pavement layer moduli were used because the available 

data were in the process of being reevaluated at the time of this study. A total of 1,950 NCHRP 

1-37A design guide runs were planned (i.e., 30 pavement sites × (5 continuous-coverage 

scenarios + 12 variable-coverage scenarios × 5 reliability levels)). For the variable-coverage 

scenarios, five runs accommodate only one-sided reliability (i.e., traffic underprediction, which 

is critical), as explained earlier. However, early in the sensitivity analysis, it was concluded that 

only scenario 1-0 is capable of providing 99.9 percent reliability. As a result, NCHRP 1-37A 

design guide simulations for this level of confidence were not conducted for all of the sections, 

nor were they considered in developing the final traffic data collection recommendations. 

Instead, a number of NCHRP 1-37A design guide runs were conducted to simulate the 

performance of some of the selected sections on the other end of the confidence interval (traffic 

overprediction) to establish the complete range of pavement life prediction estimates. NCHRP 1-

37A design guide pavement-performance predictions are in terms of particular distress 

parameters versus time. In defining pavement life, the limiting values of these parameters had to 

be assumed, as described in table 24. 

Table 24. Failure criteria for each pavement type. 

Pavement Type Failure Mode Limit 

Rutting 10 mm (0.40 inches) 

Asphalt concrete 

(AC) Longitudinal cracking 

20 percent or 200 meters per 

kilometer (m/km) 

(1,056 feet per mile (ft/mi)) 

Jointed plain 

reinforced concrete 

(JPRC) 

Slabs cracked 50 percent of total slabs 

Continuously 

reinforced concrete 

(CRC) 

Punchouts 19/km (30/mi) 

Pavement life was defined as the length of time it takes to reach the limiting value for one of 

these distresses, called the “critical distress parameter.” An example of these pavement life 

predictions is shown in figure 9 for section 181028, which was estimated to fail in rutting. Note 

that for clarity, only the results from the continuous-coverage scenarios are shown. For the 

particular site, it can be seen that the traffic data collection scenarios do have a significant effect 

on estimated pavement life. For this example, pavement life ranges from 26 years for scenario 
54 

3-0 to 43 years for scenario 4-4, but where the true estimate (i.e., obtained under scenario 1-0) is 

28 years. 

0

2

4

6

8

10

12

14

16

18

0 60 120 180 240 300 360 420 480

Time (months)

Total Rutting (mm)

1-0 2-0 3-0 4-0 4-4

Scenarios:

Rutting Failure 

Figure 9. Example of NCHRP 1-37A design guide output, site 181028 in Indiana. 

Obtaining pavement life from the output of the multitude of NCHRP 1-37A design guide 

simulations performed was automated using a macro that identified the critical pavement distress 

parameter and the number of years that it took to reach it. A summary of the estimated lives in 

years under scenario 1-0 and the critical distress parameters are given in tables 25 and 26 for the 

flexible and rigid sections, respectively. To facilitate interpretation of the pavement life 

prediction results across scenarios and confidence levels, it was decided to focus on a particular 

distress parameter, selected for each site, to be the one critical parameter under scenario 1-0. 

Furthermore, this approach allowed testing of the sensitivity of individual damage models to 

traffic input. As mentioned earlier, the life predictions in these two tables were obtained under an 

assumed annual AADTT growth rate of 4 percent. For each of the 30 sites analyzed, the results 

were summarized by plotting pavement life versus traffic data collection scenario by confidence 

level (figures 10 through 14). 
55 

Table 25. Scenario 1-0: Life prediction and critical distress, flexible pavement sites. 

Site AADTT Level Life (years) Critical Distress 

091803 > 99 No distresses 

261004 30 Rut > 10 mm (0.4 inch) 

271019 44 Rut > 10 mm (0.4 inch) 

282807 

5.9 Longitudinal cracking 

> 20 percent length 

531007 < 3 Premature failure 

182008 > 99 No distresses 

182009 > 99 No distresses 

261010 41 Rut > 10 mm (0.4 inch) 

536048 

AADTT ≤ 800 

9.8 Rut > 10 mm (0.4 inch) 

261012 

16.08 Longitudinal cracking 

> 20 percent length 

181028 30 Rut > 10 mm (0.4 inch) 

186012 26 Rut > 10 mm (0.4 inch) 

261013 26 Rut > 10 mm (0.4 inch) 

283081 

6.8 Longitudinal cracking 

> 20 percent length 

283093 

AADTT > 800 

< 3 Premature failure 

Table 26. Scenario 1-0: Life prediction and critical distress, rigid pavement sites. 

Site Configuration AADTT Level Life (years) Critical Distress 

094020 JRCP > 99 No distresses 

263069 JRCP > 99 No distresses 

284024 JRCP > 99 No distresses 

501682 JRCP 23.6 Cracking > 50 percent slabs 

533813 JRCP 18.7 Cracking > 50 percent slabs 

185022 CRCP 

AADTT 

≤ 1200 

20.7 Punchouts > 19/km (30/mile) 

94008 JRCP 5.6 Cracking > 50 percent slabs 

265363 CRCP 45 Cracking > 50 percent slabs 

274055 JRCP > 99 No distresses 

275076 CRCP 20.7 Punchouts > 19/km (30/mile) 

95001 CRCP < 3 Premature failure 

185518 CRCP 11.7 Punchouts > 19/km (30/mile) 

264015 JRCP > 99 No distress 

285006 CRCP < 3 Premature failure 

285805 CRCP 

AADTT 

> 1200 

< 3 Premature failure 
56 

0

15

30

45

60

75

90

1-0 1-1 1-2 2-0 2-1 2-2 2-3 3-0 3-1 4-0 4-1 4-2 4-3 4-4 4-5 4-6 4-7

Scenario

Mean Life Estimate 

(years)

1 23 4

= Range = Mean = Scen. 1-0

Figure 10. Summary of mean in life predictions, site 181028 in Indiana, 

confidence 50 percent. 

0

15

30

45

60

75

90

1-0 1-1 1-2 2-0 2-1 2-2 2-3 3-0 3-1 4-0 4-1 4-2 4-3 4-4 4-5 4-6 4-7

Scenario

75th Percentile Range in Life Est.

 (years)

1 23 4

= Range = Mean = Scen. 1-0

Figure 11. Summary of the range in predictions, site 181028 in Indiana, 

confidence 75 percent. 
57 

0

15

30

45

60

75

90

1-0 1-1 1-2 2-0 2-1 2-2 2-3 3-0 3-1 4-0 4-1 4-2 4-3 4-4 4-5 4-6 4-7

Scenario

85th Percentile Range in Life Est. 

(years)

1 23 4

= Range = Mean = Scen. 1-0

Figure 12. Summary of the range in predictions, site 181028 in Indiana, 

confidence 85 percent. 

0

15

30

45

60

75

90

1-0 1-1 1-2 2-0 2-1 2-2 2-3 3-0 3-1 4-0 4-1 4-2 4-3 4-4 4-5 4-6 4-7

Scenario

95th Percentile Range in Life Est. 

(years)

1 23 4

= Range = Mean = Scen. 1-0

Figure 13. Summary of the range in predictions, site 181028 in Indiana, 

confidence 95 percent. 
58 

0

15

30

45

60

75

90

1-0 1-1 1-2 2-0 2-1 2-2 2-3 3-0 3-1 4-0 4-1 4-2 4-3 4-4 4-5 4-6 4-7

Scenario

99.9th Percentile Range in Life Est. 

(years)

1 2 3 4

= Range = Mean = Scen. 1-0

Figure 14. Summary of the range in predictions, site 181028 in Indiana, 

confidence 99.9 percent. 

These plots show life predictions for both ends of the confidence interval to offer an idea of the 

range in pavement life predictions obtained from the discontinuous data coverage scenarios. 

Appendix D contains a series of tables summarizing the estimated lives by section for all 

scenarios and confidence levels. 

EFFECT OF AADTT GROWTH RATE 

The sensitivity analysis conducted so far considered that the annual growth rate in AADTT was 

4 percent for all of the scenarios simulated. Additional analyses were conducted to establish the 

actual annual growth rate in AADTT for the sections that had multiple traffic data years. A 

compound traffic formula was used for this purpose. Pavement life predictions were obtained 

with the NCHRP 1-37A design guide software using the actual AADTT growth rate under 

scenario 1-0 input. The actual growth rates calculated and the resulting pavement life predictions 

are shown in tables 27 and 28 for the flexible and rigid sections, respectively. In general, the 

actual annual AADTT growth rates differed significantly from the assumed value of 4 percent, 

ranging from −29 percent to +28 percent. Where actual annual AADTT growth rates were 

estimated to be negative, they were assumed to be equal to zero in predicting pavement 

performance. Furthermore, it was not possible to capacity constrain future AADTT, where the 

calculated annual AADTT growth rates were unusually high (i.e., the NCHRP 1-37A design 

guide software does not allow changing of the traffic growth rates during the analysis period). A 

comparison of the resulting pavement life predictions between scenario 1-0 with the assumed 

4 percent annual AADTT growth rate and the actual annual AADTT growth rate are shown in 

figures 15 and 16, respectively. 
59 

Table 27. Summary of computed AADTT growth rates and corresponding scenario 1-0 

pavement lives, flexible pavement sites. 

Table 28. Summary of computed AADTT growth rates and corresponding scenario 1-0 

pavement lives, rigid pavement sites. 

