Interstate Pavement Condition Sampling 

Final Report 

PUBLICATION NO. FHWA-HIF-17-022 August 2016 

U.S. Department of Transportation 

Federal Highway Administration 
FOREWORD 

With the passing of the Moving Ahead for Progress in the 21st Century Act (MAP-21), the 

Federal Highway Administration (FHWA) is required to adopt a set of national pavement 

performance measures for evaluating the condition of the Interstate and National Highway 

System. In support of this legislation, FHWA considered various data sources. One of those 

sources was the Highway Performance Monitoring System (HPMS) data set, which is the official 

Federal government source of data on the extent, condition, performance, use, and operating 

characteristics of the nation’s highways. 

As a result, the FHWA decided to undertake the research effort documented in this report, whose 

objectives are to: 

 Collect an unbiased baseline study of a statistically significant sample of the entire

Interstate Highway System (IHS) and produce a report indicating the pavement condition

on the IHS nationally and in each State where data were collected.

 Determine if HPMS is an unbiased representation of the pavement condition of the IHS.

 Recommend improvements to HPMS data collection and reporting that are necessary to

either make HPMS unbiased or improve its precision, in regard to performance

management and FHWA’s use of HPMS data.

Notice

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

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

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

specification, or regulation. 

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

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

objective of the document. 

Quality Assurance Statement

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

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

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

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

ensure continuous quality improvement. 
TECHNICAL DOCUMENTATION PAGE 

1. Report No. FHWA-HIF-17-022 2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Interstate Pavement Condition Sampling 

5. Report Date

August 2016 

6. Performing Organization Code

NONE

7. Author(s)

Amy L. Simpson, Gonzalo R. Rada, Beth A. Visintine, and 

Jonathan L. Groeger 

8. Performing Organization Report No.

.

9. Performing Organization Name and Address

Amec Foster Wheeler Environment & Infrastructure, Inc. 

12000 Indian Creek Court, Suite F 

Beltsville, MD 20705 

10. Work Unit No. (TRAIS)

11. Contract or Grant No.

DTFH61-13-A-00001 

12. Sponsoring Agency Name and Address

U.S. Department of Transportation 

Federal Highway Administration 

Office of Asset Management 

1200 New Jersey Avenue, SE 

Washington, D.C. 20590

13. Type of Report and Period Covered

Final Report; September 2014 –

July 2016 

14. Sponsoring Agency Code

15. Supplementary Notes

The Task Monitor was Mr. Thomas Van, P.E., FHWA 

16. Abstract

With the passing of the Moving Ahead for Progress in the 21st Century Act (MAP-21), the Federal Highway 

Administration (FHWA) has proposed that the Highway Performance Monitoring System (HPMS) data set 

is the data source for the national pavement performance measures. The objectives of this project are to: 1. 

Collect an unbiased baseline study of a statistically significant sample of the entire Interstate Highway 

System (IHS) and produce a report indicating the pavement condition on the IHS nationally and in each 

State where data were collected. 2. Determine if HPMS is an unbiased representation of the pavement 

condition of the IHS. 3. Recommend improvements to HPMS data collection and reporting that are 

necessary to either make HPMS unbiased or improve its precision, in regard to performance management 

and FHWA’s use of HPMS data. 

The project answers the following questions regarding HPMS data collection: Is two-way data collection 

necessary? Does data need to be collected in more than one lane in a direction? What is the optimum HPMS 

section length? Do all distress items require full extent reporting or is sampling adequate? Are protocols 

proposed by FHWA adequate for collecting and reporting distress or do they need improvement? This report 

documents the entire research effort, with particular emphasis on the data collection and data analyses 

activities.

17. Key Words

Highway Performance Monitoring System, MAP-21, data 

collection, Interstate Highway System, performance measures 

18. Distribution Statement

No restrictions. 

19. Security Classif. (of this report)

Unclassified 

20. Security Classif. (of this page)

Unclassified

21. No. of Pages

149 

22. Price

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

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 

in2square inches 645.2 square millimeters mm2

ft2 square feet 0.093 square meters m

2

yd2 square yard 0.836 square meters m

2

ac acres 0.405 hectares ha 

mi2square 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 m

3 

yd3 cubic yards 0.765 cubic meters m

3 

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/in2poundforce 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 

mm2square millimeters 0.0016 square inches in2 

m

2

square meters 10.764 square feet ft2 

m

2

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 

m

3 cubic meters 35.314 cubic feet ft3 

m

3 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)

oC Celsius 1.8C+32 Fahrenheit o

F 

ILLUMINATION 

lx lux 0.0929 foot-candles fc 

cd/m2candela/m20.2919 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 e System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380. 

(Revised March 2003)
v

TABLE OF CONTENTS 

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

1.1. BACKGROUND ................................................................................................................1

1.2. PROJECT OBJECTIVES ...................................................................................................4

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

CHAPTER 2. LITERATURE REVIEW .....................................................................................7

2.1. IMPROVING FHWA’S ABILITY TO ASSESS HIGHWAY INFRASTRUCTURE 

HEALTH STUDIES ..................................................................................................................7

2.2. OTHER REFERENCES ...................................................................................................12

2.3. SUMMARY ......................................................................................................................16

CHAPTER 3. DATA COLLECTION .......................................................................................17

3.1. INTERSTATE HIGHWAY SYSTEM .............................................................................17

3.2. REVIEW OF 2013 HPMS DATA ....................................................................................21

3.3. DATA COLLECTION PLAN ..........................................................................................24

3.4. EXECUTION OF DATA COLLECTION PLAN ............................................................32

CHAPTER 4. DATA ANALYSIS ..............................................................................................45

4.1. DATA ANALYSIS PLAN ...............................................................................................45

4.2. COMPARISON WITH QUALITY CONTROL DATA ..................................................46

4.3. DISTRIBUTION OF THE CONDITION METRICS ......................................................51

4.4. NETWORK CONDITION ...............................................................................................52

4.5. COMPARISON WITH THE HPMS ................................................................................58

4.6. IMPROVEMENTS TO DATA COLLECTION AND REPORTING .............................63

4.7. SUMMARY ......................................................................................................................75

CHAPTER 5. SUPPLEMENTAL ANALYSIS .........................................................................77

5.1. THRESHOLD ANALYSIS ..............................................................................................77

5.2. IMPACT OF SECTION LENGTH ..................................................................................82

5.3. POINT-BY-POINT COMPARISON................................................................................85

5.4. SUMMARY ......................................................................................................................90

CHAPTER 6. CONCLUSIONS AND RECOMMENDATIONS ............................................93

REFERENCES .............................................................................................................................97

APPENDIX A. DATA STORAGE AND QUALITY REVIEW PLAN ................................101

APPENDIX B. PROJECT DATABASE DATA DICTIONARY ..........................................111

APPENDIX C. SAMPLE LENGTH PLOTS ..........................................................................127
vi 

LIST OF FIGURES 

Figure 1. Map. Interstate Highway System. ..................................................................................18

Figure 2. Illustration. Climate zones used in HPMS database. ......................................................20

Figure 3. Map. Data collection route. ............................................................................................25

Figure 4. Chart. Data completeness comparison based on Sample Panel in HPMS 2013. ...........29

Figure 5. Photo. Data collection vehicle – rear view. ....................................................................34

Figure 6. Photo. Data collection vehicle – side view. ....................................................................34

Figure 7. Sketch. Illustration of cracking definitions. ...................................................................43

Figure 8. Graph. Distribution function of IRI. ...............................................................................53

Figure 9. Graph. Distribution function of rut depth. ......................................................................53

Figure 10. Graph. Distribution function of faulting. ......................................................................54

Figure 11. Graph. Distribution function of AC percent HPMS cracking. .....................................54

Figure 12. Graph. Distribution function of AC percent wheelpath cracking.................................55

Figure 13. Graph. Distribution function of AC NPRM under. ......................................................55

Figure 14. Graph. Distribution function of AC NPRM over. ........................................................56

Figure 15. Graph. Distribution function of AC NPRM step. .........................................................56

Figure 16. Graph. Distribution function of PCC cracking. ............................................................57

Figure 17. Chart. Condition of the IHS..........................................................................................57

Figure 18. Chart. Comparison of performance measures. .............................................................58

Figure 19. Chart. HPMS condition details. ....................................................................................59

Figure 20. Graph. Power spectral density curve for IRI on asphalt section. .................................65

Figure 21. Graph. Power spectral density curve for rut depth on asphalt section. ........................65

Figure 22. Graph. Power spectral density curve for IRI on jointed concrete section. ...................66

Figure 23. Graph. Power spectral density curve for faulting on jointed concrete section. ............66

Figure 24. Graph. Impact of section length on average IRI – 0.1 vs 0.01-mile.............................67

Figure 25. Graph. Impact of section length on rut depth – 0.1 vs 0.5-mile. ..................................68

Figure 26. Graph. Impact of section length on percent cracking. ..................................................68

Figure 27. Chart. Comparison of condition metrics. .....................................................................78

Figure 28. Chart. Comparison of condition metrics for HPMS data. ............................................80

Figure 29. Graph. Impact of section length on percent cracking. ..................................................82

Figure 30. Graph. Impact of section length on IRI. .......................................................................83

Figure 31. Graph. Impact of section length on rut depth. ..............................................................83

Figure 32. Graph. Change in standard deviation of percent cracking by section length. ..............84

Figure 33. Graph. Change in standard deviation of IRI by section length. ...................................84

Figure 34. Graph. Change in standard deviation of rut depth by section length. ..........................85

Figure 35. Performance comparison by section length. .................................................................86

Figure 36. Map. Example of good point-by-point comparison. ....................................................87

Figure 37. Map. Example of poor point-by-point comparison. .....................................................88

Figure 38. Map. Example of mixed point-by-point comparison. ..................................................89

Figure 39. Graph. Comparison of IRI at 1-mile section length to 0.1-mile section length. ........127

Figure 40. Graph. Comparison of IRI at 0.5-mile section length to 0.1-mile section length. .....127

Figure 41. Graph. Comparison of IRI at 0.05-mile section length to 0.1-mile section length. ...128

Figure 42. Graph. Comparison of IRI at 0.01-mile section length to 0.1-mile section length. ...128

Figure 43. Graph. Comparison of IRI at 0.4-mile section length to 0.1-mile section length. .....129

Figure 44. Graph. Comparison of IRI at 0.3-mile section length to 0.1-mile section length. .....129
vii 

Figure 45. Graph. Comparison of IRI at 0.2-mile section length to 0.1-mile section length. .....130

Figure 46. Graph. Comparison of rut depth at 1-mile section length to 0.1-mile section 

length............................................................................................................................................130

Figure 47. Graph. Comparison of rut depth at 0.5-mile section length to 0.1-mile section 

length............................................................................................................................................131

Figure 48. Graph. Comparison of rut depth at 0.05-mile section length to 0.1-mile section 

length............................................................................................................................................131

Figure 49. Graph. Comparison of rut depth at 0.01-mile section length to 0.1-mile section 

length............................................................................................................................................132

Figure 50. Graph. Comparison of rut depth at 0.4-mile section length to 0.1-mile section 

length............................................................................................................................................132

Figure 51. Graph. Comparison of rut depth at 0.3-mile section length to 0.1-mile section 

length............................................................................................................................................133

Figure 52. Graph. Comparison of rut depth at 0.2-mile section length to 0.1-mile section 

length............................................................................................................................................133

Figure 53. Graph. Comparison of percent cracking at 1-mile section length to 0.1-mile section 

length............................................................................................................................................134

Figure 54. Graph. Comparison of percent cracking at 0.5 mile section length to 0.1-mile section 

length............................................................................................................................................134

Figure 55. Graph. Comparison of percent cracking at 0.05-mile section length to 0.1-mile section 

length............................................................................................................................................135

Figure 56. Graph. Comparison of percent cracking at 0.01-mile section length to 0.1-mile section 

length............................................................................................................................................135

Figure 57. Graph. Comparison of percent cracking at 0.4-mile section length to 0.1-mile section 

length............................................................................................................................................136

Figure 58. Graph. Comparison of percent cracking at 0.3-mile section length to 0.1-mile section 

length............................................................................................................................................136

Figure 59. Graph. Comparison of percent cracking at 0.2-mile section length to 0.1-mile section 

length............................................................................................................................................137
viii 

LIST OF TABLES 

Table 1. Proposed pavement condition rating thresholds. (1) ...........................................................3

Table 2. Mileage of HPMS 2013 data by State. ............................................................................19

Table 3. Mileage of IHS by climate zone in HPMS 2013. ............................................................20

Table 4. Mileage of IHS by population zone in HPMS 2013. .......................................................21

Table 5. Mileage of IHS by surface type in HPMS 2013. .............................................................21

Table 6. Data completeness in terms of distress type in HPMS 2013. ..........................................22

Table 7. Data completeness in terms of distress type based on Sample Panel in HPMS 2013. ....22

Table 8. HPMS 2013 data set climate zone distribution. ...............................................................22

Table 9. HPMS 2013 data set urban vs. rural distribution. ............................................................22

Table 10. HPMS 2013 data set surface type distribution...............................................................23

Table 11. HPMS 2013 data set surface type distribution for Sample Panel. .................................23

Table 12. HPMS 2013 data set condition data distribution by surface type for Sample Panel. ....24

Table 13. Data collection miles and direction. ..............................................................................27

Table 14. Climate zone composition. ............................................................................................29

Table 15. Urban versus rural composition. ....................................................................................29

Table 16. Surface type composition...............................................................................................29

Table 17. Summary of data collection schedule. ...........................................................................32

Table 18. Summary of processed data shipments. .........................................................................36

Table 19. Comparison of routine and QC data. .............................................................................47

Table 20. Comparison of mileage by State. ...................................................................................48

Table 21. Comparison of condition metrics between project data and Infrastructure Health 

project data .....................................................................................................................................48

Table 22. Comparison of performance measures. .........................................................................49

Table 23. Average and standard deviation of each condition metric for comparison with LTPP 

data. ................................................................................................................................................50

Table 24. Condition metric summary statistics..............................................................................52

Table 25. Comparison of performance measures by State. ...........................................................60

Table 26. Comparison of HPMS 2014 and project data performance measures. ..........................60

Table 27. Comparison of HPMS 2014 and project data variances. ...............................................61

Table 28. Comparison of HPMS 2014 and project data condition metrics. ..................................61

Table 29. Comparison of HPMS 2014 and project data statistics. ................................................62

Table 30. Comparison of the Condition Metrics by State .............................................................63

Table 31. Comparison of bridge location and length. ....................................................................63

Table 32. Comparison of adjacent lane and opposing direction with primary lane. .....................64

Table 33. Sampling requirements at the national level. .................................................................69

Table 34. Minimum sampling requirements at the State level. .....................................................70

Table 35. Maximum sampling requirements at the State level. .....................................................71

Table 36. Average sampling requirements at the State level. ........................................................71

Table 37. Comparison of data with and without bridges. ..............................................................72

Table 38. Comparison of faulting from LCMS and RSP...............................................................73

Table 39. Comparison of rutting from LCMS and 5-point profile. ...............................................73

Table 40. Comparison of definitions of cracking on AC pavements. ............................................74

