Optimization of Traffic Data Collection for Specific Pavement Design Applications
May 2006 Publication No. FHWA-HRT-05-079

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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 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.

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.

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Table Of Contents

• Executive Summary
• Chapter 1. Introduction
  • Objective
  • Report Organization
  • Literature Review
• Chapter 2. Identify Scenarios And Knowledge Gaps
  • Methodology For Identifying Key Pavement Design Scenarios
  • Knowledge Gaps In The Sensitivity Of The Pavement Design Process To Traffic Input
  • Knowledge Gaps In Data Variation From Different Traffic Collection Scenarios
  • Summary
• Chapter 3. Define Traffic Data Collection Requirements For Each Selected Application
• Chapter 4. LTPP Data Analysis
  • LTPP Wim Data Extracted
  • Rationale For Selecting Sites For The Detailed Sensitivity Analysis
  • Identifying Groups Of Sites For Obtaining Regional Data
  • Simulating Traffic Data Collection Scenarios
  • Scenario 1-0: Site-Specific Continuous WIM Data
  • Scenario 1-1: Site-Specific WIM Data for 1 Month/4 Seasons
  • Scenario 1-2: Site-Specific WIM Data for 1 Week/Season
  • Scenario 2-0: Continuous Site-Specific AVC Data and Regional WIM Data
  • Scenario 2-1: Site-Specific AVC Data for 1 Month/Season and Regional WIM Data
  • Scenario 2-2: Site-Specific AVC Data for 1 Week/Season and Regional WIM Data
  • Scenario 2-3: Site-Specific AVC Data for 1 Week/Year and Regional WIM Data
  • Scenario 3-0: Continuous Site-Specific ATR Data, Regional AVC Data, and Regional WIM Data
  • Scenario 3-1: Site-Specific ATR Data for 1 Week/Season, Regional AVC Data, and Regional WIM Data
  • Scenario 4-0: Continuous Site-Specific ATR Data, Regional AVC Data, and National WIM Data
  • Scenario 4-1: Site-Specific ATR Data for 1 Week/Season, Regional AVC Data, and National WIM Data
  • Scenario 4-2: Site-Specific ATR Data for 1 Week/Year, Regional AVC Data, and National WIM Data
  • Scenario 4-3: Site-Specific ATR Data for 1 Weekday Plus 1 Weekend/Year, Regional AVC Data, and National WIM Data
  • Scenarios 4-4 through 4-7: Various-Coverage, Site-Specific ATR Data, National AVC Data, and National WIM Data
  • Estimating Traffic Input
• Chapter 5. Sensitivity Analysis
  • Effect Of Traffic Data Collection Scenario
  • Effect Of AADTT Growth Rate
• Chapter 6. Define Traffic Collection Requirements
• Chapter 7. Summary
• Appendix A. CARD-4 AND CARD-7 Descriptions
• Appendix B. Cluster Analysis Results
• Appendix C. Structural And Climatic Input
  • Structure: Jointed Portland Cement Concrete Pavement
  • Structure: Continuously Reinforced Concrete Pavement
  • Structure: Asphalt Concrete Pavement
  • Structure: Unbound Base
  • Climate
• Appendix D. Pavement Performance Results
• References

List Of Figures

• Figure 1. Schematic of the sensitivity of distress predictions to load spectra input.
• Figure 2. LTPP sites with WIM data available for periods longer than 359 days per year.
• Figure 3. LTPP sites with WIM data available for periods longer than 299 days per year.
• Figure 4. Flexible pavement site selection by AADTT and structural number.
• Figure 5. Rigid pavement site selection by AADTT and slab thickness.
• Figure 6. Annual distributions of tandem-axle loads, Washington State LTPP sites.
• Figure 7. Tandem-axle load distributions for the cluster of Washington State LTPP site 6048.
• Figure 8. Tandem-axle load distributions for the cluster of Washington State LTPP site 1007.
• Figure 9. Example of NCHRP 1-37A design guide output, site 181028 in Indiana.
• Figure 10. Summary of mean in life predictions, site 181028 in Indiana, confidence 50 percent.
• Figure 11. Summary of the range in predictions, site 181028 in Indiana, confidence 75 percent.
• Figure 12. Summary of the range in predictions, site 181028 in Indiana, confidence 85 percent.
• Figure 13. Summary of the range in predictions, site 181028 in Indiana, confidence 95 percent.
• Figure 14. Summary of the range in predictions, site 181028 in Indiana, confidence 99.9 percent.
• Figure 15. Pavement life prediction comparison between actual annual AADTT growth rate and 4 percent annual AADTT growth rate, flexible pavement sites.
• Figure 16. Pavement life prediction comparison between actual annual AADTT growth rate and 4 percent annual AADTT growth rate, rigid pavement sites.
• Figure 17. Components of the percentage difference between pavement life predictions for scenario X and those for scenario 1-0.
• Figure 18. Statistics for error component "A" in life predictions (percent), flexible pavement sites with AADTT &804; 800 trucks/day/lane.
• Figure 19. Statistics for error component "A" in life predictions (percentage), flexible pavement sites with AADTT > 800 trucks/day/lane.
• Figure 20. Statistics for error component "A" in life predictions (percentage), rigid pavement sites with AADTT &804; 1,200 trucks/day/lane.
• Figure 21. Statistics for error component "A" in life predictions (percentage), rigid pavement sites with AADTT > 1,200 trucks/day/lane.
• Figure 22. Estimated range in NCHRP 1-37A design guide pavement life prediction errors from mean traffic input.
• Figure 23. Estimated range in NCHRP 1-37A design guide pavement life prediction errors from low-percentile traffic input.
• Figure 24. Clusters of LTPP sites by annual tandem-axle load distribution, Washington State.
• Figure 25. Clusters of LTPP sites by annual tandem-axle load distribution, Vermont.
• Figure 26. Clusters of LTPP sites by annual tandem-axle load distribution, Mississippi.
• Figure 27. Clusters of LTPP sites by annual tandem-axle load distribution, Minnesota.
• Figure 28. Clusters of LTPP sites by annual tandem-axle load distribution, Michigan.
• Figure 29. Clusters of LTPP sites by annual tandem-axle load distribution, Indiana.
• Figure 30. Clusters of LTPP sites by annual tandem-axle load distribution, Connecticut.
• Figure 31. Clusters of LTPP sites by annual average truck class distribution, Washington State.
• Figure 32. Clusters of LTPP sites by annual average truck class distribution, Vermont.
• Figure 33. Clusters of LTPP sites by annual average truck class distribution, Mississippi.
• Figure 34. Clusters of LTPP sites by annual average truck class distribution, Minnesota.
• Figure 35. Clusters of LTPP sites by annual average truck class distribution, Michigan.
• Figure 36. Clusters of LTPP sites by annual average truck class distribution, Indiana.
• Figure 37. Clusters of LTPP sites by annual average truck class distribution, Connecticut