Site 

Compound Annual Growth Rate 

in AADTT for 

Truck Classes 4 Through 13 

(percentage) 

Life (years) 

091803 18.6 21.8 

261004 28.1 11.8 

271019 6.2 32.4 

282807 6.3 5.1 

531007 9.4 Premature failure 

182008 11.5 No distress 

182009 – – 

261010 11.2 23 

536048 – – 

261012 13.9 10.8 

181028 28.1 11.7 

186012 1.8 No distress 

261013 22.5 11.8 

283081 – – 

283093 9.1 Premature failure 

– Indicates only one data year available. 

Site 

Compound Annual Growth Rate 

in AADTT for 

Truck Classes 4 Through 13 

(percentage) 

Life (years) 

094020 – – 

263069 15.6 No distress 

284024 −29.3 (assumed to be 0.0) No distress 

501682 3.8 23.9 

533813 8.7 14.0 

185022 – – 

94008 3.7 5.6 

265363 −4.1 (assumed to be 0.0) No distress 

274055 2.9 21.8 

275076 – – 

095001 – – 

185518 6.8 8.3 

264015 10.0 No distress 

285006 7.9 Premature 

failure 

285805 6.0 Premature 

failure 

– Indicates only one data year available. 
60 

0

5

10

15

20

25

30

35

40

45

50

091803261004271019282807531007182008182009261010536048261012181028186012261013283081283093

Site ID

Life (years)

4% life actual life 

Figure 15. Pavement life prediction comparison between actual annual AADTT growth 

rate and 4 percent annual AADTT growth rate, flexible pavement sites. 

0

5

10

15

20

25

30

35

40

45

50

094020263069284024501682533813185022094008265363274055275076095001185518264015285006285805

Site ID

Life (years)

4% life Actual life 

Figure 16. Pavement life prediction comparison between actual annual AADTT growth 

rate and 4 percent annual AADTT growth rate, rigid pavement sites. 

As anticipated, the greater the difference between the annual AADTT growth rate and the 

assumed 4 percent value, the greater the difference in estimated pavement lives. These 

differences in pavement life were as high as 256 percent (i.e., for flexible section 181028). 
61 

CHAPTER 6. DEFINE TRAFFIC COLLECTION REQUIREMENTS 

From a pavement design point of view, traffic data collection requirements need to be 

established as a function of the tolerable error and the desirable confidence in NCHRP 1-37A 

design guide life predictions. The error is defined as the difference between pavement life 

predictions obtained under a particular scenario and those obtained under true traffic (scenario 

1-0). This error, expressed in percentage, was determined from the series of pavement life 

prediction plots presented in appendix D. It comprises two components, as shown schematically 

in figure 17, which are labeled as “A” and “B.” “A” is the estimated error from the traffic input 

of a continuous scenario or the mean traffic input of a discontinuous time-coverage scenario. “B” 

is the additional error possible in discontinuous-coverage scenarios by considering the lowest 

percentile input for all traffic input estimates simultaneously. Although doing so is very 

conservative, it allows establishment of the statistical maximum error in predicting pavement 

life, and therefore, it answers the question of reliability, as explained earlier. 

Figure 17. Components of the percentage difference between pavement life predictions for 

scenario X and those for scenario 1-0. 

Statistics for quantity “A” were computed by scenario type using the NCHRP 1-37A design 

guide performance predictions for the sites analyzed. These results are plotted by pavement type 

and traffic level in figures 18 through 21. In interpreting these results, consideration was given to 

the source of traffic input for each scenario, as outlined in table 22. To facilitate interpretation, 

the scenarios in these figures were arranged in three groups: 

• SS AVC, SS, or R WIM, which indicates site-specific vehicle classification distribution data 

combined with site-specific or regional axle-load distribution data. 

• R AVC, R WIM, which indicates regional vehicle classification distribution data combined 

with regional axle-load distribution data. 

• R or N AVC, N WIM, which indicates regional or national vehicle classification distribution 

data combined with national axle-load distribution data. 

Scenario 1-0 Life A

B

Life Estimate Error 

Life prediction using the lowest 

percentile for all traffic input 

under discontinuous Scenario X 

Life prediction from a continuous 

scenario or from mean traffic input 

for discontinuous Scenario X 
62 

Analysis of figures 18 through 21 reveals the following information: 

• For scenarios 1-1 through 2-3 (i.e., site-specific AVC, site-specific or regional WIM), the 

mean errors are relatively small and, in general, increase as site-specific information and time 

coverage decreases. They range from −10.8 percent to +9.9 percent. 

• For the remaining scenarios (i.e., 3-0 through 4-7), the mean errors are considerably larger. 

This is not surprising because they rely increasingly on regional or national vehicle 

classification and axle-load distribution data. 

• The sign of the mean errors seems random (i.e., some scenarios underpredict pavement life, 

while others overpredict it). For scenarios 1-1 through 2-3, this largely depends on whether 

the estimated MAFs resulted in higher or lower seasonal damage accumulation, respectively, 

than the actual MAFs. For scenarios 3-0 through 4-7, this depends on whether the regional or 

national vehicle classification and axle-load distributions used were heavier or lighter, 

respectively, than the actual ones. Given the random nature of the signs of these mean errors, 

it is anticipated that if a considerably larger sample of pavement-performance predictions 

was available, the mean errors for all of the scenarios would become negligible. 

• Overall, it is observed that the standard deviation in the errors decreases as the truck traffic 

volumes increase (i.e., the AADTT > 800 and AADTT > 1,200 sections exhibit lower error 

variation than the AADTT ≤ 800 and AADTT ≤ 1,200 sections, respectively). This is not 

surprising, considering that short-term sampling yields more accurate AADTT estimates 

where traffic volumes are high. 

• For scenarios 1-1 through 2-3, the standard deviation in the errors increases as site-specific 

information and time coverage decreases. In general, it ranges from 1.8 percent to 23 percent. 

• For the remaining scenarios (i.e., 3-0 through 4-7), the standard deviation in the errors is 

considerably higher and, in general, it increases as site-specific information and time 

coverage decreases. In general, it ranges from 14.3 percent to as high as 70 percent. 

Statistics for pavement prediction error component “A” were computed for all 17 sites analyzed 

(table 29). 

Indeed, the mean values of this error component are negligible. Ranges in these errors were 

estimated as a function of the selected level of confidence (i.e., 100 percent minus the probability 

of exceeding that error), assuming that the mean error values are zero. Because the sample size is 

small, the Student’s t-standard deviate was used. The values corresponding to one-sided 

probabilities of 75 percent, 85 percent, and 95 percent for 17 observations are 0.69, 1.07, and 

1.74, respectively. The error ranges obtained as a function of the probability of exceeding them 

are listed in table 29 and plotted in figure 22. 
63 

Flexible, AADTT<=800, n=5 

-60%

-40%

-20%

0%

20%

40%

60%

80%

1-1 1-2 2-0 2-1 2-2 2-3 3-0 3-1 4-0 4-1 4-2 4-3 4-4 4-5 4-6 4-7

Scenario

Error in Life Predicitons Relative to Scenario 1-0

mean st. dev.

SS AVC,SS or R WIM R AVC,R WIM R or N AVC,N WIM

Figure 18. Statistics for error component “A” in life predictions (percent), flexible 

pavement sites with AADTT ≤ 800 trucks/day/lane. 

Flexible, AADTT>800, n=5 

-60%

-40%

-20%

0%

20%

40%

60%

80%

1-1 1-2 2-0 2-1 2-2 2-3 3-0 3-1 4-0 4-1 4-2 4-3 4-4 4-5 4-6 4-7

Scenario

Error in Life Predicitons Relative to Scenario 1-0

mean st. dev.

SS AVC,SS or R WIM R AVC,R WIM R or N AVC,N WIM

Figure 19. Statistics for error component “A” in life predictions (percentage), flexible 

pavement sites with AADTT > 800 trucks/day/lane. 
64 

Rigid, AADTT<=1200, n=3 

-60%

-40%

-20%

0%

20%

40%

60%

80%

1-1 1-2 2-0 2-1 2-2 2-3 3-0 3-1 4-0 4-1 4-2 4-3 4-4 4-5 4-6 4-7

Scenario

Error in Life Predicitons 

Relative to Scenario 1-0

mean st. dev.

SS AVC,SS or R WIM R AVC,R WIM R or N AVC,N WIM

Figure 20. Statistics for error component “A” in life predictions (percentage), rigid 

pavement sites with AADTT ≤ 1,200 trucks/day/lane. 

Rigid, AADTT>1200, n=4 

-60%

-40%

-20%

0%

20%

40%

60%

80%

1-1 1-2 2-0 2-1 2-2 2-3 3-0 3-1 4-0 4-1 4-2 4-3 4-4 4-5 4-6 4-7

Scenario

Error in Life Predicitons 

Relative to Scenario 1-0

mean st. dev.

SS AVC,SS or R WIM R AVC,R WIM R or N AVC,N WIM

Figure 21. Statistics for error component “A” in life predictions (percentage), rigid 

pavement sites with AADTT > 1,200 trucks/day/lane. 
65 

Table 29. Statistics and ranges for the percentage life prediction errors from mean traffic 

input (i.e., quantity “A”), n=17. 