Table 41. Correlations between cracking definitions. ...................................................................75

Table 42. Comparison of NPRM and revised thresholds. .............................................................77
ix

Table 43. Comparison of performance measures from project data using different thresholds by 

State................................................................................................................................................79

Table 44. Comparison of HPMS 2014 performance using different thresholds by State..............81

Table 45. Mileage by comparison category. ..................................................................................90

Table 46. Data Submittal Log ......................................................................................................109

Table 47. 0.01mile_Crack_AC ....................................................................................................111

Table 48. 0.01mile_Crack_PCC ..................................................................................................114

Table 49. 0.01mile_Fault .............................................................................................................116

Table 50. 0.01mile_IRI ................................................................................................................118

Table 51. 0.01mile_Rut................................................................................................................120

Table 52. Event_Table .................................................................................................................121

Table 53. Pavement_Change .......................................................................................................122

Table 54. Tenth_Mile_Data .........................................................................................................122
x

LIST OF ACRONYMS 

AASHTO American Association of State Highway and Transportation Officials 

AC Asphalt Concrete 

C&P Conditions and Performance 

CRCP Continuously Reinforced Concrete Pavement 

DIM Distress Identification Manual 

DMI Distance Measuring Instrument 

DOT Department of Transportation 

FHWA Federal Highway Administration 

GPS Global Positioning System 

HERS Highway Economics Requirements System 

HPMS Highway Performance Monitoring System 

IHS Interstate Highway System 

IMU Inertial Measurement Unit 

IRI International Roughness Index 

JCP Jointed Concrete Pavement 

JPCP Jointed Plain Concrete Pavement 

JRCP Jointed Reinforced Concrete Pavement 

LCMS Laser Crack Measurement System 

LTPP Long-Term Pavement Performance 

MAP-21 Moving Ahead for Progress in the 21st Century Act 

MEPDG Mechanistic Empirical Pavement Design Guide 

NAPCOM National Pavement Cost Model 

NHS National Highway System 
xi

NPRM Notice of Proposed Rulemaking 

PCI Pavement Condition Index 

PCC Portland Cement Concrete 

PHT Pavement Health Track 

PMS Pavement Management System 

POS Position Orientation System 

PPDB Pavement Performance Database 

QA Quality Assurance 

QC Quality Control 

QM Quality Management 

RFP Request for Proposal 

ROW Right-of-Way 

RSL Remaining Service Life 

RSP Road Surface Profilometer 

SHA State Highway Agencies 

TRB Transportation Research Board 

TRIS Transportation Research Information Service

1

CHAPTER 1. INTRODUCTION 

1.1. BACKGROUND 

With the passing of the Moving Ahead for Progress in the 21st Century Act (MAP-21), the 

Federal Highway Administration (FHWA) was required to adopt a set of national pavement 

performance measures for evaluating the condition of the Interstate and National Highway 

System (NHS). In support of this legislation, FHWA considered various data sources. One of 

those sources was the pavement management systems (PMS) developed and maintained by 

various State Highway Agencies (SHA); i.e., external data sources to FHWA. Another data 

source, an internal one, was the Highway Performance Monitoring System (HPMS) data set, 

which is the official Federal government source of data on the extent, condition, performance, 

use, and operating characteristics of the nation’s highways. 

HPMS data are used for assessing and reporting highway system performance under FHWA’s 

strategic planning process. They also form the basis of analyses that support the Conditions and 

Performance (C&P) Report to Congress and are the source for a substantial portion of the 

information in the annual Highway Statistics publication and in other FHWA publications. In 

addition, HPMS data are widely used throughout the transportation community for research 

purposes, including performance measurement purposes. Accordingly, HPMS data were selected 

as the basis for defining the required national pavement performance measures. 

In January 2015, the FHWA issued a Notice of Proposed Rulemaking (NPRM) to establish 

performance measures to assess the condition of the pavements on the NHS and Interstate 

System. (1) According to the NPRM, FHWA considered use of existing methods such as 

Pavement Condition Index (PCI), remaining service life (RSL) and other methods used within 

State Departments of Transportation (DOTs), but found nothing to be considered a national 

standard and that implementing such measures would be challenging and a burden. (1) This 

resulted in FHWA proposing four pavement performance measures to assess pavement condition 

as follows: (1)

 Percentage of pavements on the Interstate Highway System (IHS) in good condition. 

 Percentage of pavements on the IHS in poor condition.

 Percentage of pavements on the NHS (excluding IHS) in good condition. 

 Percentage of pavements on the NHS (excluding IHS) in poor condition.

In the NPRM, FHWA proposed that the performance measures used to assess pavement 

condition meet the following criteria and, as noted earlier, be based on data within HPMS: (1)

 Consider more than roughness.

 Utilize pavement condition attributes currently reported at a national level. 
2

 Utilize pavement condition attributes where data collection and reporting standards exist 

today. 

 Result in an assessment approach that is consistent with typical conceptual approaches 

used today by State DOTs to assess condition. 

 Consider an approach that can be implemented so that State DOTs can establish targets 

within a 12-month time period after FHWA establishes the performance measures 

without introducing a considerable burden on State DOTs. 

The performance measures, as proposed by FHWA, to assess the condition of the pavement are 

based on the percentage of pavements on both the IHS and NHS (excluding the IHS) in good and 

poor condition. Condition of the pavements is to be determined based on the following metrics:(1)

 International Roughness Index (IRI) 

 Rutting 

 Cracking percent 

 Faulting 

The proposed pavement condition rating thresholds are provided in table 1.

(1) The overall 

condition of the pavement is determined based on the individual metric conditions, as follows: 

 For asphalt and jointed concrete pavements, the pavement is classified as good condition 

if all three metrics are in good condition. The pavement is classified as poor condition if 

two or more of the metrics are in poor condition. All other combination of metric 

conditions classify a pavement as fair. (1)

 For continuously reinforced concrete pavements (CRCP), if both of the metrics are in 

good condition, the pavement is classified as good. The pavement is classified as poor if 

both of the metrics are in poor condition. All other combination of metric conditions 

classify the pavement as fair. (1) 

Three of the four NPRM pavement condition metrics are used to determine the overall condition 

for asphalt and jointed concrete HPMS pavement sections, while only two pavement condition 

metrics are used to determine the overall condition for CRCP. The NPRM notes that each of the 

above pavement condition data metrics are to be collected on the full extent of the IHS in the 

rightmost travel lane in both directions of travel on an annual basis. (1) For the non-Interstate 

NHS pavements, data are to be collected for the full extent of the rightmost lane in one direction 

of travel on a biennial frequency. (1) Percent cracking, rutting, and faulting are not required for 

the full extent of the non-interstate NHS until the 2019 data collection cycle and may be reported 

as sampled data until that time. (1)
3

Table 1. Proposed pavement condition rating thresholds. (1) 

Surface Type Metric Metric range Rating 

All Pavements IRI 

< 95 in/mi Good 

95-170 in/mi: Areas with a 

population less than 100,000 Fair 95-220 in/mi: Urbanized areas with a 

population of at least 1,000,000 

> 170 in/mi: Areas with a population 

less than 100,000 Poor > 220 in/mi: Urbanized areas with a 

population of at least 1,000,000 

Asphalt, Jointed 

Concrete, and 

CRCP

Cracking 

Percent 

< 5% Good 

5 – 10% Fair 

> 10% Poor 

Asphalt Pavement Rutting 

< 0.20 Good 

0.20 – 0.40 Fair 

> 0.40 Poor 

Jointed Concrete 

Pavement Faulting 

< 0.05 Good 

0.05 – 0.10 Fair 

> 0.10 Poor 

FHWA and other agencies have performed various studies to determine the “truth” of the data 

and its reliability as a tool for the measurement and reporting required by MAP-21. For example, 

NCHRP 20-24, Task 82, looked at this issue in a report titled, “Increasing Consistency in the 

Highway Performance Monitoring System for Pavement Reporting, Final Report.”

(2) This report 

cited several findings and observations on current practice among States and made five 

recommendations to improve HPMS data for use as a national pavement performance 

management system. 

At the same time, FHWA performed a study to look at similar concerns. This study resulted in a 

series of reports with the core title, “Improving FHWA’s Ability to Assess Highway 

Infrastructure Health.” (See references 3, 4, 5 and 6.) For this study, Interstate 90 through 

Wisconsin, Minnesota, and South Dakota was evaluated. This study resulted in several 

recommendations for HPMS data collection. 

The above referenced reports along with various discussions with and among Transportation 

Research Board (TRB) and American Association of State Highway Transportation Officials 

(AASHTO) committees have raised concerns about the validity and availability of HPMS 

pavement data. However, use of HPMS data are necessary by FHWA as the agency goes through 

the development of regulations and policy. Additionally, FHWA and State and local agencies 

will use these data to set goals and measure the performance of their systems. 
4

1.2. PROJECT OBJECTIVES 

As a result of the issues summarized at the end of the previous section, the FHWA decided to 

undertake the research effort documented in this report. The objectives of the project were to: 

 Collect an unbiased baseline data set for a statistically significant sample of the entire 

IHS and produce a report indicating pavement condition on the IHS nationally and in 

each State where data were collected. 

 Determine if HPMS is an unbiased representation of pavement condition on the IHS. 

 Recommend improvements to HPMS data collection and reporting necessary to either 

make HPMS unbiased or improve its precision, in regard to performance management 

and FHWA's use of HPMS data, which in turn will enable responses to questions such as: 

o Is two-way data collection necessary? 

o Does data need to be collected in more than one lane in a direction? 

o What is the optimum HPMS section length? 

o Do all distress items require full extent reporting or is sampling adequate? 

o Are protocols proposed by FHWA adequate for collecting and reporting distress 

or do they need improvement? 

Towards successful accomplishment of the above referenced objectives, the following phases 

and tasks were undertaken: 

 Phase 1: Development of Data Collection and Analysis Plan 

o Task 1.1 Kick-Off Meeting/Teleconference 

o Task 1.2 Literature Review 

o Task 1.3 HPMS Data for IHS Pavement Conditions 

o Task 1.4 Data Collection and Analysis Plan 

o Task 1.5 Project Update Meeting 

o Task 1.6 Draft Phase 1 Report 

o Task 1.7 Final Phase 1 Report 

 Phase 2: Implementation of Data Collection and Analysis Plan 

o Task 2.1 Data Collection 
5

o Task 2.2 Data Analysis 

o Task 2.3 Progress Meetings 

o Task 2.4 Draft Phase 2 Report 

o Task 2.5 Final Phase 2 Report 

1.3. REPORT ORGANIZATION 

This report documents the entire research effort, with particular emphasis on the data collection 

and data analyses activities. The report chapters are summarized below along with a brief 

description of their contents: 

1. Introduction – provides the project background, project objectives, and organization of 

the report.

2. Literature Review – identifies recent developments in the areas of HPMS data 

collection, HPMS practices and SHA practices, which provided much of the foundation 

for the data collection and analysis efforts presented in the next two chapters. 

3. Data Collection – details the data collection effort, from the planning stages to its 

completion, including data processing and quality review. 

4. Data Analysis – details the data analysis effort, from the planning stages to its 

completion, including quality review of the results and major findings. 

5. Supplemental Analysis – details the supplemental analysis effort, including threshold 

values, impact of the section length and comparison of the HPMS and project data. 

6. Conclusions and Recommendations – documents the major conclusions from the effort, 

including responses to the various issues pursued in the project, and recommendations 

for improving HPMS data collection practices. 

In addition, three appendices are provided to more completely document the data collection and 

data analyses efforts. These appendices are as follows: 

 Appendix A – Data Storage and Quality Review Plan 

 Appendix B – Project Database Data Dictionary 

 Appendix C – Sample Length Graphs 

7

CHAPTER 2. LITERATURE REVIEW 

The objective of the literature review was to identify recent developments in the areas of HPMS 

and State DOTs data collection practices. This effort was considered important because the 

resulting information provided much of the foundation for the data collection and analysis efforts 

presented in chapters 3 through 5. Towards accomplishing this objective, the project team 

performed the following activities: 

 Built on literature reviews and surveys recently conducted by the project team and others 

on the focus subject area, including the series of “Improving FHWA’s Ability to Assess 

Highway Infrastructure Health” reports. 

 Built on other recently completed and relevant literature reviews for FHWA’s Long-Term 

Pavement Performance (LTPP) effort to develop HPMS data sets for LTPP test sections. 

 Conducted searches utilizing the TRB Research in Progress database, Transportation 

Research Information Service (TRIS) database, FHWA information resources, online 

libraries, State and regional transportation agencies, industry organizations, academic 

institutions, military departments, and other related information sources. 

The remainder of this chapter presents the results of the literature review effort – it summarizes 

relevant information extracted from the literature that supports accomplishment of the stated 

study objectives. This information is presented in two separate sections based on the reference –

(1) reports and other references that resulted from the “Improving FHWA’s Ability to Assess 

Highway Infrastructure Health” studies and (2) other reports and references. 

2.1. IMPROVING FHWA’S ABILITY TO ASSESS HIGHWAY INFRASTRUCTURE 

HEALTH STUDIES 

Over the past five years, FHWA conducted a series of studies under the “Improving FHWA's 

Ability to Assess Highway Infrastructure Health” effort, which led to highly relevant references

for the study in question. (See references 3, 4, 5, 6, 7 and 8.) The studies were conducted under 

three phases. Phase I focused on defining an approach for assessing pavement and bridge 

condition and health. In Phase II, the approach was refined and tested via a pilot study on a 

sample corridor. Phase III consisted of a national meeting to review the project results with 

practitioners. The more relevant references associated with the Phase II and III efforts are 

summarized next. 

This project included a pilot study conducted by FHWA aimed at enhancing FHWA’s ability to 

assess the health of the nation’s highway infrastructure. As part of this study, a section of 

Interstate 90 through South Dakota, Minnesota, and Wisconsin was evaluated in order to: (3) 

 Define a consistent and reliable method of assessing infrastructure health with a focus on 

bridges and pavements on the IHS.

 To develop tools to provide FHWA and SHA personnel access to key information that 

will allow for a better and more complete view of infrastructure health nationally. 
8

Major conclusions from the pilot study relating to the level of confidence associated with the 

various pavement condition measures evaluated include: (3)

 There is a high-level of confidence with IRI given the acceptable correlation found in the 

study between the HPMS, State DOT PMS, and field data sources. 

 A medium-level of confidence exists for the rut depth data and additional investigation is 

required to resolve the bias issue between the HPMS or State DOT PMS data and the 

field data. 

 For the remaining condition measures (cracking percentage, cracking length and 

faulting), additional work is required to standardize data collection and processing at the 

national level. 

 Given the need for consistent, high-quality data at the national level, use of the HPMS 

data set to drive the good/fair/poor indicator is considered the best option at present and 

in the near future. However, this does not imply that improvements to the HPMS data are 

not possible and/or required, as discussed next. Using State DOT PMS data does not 

seem feasible at this time due to the differences between States. Collecting field data on 

the entire interstate system likewise does not appear economically justified at this time. 