List Of Tables

• Table 1. Range in life prediction percentage errors from mean traffic input
• Table 2. Range in combined life prediction errors from low-percentile traffic input
• Table 3. Accuracy of AADT predictions as a function of factoring procedure
• Table 4. Traffic input levels in the NCHRP 1-37A design guide
• Table 5. Detailed description of the NCHRP 1-37A design guide traffic data input levels
• Table 6. NCHRP 1-37A design guide flow of calculations in assembling axle-load spectra
• Table 7. Suggested levels of reliability for roads of various classes
• Table 8. Selected traffic data collection scenarios
• Table 9. Relationship between two-sided probability of survival/failure and standard normal deviate in pavement life predictions
• Table 10. Definition of variables extracted from the CTDB
• Table 11. Background information on the flexible LTPP sites selected
• Table 12. Background information on the rigid LTPP sites selected
• Table 13. Identification codes for roadway functional classes as defined by LTPP database field FUNCTIONAL_CLASS in table INV_ID
• Table 14. Euclidean distance matrix: Annual distributions of tandem-axle loads, Washington State LTPP sites
• Table 15. Summary of clustering strategies and associated Euclidean distance: Annual distributions of tandem-axle loads, Washington State LTPP sites
• Table 16. LTPP sites used for obtaining regional vehicle classification data
• Table 17. LTP sites used for obtaining regional axle-load data
• Table 18. Example of computing MAFs from regional data
• 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
• Table 21. Number of tandem axles per vehicle, annual Washington State data
• Table 22. Summary of the source of traffic data input to the NCHRP 1-37A design guide for the selected scenarios
• Table 23. Number of possible traffic sampling combinations by scenario
• Table 24. Failure criteria for each pavement type
• Table 25. Scenario 1-0: Life prediction and critical distress, flexible pavement sites
• Table 26. Scenario 1-0: Life prediction and critical distress, rigid pavement sites
• 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
• Table 29. Statistics and ranges for the percentage life prediction errors from mean traffic input (i.e., quantity "A"), n=17
• Table 30. Statistics for percentage additional error in life predictions from lowest percentile traffic input (i.e., quantity "B")
• Table 31. Range in mean "B" errors
• 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
• Table 34. Card-7: Truck weight record
• Table 35. PC strength properties for level 2 input
• Table 36. Layer types and thicknesses for all sites
• Table 37. Assumed layer moduli
• Table 38. Site locations used for interpolation of weather station
• Table 39. Life prediction estimates by scenario and traffic input percentile level, section 181028
• Table 40. Life prediction estimates by scenario and traffic input percentile level, section 261010
• Table 41. Life prediction estimates by scenario and traffic input percentile level, section 282807
• Table 42. Life prediction estimates by scenario and traffic input percentile level, section 536048
• Table 43. Life prediction estimates by scenario and traffic input percentile level, section 185518
• Table 44. Life prediction estimates by scenario and traffic input percentile level, section 275076
• Table 45. Life prediction estimates by scenario and traffic input percentile level, section 094008
• Table 46. Life prediction estimates by scenario and traffic input percentile level, section 501682
• Table 47. Life prediction estimates by scenario and traffic input percentile level, section 186012
• Table 48. Life prediction estimates by scenario and traffic input percentile level, section 261004
• Table 49. Life prediction estimates by scenario and traffic input percentile level, section 261012
• Table 50. Life prediction estimates by scenario and traffic input percentile level, section 261013
• Table 51. Life prediction estimates by scenario and traffic input percentile level, section 271019
• Table 52. Life prediction estimates by scenario and traffic input percentile level, section 28308
• Table 53. Life prediction estimates by scenario and traffic input percentile level, section 185022
• Table 54. Life prediction estimates by scenario and traffic input percentile level, section 265363
• Table 55. Life prediction estimates by scenario and traffic input percentile level, section 533813

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