1-1

1-2

2-0

2-1

2-2

2-3

3-0

3-1

4-0

4-1

4-2

4-3

4-4

4-5

4-6

4-7

25%

15%

5%

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

Range in Error 

Scenario

Probability of Exceeding Error = 25% 15% 5%

Figure 22. Estimated range in NCHRP 1-37A design guide pavement life prediction errors 

from mean traffic input. 

 Error Statistics Error Range by Probability 

of Exceeding Them 

Scenario Mean SD 25 percent 15 percent 5 percent 

1-1 1 8 5.32 8.26 13.42 

1-2 −1 10 6.87 10.66 17.33 

2-0 −2 16 10.74 16.65 27.08 

2-1 −3 15 10.28 15.94 25.91 

2-2 −4 16 10.72 16.62 27.03 

2-3 −4 16 10.94 16.97 27.59 

3-0 −3 37 25.29 39.22 63.78 

3-1 −3 36 25.00 38.77 63.05 

4-0 1 39 27.08 41.99 68.29 

4-1 0 38 26.38 40.90 66.51 

4-2 0 36 25.11 38.94 63.32 

4-3 10 41 28.48 44.17 71.83 

4-4 1 44 30.17 46.78 76.08 

4-5 −1 44 30.38 47.10 76.60 

4-6 −6 44 30.12 46.71 75.96 

4-7 0 47 32.49 50.39 81.94 
66 

Assuming mean traffic input, figure 22 can be used to establish the least effort traffic data 

collection scenario that will provide a maximum acceptable pavement life prediction error under 

a selected level of confidence. For example, scenarios 3-0, 3-1, and 4-2 are among the least effort 

scenarios capable of a 25-percent maximum error in predicting pavement life (e.g., 3.5 years in a 

14-year design period) with a 75-percent confidence. A better traffic data collection scenario 

would be needed to either decrease the level of the acceptable error or increase the confidence 

that it will not be exceeded. 

The error ranges described above were obtained assuming mean traffic input for each 

discontinuous traffic data collection scenario. Additional errors were computed because of the 

variation in traffic input resulting from the sampling scheme used within each of the traffic data 

collection scenarios that involved discontinuous time coverage. These reflect the error 

component “B” defined in figure 17. As described earlier, this error component in predicting 

pavement life was computed from the NCHRP 1-37A design guide life estimates obtained by 

inputting the low percentile for all of the traffic elements simultaneously. Table 30 shows the 

mean and the standard deviation of these errors by traffic input percentile level. It also shows the 

standard deviation in the mean errors computed by dividing the standard deviation of the errors 

by the square root of the number of degrees of freedom (i.e., 16 =4), according to the central 

limit theorem. Table 31 gives the ranges in the mean error component “B” by traffic input 

percentile level. These were computed from the data in table 30 by summing the mean error plus 

the product of the standard deviation of the mean error multiplied by the Student’s t-deviate (i.e., 

0.69, 1.07, and 1.74, as described earlier). 

The combined range in the two error components “A” and “B” was computed by percentile level 

by adding the range in the error component “A” to the range in the mean of the error of 

component “B.” The results are shown in table 32 and plotted in figure 23. Figure 23 was 

compiled assuming that the lowest percentile of all of the traffic input for a discontinuouscoverage scenario could be input simultaneously during design. As mentioned earlier, this is very 

conservative; however, it addresses the question of reliability to guarantee the designer that a 

particular error level will not be exceeded given a level of confidence. 

Assuming low-percentile traffic input, figure 23 can be used to establish the least-effort traffic 

data collection scenario that will provide a maximum acceptable pavement life prediction error 

under a selected level of confidence. Compared to the earlier example, scenario 3-0 is the only 

one among the least effort scenarios identified earlier capable of a 25-percent minimum error in 

predicting pavement life with a 75-percent confidence. A better traffic data collection scenario 

would be needed to either decrease the level of the acceptable error or increase the confidence 

that it will not be exceeded. 

The main observations drawn from figure 23 are summarized below: 

• Discontinuous traffic data collection scenarios involving site-specific WIM data (scenarios 

1-1 and 1-2) are inferior to continuous-coverage, site-specific AVC data (scenario 2-0). This 

is because partial WIM coverage does not yield site-specific MAFs, which are necessary for 

accurately modeling seasonal damage in the NCHRP 1-37A design guide. 
67 

• Scenario 2-0 is capable of predicting pavement life with an error lower than 10 percent, 

16 percent, and 27 percent for confidence levels of 75 percent, 85 percent, and 95 percent, 

respectively. 

• Where continuous site-specific truck counts are combined with regional load and 

classification data (scenario 3-0), life prediction errors may range from 25 percent to 

64 percent, depending on the desired confidence level. 

• Where continuous site-specific truck counts are combined with regional classification and 

national load data (scenario 4-0), life prediction errors may range from 27 percent to 

68 percent, depending on the desired confidence level. 

• Where continuous site-specific truck counts are combined with national axle-load and 

classification data (scenario 4-4), life prediction errors may range from 30 percent to 

76 percent, depending on the desired confidence level. 

Table 30. Statistics for percentage additional error in life predictions from lowest 

percentile traffic input (i.e., quantity “B”). 

Mean Error 

by Traffic Input Percentile 

Stand. Dev. of Error 

by Traffic Input Percentile 

Stand. Dev. in Mean Error 

by Traffic Input Percentile 

Scenario 25th 15th 5th 25th 15th 5th 25th 15th 5th

1-1 12.69 15.79 23.74 16.65 19.72 25.52 4.16 4.93 6.38 

1-2 17.75 20.00 2.80 22.99 30.50 32.94 5.75 7.63 8.24 

2-0 – – – – – – – – – 

2-1 19.17 21.40 27.82 30.49 30.81 30.30 7.62 7.70 7.57 

2-2 11.24 16.89 15.31 10.62 17.61 11.42 2.65 4.40 2.85 

2-3 22.82 29.75 51.49 20.19 29.38 62.59 5.05 7.35 15.65 

3-0 – – – – – – – – – 

3-1 4.32 6.06 9.53 4.33 5.49 8.31 1.08 1.37 2.08 

4-0 – – – – – – – – – 

4-1 4.91 5.38 8.59 4.92 6.94 9.59 1.23 1.74 2.40 

4-2 18.83 28.31 36.16 19.05 30.77 35.84 4.76 7.69 8.96 

4-3 30.37 41.31 68.03 27.84 40.35 69.18 6.96 10.09 17.30 

4-4 – – – – – – – – – 

4-5 4.08 5.99 8.40 5.21 7.34 10.24 1.30 1.84 2.56 

4-6 34.55 55.29 81.06 31.88 59.85 102.17 7.97 14.96 25.54 

4-7 45.22 74.99 110.08 35.48 80.46 85.41 8.87 20.11 21.35 a

16 degrees of freedom 

– Indicates no data available.
68 

 Table 31. Range in mean “B” errors. 

Range in Mean “B” Errors 

by Traffic Input Percentile 

Scenario 25th 15th 5th

1-1 15.56 19.19 28.15

1-2 21.72 25.26 38.48

2-0 – – – 

2-1 24.43 26.72 33.05

2-2 13.07 19.93 17.28

2-3 26.30 34.82 62.28

3-0 – – – 

3-1 5.07 7.00 10.96

4-0 – – – 

4-1 5.76 6.57 10.24

4-2 22.11 33.62 42.35

4-3 35.17 48.27 79.96

4-4 – – – 

4-5 4.98 7.26 10.17

4-6 40.05 65.61 98.69

4-7 51.34 88.87 124.81

Table 32. Overall range in pavement life prediction errors (“A” plus “B” components) by 

probability of exceeding them. 

Range in Combined “A” and “B” Errors 

by Percentile of Probability of Exceeding Them 

Scenario 25 percent 15 percent 5 percent 

1-1 20.89 27.45 41.57 

1-2 28.59 35.91 55.81 

2-0 10.74 16.65 27.08 

2-1 34.70 42.65 58.96 

2-2 23.79 36.55 44.31 

2-3 37.24 51.79 89.88 

3-0 25.29 39.22 63.78 

3-1 30.07 45.78 74.02 

4-0 27.08 41.99 68.29 

4-1 32.14 47.47 76.75 

4-2 47.22 72.55 105.66 

4-3 63.66 92.44 151.79 

4-4 30.17 46.78 76.08 

4-5 35.36 54.36 86.77 

4-6 70.17 112.32 174.65 

4-7 83.84 139.25 206.75 
69 

1-1

1-2

2-0

2-1

2-2

2-3

3-0

3-1

4-0

4-1

4-2

4-3

4-4

4-5

4-6

4-7

25%

15%

5%

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

Range in Error 

Scenario

Probability of Exceeding Error = 25% 15% 5%

 Figure 23. Estimated range in NCHRP 1-37A design guide pavement life prediction errors 

from low-percentile traffic input. 

71 

CHAPTER 7. SUMMARY 

This study presented a comprehensive approach for establishing the minimum traffic data 

collection effort required for pavement design applications satisfying a maximum acceptable 

error under a prescribed confidence level. This approach consists of simulating the traffic data 

input to the new NCHRP 1-37A design guide for 17 distinct traffic data collection scenarios 

using extended-coverage WIM data from the LTPP database. 