In turn, based on the study findings, the following list highlights some of the higher priority 

HPMS data improvement opportunities: (3)

 HPMS data summary lengths should be investigated to resolve the analysis bias when 

using variable sample lengths. At present, the summary lengths are highly variable, which 

can lead to pavement condition measures being either exaggerated in the case of short 

lengths or being lost due to averaging over long lengths. 

 Incorporate additional checks in the HPMS software to flag HPMS data that are not 

consistent (for example sections that have a high PSR value but show high distress levels 

or vice versa). These checks should be applied at the State level, prior to submission of 

data to FHWA. Having up-to-date information on maintenance and rehabilitation is 

important to resolve potential issues associated with the temporal analysis of pavement 

condition data. 

 The HPMS rut depth data collection procedure and analysis algorithm should be codified 

for purposes of the good/fair/poor indicator. 

 HPMS cracking data collection should be better defined and a manual for its 

implementation prepared along with the recommended quality assurance (QA) standards. 

 Faulting data should be investigated to resolve the inconsistencies in data collection and 

analysis. Use of the ProVAL tool to analyze faulting may be a suitable method to 

standardize the analysis of faulting data. 
9

As part of the project a national meeting was conducted by FHWA, in coordination with 

AASHTO, to: (4)

 Present the results of the FHWA Highway Infrastructure Health Assessment Study. 

 Solicit feedback on project findings and recommendations, with a particular focus on 

their benefits, potential implementation challenges, and recommendations for addressing 

these issues. 

 Identify critical next steps for advancing national performance measures for 

infrastructure. 

The meeting entitled “AASHTO/FHWA Workshop on the Highway Infrastructure Health 

Assessment Study” took place on October 13, 2011 in Detroit, Michigan. Some of the more 

relevant discussion points and common themes for the project in question include: 

 One group urged national condition measurement should remain confined to the IHS for 

now. 

 Some participants expressed concern over consistency with State efforts. 

 There was a desire to find measures that would indicate the adequacy of condition over 

time or the timeframe when condition might deteriorate significantly, as opposed to a 

snapshot of condition at one time. 

 There was general consensus that, while IRI is ready and available for use as a pavement 

condition rating, it does not tell the whole story of pavement condition. 

 Participants felt it was important to acknowledge the limitations of the condition ratings. 

 There was a general consensus on the need for better data collection standards and 

protocols.

 The importance of continual improvements to the national data sets, which are the 

foundation for all of the measures being investigated by FHWA and AASHTO. 

 The importance of viewing measurement and reporting efforts from the perspective of the 

traveling public. 

Subsequent to the pilot study, FHWA conducted two follow-up studies. The first one was 

intended to investigate the rutting bias issue identified during the pilot study. The objectives of 

this follow-up study were to: (5)

 Investigate the discrepancy between rutting observed from field data collection versus 

that retrieved from HPMS/State data to determine the cause of the bias. 

 Develop data requirements and an algorithm that can be applied to rutting to produce 

consistent, high-quality data. 
10

The following recommendations were made based on the study findings: (5)

 A maximum longitudinal spacing of 50 ft should be used for the collection of transverse 

profile data, but a 10-ft spacing provides a more optimal approach for estimating rut 

depth. 

 A minimum of 400 data points should be used to characterize the transverse profile. 

 For transverse profiles containing 1,000 points or more, a moving average of 2 inches 

may be used to reduce the white noise in the signal obtained during data collection. 

 A lane width wireline should be used to calculate rut depth from the transverse profile. 

 The gage width should be set to between 1.2 and 1.57-in. for calculating rut depth. 

These requirements are similar to those in AASHTO PP 70 protocol, (9) but this protocol is based 

on reviewing data by lane-half and it does not address the required number of points per profile. 

The second follow-up FHWA study was intended to develop a next generation pavement 

performance measure that (1) provided an accurate and repeatable assessment of functional 

condition and (2) relied solely on HPMS pavement condition data. (6) It was determined that to 

accomplish the stated objectives, changes to the HPMS data collection processes were required 

to ensure consistent and uniform data from one State to another, as well as accurate and 

repeatable data. It was also determined that differences exist on how State DOTs collect data for 

input to HPMS. Similarly, data processing can vary from one State to another. Accordingly, it 

was concluded that a number of data collection, processing and reporting requirements needed to 

be addressed. 

Moreover, the focus of the study shifted from developing a single composite index to using 

individual distresses, which led to the development of recommendations for data collection, 

processing and QC/QA. Selected examples of the recommended data collection and processing 

requirements are provided below: (6)

 For rutting data collection, (1) the data points should cover a minimum width of 13 feet to 

help ensure the full width of the lane is covered and those data points should have a 

separation less than or equal to 0.4 inch, (2) the maximum longitudinal spacing between 

profiles should be 10 feet, (3) a 2-inch moving average filter should be applied to the 

transverse profile, (4) the wireline method is recommended for the rut depth computation, 

(5) a gage width of 1.2 to 1.5 inches should be used for calculating the rut depth, and (6) 

the base length for summarization of these data should be set to 0.1 mile. 

 For ride quality data, (1) the data collection interval should be 2 inches or less and on 

concrete pavements where the data may be used for faulting measurement, the interval 

should be 0.75 inch or less, (2) a height sensor should be selected with a sufficient (2.75 

inches) footprint to not be impacted by the surface texture, (3) data collection should 

ideally occur at the same time of day and time of year each time it is collected to 

minimize the impact of diurnal and seasonal variations, (4) the full extent of the system 
11

(including bridges and pavement changes) should be included in the IRI calculation, and 

(5) the base length for summarization of these data should be set to 0.1 mile. 

 For faulting data collection, (1) the equipment should be set to collect and store an 

elevation measurement every 0.75 inch, (2) the data should ideally be collected at the 

same time of day and time of year, (3) ProVAL version 3.3 (or later version) should be 

used for calculation of faulting, (4) both joints and cracks should be analyzed and 

reviewed for faulting on jointed concrete pavements, and (5) the base length for 

summarization of these data should be set to 0.1 mile. 

 For cracking data collection, (1) an automated method for collection and processing of 

cracking is recommended, (2) a 100 percent sampling rate should be used to reduce the 

likelihood that outlier areas of condition will be missed in the evaluation, and (3) the base 

length for summarization of these data should be set to 0.1 mile. 

The reader is referred to the study report for a complete list of recommendations, including 

quality control and storage recommendations. In addition, future research was recommended to 

improve current capabilities in data collection and processing, such as gaining a better 

understanding of the impact of changes in curling on faulting measurements, improving faulting 

measurements, and consideration of sealed cracks and length of ruts, which are not currently 

considered by the HPMS Field Manual. 

Van Hecke and Ebright-McKeehan 2012, documents investigations into the completeness of IHS

pavement condition data in the FHWA 2010 and 2011 HPMS databases.(7) Based on the review 

of five pavement condition data items – IRI, cracking, faulting, pavement serviceability rating 

(PSR), and rutting in the HPMS data set, several data gaps were identified. Highlights from the 

memorandum that are important to this project are summarized below: (7)

 The most common condition item in the 2010-2011 HPMS databases is IRI. 

 Although less common than IRI, the extent to which cracking and rutting are available 

was a positive finding. 

 Rutting data appears to have improved significantly recently. 

 Despite the wide availability of some of the data items, the number of segments with 

multiple pavement condition items is not as comprehensive as anticipated. Two data 

gaps exist which contribute to the lack of completeness: horizontal gaps within individual 

data items and vertical gaps between data items within segments. 

 Gaps often occur across State lines when one DOT reports a pavement condition 

attribute, but the neighboring State does not. Even within States for certain data items, 

data appears to have been collected or reported in a checkerboard fashion, creating gaps 

in the HPMS network for that particular attribute. 

 Vertical gaps occur where the depth of data collection is shallow. For instance, some 

States may collect and submit IRI and cracking values for all of their system, but nothing 
12

else. Most interstate highway segments in the HPMS database contain attribute values 

for two pavement condition data items. 

 The best combinations of pavement data are IRI and rutting, and IRI and cracking. 

However, while cracking data often appears in States where rutting data are reported, 

cracking data are slightly less prevalent and have more gaps. 

In a follow-up effort to the previous referenced FHWA studies, an assessment of two additional 

corridors beyond the original pilot study was performed using the RSL concept contained in the 

FHWA Pavement Health Track (PHT) analysis tool using HPMS data. (8) In order to select the 

two corridors for assessment, an investigation into the completeness and utility of the interstate 

pavement condition data in the 2010 and 2011 HPMS databases was performed – this 

investigation was documented under the previous reference. Analysts identified several potential 

tri-State corridors with key HPMS data that could support the analysis. The two corridors 

selected were I-15 through Idaho, Utah and Arizona and I-85 through Virginia, North Carolina, 

and South Carolina. The health of these two corridors was assessed using the PHT analysis tool. 

The following observations and conclusions relevant to the project in question were derived from 

the assessment: (8)

 It is evident that the HPMS data set has significant data completeness issues for 

conducting this type (i.e., PHT) of analysis. No tri-State corridor study areas could be 

identified from interstate highway segments containing values for all pavement condition 

data elements. 

 As the PHT analysis tool requires data elements only required for HPMS sample panels, 

only 77% of the pavement sections for I-15 and 45% of pavement sections for I-85 

contained the required data elements for PHT analysis. The number of analysis sections 

was further reduced once a quality check on the data was conducted and sections that 

were missing required data elements were eliminated. Only 61% of the pavement sections 

for I-15 and 13% of pavement sections for I-85 contained all the required data elements. 

 Given the significant data limitations and anomalies associated with the input HPMS 

data, the tri-State corridor analysis was somewhat limited. It can be rationally concluded 

that such an analysis for the entire IHS would similarly be very severely limited given 

current HPMS data completeness. 

2.2. OTHER REFERENCES 

The HPMS Field Manual provides the data collection and reporting requirement of HPMS data. 

(10) Although the field manual contains requirements for all 69 items contained in the HPMS data 

set, only the pavement condition requirements are summarized below. Differences between the 

2013 HPMS Field Manual and 2014 HPMS Field Manual are also noted. 

 IRI data should be collected according to AASHTO Standard R 43-07 (11) (and associated 

PP 69-10 (12) and PP 70-10 (9) as applicable). The 2014 HPMS Field Manual included the 

addition of PP 69-10 (12) and PP 70-10. (9)

 This addition allows for the collection of 

rutting data concurrent with collection of ride quality data. 
13

 Rutting data should be collected according to AASHTO R 48-10 (13) (and associated PP 

69-10 (12) and PP 70-10 (9) as applicable) specifications or the LTPP protocol. 

 Faulting data should be collected according to AASHTO R 36-04 (14) or the LTPP 

protocol. 

 Cracking percent and cracking length data should be collected according to AASHTO R 

55-10 (15) (and associated PP 67-14

(16) and PP 68-10 (17) as applicable) or the LTPP 

Distress Identification Manual. (18)

Concurrent with FHWA studies synthesized in the previous section, the NCHRP 20-24 Task 82 

effort was on-going. (2) The study in question focused on the following topics: 

 Availability of comparable information in the pavement management and HPMS data 

sets. 

 Amount of processing required for comparing HPMS and pavement management data. 

 Differences in pavement conditions reported using HPMS and pavement management 

data. 

 Impact of the reporting length (e.g., segment or route) on pavement condition statistics. 

The findings from the study suggest that changes to the current HPMS requirements are needed 

to improve the consistency in pavement management and HPMS data. Some of the major study 

findings and observations are provided below: (2)

 There is a relatively high degree of confidence in the HPMS data on a State-by-State 

basis, but inconsistencies in how the information is collected nationwide result in a 

moderate to low degree of confidence in the ability to use the information to compare 

conditions across States. 

 The HPMS pavement data requirements were implemented to populate new performance 

models based on the Mechanistic-Empirical Pavement Design Guide (MEPDG). 

However, the information needed for the models is not readily available in many States, 

and therefore, the States do not place a high degree of confidence in the reliability of the 

off-State system data. 

 There are significant differences in the pavement condition information that States use to 

report network conditions. IRI is often a component of the overall condition, but ride is 

typically not a significant factor in selecting and prioritizing pavement preservation 

activities. 

 The variability in the reported pavement conditions varies based on the method of 

aggregating the data; i.e., whether the data were summarized at the State, route, or 

segment levels. 
14

 Over three quarters of the States responding to the study survey indicated that the 

methodology used in their PMS for cracking differs from the HPMS requirements for 

reporting cracking information. As a result, some agencies have developed equations for 

converting their pavement management information for HPMS purposes and other States 

have developed other approaches for responding to the requirement. 

 The requirement to report faulting data is also problematic for some States since several 

States do not include faulting in their pavement management survey procedures. 

 The comparison of HPMS data and pavement management data required a lot of 

manipulation to match sections, which illustrates the issues that would arise if the FHWA 

chose to develop a process for verifying the quality of the State data submitted to HPMS 

or if the pavement management personnel wanted to check the quality of the HPMS 

submittal before being sent to FHWA. 

 There was no evidence that the size of the agency or the method of collecting and 

processing the data influenced the results of the analysis or the likelihood of finding 

variability in the data. 

In turn, the above findings and observations led to the following recommendations for changes to 

the HPMS data requirements, which have been prioritized (from highest to lowest priority) based 

on their potential impact on the States: (2)

 Determine the appropriateness of the models being used for national performance 

management and the need for the level of detail currently required. 

 Address the inconsistencies in the pavement management data collection activities that 

are impacting the ability to use HPMS data to compare pavement conditions across 

States. 

 For pavement-related data only, require States to submit HPMS data using a consistent 

section length. Future research is needed to determine the appropriate length to minimize 

variability and processing time for reporting purposes. 

 Demonstrate to States the benefits associated with the availability and use of HPMS data. 

 Develop a strategy for obtaining the information necessary for evaluating or estimating 

the performance of the off-State system in a cost-effective manner. 

Research for improving the consistency of the HPMS data was also recommended if these data 

are to be used to report national highway conditions, to predict future highway conditions, and to 

estimate the impacts of investment levels on national highway conditions, including: (2)

 The sensitivity of predicted conditions to the variables required under HPMS to better 

determine the impact of inaccurate or default data. 
15

 The enforcement of a consistent HPMS reporting length that minimizes variability and 

reporting time.

 A suitable pavement condition metric that more closely matches State conditions reported 

for pavement management. 

Many of the findings, conclusions and recommendations from this NCHRP study are similar to 

those presented earlier, under the discussion of the “Improving FHWA’s Ability to Assess 

Highway Infrastructure Health” references. 

A Practical Guide on Quality Management (QM) Procedures for network-level pavement 

condition data was developed under a study sponsored by FHWA. (19) The guide provides 

information related to the development and implementation of a QM program, incorporating 

proven QM practices, and showcasing examples or case studies using pavement condition data 

from a variety of State DOTs. Specific elements covered by the guide include: 

 Location Referencing Systems – presents methods of geospatially locating the data. 

 Network-Level Data Collection Background – presents an overview of the data collection 

process, the types of surveys conducted, data items collected, and rating protocols used. 

 Principles of Data Quality Management – presents the principles, definitions, and key 

concepts of data QM. 

 Development and Implementation of a Data Quality Management Plan – presents an 

overview of the key steps to develop and implement a comprehensive QM plan. 

 Data Quality Standards and Acceptance Criteria – describes the process used to establish 

data quality standards and acceptance criteria. 

 Quality Control – presents the key activities utilized for QC. 

 Acceptance – describes the procedures used for acceptance. 

 Quality Management Reporting – describes the procedure for documenting all phases of 

the QM process. 

While the guide is primarily geared towards State DOT network-level pavement condition data 

for use in PMS analyses or treatment decisions, the same data are most often used for HPMS 

reporting. As such, the quality of network-level pavement condition data is vital not only for 

pavement management purposes, but also for other applications such as incorporation into 

HPMS. In turn, the ability to evaluate and determine the quality of pavement condition data is 

essential for establishing the accuracy and reliability of analyses made using pavement condition 

data. 

This reference has been included because of its potential value to the project in addressing 

quality issues related to the collection, processing, and storage of HPMS data. 
16

A review of the AASHTO R 43-13 (11) standard was important to compare how the test has 

changed from the version (R 43-07) referenced in the HPMS Field Manual. (10) Performing tests 

with the most recent version of the test standard maintains consistency with current technology. 

Based on this comparison, it was determined that changes made to the standard included

reverting to only English units within the standard. Also, the 2013 version references the 

ProVAL software for use in calculating the IRI from longitudinal profile. 

The AASHTO M 328-14 (20)standard was compared to the 2010 version similar to the 

comparison conducted between versions of the AASHTO R 43 standard. (11) The only change of 

note between the two versions was a change to the height sensor resolution. The resolution 

required in the 2010 version of the standard was 0.001 inch while the 2014 version requires a 

resolution of 0.002 inch. This revision is expected to have only a nominal impact on the 

collected IRI. 

In a memo to FHWA, the creation of HPMS data sets from the FHWA LTPP test sections using 

information presently, or soon to be contained, in the LTPP pavement performance database 

(PPDB) was outlined. (21) The document provides for the potential use of LTPP data in support of 

improved, higher-quality HPMS data. The overall objective of the proposed effort is to develop 

data sets for the LTPP test sections that are compatible with the HPMS 2014 data format to 

enable the following: 

 Validation and/or routine usage of national pavement analysis tools, including the 

Highway Economics Requirements System (HERS) models, the PHT analysis tool, and 

the NAtional Pavement COst Model (NAPCOM). 

 Validation of the MAP-21 performance measures. 

 Development and/or validation of other national, State and/or local performance 

measures and/or indices. 

Again, the memorandum is relevant to this study because LTPP data could potentially be used in 

addressing various data quality aspects, which are so critical to the successful outcomes of the 

project, including data collection, data processing, and data storage quality issues. 

2.3. SUMMARY 

The information gathered as part of the literature review and summarized in this chapter built a 

foundation and provided direction for the conduct of the data collection and analysis efforts 

which are presented in the next two chapters of this report. Also, the data collection protocols 

that were followed in the actual data collection have been listed as part of the literature review. 
17

CHAPTER 3. DATA COLLECTION 

The objective of the data collection effort was to gather high-quality pavement condition metric 

data – IRI, rutting, percent cracking and faulting – and supporting information to enable 

successful accomplishments of the overall project objectives stated in the introductory chapter. 

The major steps taken to achieve this objective included: 

 Review of information relating to the IHS. 

 Review of 2013 HPMS data (more recent data were not available for data collection 

planning purposes). 

 Formulation of data collection plan.

 Field data collection.

 Data processing and quality review. 

 Creation of project database. 

Each of these steps are detailed over the remainder of this chapter, including issues encountered 

during the course of the data collection effort and their resolution. 

3.1. INTERSTATE HIGHWAY SYSTEM 

The review of the IHS (and 2013 HPMS data in the next section) concentrated on those routes 

located within the contiguous 48 States and the District of Columbia. 

In the fall of 2014, which is when this project was started, the IHS was estimated to incorporate 

over 46,000 centerline miles. This system is illustrated in figure 1. The system includes 62 twodigit routes with 29 in the primarily east-west alignment, as designated by their even-numbered 

route number. The other 33 odd-numbered routes predominantly follow a north-south alignment. 

The 2013 HPMS data were reviewed for the distribution of the network across the US. Table 2

identifies the mileage of the IHS within each of the 48 contiguous States and the District of 

Columbia along with the mileage of condition data available. In addition, as part of the IHS 

review, a number of factors considered important in establishing the data collection plan were 

looked at, including: 

o Climate zone. 

o Urban / rural.

o Surface type. 
18

Figure 1. Map. Interstate Highway System. 
19

Table 2. Mileage of HPMS 2013 data by State. 

State Total 

Miles 

Miles of Condition Data 

IRI Rutting Faulting Cracking 

Alabama 1,002 1,002 429 57 429

Arizona 1,169 1,166 0 0 886

Arkansas 656 628 434 0 391

California 2,453 2,368 1,181 0 1,213

Colorado 952 952 324 199 522

Connecticut 346 346 139 4 143

Delaware 41 41 18 18 18

District of Columbia 12 12 12 12 12

Florida 1,495 1,492 1,225 21 449

Georgia 1,248 1,207 196 212 445

Idaho 612 612 485 126 612

Illinois 2,185 2,185 467 62 529

Indiana 1,239 1,051 1,052 1,001 1,052

Iowa 782 721 94 94 94

Kansas 874 874 239 122 241

Kentucky 801 777 0 144 740

Louisiana 895 879 880 880 893

Maine 367 367 367 0 367

Maryland 480 480 192 0.1 180

Massachusetts 574 574 573 573 573

Michigan 1,244 1,244 339 354 692

Minnesota 914 907 433 482 911

Mississippi 700 700 423 423 421

Missouri 1,379 1,338 1,338 1,338 572

Montana 1,192 1,192 318 9 327

Nebraska 482 482 482 0 0 

Nevada 596 587 205 39 168

New Hampshire 225 225 225 0 92

New Jersey 431 428 107 81 133

New Mexico 1,000 998 430 13 985

New York 1,724 1,717 894 50 714

North Carolina 1,254 1,239 1,239 104 466

North Dakota 571 571 106 164 309

Ohio 1,573 1,573 525 525 525

Oklahoma 933 933 167 0 291

Oregon 729 727 722 9 113

Pennsylvania 1,856 1,853 1,793 1,793 1,793

Rhode Island 70 70 70 0 51

South Carolina 851 851 292 45 365

South Dakota 679 679 180 482 678

Tennessee 1,104 1,086 297 8 298

Texas 3,415 3,415 615 232 846

Utah 937 937 936 930 931

Vermont 320 320 320 0 320

Virginia 1,119 1,119 987 75 1,045

Washington 764 764 440 325 764

West Virginia 555 555 392 129 520

Wisconsin 743 743 640 628 661

Wyoming 913 913 913 913 913
20

The intent was to identify how much of the IHS fell within the different categories associated 

with each of the above three factors. 

Four climate zones are used within the HPMS system: wet freeze, wet no-freeze, dry freeze, and 

dry no-freeze. The climate zones are set by the FHWA based on a standard definition associated 

with the location. Figure 2 illustrates the location of the four climate zones around the contiguous 

US. Table 3 illustrates the quantity of the network within each climate zone as well as the 

quantity of pavement condition data available for each network. 

Figure 2. Illustration. Climate zones used in HPMS database.

Table 3. Mileage of IHS by climate zone in HPMS 2013. 

Climate Zone Mileage Mileage of Condition Data 

IRI Rutting Faulting Cracking 

Wet Freeze 18,954 18,645 10,974 7,361 11,734

Wet No-Freeze 11,666 11,513 6,138 2,003 4,920

Dry Freeze 9,157 9,129 4,901 3,074 5,588

Dry No-Freeze 6,684 6,613 2,139 235 3,448

From table 3, the largest percentage of the network falls within the wet freeze climate zone and 

the dry-no freeze contains the smallest portion. The availability of IRI data across the network 

was fairly consistent between the climate zones with 98 to 100 percent of the IHS having IRI 

data within each climate zone. There was a bit more variability in the availability of the other 

data from the climate zones in terms of percentage of mileage of the IHS within that area. The 

dry no-freeze zone has the least rutting and faulting data available. The wet no-freeze zone has 

the least cracking data available. 

Another factor considered was whether the section fell in an urban or rural region. A section is 

considered urban if it falls within an area with a population of at least 5,000. More specifically, 
21

the HPMS database identifies which specific urban area the section occupies. If the section does 

not fall within one of the named areas, but is in an area with a population of at least 5,000, it is 

coded as occurring in a small urban area. All other sections are identified as rural. Table 4

identifies the total mileage within each category (urban, small urban, and rural) along with the 

mileage of condition data available within each category. There were approximately 32 miles of 

highway for which the population zone was not designated. These roadways are not included in 

the summations in table 4.

Table 4. Mileage of IHS by population zone in HPMS 2013. 

Population 

Zone Mileage Mileage of Condition Data 

IRI Rutting Faulting Cracking 

Rural 28,654 28,361 14,787 7,541 15,993

Small Urban 2,669 2,654 1,553 868 1,679

Urban 15,105 14,863 7,791 4,245 7,997

The final factor reviewed within the HPMS data is the surface type. HPMS provides eleven 

classifications for pavement type. Table 5 identifies the mileage of the IHS within each surface 

type. There were approximately 17,513 miles of the IHS for which the surface type was not 

identified within the HPMS. However, surface type is only required to be reported for Sample 

Panel sections. The HPMS Sample Panel sections represented approximately 50 percent of the 

interstate data within the HPMS system, or 23,588 miles. Of these miles, there were 

approximately 612 miles of the IHS for which surface type was not identified, or approximately 

3 percent. 

Table 5. Mileage of IHS by surface type in HPMS 2013.

Surface Type Mileage Mileage of Condition Data 

IRI Rutting Faulting Cracking 

Unpaved 6 6 6 0 6

Bituminous 8,410 8,384 7,714 2,571 7,758

JPCP 4,571 4,532 2,282 3,218 3,622

JRCP 1,103 1,094 263 1,006 968

CRCP 989 984 275 627 865

AC over AC 7,265 7,215 5,237 1,347 5,884

AC over JCP 5,188 5,166 4,853 2,365 4,492

AC over CRCP 903 902 887 212 787

Unbonded PCC overlay 351 350 49 330 338

Bonded PCC overlay 69 69 45 55 67

Other 90 89 64 52 56

3.2. REVIEW OF 2013 HPMS DATA 

3.2.1 Data Completeness 

To obtain an accurate picture of data completeness based on each distress type, the 2013 HPMS 

data set mileage was subdivided based on surface type for the various distresses. That is, the total 

mileage for rutting and crack length is only based on mileage for surface types where rutting and 
22

crack length data are collected (e.g., bituminous), the total mileage for faulting is only based on 

the mileage for surface types where faulting data is collected (e.g., jointed concrete), IRI is 

collected on all surface types except unpaved and cracking percent is collected on all surface 

types except unpaved and other. Table 6 contains the mileage for each distress that data was 

reported, the total mileage for each distress and the percent completeness for each distress. With 

the exception of cracking percent, all other distress show good data completeness ranging from 

74 percent to 99 percent. 

Table 6. Data completeness in terms of distress type in HPMS 2013. 

Distress Distress Mileage Total Mileage Percentage 

IRI 47,030 47,590 99% 

Cracking % 24,295 47,500 51% 

Rutting 19,778 22,870 86% 

Faulting 5,267 7,117 74% 

With the exception of IRI, all distress types are only required for Sample Panel sections. IRI is 

required for the Full Extent of the IHS. Therefore, the data completeness should reflect only the 

Sample Panel sections for all distresses besides IRI. Table 7 contains the mileage for each 

distress that data was reported, the total mileage for each distress and the percent completeness 

for each distress, based only on the Sample Panel. It should also be noted that crack length is an 

optional Sample Panel data item to be reported. 

Table 7. Data completeness in terms of distress type based on Sample Panel in HPMS 2013. 

Distress Distress Mileage Total Mileage Percentage 

IRI 47,030 47,590 99% 

Cracking % 18,864 22,970 82% 

Rutting 15,844 17,887 89% 

Faulting 3,525 4,079 86% 

The stratification factors considered for assessing the composition of the 2013 HPMS data set 

were climate zone, urban versus rural, and surface type. Table 8 through table 10 contain the 

mileage for each stratification factor and the percentage of the data set it represents. Table 11

contains the 2013 HPMS data set surface type distribution based on Sample Panel data. 

Table 8. HPMS 2013 data set climate zone distribution. 

Climate Zone Mileage Percentage 

Wet-Freeze 18,954 41% 

Wet-Non Freeze 11,666 25% 

Dry-Freeze 9,157 20% 

Dry-Non Freeze 6,684 14% 

Total 46,460 100% 
23

Table 9. HPMS 2013 data set urban vs. rural distribution. 

Urban Code Mileage Percentage 

Rural 28,654 62% 

Small urban 2,669 6% 

Urban 15,105 33% 

Total 46,428 100% 

Table 10. HPMS 2013 data set surface type distribution. 

Surface Type Mileage Percentage 

1 Unpaved 6 ~0% 

2 Bituminous 8,410 18% 

3 Jointed Plain Concrete Pavement (JPCP) 4,571 10% 

4 Jointed Reinforced Concrete Pavement (JRCP) 1,103 2% 

5 CRCP 989 2% 

6 AC Overlay over Existing AC Pavement 7,265 16% 

7 AC Overlay over Existing JCP 5,188 11% 

8 AC Overlay over Existing CRCP 903 2% 

9 Unbonded Jointed Concrete Overlay on PCC Pavements 351 1% 

10 Bonded PCC Overlays on PCC Pavements 69 ~0% 

11 Other 90 ~0% 

No Data 17,514 38% 

Total 46,460 100% 

Table 11. HPMS 2013 data set surface type distribution for Sample Panel. 

Surface Type Mileage Percentage 

1 Unpaved 6 ~0% 

2 Bituminous 6,760 29% 

3 JPCP 2,952 3% 

4 JRCP 976 4% 

5 CRCP 943 4% 

6 AC Overlay over Existing AC Pavement 5,986 25% 

7 AC Overlay over Existing JCP 4,419 19% 

8 AC Overlay over Existing CRCP 722 3% 

9 Unbonded Jointed Concrete Overlay on PCC Pavements 84 ~0% 

10 Bonded PCC Overlays on PCC Pavements 67 ~0% 

11 Other 60 ~0% 

No Data 612 3% 

Total 23,588
24

3.2.2 Data Comparisons 

As part of the review of the HPMS data, cursory checks were made comparing various data 

elements. In particular, the quantity of condition data by surface type was reviewed for Sample 

Panel data. Table 12 presents the mileage of condition data by surface type. This table shows that 

rutting data have been presented for approximately 1,935 miles of concrete-surfaced pavement. 

Further, faulting data have been presented for approximately 4,991 miles of asphalt-surfaced 

pavement. 

The presence of condition data for inappropriate surface types suggests potential quality control 

issues with the HPMS data. In turn, these issues suggest that one potential improvement in 

HPMS data collection is a series of automated quality control checks allowing these issues to be 

identified quickly and the records marked for data users. 

Table 12. HPMS 2013 data set condition data distribution by surface type for Sample 

Panel.