Extended coverage was defined as 299 or more days per year of level E WIM data. Analysis of 

Data Release 16.0 revealed a total of 178 GPS sites that satisfied this requirement. For all of 

these sites, CTDB data were extracted in the form of daily summaries (level 3). From these sites, 

a total of 30 sites (15 flexible and 15 rigid) were selected for NCHRP 1-37A design guide 

simulation. The selection was based on the widest possible distribution of AADTT volumes and 

structural thicknesses. 

A number of the traffic data collection scenarios simulated involved continuous site-specific data 

coverage for axle loads, classification, or counts, while others involved discontinuous sitespecific data coverage (e.g., 1 month per season, 1 week per season, and so on). Data elements 

that were assumed to be unavailable at a site for simulation purposes were estimated from 

regional data. Regional vehicle classification and load data were obtained from the remaining 

LTPP sites identified using clustering techniques. Scenarios involving national data used the 

default traffic input in the NCHRP 1-37A design guide. For each of the traffic data collection 

scenarios involving discontinuous coverage of site-specific data, statistics for each traffic data 

element were computed by considering all possible time-coverage combinations. This allowed 

establishment of the lowest percentiles for each of these input to simulate underestimation of the 

actual traffic volumes/loads at a site. This was considered to be critical because it would result in 

thinner pavement designs that failed prematurely. Three confidence levels were selected: 

75 percent, 85 percent, and 95 percent. Traffic inputs for the continuous-coverage traffic data 

collection scenarios involved no variation because of the sampling scheme used. All scenarios 

were simulated using a 4-percent annual growth in AADTT. Additional analyses were conducted 

to compute the annual growth rate in AADTT and its effect on pavement life predictions. 

The NCHRP 1-37A design guide pavement life predictions for each scenario were analyzed to 

compute percentage errors in pavement life predictions with respect to the life predictions 

obtained under continuous site-specific WIM data (scenario 1-0). Reasonable life predictions 

were obtained for 17 of the 30 sections analyzed (the remainder experienced either premature 

failures or no failure at all). Two error components were identified: 

• “A” is the estimated error from the traffic input of a continuous scenario or from the mean 

traffic input of a discontinuous time-coverage scenario. 

• “B” is the additional error possible in discontinuous-coverage scenarios by inputting the 

lowest percentile input for all of the traffic input estimates simultaneously. 

Computing statistics for error component “A” for all 17 sections revealed that its mean is 

negligible for all of the scenarios analyzed. Its standard deviation allowed for the establishment 

of a range of errors by confidence level (table 29 and figure 22). Statistics for error component 

“B” were processed to yield the mean error and the standard deviation in the mean error by 
72 

traffic data collection scenario. This allowed computation of the range in mean error resulting 

from specifying the lowest percentile for all of the traffic input simultaneously. It was noted that 

this is very conservative; however, it addresses the question of reliability, guaranteeing the 

designer that given a level of confidence, a particular error level will not be exceeded. Overall 

error was computed by adding the range in error from component “A” to the range in mean error 

from component “B.” The results were plotted in a three-dimensional plot, indicating the 

maximum error by confidence level for each of the traffic data collection scenarios analyzed 

(table 32 and figure 23). Figure 23 can be used to establish the minimum required traffic data 

collection effort given the acceptable error and the desirable level of confidence. 
73 

APPENDIX A. CARD-4 AND CARD-7 DESCRIPTIONS 

Table 33. Card-4: Vehicle classification record. 

Columns Width Description 

1 1 Vehicle classification record code (4) 

2–3 2 State code 

4−5 2 Functional classification 

6−8 3 Station identification number 

9 1 Direction of travel 

10−11 2 Year of data 

12−13 2 Month of data 

14−15 2 Date of month 

16−17 2 Hour of day 

18−19 2 Number of motorcycles (optional) 

20−23 4 Number of passenger cars or all two-axle, four-tire, single-unit vehicles 

24−26 3 Number of other two-axle, four-tire, single-unit vehicles 

27−28 2 Number of buses 

29−31 3 Number of two-axle, six-tire, single-unit trucks 

32−33 2 Number of three-axle, single-unit trucks 

34−35 2 Number of four- or more axle, single-unit trucks 

36−37 2 Number of four- or less axle, single-trailer trucks 

38−40 3 Number of five-axle, single-trailer trucks 

41−42 2 Number of six- or more axle, single-trailer trucks 

43−44 2 Number of five- or less axle, multi-trailer trucks 

45−46 2 Number of six-axle, multi-trailer trucks 

47−48 2 Number of seven- or more axle, multi-trailer trucks 

49 1 Motorcycle reporting indicator 

50 1 Vehicle class combination indicator 

51 1 Lane of travel: 0 = combined lanes, 1 = outside (rightmost) lane, 

2 through 9 = other lanes in order toward innermost lane 

52−80 31 Blank or optional State data 
74 

Table 34. Card-7: Truck weight record. 

Columns Width Description 

FACE RECORD 

1 1 Truck weight record code (7) 

2−3 2 State code 

4−5 2 Functional classification 

6−8 3 Station identification number 

9 1 Direction of travel 

10−11 2 Year of data 

12−13 2 Month of data 

14−15 2 Date of month 

16−17 2 Hour of day 

18−23 6 Vehicle type code 

24−27 4 (open) 

28 1 Day of week (optional) 

29−34 6 (open) 

35 1 Lane of travel 

36−41 6 (open) 

42−45 4 Total weight of truck or combination 

46−48 3 A axle weight (hundreds of pounds) 

49−51 3 B axle weight (hundreds of pounds) 

52−54 3 C axle weight (hundreds of pounds) 

55−57 3 D axle weight (hundreds of pounds) 

58−60 3 E axle weight (hundreds of pounds) 

61−63 3 A-B axle spacing (feet and tenths) 

64−66 3 B-C axle spacing (feet and tenths) 

67−69 3 C-D axle spacing (feet and tenths) 

70−72 3 D-E axle spacing (feet and tenths) 

73−76 4 Total wheelbase (feet and tenths) 

77−79 3 Record serial number 

80 1 Continuation indicator: 0 = no continuation record, 

1 = has a continuation record 

CONTINUATION RECORD* 

1−23 23 Same as columns 1 through 23 of the face record 

23−28 5 (open) 

29−31 3 F axle weight (hundreds of pounds) 

32−34 3 G axle weight (hundreds of pounds) 

35−37 3 H axle weight (hundreds of pounds) 

38−40 3 I axle weight (hundreds of pounds) 

41−43 3 J axle weight (hundreds of pounds) 

44−46 3 K axle weight (hundreds of pounds) 

47−49 3 L axle weight (hundreds of pounds) 

50−52 3 M axle weight (hundreds of pounds) 

53−55 3 E-F axle spacing (feet and tenths) 

56−58 3 F-G axle spacing (feet and tenths) 

59−61 3 G-H axle spacing (feet and tenths) 

62−64 3 H-I axle spacing (feet and tenths) 

65−67 3 I-J axle spacing (feet and tenths) 

68−70 3 J-K axle spacing (feet and tenths) 

71−73 3 K-L axle spacing (feet and tenths) 

74−76 3 L-M axle spacing (feet and tenths) 
75 

Table 34. Card-7: Truck weight record (continued). 

CONTINUATION RECORD* 

Columns Width Description 

77−79 3 Record serial number (same as face record) 

80 1 Continuation indicator: 2 = first continuation record for a vehicle 

with more than 13 axles, 9 = last continuation record a Used only for truck combinations having six or more axles. Immediately follows the face record. 

77 

APPENDIX B. CLUSTER ANALYSIS RESULTS 

Figure 24. Clusters of LTPP sites by annual tandem-axle load distribution, 

Washington State. 
78 

Figure 25. Clusters of LTPP sites by annual tandem-axle load distribution, Vermont. 
79 

Figure 26. Clusters of LTPP sites by annual tandem-axle load distribution, Mississippi. 
80 

Figure 27. Clusters of LTPP sites by annual tandem-axle load distribution, Minnesota. 
81 

Figure 28. Clusters of LTPP sites by annual tandem-axle load distribution, Michigan. 
82 

Figure 29. Clusters of LTPP sites by annual tandem-axle load distribution, Indiana. 
83 

Figure 30. Clusters of LTPP sites by annual tandem-axle load distribution, Connecticut. 
84 

Figure 31. Clusters of LTPP sites by annual average truck class distribution, 

Washington State. 
85 

Figure 32. Clusters of LTPP sites by annual average truck class distribution, Vermont. 
86 

Figure 33. Clusters of LTPP sites by annual average truck class distribution, Mississippi. 
87 

Figure 34. Clusters of LTPP sites by annual average truck class distribution, Minnesota. 
88 

Figure 35. Clusters of LTPP sites by annual average truck class distribution, Michigan. 
89 

Figure 36. Clusters of LTPP sites by annual average truck class distribution, Indiana. 
90 

Figure 37. Clusters of LTPP sites by annual average truck class distribution, Connecticut. 
91 

APPENDIX C. STRUCTURAL AND CLIMATIC INPUT 

This appendix lists the structural and climatic input values used for the 2002 Pavement Design 

Guide (PDG) for the detailed sensitivity analysis LTPP sites. The structural input is listed in the 

order in which it is entered into the PDG. 