Surface Type IRI, miles Rutting, 

miles 

Faulting, 

miles 

Percent 

Cracking, miles 

Unpaved 6 6 0 6

Bituminous 6,738 6,617 1,559 5,444

JPCP 2,929 1,465 2,497 2,561

JRCP 968 170 898 857

CRCP 938 238 607 845

AC over AC 5,962 4,388 1,262 4,661

AC over JCP 4,401 4,132 2,086 3,698

AC over CRCP 721 706 84 606

Unbonded PCC overlay 84 19 76 84

Bonded PCC overlay 67 43 54 65

Other 59 42 39 43

3.3. DATA COLLECTION PLAN 

3.3.1 Planned Route 

In light of the information gathered and reviewed, the route depicted in figure 3 was selected for 

data collection. The basis for selecting this route was to collect a 10,000 mile sample of the IHS 

that reflected the actual stratification of the overall IHS plus other considerations such as data 

completeness. The route covered 8,623 miles (18 percent of the total IHS centerline miles), 9 

different interstates (15 percent of the total number of named routes), and 38 States and D.C. (80 

percent of the States – but as noted later in this chapter data were collected in a 39th State). 
25

Figure 3. Map. Data collection route. 
26

The proposed route included mileage on the following interstates along with the States covered 

listed next to the route: 

 I-10 through California, Arizona, New Mexico, Texas, Louisiana, Mississippi, Alabama, 

and Florida. 

 I-15 through Arizona, Nevada, and California. 

 I-64 through Kentucky, Indiana, and Illinois. 

 I-65 through Tennessee, Kentucky, and Indiana. 

 I-70 through Utah, Colorado, Kansas, and Missouri. 

 I-79 through West Virginia. 

 I-81 through Pennsylvania and New York. 

 I-90 through Washington, Idaho, Wyoming, South Dakota, Minnesota, and Wisconsin. 

 I-95 through Georgia, South Carolina, North Carolina, Virginia, Maryland, Delaware, 

Pennsylvania, New Jersey, New York, Connecticut, Rhode Island, Massachusetts, New 

Hampshire, and Maine. 

Data collection on the planned route was to occur in the right most through lane in the eastbound 

or northbound direction along the full length of the route, except where specified otherwise, in 

order to match the HPMS direction of collection. In addition to those 8,623 miles, another 500 

miles of data were to be collected on the inside lane, 500 miles were to be collected in the 

opposite direction of stated data collection, and 500 miles of data were to be collected for quality 

control (QC) purposes. 

Table 13 summarizes the number of miles collected on each interstate by State as well as the 

number of miles collected in the inside lane or opposite direction and the number of miles 

collected for QC purposes. 

To obtain an accurate picture of data completeness based on each distress type, the planned route 

mileage was subdivided based on surface type for the various distresses for the Sample Panel. 

That is, the total mileage for rutting and crack length is only based on mileage for surface types 

where rutting and crack length data are collected (e.g., bituminous) and the total mileage for 

faulting is only based on the mileage for surface types where faulting data is collected (e.g., JCP) 

while IRI and cracking percent are collected on all surface types and therefore are based on the 

total number of miles on the route. Figure 4 depicts the comparison between the data 

completeness for the 2013 HPMS data set and the route. The figure shows good comparison with 

the data completeness being slightly higher for the route than the 2013 HPMS data set with the 

exception of rutting. All distresses show good data completeness ranging from 79 percent to 100 

percent. 
27

Table 13. Data collection miles and direction.

Interstate State Miles 

Direction 

of Data 

Collection 

Opposing 

Direction, 

Miles/Direction 

Adjacent Lane, 

Miles/Direction 

QC 

Purposes, 

Miles and 

Direction 

90 

WA 297 East 25, West 25, East 

ID 74 East 50, East 

WY 207 East 25, West 25, East 

SD 412 East 

MN 276 East 

WI 188 East 

15 

AZ 29 North 25, South 25, North 

NV 124 North 25, South 25, North 

CA 178 North 25, South 25, North 

10 

CA 185 East 50, East 

AZ 392 East 25, West 25, East 

NM 164 East 25, West 25, East 

TX 881 East 25, West 25, East 

LA 274 East 25, West 25, East 

MS 77 East 50, East 

AL 66 East 

FL 362 East 25, West 25, East 

70 

UT 232 East 25, West 25, East 

CO 451 East 25, West 25, East 

KS 424 East 50, East 

MO 252 East 

95 

GA 112 North 25, South 25, North 

SC 199 North 25, South 25, North 

NC 182 North 50, North 

VA 179 North 25, South 25, North 

MD 110 North 

DE 23 North 

PA 51 North 

NJ 98 North 

NY 24 North 

CT 112 North 25, South 25, North 

RI 43 North 

MA 92 North 50, North 

NH 16 North 

ME 303 North 25, South 25, North 
28

Interstate State Miles 

Direction 

of HPMS 

Data 

Collection 

Opposing 

Direction, Miles 

and Direction 

Adjacent Lane, 

Miles and 

Direction 

QC 

purposes, 

Miles and 

Direction 

81 PA 233 North 25, South 25, North 

NY 184 North 50, North 

79 WV 161 North 50, North 

64 

KY 185 East 

IN 123 East 25, West 25, East 

IL 128 East 50, East 

65 

TN 122 North 25, South 25, North 

KY 137 North 

IN 261 North 50, North 

Total 8,623 500 500 500

In selecting the route, it was also important to have a statistically representative sample of the 

entire interstate system. To evaluate this, three stratification factors were considered to compare 

to the 2013 HPMS data set. These factors included climate zone, urban/rural, and surface type. 

Table 14 through table 16 show the comparison between the composition of the 2013 HPMS 

data set and the route for these stratification factors. Based on these comparisons, the route 

provides a representative sample of the entire interstate system.

The proposed route was also compared against the two freight networks that the FHWA is 

considering for possible adoption – the 27,000-mile and the 41,000-mile freight networks. All 

routes appear to be on one or both of the proposed freight networks, except for the section of 

I-10 in western Arizona and I-79 in West Virginia.

3.3.2 Data Collection Equipment, Protocols and Other Requirements 

While definition of the data collection route was an important activity, just as important or 

perhaps more so was the definition of how high-quality data were to be collected, which required 

addressing data collection equipment, protocols and other requirements. To meet the project 

objectives, the equipment to be used for data collection had to meet the requirements identified 

in the NRPM, which included: 

 AASHTO PP 68-10, “Collecting Images of Pavement Surfaces for Distress 

Detection.”

(17)

 AASHTO M 328-14, “Standard Specification for Inertial Profiler.” (20)

 AASHTO PP 70-14, “Standard Practice for Collecting the Transverse Pavement 

Profile.”(9) 
29

Figure 4. Chart. Data completeness comparison based on Sample Panel in HPMS 2013.

Table 14. Climate zone composition. 

2013 HPMS Data Route 

Wet Freeze 41% 35% 

Wet No-Freeze 25% 18% 

Dry Freeze 20% 24% 

Dry No-Freeze 14% 23% 

Table 15. Urban versus rural composition. 

2013 HPMS Data Route 

Rural 62% 70% 

Small Urban 6% 5% 

Urban 33% 25% 

Table 16. Surface type composition. 

2013 HPMS Data Route 

Bituminous 18% 22% 

JPCP 10% 11% 

JRCP 2% 2% 

CRCP 2% 3% 

AC over AC 16% 15% 

AC over JCP 11% 10% 

AC over CRCP 2% 3% 

Unbonded PCC Overlay 1% 1% 

No data 38% 33% 
30

In addition to the equipment requirements, the following specific activities were stipulated as 

part of the data collection plan: 

 Project Preparation: included those items required prior to mobilization to the site –

e.g., project-specific documentation, clarification of data collection, processing and/or 

reporting protocols, questions related to coordination and/or communications protocols, 

safety-related matters and other – required to ensure a successful data collection effort.

 Calibration: required proof of current calibration prior to mobilization to the site for data 

collection. 

 Data Collection: required collection of cracking, longitudinal profile, transverse profile, 

and faulting data. Data collection was to be performed in the outside through lane in the 

eastbound or northbound direction except where specified otherwise. All raw data were 

to be geo-referenced. All summary data were to be referenced to a State/route number/ 

milepoint/direction/lane linear referencing system. If questionable data and/or 

improperly functioning equipment were suspected during data collection, activities were 

ceased immediately until the issue(s) in question were resolved. 

In addition to the above referenced calibration requirements, to ensure to the extent 

possible that the equipment was collecting high-quality data, repeat checks of the 

equipment on a monthly basis were required. These checks required the conduct of five 

repeat runs on a short (but no less than 1,000-ft section) pavement section and the review 

of those data. It was anticipated that pavement condition values resulting from the repeat 

runs would be within 5 percent of each other for each data collection item – IRI, 

cracking, rutting and/or faulting. 

The quality of the data collection was assessed after the first 250 miles of data collection 

and the next 500 miles of data collection by the project team. The data was processed and 

reviewed for quality based on the Data Storage and Quality Review Plans as contained in 

appendix A. 

3.3.3 Data Collection Quality Requirements 

While the goal behind the quality considerations presented in this section was to achieve perfect 

data, it was recognized that such a goal is difficult to achieve. Nonetheless, it was felt that the 

quality considerations identified as part of the project went a long way towards achievement of 

complete, high-quality data. This concentration on quality would help ensure the outcomes, 

conclusions, and recommendations resulting from implementation of the data collection and 

analysis plan were as accurate, precise, and reliable as possible. In turn, this would provide 

confidence that the project outcomes, conclusions, and recommendations were defensible. 

The following quality-related activities were to be carried out during the data collection effort: 

 Required quality considerations prior to data collection 

o Data collection plan 
31

o Data collection equipment and protocols in accordance with the 2014 HPMS 

Manual. (10)

 Required quality considerations during data collection 

o Weather considerations 

o Field data collection considerations and checks 

 Required quality considerations after data collection 

o Office quality checks 

On completion of planning the quality requirements, it was considered acceptable to commence 

data collection, but the quality mentality was to continue through the remainder of the data 

collection effort. An important consideration during the data collection effort was to be ambient 

conditions as they can significantly affect the quality of the data. For example, profiler 

measurements may be affected by ambient conditions. Laser height sensors can malfunction at 

very high and very low temperatures and begin to produce errors and/or turn off to prevent 

damage. Similarly, the distance measured by the Distance Measuring Instrument (DMI) is 

directly affected by the rolling radius of the tire, which is directly related to tire pressure which is 

affected by ambient conditions. In addition the interior vehicle environment is critical to the 

operation of on-board computers. 

In addition to the equipment, ambient conditions can also affect the measurements being taken. 

There is, for example, the case of the self-healing hot-mix asphalt pavements in some southern 

US States, which show less cracking during the hot summer months. Likewise, temperature can 

affect the distresses visible in PCC pavement including crack and joint openings. Furthermore, 

rainfall prevents the collection of distress and profile data. 

Accordingly, data collection personnel were required to document how ambient conditions 

would be addressed and, as appropriate, mitigated. It was envisioned that the planned route 

would help mitigate some of the ambient condition effects; e.g., data collection route starting in 

the southern States and moving north. Data collection personnel were asked to stop data 

collection immediately once ambient conditions beyond those under which the equipment 

sensors can properly operate occurred. 

Assuming acceptable ambient conditions and once data collection began, the most critical issues 

to then be considered by the data collection personnel were to follow the established protocols 

and other stipulated requirements, and to continuously monitor the data being collected to ensure 

its reasonableness and that equipment was properly functioning. If questionable data and/or 

improperly functioning equipment were suspected, data collection personnel were to cease data 

collection activities immediately until the issue(s) was resolved. 

In addition, because modern equipment provides software that allows for the review of the data 

while in the field, it was considered important that the data be checked at the end of the day to 

make sure things look acceptable; e.g., good images, no profile spikes, etc. 
32

On completion of the field activities and once data were received in the office, data collection 

personnel were required to check those data to ensure that (1) the files were not corrupt and (2) 

the data collected were complete. In addition, within one week after completion of data 

collection for a given State-route combination, data collection personnel were to confirm that the 

data collected for that State-route had been reviewed and that it was either ready for processing 

or those data needed to be recollected. 

3.4. EXECUTION OF DATA COLLECTION PLAN 

3.4.1 Data Collection 

On completion of the data collection plan, the project team issued a request for proposals (RFP) 

soliciting a vendor to perform the required data collection in accordance with the plan. The 

procurement process began in early February 2015 and it was completed at the end of April 

2015.

With few exceptions, which are noted later in this section, data collection was completed in 

accordance to the plan. Actual data collection began in June 2015 after successful completion of 

the required equipment checks and calibrations. Prior to the start of data collection, the project 

team met with the data collection vendor to review, refine and finalize the data storage and 

quality review requirements. 

Once data collection began in June 2015, the project team provided a weekly data collection 

status report to the FHWA. Table 17 summarizes the data collection schedule from start to end, 

by route and State. As shown, data collection was completed in August 2015. 

Table 17. Summary of data collection schedule.

Route State Start Date Completion Date 

I-15 CA, NV, AZ 6/15/15 6/21/15 

I-10 CA, AZ, NM, TX, LA, MS, AL, FL 6/22/15 7/9/15

I-95 GA, SC, NC, VA, MD, DE, NJ, 

NY, CT, RI, MA, NH, ME 

7/10/15 7/28/15 

I-81 PA, NY 7/29/15 8/1/15 

I-79 WV 8/2/15 8/2/15 

I-65 TN, KY, IN 8/3/15 8/10/15 

I-70 UT, CO, KS, MO 8/11/15 8/18/15 

I-64 IL, IN, KY 8/19/15 8/21/15 

I-90 WA, ID, MT, WY, SD, MN, WI 8/22/15 8/31/15 

A single data collection unit was used by the data collection vendor, at the request of the project 

team. While the data collection effort would have been completed faster with two or more units, 

the project team concluded that multiple units would introduce data variability and quality issues 

that could not be afforded on a project of this nature. The components of the equipment used by 

the data collection vendor are summarized below: 

 Right-of-Way Imagery. The digital imagery system included two forward-facing 

cameras for the collection of right-of-way images. The cameras were capable of capturing 
33

images at a custom frame rate, and running at a minimum resolution of 3,296 x 2,472 

pixels each – a capture rate of 200 frames per mile was used. The cameras automatically 

adjusted to changes in lighting conditions to capture images of the highest quality. 

Additionally, the operator was able to monitor image collection in real-time and to 

compensate for additional factors that could have affected the quality of the images. 

 Pavement Analysis. A Laser Crack Measurement System (LCMS) was used for the 

collection of the pavement distress, rutting and faulting data. The LCMS used high-speed 

cameras, custom optics, and laser line projectors to acquire both 2D images and high 

resolution 3D profiles of the road. The LCMS allowed for the automatic extraction of 

distress information as well as the collection of rutting data. Other specifications of the 

system included: 

o Sampling rate: 5,600 profiles/s 

o Transversal field of view: 13.1 ft 

o Transversal resolution: 0.04 inch 

o Depth range of operation: 9.8 inches 

o Depth resolution: 0.02 inch 

A Mark IV Road Surface Profilometer (RSP) was used for the collection of IRI data. The 

RSP has two laser sensors and a Class I profiling certification to collect IRI and faulting 

data. System specifications include: 

o Vertical displacement measuring resolution of the laser sensors: +/- 2 mil 

o Fault/bump height detection threshold: 0.2 inch (bump heights reported to nearest 

0.1 inch) 

o Number of detected fault/bumps per 0.1 mile measured and reported as “bump 

count”

 Positional Information. The Position Orientation System (POS) LV 220 was used for 

the collection of Global Positioning System (GPS) data. The POS collects vehicle 

position, velocity, attitude, track, speed, and dynamics for the collection vehicle as it 

travels along the roadway. It includes an Inertial Measurement Unit (IMU) and a DMI. 

The system is capable of providing latitude, longitude and elevation data that is accurate 

to within +/- 3.28 ft.

The data collection vehicle was a Ford E-350 passenger van that was outfitted with the custom 

hardware, cabling, and safety features required for a network-level data collection project. Figure 

5 and figure 6 show the data collection vehicle that was used on the project. In addition to the 

technology already described, the vehicle included the following technology: 
34

Figure 5. Photo. Data collection vehicle – rear view. 

Figure 6. Photo. Data collection vehicle – side view. 
35

 Pavement Surface Temperature. An infrared thermometer designed for mobile use at 

highway speeds was used to collect pavement surface temperature. The non-contact 

infrared sensor featured a robust housing and was mounted under the vehicle in order to 

scan the pavement surface temperature at a rate of once every 15 minutes. 

 Vehicle Tracking. To more efficiently manage and maintain the data collection vehicle 

and schedule, the collection vehicle was monitored while it was in the field. The 

monitoring system featured a multi-network, rugged communications platform that 

delivered a secure, wireless wide area networking for the vehicle, which helped ensure 

the vehicle was on track and schedule. 

As noted earlier, data collection was completed in full accordance with the protocols and 

requirements stipulated in the Data Collection Plan. For the most part, data collection progressed 

as planned, but there were issues that affected the schedule (in order to ensure the quality of the 

data was not affected). Those issues and their resolution are summarized below: 

 Collection vehicle overheating. Data collection commenced in June 2015 along I-10 and 

I-15 in California with temperatures in excess of 100oF. These high temperatures caused 

the collection vehicle to overheat. To resolve this issue, engine maintenance took place at 

a Ford Dealership in Las Vegas. Because of the excess heat, overheating was 

commonplace in the area and the local repair shops were inundated with vehicles, which 

resulted in four days lost. The heat did not affect the data collection systems. 

 Detours. Data collection was to occur in the right most through lane in the eastbound or 

northbound direction along the full length of the route; i.e., 8,623 miles (remaining 1,500 

miles included data collection on inside lane, in opposite direction and QC repeats). 

Detours were anticipated during the planning stages due to construction or other reasons, 

however, these detours only materialized once. In Indiana, the replacement of a bridge on 

I-65 prevented data collection on approximately 50 miles of that route. Given the data 

collection schedule constraints, the decision was made by FHWA that, instead of waiting 

until the bridge replacement had been completed, 50 miles of data would be collected on 

I-90 in Montana (thus one more State on the route, or 39 States instead of 38). Because 

data collection on I-90 was done from west to east, the 50 miles of data in question were 

collected in western Montana, starting from the border with Idaho. This was the only 

deviation to the data collection route. 

 Traffic delays. Like detours, traffic delays were also anticipated during the data 

collection planning stages, especially in or near urban areas. Unlike the detours, however, 

the traffic delays did materialize, which required that data collection be delayed or 

postponed to ensure a minimum speed of at least 13 mph whenever possible. When the 

speed did drop below this threshold, IRI data collected at those speeds was excluded from 

the summaries. The bulk of the delays took place along the northern half of the I-95 

corridor and their impact on the data collection effort was limited. 

 Rain delays. Like traffic detours and delays, delays in the data collection effort due to 

weather were also anticipated. In addition to the temperature addressed above, rain also 

caused delays along the eastern end of the I-10 corridor and the southern end of the I-95
36

corridor. Fortunately, like traffic delays, the impact of rain on the data collection effort 

was limited. 

3.4.2 Data Processing 

Processing of the more than 10,000 miles worth of data was completed in accordance with the 

protocols and requirements specified in the data collection plan. Processing of the data was done 

in seven separate batches. Those batches, along with the number of miles and shipment date (to 

project team) are summarized in table 18. The following sections provide details on the various 

types of data processing. 

Table 18. Summary of processed data shipments. 

Shipment No. Number of Miles Shipment Date 

1 215 June 25, 2015 

2 507 July 9, 2015 

3 2,105 July 27, 2015 

4 1,651 August 7, 2015 

5 1,376 August 17, 2015 

6 2,252 September 3, 2015 

7 2,021 September 21, 2015 

Crack Identification 

The two-dimensional intensity profiles provided by the LCMS were used to form a continuous 

image of the road surface. The first role of the intensity information was for the detection of road 

limits. This algorithm relies on the detection of the painted lines used as lane markings to 

determine the width and position of the road lane in order to compensate for driver wander. The 

lane position data was then used by the other detection algorithms to circumscribe the analysis 

within this region of interest in order to avoid surveying defects outside the lane. 

Two separate software applications were used to analyze the collected data and automatically 

detect pavement distresses. The data were first run through software called RoadAnalyser, which 

automatically detects cracks and assigns crack width by analyzing the intensity and range data. 

RoadAnalyser outputs three sets of viewable images: range, intensity, and both range and 

intensity with detected distress overlay. The data was then run through the data collection 

vendor’s internally-developed classifier application which analyzed the marked cracks and 

classified them according to the specifications of the project. Transverse profiles were also 

processed and reported at the same interval as the images. 

The three-dimensional data acquired by the LCMS system measures the distance from the sensor 

to the surface for every sampled point on the road. The lower the surface (i.e., farther from the 

sensor), the darker the point will be on the image. In a range image the height varies along the 

cross section of the road. The areas in the wheel path are usually deeper than the sides and thus 

appear darker, corresponding to the presence of ruts. Height variations can also be observed in 

the longitudinal direction due to variations in longitudinal profiles of the road causing 

movements in the suspension of the vehicle holding the sensors. These large-scale height 
37

variations correspond to the low-spatial frequency content of the range information in the 

longitudinal direction. The features that need to be detected are located in the high-spatial 

frequency portion of the range data. 

The process of automatically detecting the cracks began by applying a threshold algorithm to the 

range image. The 3D profile data was detrended from the effects of rutting and vehicle 

movements. Once the detection operation was performed, a binary image is obtained where the 

remaining active pixels are potential cracks. This binary image was then filtered to remove any 

false detections that were caused by asperities and other features in the road surface which were 

not cracks on the pavement. After the detection process, the next step consisted of the 

characterization of the cracks. The severity level of a crack was determined by evaluating its 

width (opening). The cracks were also grouped into three main categories: longitudinal, 

transverse, and pattern cracks. Transverse cracks were divided into complete and incomplete 

types and joints were classified separately. Pattern cracks are groups of cracks within a close 

proximity to each other. Longitudinal cracks are single cracks that run parallel along the 

roadway. 

Although data processing went as planned, a couple of issues were encountered, which required 

re-processing of the data. They are: 

 Sand-filled cracks. During the data quality review process (described later in this 

chapter), which included a review of the right-of-way images as well as pavement 

images, it was discovered that cracks filled with sand (most likely due to wind) were not 

being properly identified by the automated crack detection software. Because the cracks 

were filled with sand, there was no depth to those cracks, and hence they could not be 

detected. Efforts to automate the detection of these cracks were unsuccessful. As a result, 

a significant effort was spent in (1) identifying where those cracks occurred, (2) 

performing manual take-offs of those cracks, and (3) integrating the resulting take-offs 

with the rest of the cracking data. Fortunately, the occurrence of the sand-filled cracks on 

the data collection route was rare, with most of it occurring along I-10 in California and 

Arizona as well as I-15 in California. 

 Sealed cracks. While somewhat vague, the NPRM defers to the 2014 HPMS Guide (10)

for the treatment of sealed cracks – sealed cracks are to be incorporated into the cracking 

count for purposes of estimating percent cracking. The LCMS sensor and associated 

processing algorithms are able to identify unsealed cracks and sealed cracks. However, 

when processing was initiated, the sealed crack portion of the algorithm was not initiated. 

Therefore, processing of data for a significant portion of the route (all but I-90 and 

portions of I-64), was completed without taking into account sealed cracks. Accordingly, 

re-processing of the data affected was complete, which required a significant effort. 

The crack identification on the PCC sections was performed manually. When the reprocessing was performed, the PCC pavements were not included in that effort. A review 

of these sections indicated there were approximately 33 miles of PCC pavement with 

sealed cracks which were not re-processed amounting to approximately 0.3 percent of the 

collected data. In reviewing these segments, an exercise was performed to identify the 

impact of these data on the results. In this exercise, it was assumed that the cracking 
38

condition for all 33 miles should have been poor and the overall condition was 

determined using the cracking condition as poor. The condition changed for 

approximately 5.5 miles amounting to less than 0.1 percent of the collected data. 

Therefore, because the impact would have been minimal in comparison with the effort 

required to re-process these data, the sealed cracking on PCC pavements was omitted 

from consideration in these analyses. 

There were portions of the route where both of the above issues occurred. In those cases, which 

were only a handful, the manual take-off of sand-filled cracks was done after re-processing of the 

data for sealed cracks had been completed. 

Rutting Identification 

Rutting was measured and characterized by the LCMS using more than 4,000 points, and rut 

depth and type (short, multiple, long radius) were evaluated. During post-processing the software 

retrieved the rutting information (rut depth, rut width, cross-section, etc.) for a specific road 

section. The rutting computation was based on the ASTM E1703 – Standard Test Method for 

Measuring Rut-Depth of Pavement Surfaces Using a Straightedge. (22) This method measures the 

depth of the rut at a chosen location in a pavement surface using a straightedge and a gauge. The 

LCMS processing library computes two ruts per profile, the left and right wheel paths. The 

number of rut measurements that were calculated per road section was 528 per 0.1-mile segment. 

The distance between successive rut measurements was 1 ft, the length of the straightedge used 

for simulating the manual rut measurement was variable depending on lane width, but never 

greater than 6.56 ft, and the width of the gauge utilized for rut measurement was 1.57 inches. 

3.4.3 Data Quality Review 

The Quality Management Plan developed for the project and approved by FHWA included the 

data collection daily checks and system inspections to be performed by the vendor. No precision 

and bias statements are available for rutting, cracking, and faulting. The checks to be performed 

as part of data collection included: 

 Check and monitor GPS accuracy. 

 Monitor images to assure collection meets specifications.

 Run internal verification program to confirm completeness of all data sets after the 

collection of every route. 

No errors or system issues were discovered in the field. 

In addition, the following weekly checks were performed. 

 Inertial Profiler Repeatability 

o 10 runs of inertial profiler data have less than 5 percent standard deviation from 

the mean.
39

o Average IRI values from the last historical validation at the validation site has less 

than a 10 percent standard deviation from current data. 

o Continuous IRI graph plots well. 

o Power spectral density graphs look normal. 

 Rutting Repeatability 

o 10 runs of LCMS data have a standard deviation of less than 1 mm rut depth. 

o Average rutting values from the last historical validation at the validation site has 

less than a 0.04 inch standard deviation from current data. 

 Distress Repeatability 

o 10 runs of LCMS data have a standard deviation of less than 15 percent total 

distress length. 

o Average distress lengths from the last historical validation at the validation site 

has less than a 15 percent standard deviation from current data. 

 Linear Reference 

o DMI pulse counts from 5 runs have less than a 0.1 percent difference from each 

other. 

o Utilize validation runs to confirm proper linear reference values have been input 

for all collection systems. 

No issues were found during the weekly repeatability tests. 

The following checks were performed in the office as part of the data reduction and review. 

 Daily checks 

o Use project Keyhole Markup Language (KML) files to assure 100% coverage. 

o Review sample images for clarity. 

o Review sample images for color balance. 

o Review sample images for luminance. 

o Review bounce test output in ProVAL. 

o Load inertial profiler files into ProVAL for power spectral density anomalies. 

o Process a sample of LCMS images and review for anomalies. 
40

o Bounce test review. 

o Block check review. 

As part of these daily checks, there were 6 miles on I-15 in California that were found to have 

shadows on the pavement in the forward images created by the vehicle due to collection later in 

the day with the sun behind the vehicle. As a result, the affected mileage was recollected. 

 Reduction checks 

o Review a sample of right-of-way images to assure they meet standards. 

o Utilize image enhancer program to adjust unacceptable images. 

o Check LCMS images for null rutting values. 

o Check LCMS for invalid rutting values (= 0 or > 0.75 inch). 

o Check for profile data collected outside of acceptable temperature limits (< 32 °F 

> 104 °F). 

o Review automated distress to assure algorithms are working appropriately. 

o Confirm placement of roadway features such as locations of bridges and 

pavement changes. 

o Use collected QC roadway sections to perform data set comparison. 

No issues were found during the reduction checks. A number of records were identified where 

data was recorded outside of the acceptable temperature limits. These records were reviewed 

further to identify the potential for errors. The ride quality values collected at these temperatures 

were similar in size to other data collected in these areas. Further, the sensors used in data 

collection typically will automatically turnoff once an unacceptable temperature is reached. The 

check on data collected outside of the temperature range was performed and all data were 

deemed reasonable.

Prior to delivering the data, the data collection vendor reviewed the data for quality assurance in 

the following ways: 

 Confirm completeness for all deliverables. 

 Utilize outlier checks to flag improbable values for further review. 

Once the data collection vendor had reviewed the data for quality assurance, the data was 

delivered to the project team. 
41

As presented earlier, the data was processed in seven shipments. The data was received from the 

data collection vendor on hard drives. Once the data was received, the initial processing 

included: 

 Data files scanned for viruses. 

 Data were stored on the network under the Data Collection Task in the project file. 

 Original data file was maintained under the data collection task.

 Copy of data then placed under Data Analysis Task in project file as working file. 

 Original USB hard drive maintained until all data collection complete and received. 

The data was then reviewed for quality and consistency. The review of data was performed per 

the requirements of the Data Storage and Quality Review Plan contained in appendix A. 

Data that failed any of the above checks were flagged and reviewed by the project team. Some of 

these flags included exceeding the maximum IRI, cracking, rutting and faulting values, and 

exceeding the allowable difference between wheelpath values for IRI or rutting suggested in the 

Data Storage and Quality Review Plan. 

The flagged data were reviewed by the QA engineer. Reviews included comparing the flagged 

data to the images to check for causes of being flagged. For example, for a record exceeding the 

maximum specified rutting value in the Data Storage and Quality Review Plan, the image at that 

location was reviewed to determine if the reported value was reasonable. If rutting was 

observable in the image, the record was considered valid. 

It was through these QC reviews that both the sand-filled cracks and sealed cracks referenced 

earlier were found. No other systematic issues were found during the QC review process. 

After the review by the QA engineer and resolution of any issues, the data was approved and 

included in the database. 