STRUCTURE: JOINTED PORTLAND CEMENT CONCRETE PAVEMENT 

1) Design features 

a) Slab thickness: Variable (table 9) 

b) Permanent curl/warp effective temperature difference: 23.3 degrees Celsius (oC) 

(75 degrees Fahrenheit (°F)) 

c) Joint spacing: 3.7 m (12 ft) 

d) Sealant type: Liquid 

e) Doweled transverse joints 

f) Dowel diameter: 32 mm (1.25 inches) 

g) Dowel bar spacing: 305 mm (12 inches) 

h) Edge support: None 

i) Base type: Granular 

j) PCC-base interface: Bonded 

k) Erodibility index: Erosion resistant (3) 

l) Loss of bond age (months): 60 

2) Drainage and surface properties 

a) Surface shortwave absorptivity: 0.85 

b) Infiltration: Minor (10 percent) 

c) Drainage path length: 3.7 m (12 ft) 

i) Pavement cross slope (percent): 2 

3) Layers 

a) PCC 

i) Thermal 

(1) PCC material: JPCP 

(2) Layer thickness: Variable (table 9) 

(3) Unit weight: 2400 kilograms per cubic meter (kg/m3

)

 

(159 pounds per cubic foot (lb/ft3) 

(4) Poisson’s ratio: 0.20 

(5) Coefficient of thermal expansion: 9.9 x 10-6 oC-1 (5.5 x 10-6 oF-1) 

(6) Thermal conductivity: 2.16 watts (W)/m-oC (1.25 British thermal units 

(BTU)/h-ft-oF) 

(7) Heat capacity: 0.33 W-h/kg-oC (0.28 BTU/lb-oF) 

ii) Mix 

(1) Cement type: Type II 

(2) Cementitious material content: 178 kg/m3

 (600 pounds per cubic yard (lb/yd3)) 

(3) Water-cement ratio: 0.4 

(4) Aggregate type: Gabbro 

(5) PCC zero-stress temperature (oF): Computed by PDG 
92 

(6) Ultimate shrinkage at 40 percent relative humidity (RH) (microstrain): Computed 

by PDG 

(7) Reversible shrinkage (percent of ultimate shrinkage): Computed by PDG 

(8) Time to develop 50 percent of ultimate shrinkage (days): Computed by PDG 

(9) Curing method: Curing compound 

iii) Strength: Level 2 (table C.1) 

Table 35. PC strength properties for level 2 input. 

Time Compressive Strength

(kilopascals (kPa)) 

Compressive Strength 

(pounds force per square 

inch (lbf/inch2

)) 

7 days 24,500 3560 

14 days 26,900 3900 

28 days 29,000 4200 

90 days 32,500 4700 

20 years/28 days 1.44 

STRUCTURE: CONTINUOUSLY REINFORCED CONCRETE PAVEMENT 

1) Design features 

a) Slab thickness: Variable (table 9) 

b) Shoulder type: Asphalt 

i) Permanent curl/warp effective temperature difference: 23.3 oC (−10 oF) 

c) Steel reinforcement 

i) Percent steel (percent): 0.7 

ii) Bar diameter: 16 mm (0.625 inch) 

iii) Steel depth: 356 mm (4 inches) 

d) Base properties 

i) Base type: Granular 

ii) Erodibility index: Erosion resistant (3) 

iii) Base/slab friction coefficient: 4 

e) Crack spacing: Generate using model 

2) Drainage and surface properties 

a) Surface shortwave absorptivity: 0.85 

b) Infiltration: Minor (10 percent) 

c) Drainage path length: 3.7 m (12 ft) 

i) Pavement cross slope (percent): 2 

3) Layers 

a) PCC 

i) Thermal 

(1) PCC material: CRCP 

(2) Layer thickness: Variable (table 9) 

(3) Unit weight: 2400 kg/m3

 (150 lb/ft3) 

(4) Poisson’s ratio: 0.20 

(5) Coefficient of thermal expansion: 9.9 x 10-6 oC-1 (5.5 x 10-6 oF-1) 
93 

(6) Thermal conductivity: 2.16 W/m-oC (1.25 BTU/h-ft-oF) 

(7) Heat capacity: 0.33 W-h/kg-oC (0.28 BTU/lb-oF) 

ii) Mix 

(1) Cement type: Type II 

(2) Cementitious material content: 178 kg/m3

 (600 lb/yd3) 

(3) Water-cement ratio: 0.4 

(4) Aggregate Type: Gabbro 

(5) PCC zero-stress temperature (oF): Computed by PDG 

(6) Ultimate shrinkage at 40 percent RH (microstrain): Computed by PDG 

(7) Reversible shrinkage (percent of ultimate shrinkage): Computed by PDG 

(8) Curing method: Curing compound 

iii) Strength: Level 2 (table 35) 

STRUCTURE: ASPHALT CONCRETE PAVEMENT 

1) Drainage and surface properties 

a) Surface shortwave absorptivity: 0.85 

2) Layers 

a) AC 

i) Input level: 3 

ii) Asphalt material type: Asphalt concrete 

iii) Layer thickness: Variable (table 36) 

Table 36. Layer types and thicknesses for all sites. 

Site Layer 1 

(Surface) 

h1 (mm 

(inches)) 

LAYER 

2 

(USCS)a

h2 (mm 

(inches)) 

Layer 3 

(USCS) a

h3 (mm 

(inches)) 

Layer 4 

(USCS)a

h4 (mm 

(inches)) 

181028 AC 401 (15.8) CL 305 (12.0) CL ∞ – – 

261010 AC 58 (2.3) GW 279 (11.0) SW 508 

(20.0) ML ∞

282807 AC 267 (10.5) GW 762 (30.0) ML ∞ – – 

531007 AC 4.6 (1.8) GW 335 (13.2) MH ∞ – – 

536048 AC 152 (6.0) GW 86 (3.4) GW 254 

(10.0) GW ∞

091803 AC 178 (7.0) GW 254 (10.0) ML ∞ – – 

182008 AC 401 (15.8) ML ∞ – – – – 

182009 AC 450 (17.7) SW 610 (24.0) ML ∞ – – 

186012 AC 523 (20.6) GW CL ∞ – – 

261004 AC 64 (2.5) GW 102 (4.0) ML ∞ – – 

261012 AC 152 (6.0) GW 114 (4.5) SW ∞ – – 

261013 AC 191 (7.5) GW 114 (4.5) SW ∞ – – 

271019 AC 122 (4.8) GW 152 (6.0) ML ∞ – – 

283081 AC 229 (9.0) GW ∞ – – – – 

283093 AC 305 (12.0) GW ∞ – – – – a Codes based on Unified Soil Classification System (USCS) standards.(19)
94 

iv) Asphalt mix 

(1) Cumulative percent retained on the 19.0-mm (0.75-inch) sieve: 0 

(2) Cumulative percent retained on the 12.5-mm (0.375-inch) sieve: 5 

(3) Cumulative percent retained on the 4.75-mm (#4) sieve: 30 

(4) Percent passing the 0.075-mm (#200) sieve: 5 

v) Asphalt binder 

(1) Conventional viscosity grade: AC 20 

vi) Asphalt general 

(1) Reference temperature: 21.1 oC (70 Fo) 

(2) Volumetric properties: As built 

(a) Effective binder content (percent): 11.0 

(b) Air voids (percent): 8.5 

(c) Total unit weight: 2370 kg/m3

 (148 lb/ft3) 

(3) Poisson’s ratio: 0.35 

(4) Thermal properties 

(a) Thermal conductivity of asphalt: 1.16 W/m-oC (0.67 BTU/h-ft oF) 

(b) Heat capacity of asphalt: 0.27 W-h/kg-oC (0.23 BTU/lb-oF) 

STRUCTURE: UNBOUND BASE 

1) Unbound material: Variable (table 37) 

 

Table 37. Assumed layer moduli. 

Layer Type 

(USCS)a

Layer 

Description 

Modulus 

(megapascals 

(MPa) (kips 

per square 

inch (ksi)) 

GW Gravel 690 

(100) 

SW Sand 276 (40) 

SM Silty Sand 138 (20) 

CL Silty Clay 138 (20) 

ML Clayey Silt 138 (20) 

MH Silt 138 (20) 

 a Codes based on Unified Soil Classification System (USCS) standards. (19)

2) Thickness: Variable (table 36) 

3) Strength properties 

a) Input level: 3 

b) Poisson’s ratio: 0.35 

c) Coefficient of lateral earth pressure: K=0.5 

d) Material property 

i) Modulus: Variable (table 37) 
95 

e) Analysis type: 

i) Integrated Climatic Model (ICM) calculated modulus: ICM input 

4) Integrated Climate Model (ICM) 

a) Gradation and plasticity index: Computed by PDG 

b) Calculated/Derived parameters: Computed by PDG 

c) Soil characteristic curve fit parameters: Computed by PDG 

Compacted, unbound materials 

CLIMATE 

The climatic data for the detailed analysis sites were computed by the PDG as a function of the 

latitude/longitude of each site (table 38). Weather station data from the nearest three or four 

weather sites were used to estimate the site-specific weather data. The actual elevation of each 

site was used; however, all groundwater table elevations were set at 30.5 m (100 ft) to eliminate 

the resulting variations in pavement performance. 
96 

Table 38. Site locations used for interpolation of weather station data. 