3.4.4 Project Database 

The data were exported to an Access database to be used for all analyses. The database contains 

both the original data provided at the 0.01-mile intervals and the computed values at 0.1-mile 

intervals. 

The database contains seven tables: five containing the original data collected at 0.01-mile 

intervals; one containing data on the locations of bridges, required lane changes, and 

construction encountered during data collection; and one containing the data calculated at the 

0.1-mile interval. 

The five tables for the 0.01-mile interval data include a table for cracking on AC pavements, a 

table for cracking on PCC pavements, a table for IRI on all pavements, a table for rut depth on 

AC pavements, and a table for faulting on JPCP segments. Each of these five tables contains 
42

basic identification information including the State, route, milepost, latitude and longitude, 

direction, and lane of data collection. These tables all have a column identifying if the data is 

“QC” data or not, where QC data is repeat data collection on select segments of the route. The 

tables contain data regarding the details of the data collection effort including the vehicle, driver, 

operator, speed of travel, date, time, air temperature, surface temperature, and surface type. 

These tables also contain flags identifying the locations of bridges, construction, and required 

lane changes. These flags are intended to explain where condition data are not provided. 

Longitudinal profile data were collected and are provided on bridge decks but no other condition 

data were collected on these areas. 

A complete Data Dictionary is provided in appendix B which provides definitions for each data 

element included in the database. A few specific definitions are provided here to provide clarity 

in the analysis discussed in the next chapter. 

For the analysis several definitions were used for percent cracking on asphalt surfaces. This 

section covers these definitions and provides the names by which these values are referenced for 

the remainder of this report. 

 AC HPMS – Total area of wheelpath cracking divided by the total lane area.

 AC Percent Wheelpath – Total area of wheelpath cracking divided by the area of the 

wheelpaths.

 NPRM Under – Area of affected wheelpath cracking plus the area of transverse cracking 

(transverse cracking length multiplied by 1 ft). The length of transverse cracking is 

reduced by the amount of the transverse cracking within the wheelpath. Total crack area 

is divided by the total lane area. This definition is illustrated in Figure 7. 

 NPRM Over – Area of affected wheelpath cracking plus the area of transverse cracking 

(transverse cracking length multiplied by 1 ft) divided by the total lane area. This 

definition is illustrated in Figure 7. 

 NPRM Step – The area of transverse cracking is reduced in a graduated manner based on 

the area of wheelpath cracking. Where the percentage of wheelpath cracking is greater 

than 80 percent of the total wheelpath area, the transverse cracking length is reduced by 

the amount of transverse cracking within the wheelpath. The full transverse cracking 

length is used where the percent of wheelpath cracking is less than 20 percent. If the 

percent of wheelpath cracking is between 20 and 40 percent, 75 percent of the transverse 

crack length within the wheelpath is used. If the percent of wheelpath cracking is 

between 40 and 60 percent, 50 percent of the transverse crack length within the 

wheelpath is used. Finally, if the percent of wheelpath cracking is between 60 and 80 

percent, 25 percent of the transverse crack length within the wheelpath is used. The total 

crack area is divided by the total lane area. This definition is illustrated in figure 7. 

In addition to the varying types of cracking used in analyses, there are two types of faulting 

reported within the database. The LCMS was used to estimate faulting at each joint as was the 

inertial profiler. 
43

Two types of rutting were also provided with the data. The first is based on the transverse 

profile collected from the LCMS and the second is a rut depth estimate based on the 5-point 

method. 

Comparisons between these definitions for the condition data are provided in chapter 4. 

Figure 7. Sketch. Illustration of cracking definitions.

45

CHAPTER 4. DATA ANALYSIS 

The data resulting from the effort described in the previous chapter served as the reference data 

for the analysis presented in this chapter. The objectives of these analyses activities, which were 

the same as for the project, were to: 

 Produce a report indicating the pavement condition on the IHS nationally and in each 

State where data were collected, which was to be accomplished using project collected 

data.

 Determine if HPMS is an unbiased representation of the pavement condition of the IHS, 

which was to be accomplished by comparing project collected data with 2014 HPMS 

data.

 Recommend improvements to HPMS data collection and reporting that are necessary to 

either make HPMS unbiased or improve its precision, in regard to performance 

management and FHWA’s use of HPMS data, which in turn would enable responses to 

questions such as: 

o Is two-way data collection necessary? 

o Does data need to be collected in more than one lane in a direction? 

o What is the optimum HPMS section length? 

o Do all distress items require full extent reporting or is sampling adequate? 

o Are the protocols proposed by FHWA adequate for collecting and reporting 

distress or do they need improvement? 

To accomplish the above stated objectives, a two-pronged approach was undertaken. First, a data 

analysis plan was formulated, which addressed not only those analyses to be carried out, but also 

the quality review activities to be performed to help ensure the quality of the outcomes. Second, 

the implementation of the analysis plan, including issues encountered and their resolution. Both 

of these items are addressed, in detail, in this chapter. 

4.1. DATA ANALYSIS PLAN 

A detailed analysis plan was developed to identify the steps to be followed to meet the objectives 

as stated above. The outline of the analysis plan is as follows: 

 Quality review of analyses results 

o Paired t-test of QC data collected 

o Comparison to I-90 data 
46

o Comparison to LTPP data 

 Detailed analyses 

o Distribution of condition metrics 

o Computation of overall network condition 

o Comparison of pavement condition metrics data between HPMS and project data 

o Calculation of overall condition by State 

o Comparison of overall condition between HPMS and project data 

o Paired t-tests of condition metrics to data collected in adjacent lane 

o Paired t-tests of condition metrics to data collected in opposing direction 

o Review of accumulation distance 

o Estimated sampling requirement 

o Review of impact of bridges on overall condition 

o Review of impact of equipment type on condition metrics and overall condition 

The following sections will cover each of these items. 

4.2. COMPARISON WITH QUALITY CONTROL DATA 

Several analyses were performed as part of a check on the quality of the data collected. The first 

is a comparison with the QC data collected as part of the project. The second is a comparison of 

the data collected for this project with the data collected for the pilot project on the FHWA Study 

Improving FHWA’s Ability to Assess Highway Infrastructure Health - Pilot Study Report. (3) The 

third comparison was with data collected for the LTPP program. 

4.2.1 Comparison with Quality Control Data 

The first analysis performed on the collected data was a review of these data as compared to the 

quality control data collected. As noted in chapter 3, approximately 500 miles of data were 

collected twice to provide an opportunity to review the repeatability of the data collection effort. 

The comparisons were performed using the 0.01-mile interval data collected. A paired t-test was 

used to compare the four condition metrics from the “routine” data collection and the QC data 

collection. The paired t-test compares the average of the differences in the two values for each 

interval to zero. Table 19 provides the results of these comparisons. 

The differences observed in the QC data and the routine data are small and of little importance 

even though the statistics indicate that they are significantly different. The statistical 
47

significance is likely due to the size of the data set. The correlation coefficients for each metric 

indicate that the condition metrics are quite repeatable. The comparisons indicate that the data 

are of good quality. 

Table 19. Comparison of routine and QC data. 

Element Routine 

Data 

Average 

QC Data 

Average 

Average 

Difference 

Correlation 

Coefficient 

Statistical 

Significance 

Average IRI 65 in/mile 64 in/mile 0.5 in/mile 0.92 Yes 

Average Rut 

Depth 0.13 inch 0.14 inch 0.01 inch 0.95 Yes 

AC HPMS 

Percent Cracking 1.8% 1.6% 0.2% 0.90 Yes 

AC Percent 

Wheelpath 4.0% 3.6% 0.4% 0.90 Yes 

AC NPRM 

Under 2.2% 2.0% 0.2% 0.91 Yes 

AC NPRM Over 2.4% 2.2% 0.2% 0.90 Yes 

AC NPRM Step 2.3% 2.1% 0.2% 0.90 Yes 

PCC Cracking 2.3% 2.1% 0.2% 0.71 No

Faulting 0.03 inch 0.03 inch 0.001 inch 0.78 Yes 

4.2.2 Comparison with Infrastructure Health Data 

Data for the FHWA project Improving FHWA’s Ability to Assess Highway Infrastructure Health 

- Pilot Study Report were collected along the I-90 corridor through South Dakota, Minnesota, 

and Wisconsin. (3) Data were collected in 2011 along the outside lane of the primary direction of 

travel (east). Data collected for this project included IRI, cracking, rut depth, and faulting at 0.1-

mile intervals. 

The first aspect of the comparison was a check on location referencing. A total of 46 locations 

were selected randomly along the corridor and the GPS coordinates for the milepoints identified 

within both data sets were used to compare how closely the data sets compared. The distance 

between the two points was measured in Google Earth. This measurement is not intended to 

have a high degree of accuracy, rather, the measurement is intended to provide a general 

understanding of the differences in the two data sets. 

For the first 10 miles (approximately) within each of the three States, the differences in location 

were smaller than those at higher mileposts and were generally less than 200 ft. While after the 

first 10 miles, the differences were larger, the difference in milepost location did not increase 

with increasing milepost. In Minnesota and South Dakota, the largest differences were 

approximately 2,000 ft and the differences in location reached a total of approximately 3,600 ft 

in Wisconsin.

Table 20 provides a comparison of the total mileage for each State on the I90 corridor. 

Differences were observed in the total mileage for each State. However, the lengths of data 

collected for this project were much closer to the State published mileage than the Infrastructure 
48

Health data. This review suggests that the location referencing for the IS Condition data is more 

accurate than the Infrastructure Health data. 

Table 20. Comparison of mileage by State. 

State IS Condition Mileage Infrastructure Health Mileage State Published Mileage 

MN 275.60 276.11 275.70

SD 412.75 412.47 412.76

WI 187.16 187.88 187.13

Next a comparison was made of the surface type between the two projects. The objective of this 

comparison is primarily to understand how the corridor changed between the two data sets rather 

than for quality purposes. A difference in surface type was observed for 18 percent of the I90 

corridor through these three States. There is a 4-year difference in time between the two data 

collection efforts. The differences in surface type are expected to be caused by two factors: (1) 

construction that has taken place since the 2011 data collection, and (2) the shift in location 

referencing that occurs between the two data sets. 

The next step was to compare the condition metrics between the two data sets. These results are 

summarized in table 21. The data were filtered such that only the portion of the corridor with the 

same surface in both data sets were compared. The difference in the IRI data between the two 

data sets was not statistically significantly different. The average value differs by 0.3 in/mile 

between the two data sets. Both data sets have an average of 75 in/mile. 

Table 21. Comparison of condition metrics between project data and Infrastructure Health 

project data.

Metric IS Condition Infrastructure Health Statistically 

Average Standard Significant 

Deviation 

Average Standard 

Deviation 

IRI 75 in/mile 34 in/mile 75 in/mile 31 in/mile No

Rut Depth 0.13 in 0.07 in 0.12 in 0.08 in Yes 

Faulting 0.03 in 0.02 in 0.08 in 0.10 in Yes 

Percent Cracking 3.6% 8.7% 4.0% 11.1% Yes 

The other three condition metrics were all statistically significant. The average rut depths are 

0.13 inch for IS Condition and 0.12 inch for Infrastructure Health. So, even though the 

difference in these values is statistically significant, it is not significant from an engineering 

viewpoint. The average percent cracking is 3.6 percent for IS Condition versus 4.0 percent for 

Infrastructure Health; so, again, statistically significant but not from an engineering viewpoint. 

Last is the faulting. The difference here is much larger with an average value of 0.03 inch for IS 

Condition and 0.08 inch for Infrastructure Health. The larger difference in the average values is 

hypothesized to be a combination of factors – time and equipment. The vendor collected the 

faulting data using the RSP for the Infrastructure Health project. The LCMS methods for 

collecting faulting were not ready for general use at the time of the Infrastructure Health project 

and so the profiler was used for faulting for that project. A further comparison of the impact of 

the device is reviewed in Section 4.6.5. 
49

A comparison was also made between the variances for each condition metric between the two 

data sets. Differences in variances are all identified as statistically significant. For rutting, 

faulting, and cracking, the Infrastructure Health data have a larger variance than the IS Condition 

data. For IRI, the IS data have a larger variance than the Infrastructure Health. The differences 

in variances for rutting, faulting, and cracking are expected to be part of a process improvement 

effort. In the time between the collection of the two data sets, improvements have been made in 

the collection of rutting, faulting, and cracking. The collection of longitudinal profile data has 

reached a fairly static level where little change has occurred in the last few years. Standard 

deviation in IRI is 33 in/mile for the IS data and 31 in/mile for the Infrastructure Health data. It 

is possible that the difference in length observed in the comparison of the location referencing 

between the two data sets contributes to the difference in variance between the two data sets. 

The longer length of the Infrastructure Health data allows for the variability to spread over a 

longer distance making the variability appear to be smaller. 

The last step in the comparison was between the performance measures for each data set. Both 

data sets show 63 percent in good condition; although the Infrastructure Health data shows 10% 

poor and the IS Condition data for these three States is less than 1 percent poor. Table 22

provides the comparison of the performance measures between the two projects for the three 

States. While the performance measures for the three States compare well between the two data 

sets, a segment-by-segment comparison is not as good with differences in overall condition at the 

segment level ranging from 28 to 49 percent. Much of these differences are likely due to the 

time difference in the data sets, especially for Minnesota and Wisconsin, where overall pavement 

condition has clearly deteriorated from the Infrastructure Health project to the IS Condition 

project. Generally, segments appear to have moved from good to fair; although poor segments 

have also moved to either good or fair condition (likely due to maintenance of these areas). 

Table 22. Comparison of performance measures. 

State Data Set Good Fair Poor 

Segments with 

Different 

Condition 

Minnesota Infrastructure Health 61.9% 30.6% 7.5% 41.7% IS Condition 55.0% 45.0% 0.0% 

South 

Dakota 

Infrastructure Health 72.0% 21.4% 6.6% 28.2% IS Condition 83.9% 16.0% 0.1% 

Wisconsin Infrastructure Health 47.1% 32.9% 20.0% 49.0% IS Condition 30.9% 68.0% 1.1% 

Total Infrastructure Health 63.1% 27.1% 9.8% 37.7% IS Condition 63.2% 36.6% 0.2% 

The comparisons between the Infrastructure Health and IS Condition data confirm the 

reasonableness of the data collected for this project. These data comparisons also suggest that 

process improvements in data collection, particularly in reference to faulting and reference 

location, have made significant changes in the resulting data. 
50

4.2.3 Comparison with LTPP Data 

The third comparison for review of the quality of the data was with data from the LTPP PPDB. 

One-hundred sections were available for use in comparison to the data collected along this 

project. 

The LTPP test sections are 500 ft in length; therefore, these sections were compared to data 

aggregated to 0.1 mile. The GPS coordinates of the LTPP section were used to identify the 

closest milepost along the route to the nearest 0.01 mile. In other words, the nearest 0.01-mile 

interval may begin at milepost 107.74 as opposed to 107.7. Condition data were then aggregated 

to obtain the values for the 0.1-mile beginning at that location. 