Site Longitude 

(decimal of degree) 

Latitude 

(decimal of degree) 

Elevation 

(m (ft)) 

094008 −72.558 41.798 47 (155) 

181028 −87.016 38.196 134 (441) 

185518 −86.853 40.477 20 (65) 

261010 −83.656 43.179 241 (792) 

274055 −94.074 45.424 299 (980) 

275076 −92.975 45.034 300 (985) 

282807 −89.655 34.356 90 (295) 

284024 −91.041 33.359 300 (125) 

501682 −73.241 44.326 122 (400) 

531007 −119.602 46.048 275 (903) 

536048 −122.138 47.788 37 (120) 

091803 −72.0273 41.3949 50 (165) 

094020 −72.5677 41.7020 61 (201) 

095001 −72.4399 41.8485 163 (534) 

182008 −85.0578 40.9456 242 (793) 

182009 −86.0046 40.0308 239 (785) 

185022 −86.0719 39.6278 255 (836) 

186012 −87.4925 38.1673 144 (472) 

261004 −88.6100 47.1000 300 (984) 

261012 −83.5300 43.7100 315 (1032) 

261013 −85.4918 43.4408 274 (900) 

263069 −84.8745 43.8713 285 (935) 

264015 −82.7996 42.9779 238 (780) 

265363 −83.3915 42.1866 195 (640) 

271019 −93.6021 45.5909 300 (980) 

283081 −88.4405 34.2400 97 (317) 

283093 −88.6736 30.4327 7 (24) 

285006 −88.8100 34.3300 100 (329) 

285805 −89.0600 30.4400 9 (30) 

533813 −122.4600 45.5800 134 (440) 
97 

APPENDIX D. PAVEMENT-PERFORMANCE RESULTS 

All pavement sections were analyzed using the 2002 PDG. This section contains the life of the 

pavement sections in absolute terms (years). 

Table 39. Life prediction estimates by scenario and traffic input percentile level, 

section 181028. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 75% 85% 95% 99.9% 

Life to Rutting Failure (years) 

1-0 30.0 – – – – – – – – 

1-1 30.0 30.0 29.0 30.0 30.0 29.0 29.0 29.0 28.0 

1-2 29.0 30.0 30.0 31.0 32.0 28.0 27.0 27.0 25.0 

2-0 27.0 – – – – – – – – 

2-1 28.0 28.0 28.0 29.0 29.0 28.0 28.0 27.0 27.0 

2-2 27.0 28.0 29.0 29.0 30.0 27.0 26.0 26.0 25.0 

2-3 27.0 29.0 29.0 30.0 32.0 26.0 25.0 25.0 23.0 

3-0 27.0 – – – – – – – – 

3-1 27.0 27.0 27.0 27.0 27.0 26.0 26.0 26.0 26.0 

4-0 27.0 – – – – – – – – 

4-1 27.0 27.0 27.0 28.0 28.0 26.0 26.0 26.0 26.0 

4-2 27.0 28.0 28.0 29.0 30.0 26.0 25.0 25.0 24.0 

4-3 27.0 29.0 30.0 31.0 34.0 25.0 24.0 23.0 21.0 

4-4 45.0 – – – – – – – – 

4-5 44.0 44.0 44.0 45.0 45.0 43.0 43.0 43.0 42.0 

4-6 44.0 45.0 45.0 45.0 46.0 43.0 43.0 42.0 41.0 

181028 

4-7 46.0 49.0 50.0 52.0 58.0 42.0 40.0 38.0 35.0 

 – Indicates no data available.
98 

Table 40. Life prediction estimates by scenario and traffic input percentile level, 

section 261010. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 75% 85% 95% 99.9% 

Life to Rutting Failure (years) 

1-0 41.0 – – – – – – – – 

1-1 46.0 51.0 52.0 52.0 56.0 44.0 43.0 43.0 38.0 

1-2 46.0 55.0 57.0 60.0 49.0 41.0 40.0 37.0 34.0 

2-0 44.0 – – – – – – – – 

2-1 43.0 45.0 45.0 47.0 49.0 39.0 39.0 38.0 35.0 

2-2 42.0 46.0 47.0 49.0 54.0 37.0 36.0 35.0 31.0 

2-3 41.0 48.0 51.0 56.0 100.0 35.0 33.0 31.0 27.0 

3-0 33.0 – – – – – – – – 

3-1 33.0 34.0 34.0 35.0 36.0 32.0 32.0 31.0 30.0 

4-0 38.0 – – – – – – – – 

4-1 37.0 39.0 40.0 41.0 43.0 35.0 34.0 33.0 31.0 

4-2 37.0 42.0 44.0 47.0 61.0 31.0 30.0 28.0 25.0 

4-3 37.0 46.0 49.0 57.0 100.0 29.0 28.0 26.0 22.0 

4-4 25.0 – – – – – – – – 

4-5 24.0 26.0 27.0 28.0 31.0 22.0 22.0 21.0 18.8 

4-6 24.0 29.0 31.0 34.0 44.0 19.9 18.9 17.6 15.0 

261010 

4-7 24.0 31.0 33.0 37.0 100.0 18.9 17.9 16.8 13.9 

 – Indicates no data available. 
99 

Table 41. Life prediction estimates by scenario and traffic input percentile level, 

section 282807. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 75% 85% 95% 99.9% 

Life to Longitudinal Cracking Failure (years) 

1-0 5.9 

1-1 5.9 7.9 8.0 8.9 9.9 4.9 5.0 4.9 4.1

1-2 5.1 8.9 10.0 10.8 10.0 4.8 6.8 4.0 5.0

2-0 7.0 – – – – – – – – 

2-1 6.9 7.1 7.1 7.9 12.0 6.8 6.1 6.0 5.9

2-2 6.8 7.9 8.0 8.9 10.1 5.9 5.9 5.1 4.9

2-3 6.8 9.0 9.9 15.8 21.0 5.0 5.0 4.8 3.9

3-0 12.0 – – – – – – – – 

3-1 11.9 12.1 12.8 12.9 13.8 11.8 11.8 11.0 10.9

4-0 12.0 – – – – – – – – 

4-1 11.9 12.1 12.9 13.0 14.0 10.9 10.9 10.8 9.9

4-2 11.9 14.0 14.9 16.8 22.0 9.9 9.0 8.9 7.1

4-3 12.0 14.9 16.0 18.1 25.0 9.9 9.0 8.9 7.0

4-4 6.1 

4-5 6.0 6.8 6.9 7.0 8.0 5.8 5.1 5.0 4.9

4-6 5.9 7.9 8.8 9.9 15.0 4.9 4.9 4.0 3.9

282807 

4-7 6.8 10.0 11.8 15.0 100.0 4.9 4.1 4.0 3.0

 – Indicates no data available. 
100 

Table 42. Life prediction estimates by scenario and traffic input percentile level, 

section 536048. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 75% 85% 95% 99.9% 

Life to Rutting Failure (years) 

1-0 9.8 – – – – – – – – 

1-1 9.1 11.9 12.9 14.0 17.1 7.8 7.0 7.0 5.9 

1-2 9.2 14.0 15.1 18.1 24.0 6.8 6.1 6.0 5.0 

2-0 6.9 – – – – – – – – 

2-1 6.1 7.0 7.1 7.9 8.9 5.9 5.9 5.7 5.0 

2-2 6.1 7.1 7.9 8.0 9.9 5.8 5.5 5.0 4.8 

2-3 6.0 8.2 9.0 10.8 16.0 5.0 4.9 4.1 3.9 

3-0 6.1 – – – – – – – – 

3-1 6.0 6.1 6.2 6.7 6.8 6.0 6.0 6.0 5.9 

4-0 6.8 – – – – – – – – 

4-1 6.8 6.9 6.9 7.0 7.2 6.1 6.1 6.0 5.9 

4-2 6.1 7.1 7.8 8.0 10.0 5.8 5.6 5.0 4.8 

4-3 6.8 7.9 8.0 9.0 11.9 5.8 5.4 5.0 4.7 

4-4 4.2 – – – – – – – – 

4-5 4.1 4.5 4.7 4.8 4.9 4.0 4.0 4.0 3.9 

4-6 4.1 4.8 4.9 5.0 5.9 3.9 3.9 3.8 3.3 

536048 

4-7 4.9 7.8 8.9 12.8 100.0 3.8 3.2 2.9 2.5 

 – Indicates no data available.
101 

Table 43. Life prediction estimates by scenario and traffic input percentile level, 

section 185518. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 75% 85% 95% 99.9% 

Life to Punchout Failure (years) 

1-0 11.7 – – – – – – – – 

1-1 11.8 13.8 14.4 15.2 18.3 10.5 10.3 10.2 9.3

1-2 11.8 15.4 17.2 20.7 26.7 9.9 9.5 9.4 8.4

2-0 10.3 – – – – – – – – 

2-1 9.7 10.7 11.3 11.7 13.3 9.3 8.8 8.7 8.3

2-2 9.5 10.3 11.4 11.8 13.7 8.8 8.7 7.8 7.7

2-3 9.4 12.4 13.3 15.5 23.7 8.3 7.7 7.4 6.6

3-0 9.8 – – – – – – – – 

3-1 9.8 10.3 10.3 10.4 10.8 9.4 9.4 9.4 9.3

4-0 11.4 – – – – – – – – 

4-1 11.3 11.8 12.2 12.3 13.6 10.6 10.5 10.4 10.3

4-2 11.3 13.3 15.2 15.7 23.7 9.5 9.3 8.8 8.1

4-3 11.3 13.7 14.3 17.0 23.7 9.6 9.3 8.7 7.8

4-4 18.7 

4-5 18.5 18.7 19.4 19.6 22.7 18.1 17.7 17.6 17.3

4-6 18.5 21.7 23.7 22.7 24.7 16.5 15.8 15.3 13.9

185518 

4-7 19.2 23.7 23.7 24.7 25.7 16.3 15.5 14.6 13.3

 – Indicates no data available.
102 

Table 44. Life prediction estimates by scenario and traffic input percentile level, 

section 275076. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 75% 85% 95% 99.9% 

Life to Punchout Failure (years) 

1-0 20.7 – – – – – – – – 

1-1 20.7 23.7 23.7 24.7 26.7 18.8 18.9 18.8 17.8 

1-2 20.7 24.7 25.7 26.7 27.7 18.8 18.8 18.1 16.9 

2-0 17.8 – – – – – – – – 

2-1 17.8 18.7 22.7 19.3 20.7 16.8 16.8 16.3 15.4 

2-2 17.7 18.8 19.3 21.7 21.7 17.8 16.3 15.8 14.7 

2-3 17.7 20.7 21.7 22.7 23.7 15.5 15.2 14.3 13.0 

3-0 11.5 – – – – – – – – 

3-1 11.8 12.2 12.3 12.4 13.1 11.7 11.7 11.5 11.2 

4-0 13.9 – – – – – – – – 

4-1 13.8 14.6 14.7 14.9 15.7 13.8 13.7 13.4 12.8 

4-2 13.8 15.5 16.1 16.8 19.3 12.8 12.8 12.4 11.7 

4-3 14.1 16.3 16.8 18.2 21.7 12.8 12.5 11.8 10.9 

4-4 15.6 – – – – – – – – 

4-5 15.3 15.6 15.7 15.8 16.5 14.8 14.7 14.6 14.3 

4-6 15.3 16.8 16.9 17.8 19.8 13.8 13.8 13.8 12.6 

275076 

4-7 15.8 19.7 21.7 23.7 25.7 13.5 12.8 12.3 10.8 

 – Indicates no data available.
103 

Table 45. Life prediction estimates by scenario and traffic input percentile level, 

section 094008. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 75% 85% 95% 99.9% 

Life to Slab Cracking Failure (years) 

1-0 5.8 – – – – – – – – 

1-1 5.0 6.1 6.8 7.6 9.7 4.7 4.6 4.0 3.8 

1-2 5.0 6.8 6.9 8.0 11.7 4.6 4.0 3.9 3.7 

2-0 7.8 – – – – – – – – 

2-1 6.8 8.1 6.8 9.7 11.8 6.0 5.8 5.8 5.0 

2-2 6.9 8.1 8.8 9.8 11.8 6.1 5.9 5.8 5.1 

2-3 6.7 12.0 14.8 23.6 36.6 4.6 4.0 3.8 3.0 

3-0 6.8 – – – – – – – – 

3-1 6.8 7.0 7.1 7.7 7.9 6.6 6.1 5.9 5.8 

4-0 7.8 – – – – – – – – 

4-1 7.8 8.6 7.1 7.7 9.6 7.7 7.6 7.0 6.8 

4-2 7.1 10.8 12.8 15.7 100.0 5.8 5.1 4.8 3.9 

4-3 7.0 12.2 15.1 24.6 100.0 5.0 4.8 4.7 3.8 

4-4 9.8 – – – – – – – – 

4-5 9.8 9.8 9.9 10.1 10.8 9.6 9.2 9.0 8.8 

4-6 9.8 11.0 11.8 12.7 15.7 8.7 8.6 7.8 7.0 

094008 

4-7 10.7 14.8 16.1 20.6 35.6 8.1 7.8 7.1 6.0 

 – Indicates no data available.
104 

Table 46. Life prediction estimates by scenario and traffic input percentile level, 

section 501682. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 75% 85% 95% 99.9% 

Life to Slab Cracking Failure (years) 

1-0 23.6 – – – – – – – – 

1-1 24.6 27.6 28.6 29.6 31.6 22.6 21.6 21.6 19.7 

1-2 24.6 30.6 31.6 32.6 34.6 19.8 19.0 19.1 15.8 

2-0 26.6 – – – – – – – – 

2-1 26.6 26.6 26.6 27.6 27.6 25.6 25.6 24.6 24.6 

2-2 25.6 26.6 27.6 27.6 28.6 24.6 24.6 23.6 22.6 

2-3 25.6 28.6 28.6 29.6 31.6 23.6 23.6 22.6 20.6 

3-0 27.6 – – – – – – – – 

3-1 26.6 26.6 26.6 27.6 27.6 26.6 26.6 26.6 26.6 

4-0 40.6 – – – – – – – – 

4-1 39.6 39.6 39.6 39.6 40.6 36.6 36.6 36.6 35.6 

4-2 39.6 40.6 40.6 41.6 42.6 35.6 33.6 32.6 31.6 

4-3 39.6 42.6 43.6 44.6 100.0 33.6 32.6 30.6 27.6 

4-4 22.6 – – – – – – – – 

4-5 21.6 22.6 22.6 23.6 23.6 21.6 21.6 20.6 20.6 

4-6 21.6 23.6 23.6 24.6 26.6 20.6 19.8 19.6 18.0 

501682 

4-7 22.6 27.6 28.6 30.6 35.6 18.7 17.8 16.7 14.1 

 – Indicates no data available. 
105 

Table 47. Life prediction estimates by scenario and traffic input percentile level, 

section 186012. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 

Life to Rutting Failure (years) 

1-0 28.0 – – – – 

1-1 26.0 31.0 32.0 36.0 – 

1-2 26.0 28.0 30.0 33.0 – 

2-0 26.0 – – – – 

2-1 24.0 28.0 29.0 31.0 – 

2-2 24.0 26.0 27.0 28.0 – 

2-3 23.0 29.0 31.0 34.0 – 

3-0 26.0 – – – – 

3-1 26.0 27.0 27.0 28.0 – 

4-0 31.0 – – – – 

4-1 30.0 32.0 32.0 34.0 – 

4-2 29.0 34.0 36.0 39.0 – 

4-3 33.0 43.0 48.0 60.0 – 

4-4 50.0 – – – – 

4-5 50.0 50.0 50.0 50.0 – 

4-6 45.0 53.0 54.0 59.0 – 

186012 

4-7 51.0 65.0 72.0 85.0 – 

 – Indicates no data available. 
106 

Table 48. Life prediction estimates by scenario and traffic input percentile level, 

section 261004. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 

Life to Rutting Failure (years) 

1-0 30.0 – – – – 

1-1 28.0 29.0 30.0 31.0 – 

1-2 27.0 30.0 32.0 34.0 – 

2-0 27.0 – – – – 

2-1 29.0 32.0 33.0 35.0 – 

2-2 29.0 31.0 31.0 30.0 – 

2-3 28.0 37.0 38.0 99.0 – 

3-0 26.0 – – – – 

3-1 26.0 27.0 28.0 28.0 – 

4-0 29.0 – – – – 

4-1 29.0 32.0 33.0 36.0 – 

4-2 29.0 42.0 50.0 99.0 – 

4-3 31.0 58.0 99.0 99.0 – 

4-4 15.9 – – – – 

4-5 15.9 15.9 15.9 15.9 – 

4-6 13.0 23.0 27.0 46.0 – 

261004 

4-7 14.0 33.0 57.0 99.0 – 

 – Indicates no data available. 
107 

Table 49. Life prediction estimates by scenario and traffic input percentile level, 

section 261012. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 

Life to Longitudinal Cracking Failure (years) 

1-0 16.1 – – – – 

1-1 17.1 19.7 20.0 21.0 – 

1-2 16.9 17.7 18.0 18.8 – 

2-0 15.6 – – – – 

2-1 16.5 17.6 17.8 18.1 – 

2-2 16.0 16.1 16.1 16.0 – 

2-3 16.0 17.3 17.6 17.3 – 

3-0 13.0 – – – – 

3-1 13.0 13.3 13.6 13.7 – 

4-0 14.0 – – – – 

4-1 13.9 14.7 14.8 14.9 – 

4-2 13.6 16.1 16.9 18.7 – 

4-3 12.2 16.1 17.6 21.0 – 

4-4 14.6 – – – – 

4-5 14.6 14.6 14.6 14.6 – 

4-6 11.9 16.3 18.0 23.0 – 

261012 

4-7 13.0 17.2 18.8 23.0 – 

 – Indicates no data available. 
108 

Table 50. Life prediction estimates by scenario and traffic input percentile level, 

section 261013. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 

Life to Rutting Failure (years) 

1-0 26.0 – – – – 

1-1 28.0 37.0 39.0 48.0 – 

1-2 28.0 31.0 31.0 29.0 – 

2-0 24.0 – – – – 

2-1 26.0 34.0 37.0 41.0 – 

2-2 26.0 29.0 29.0 30.0 – 

2-3 26.0 40.0 43.0 99.0 – 

3-0 21.0 – – – – 

3-1 21.0 24.0 25.0 27.0 – 

4-0 22.0 – – – – 

4-1 22.0 26.0 27.0 30.0 – 

4-2 23.0 39.0 49.0 99.0 – 

4-3 22.0 41.0 55.0 99.0 – 

4-4 23.0 – – – – 

4-5 23.0 23.0 23.0 23.0 – 

4-6 20.0 42.0 63.0 99.0 – 

261013 

4-7 20.0 44.0 71.0 99.0 – 

 – Indicates no data available. 
109 

Table 51. Life prediction estimates by scenario and traffic input percentile level, 

section 271019. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 

Life to Rutting Failure (years) 

1-0 44.0 – – – – 

1-1 44.0 51.0 53.0 59.0 – 

1-2 41.0 45.0 43.0 45.0 – 

2-0 41.0 – – – – 

2-1 41.0 48.0 50.0 55.0 – 

2-2 38.0 41.0 42.0 43.0 – 

2-3 38.0 46.0 47.0 46.0 – 

3-0 37.0 – – – – 

3-1 37.0 39.0 39.0 40.0 – 

4-0 39.0 – – – – 

4-1 36.0 39.0 39.0 40.0 – 

4-2 36.0 41.0 42.0 46.0 – 

4-3 39.0 52.0 59.0 78.0 – 

4-4 20.0 – – – – 

4-5 20.0 20.0 20.0 20.0 – 

4-6 16.9 22.0 24.0 29.0 – 

271019 

4-7 19.0 30.0 36.0 56.0 – 

 – Indicates no data available. 
110 

Table 52. Life prediction estimates by scenario and traffic input percentile level, 

section 283081. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 

Life to Longitudinal Cracking Failure (years) 

1-0 6.8 – – – – 

1-1 6.8 6.8 6.8 6.8 – 

1-2 6.0 6.4 6.8 6.8 – 

2-0 5.8 – – – – 

2-1 5.8 5.9 5.9 5.9 – 

2-2 5.0 5.1 5.1 5.1 – 

2-3 5.1 5.9 5.9 5.9 – 

3-0 5.8 – – – – 

3-1 5.8 5.9 5.9 5.9 – 

4-0 6.9 – – – – 

4-1 6.8 7.0 7.0 7.1 – 

4-2 6.8 7.7 7.9 8.0 – 

4-3 6.8 8.9 9.8 12.0 – 

4-4 9.0 – – – – 

4-5 9.0 9.0 9.0 9.0 – 

4-6 7.9 10.9 12.8 15.1 – 

283081 

4-7 8.0 11.8 12.9 15.8 – 

 – Indicates no data available. 
111 

Table 53. Life prediction estimates by scenario and traffic input percentile level, 

section 185022. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 

Life to Punchout Failure (years) 

1-0 20.7 – – – – 

1-1 24.7 44.7 44.7 44.7 – 

1-2 25.7 44.7 44.7 44.7 – 

2-0 18.8 – – – – 

2-1 19.3 38.7 44.7 44.7 – 

2-2 21.7 29.7 34.7 24.7 – 

2-3 23.7 44.7 44.7 44.7 – 

3-0 32.7 – – – – 

3-1 32.7 36.7 37.7 40.7 – 

4-0 44.7 – – – – 

4-1 44.7 44.7 44.7 44.7 – 

4-2 44.7 44.7 44.7 44.7 – 

4-3 40.7 44.7 44.7 44.7 – 

4-4 17.7 – – – – 

4-5 17.5 19.6 20.7 22.7 – 

4-6 17.3 24.7 29.7 44.7 – 

185022 

4-7 15.3 20.1 22.7 34.7 – 

 – Indicates no data available. 
112 

Table 54. Life prediction estimates by scenario and traffic input percentile level, 

section 265363. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 

Life to Slab Cracking Failure (years) 

1-0 45.0 – – – – 

1-1 45.0 99.0 45.0 99.0 – 

1-2 44.0 99.0 32.0 99.0 – 

2-0 99.0 – – – – 

2-1 99.0 99.0 99.0 99.0 – 

2-2 99.0 99.0 99.0 99.0 – 

2-3 99.0 99.0 99.0 99.0 – 

3-0 99.0 – – – – 

3-1 99.0 99.0 99.0 99.0 – 

4-0 99.0 – – – – 

4-1 99.0 99.0 99.0 99.0 – 

4-2 99.0 99.0 99.0 99.0 – 

4-3 99.0 99.0 99.0 99.0 – 

4-4 99.0 – – – – 

4-5 99.0 99.0 99.0 99.0 – 

4-6 99.0 99.0 99.0 99.0 – 

265363 

4-7 99.0 99.0 99.0 99.0 – 

 – Indicates no data available. 
113 

Table 55. Life prediction estimates by scenario and traffic input percentile level, 

section 533813. 

Traffic Input Percentile 

Site Scenario 50% −75% −85% −95% −99.9% 

Life to Slabs Cracking Failure (years) 

1-0 18.7 – – – – 

1-1 18.7 22.0 23.0 24.0 – 

1-2 18.6 23.0 24.0 23.0 – 

2-0 19.8 – – – – 

2-1 21.0 22.0 23.0 24.0 – 

2-2 19.9 22.0 22.0 23.0 – 

2-3 19.9 24.0 25.0 28.0 – 

3-0 11.8 – – – – 

3-1 11.8 12.1 12.7 12.8 – 

4-0 14.8 – – – – 

4-1 14.7 15.8 16.6 17.0 – 

4-2 14.0 17.6 18.6 21.0 – 

4-3 15.6 24.0 28.0 48.0 – 

4-4 12.6 – – – – 

4-5 10.8 11.8 12.6 12.8 – 

4-6 9.8 19.9 28.0 50.0 – 

533813 

4-7 11.8 21.0 25.0 50.0 – 

 – Indicates no data available. 

115 

REFERENCES 

1. National Cooperative Highway Research Program, 2002 Design Guide: Design of New and 

Rehabilitated Pavement Structures, Draft Final Report, NCHRP Study 1-37A, Washington 

DC, July 2004. 

2. Long-Term Pavement Performance Program, Federal Highway Administration, Guide to 

LTPP Traffic Data Collection and Processing, McLean, VA, Revised March 12, 2001. 

3. Federal Highway Administration, Traffic Monitoring Guide, Versions 1992, 1995, and 2001, 

Report Nos. FHWA-PL-95-03, FHWA-PL-92-017, and FHWA-PL-01-021, respectively, 

Washington DC, October 1992. 

4. National Cooperative Highway Research Program, Traffic Data Collection, Analysis and 

Forecasting for Mechanistic Pavement Design, Draft Final Report, NCHRP Study 1-39, 

Washington DC, October 2003. 

5. National Cooperative Highway Research Program, “Development of the 2002 Guide for the 

Design of New and Rehabilitated Pavement Structures,” workshop presented at the 

81st Annual Transportation Research Board Meeting, Washington, DC, January 2002. 

6. National Cooperative Highway Research Program, Development of the 2002 Guide for the 

Design of New and Rehabilitated Pavement Structures: Determination of Traffic Information 

and Data for Pavement Structural Design and Evaluation, Interim Report, NCHRP 1-37A, 

Washington DC, December 1999. 

7. National Cooperative Highway Research Program, Development of the 2002 Guide for the 

Design of New and Rehabilitated Pavement Structures: Traffic Input Guide, Interim Report, 

NCHRP 1-37A, Washington DC, January 2000. 

8. Ritchie, S.G., and M.E. Hallenbeck, State-Wide Highway Data Rationalization Study, 

Research Report No. WA-RD-83.1, Washington State Department of Transportation, 

Olympia, WA, 1986. 

9. Joint Task Force on Traffic Monitoring Standards, American Association of State Highway 

and Transportation Officials, Guidelines for Traffic Data Programs, ISBN 1-56051-054-4, 

Washington DC, 1992. 

10. Federal Highway Administration, Use of Data From Continuous Monitoring Sites, Volume I: 

Recommendations, and Volume II: Documentation, Final Report, Washington DC, August 

1994. 

11. Federal Highway Administration, Accuracy of Traffic Load Monitoring and Projections 

Related to Traffic Data Collection Parameters, Pooled-Fund Study SPR-2 (193), Washington 

DC, August 1998. 

12. SAS Institute, Inc., SAS/STAT User’s Guide, Release 6.03, Cary, NC, 1988. 

13. Hallenbeck, M.E., and Soon-Gwam Kim, Final Technical Report for Task A: Truck Loads 

and Flows, Research Report No. WA-RD-320.3, Washington State Department of 

Transportation, Olympia, WA, November 1993. 
116 

14. American Association of State Highway and Transportation Officials, Guide for the Design 

of Pavement Structures, Washington, DC, 1993. 

15. Portland Cement Association, Thickness Design for Concrete Highway and Street 

Pavements, Skokie, IL, 1984, 1995. 

16. Federal Highway Administration, LTPP IMS Data Release 16.0 (Compact Disk), Volume 1: 

Primary Data Set, McLean, VA, July 2003. 

17. Statistical Functions for Microsoft Excel, www.statistixl.com. 

18. Federal Highway Administration, “Accuracy of Traffic Load Monitoring and Projections 

Related to Traffic Data Collection Parameters,” Pooled- Fund Study SPR-2 (193), workshop 

presented at the 82nd Annual Transportation Research Board Meeting, Washington, DC, 

January 2003. 

19. Howard, A.K., “Laboratory Classification of Soils—Unified Soil Classification System,” 

Earth Sciences Training Manual No. 4, U.S. Bureau of Reclamation, Denver, CO, 1977.