The LTPP test sections are evaluated on a regular basis; therefore, multiple values are available 

for each condition metric with the most recent survey used. The data are stored in metric units 

and were converted to English units to match those of the IS Condition data. Cracking data for 

the LTPP test sections is collected in accordance with the LTPP DIM (18) which is much more 

detailed than the requirements for the NPRM. These data have been used to estimate the percent 

cracking on the test section in accordance with LTPP Distress Directive D-61, “Translation of 

LTPP Cracking Data for National Applications.” (23) 

Data were compared for each condition metric. The data set for each condition metric were 

reduced to only consider data collected within the last five years except for the rut depth 

comparison. Only seven test sections exist on the route in the same direction of travel with rut 

depth collected within the last five years; therefore, comparisons with rut depth included all data. 

A paired t-test was used to compare the condition metrics and these were not statistically 

significant for all of the condition metrics. These comparisons involved a smaller number of 0.1-

mile sections than many of the other comparisons contained within this chapter; therefore, 

statistical significance requires a larger difference in the data. Table 23 provides the average and 

standard deviation for each condition metric for both data sets along with the number of sections 

used in that comparison. 

Table 23. Average and standard deviation of each condition metric for comparison with 

LTPP data. 

Condition Metric 

IS Condition Data LTPP Data Number of 

Average Sections Standard 

Deviation Average Standard Deviation 

IRI 102 in/mile 37 in/mile 100 in/mile 35 in/mile 33

Percent Cracking 23% 31% 25% 31% 32

Rut Depth 0.26 inch 0.17 inch 0.30 inch 0.17 inch 67

Faulting 0.05 inch 0.06 inch 0.03 inch 0.06 inch 25

A further review of the differences in the data between the IS Condition and LTPP data was 

performed. Within each condition metric, between 2 and 6 sections were identified as having a 

larger difference between the two data sets when compared to the other 20 plus sections 

reviewed for each condition metric. Six sections were identified with larger differences in rut 

depth between the two data sets. In all six cases, the most recent rut depth measurement was 
51

performed prior to the year 2000. The large difference in time is the cause for these differences 

in values. 

Two sections were identified with large differences in faulting. For one of this sections, it was 

noted that the faulting observed on the first half of the section was larger in the project data than 

it was in the LTPP database, but the exact cause of this difference was not observable. The other 

section had several faults identified as “N/A” in the LTPP database. These joints were typically 

located around patches. These same joints were generally identified as having larger faults than 

other joints on the section. 

Four sections were identified with differences in percent cracking for further review. Three of 

the four sections were identified as having a large quantity of map cracking. The map cracking 

in some areas included a crack that appeared to be deeper than just the surface from the images; 

however, the manual distress survey rated these areas as map cracking. It is not possible to 

determine from the images whether these cracks are definitely deeper than the surface. The 

fourth section was also a concrete pavement. On this section, there was a significant level of 

macrotexture observable in the images. The macrotexture appears to have masked cracking in 

the images that was observable to the naked eye during a manual survey. 

4.2.4 Quality Control Comparison Summary 

These comparisons were used to check for the quality of data collected. These comparisons 

illustrated that the data collection was repeatable and generally matches data collected by/for 

other studies in the last few years. These data suggest that process improvement practices have 

improved the overall capabilities of the industry to collect quality data. Further, these 

improvements in methods to collect quality data may be expected to have a significant impact on 

the performance measures. 

4.3. DISTRIBUTION OF THE CONDITION METRICS 

A key concern about the condition metrics is the type of distribution these data follow. Most of 

the standard approaches for evaluating data assume that the data follow a normal distribution. A 

series of reviews were conducted of the condition metrics to determine if these data follow a 

normal distribution. This review was performed using the data accumulated to the 0.1-mile 

interval. 

The results of this review are provided in table 24. The table contains the mean, standard 

deviation, minimum, and maximum values. In addition, it also includes the skewness and 

kurtosis of the distribution. The skewness provides a measure of the symmetry of the 

distribution. A negative skewness indicates that the tail is larger on the left side of the 

distribution and a positive skewness indicates that the tail is larger on the right side of the 

distribution. The closer the value is to zero the more symmetric the distribution. 
52

Table 24. Condition metric summary statistics. 

Element Count Mean Standard 

Deviation Minimum Maximum Skewness Kurtosis 

IRI 84,837 71 

in/mile 

37.8 

in/mile 3 in/mile 959 in/mile 2.24 11.6

Rut Depth 66,304 0.15 in. 0.10 in. 0.003 in. 1.54 in. 2.25 8.68

Fault 12,607 0.04 in. 0.04 in. 0.0 in. 0.62 in. 3.49 22.3

AC HPMS 

Percent 

Cracking 

66,206 1.8% 4.9% 0% 54.1% 4.46 23.9

AC Percent 

Wheelpath 66,284 4.0% 10.5% 0% 99.8% 4.32 22.1

AC NPRM 

Under 66,206 2.1% 5.2% 0% 54.7% 4.16 20.8

AC NPRM 

Over 66,206 2.2% 5.3% 0% 54.7% 4.20 21.3

AC NPRM 

Step 66,206 2.2% 5.3% 0% 54.7% 4.02 19.5

PCC 

Cracking 12,618 9.2% 19.4% 0% 100% 2.56 5.96

Kurtosis is a measure of the shape of the distribution. The closer the kurtosis is to a value of 3 

the more the distribution follows a normal distribution. Distributions with a kurtosis less than 3 

indicate a distribution that tends to be more uniformly distributed. Distributions with a kurtosis 

greater than 3 tend to have a sharper peak than a true normal distribution. 

Figure 8 thru figure 16 provide the cumulative distribution function of the data as compared to a 

true normal distribution. The distributions for cracking are the most skewed of the distributions 

reviewed. Generally, all of the distributions are sufficiently normal to allow for the assumption 

of normality in the statistical investigations and for those distributions that are not normal, 

comparisons using a Student’s t-test for comparison is very robust in terms of dealing with nonnormal distributions. (24)

4.4. NETWORK CONDITION 

The primary objective of this effort was to collect a statistically significant sample of the IHS 

and determine the condition of that network based on this sample. In accordance with the 

thresholds identified in table 1 of this report. Figure 17 presents the performance measures based 

on the data collected for this project as well as the condition determined from each individual 

metric. The figure shows that 63 percent of the network is in good condition and 1 percent of the 

network is in poor condition. These percentages do not reflect the 380 miles for which data 

could not be collected due to bridges or obstructions (construction, accident, etc.) in the primary 

lane.
53

Figure 8. Graph. Distribution function of IRI. 

Figure 9. Graph. Distribution function of rut depth. 
54

Figure 10. Graph. Distribution function of faulting. 

Figure 11. Graph. Distribution function of AC percent HPMS cracking.
55

Figure 12. Graph. Distribution function of AC percent wheelpath cracking. 

Figure 13. Graph. Distribution function of AC NPRM under. 
56

Figure 14. Graph. Distribution function of AC NPRM over. 

Figure 15. Graph. Distribution function of AC NPRM step.
57

Figure 16. Graph. Distribution function of PCC cracking.

Figure 17. Chart. Condition of the IHS. 
58

4.5. COMPARISON WITH THE HPMS 

The second objective of this effort is to determine if the HPMS is an unbiased representation of 

IHS condition. In order to meet this objective a series of comparisons were performed between 

HPMS data and data collected for this project. These comparisons included comparing the 

condition metrics and the performance measures. 

The HPMS data used for these comparisons represents condition data collected in 2014, one year 

prior to the data collected for this project. The condition data had not been expanded to represent 

the full network but were based on the sample data provided by the States. The database 

reviewed contained information for 47,829 miles of IHS, but only 24,149 miles contained all of 

the required condition metric data to identify the overall condition of the section reported. The 

sections of missing condition metric data are not reported as poor condition as indicated by the 

NPRM, rather these data are not incorporated into the statistics to allow for a comparison of the 

data collected rather than a comparison of data that was not collected. 

A comparison of the performance measures is shown in figure 18, which shows that the HPMS 

data tends to rate the pavement condition as fair more frequently than the project database. 

Figure 19 shows the details of the HPMS condition estimate showing the Good/Fair/Poor rating 

for each condition metric. This figure also shows that the HPMS data tended to have less area in 

the good range for each condition metric as well as the overall condition. 

Figure 18. Chart. Comparison of performance measures. 
59

Figure 19. Chart. HPMS condition details.

Table 25 provides a comparison by State of the performance measures calculated using both the 

project data and the HPMS data. The table shows the overall condition has a higher percentage in 

Good condition for the project data than the HPMS data for the majority of the States. 

The HPMS data included in the comparisons above includes all of the data collected on the IHS 

in the HPMS database. Therefore, additional review included removing data collected along 

routes other than those for which data were collected for the project. Further, the data collected 

for the project included ride quality on pavements as well as on bridge decks, but the data from 

the bridge decks were removed in developing the condition of each segment. The performance 

evaluation contained in table 25 does not include the bridge deck ride quality data. Last, the 

NPRM identifies that the segments are based on 0.1-mile intervals. Table 26 provides a 

comparison of the performance measures for each of these cases. The table shows that the same 

trend observed in the full data set is observed in the subsets. 

In order to more fully understand the differences observed in the data sets, comparisons were 

made between the condition metrics. This comparison involved performing an F-test to compare 

the variances of the two data sets and then a t-test to compare the average values. Table 27

provides the results of the F-test comparisons. The HPMS data represented by the comparisons 

in table 27 include all of the HPMS data. The F-test suggests that the variances are not similar 

between the two data sets for all of the condition metrics. 
60

Table 25. Comparison of performance measures by State. 

State Mileage, 

miles 

HPMS 2014 Data Mileage, 

miles 

Project Data 

% 

Good 

% 

Fair 

% 

Poor 

% 

Good 

% 

Fair 

% 

Poor 

All 23,680 47.1 51.2 1.7 8,250 63.0 36.0 1.0

Alabama 349 25.0 74.3 0.7 58 78.8 20.7 0.5

Arizona 1,068 56.9 42.8 0.3 418 69.0 30.8 0.2

California 310 52.8 45.6 1.6 356 59.1 40.1 0.8

Colorado 517 23.3 61.5 15.2 430 45.3 52.4 2.3

Connecticut 142 55.0 45.0 0.0 102 55.6 43.3 1.2

Delaware 18 35.9 64.1 0.0 22 62.7 36.8 0.5

Florida 404 58.9 41.0 0.1 356 60.4 37.3 2.3

Georgia 354 26.2 71.9 1.9 109 58.9 40.4 0.6

Idaho 610 37.3 61.6 1.1 67 35.7 60.7 3.6

Illinois 528 0.1 99.9 0.0 123 77.9 21.9 0.2

Indiana 968 71.8 28.2 0.0 308 64.5 35.2 0.3

Kansas 337 60.8 37.4 1.8 419 86.5 13.4 0.1

Kentucky 625 53.5 46.3 0.2 298 75.9 23.8 0.3

Louisiana 716 30.5 68.1 1.4 218 65.9 30.7 3.4

Maine 366 43.9 56.1 0.0 289 36.5 61.9 1.6

Maryland 185 70.7 29.3 0.0 97 81.4 18.6 0.0

Massachusetts* 0 #NA #NA #NA 85 83.8 16.1 0.1

Minnesota 906 58.9 39.7 1.4 252 58.0 41.9 0.1

Mississippi 416 70.1 29.3 0.6 71 69.7 29.6 0.7

Missouri 538 76.4 23.6 0.0 247 79.2 20.6 0.2

Montana 335 68.6 29.6 1.8 49 49.4 49.1 1.5

Nevada 267 62.4 37.1 0.5 124 93.0 7.0 0.0

New Hampshire 92 0.0 99.3 0.7 14 98.5 1.5 0.0

New Jersey 132 61.5 38.1 0.4 90 41.8 57.0 1.2

New Mexico 449 30.4 69.5 0.1 164 53.4 45.6 1.0

New York 216 0.0 93.7 6.3 198 70.1 29.5 0.4

North Carolina 439 60.2 39.8 0.0 181 59.7 40.2 0.1

Pennsylvania 1,798 72.0 27.9 0.1 254 51.1 47.7 1.2

Rhode Island 49 51.6 48.4 0.0 43 82.2 17.3 0.5

South Carolina 347 17.4 82.6 0.0 196 68.4 30.3 1.3

South Dakota 673 38.2 61.7 0.1 396 83.2 16.7 0.1

Tennessee 277 42.7 56.7 0.6 119 90.5 9.3 0.2

Texas 1,086 54.4 44.8 0.8 862 54.3 45.3 0.4

Utah 931 49.0 48.6 2.4 232 78.7 21.2 0.1

Virginia 1,092 39.7 58.6 1.7 174 58.7 39.2 2.1

Washington 744 34.5 61.6 3.9 285 31.4 61.6 7.0

West Virginia 544 66.9 30.0 3.1 159 75.6 24.0 0.4

Wisconsin 622 28.8 70.6 0.6 185 44.5 54.7 0.8

Wyoming 167 32.7 64.9 2.4 202 47.2 52.4 0.4

*For Massachusetts, #NA indicates that the pavement distress data was not available at the time of this report. 
61

Table 26. Comparison of HPMS 2014 and project data performance measures. 

Data Set % Good % Fair % Poor 

HPMS (All Interstate) 47.1 51.2 1.7

HPMS (Data Collection Route Only) 45.4 52.5 2.1

Project Data Including Bridges 61.7 37.3 1.0

Project Data Excluding Bridges 63.0 36.0 1.0

Project Data Excluding Bridges and segments 

less than 0.1-mile in length 63.0 35.9 1.1

Table 27. Comparison of HPMS 2014 and project data variances. 

Element HPMS Variance Project Data Variance Statistical 

Significance 

IRI 1956 1433 Yes 

Rut Depth 0.01 0.01 Yes 

Faulting 0.02 0.001 Yes 

Percent Cracking 149 84 Yes 

Once the variances were compared, the next step was to compare the means of the two data sets 

for each condition metric. This comparison involved the use of a t-test to compare those means. 

Because the variances all proved to be statistically significantly different, the t-test assumed 

unequal variances between the data sets based upon the results presented in table 27. Table 28

provides a summary of these results. As with table 27, the HPMS data represented by the results 

in table 28 includes all of the HPMS data. 

Table 28. Comparison of HPMS 2014 and project data condition metrics.

Data Element HPMS Average Project Data 

Average 

Statistical 

Significance 

IRI 80 in./mile 72 in./mile Yes 

Rut Depth 0.12 in. 0.15 in. Yes 

Faulting 0.05 in. 0.04 in. Yes 

Percent Cracking 4.7% 3.0% Yes 

The comparisons of the HPMS data and project data suggest that the biases in the data are 

insufficient to be considered important. The comparison of the variances suggests that the 

variance of the HPMS data is larger than the variance of the project data. Absolute differences in 

condition by metric is 11 percent for IRI, 20 percent for rutting, 25 percent for faulting and 57 

percent for cracking. These values are large and statistically significant, but, for example, the 

difference in IRI would be difficult for anyone to judge given the 8 in/mile difference in the 

average values. 

As a further review of the differences in variability between the two data sets, table 29 provides 

the mean, minimum, and maximum values for each of the condition metrics for the two data sets. 

The range in the rut depth and faulting is larger for the HPMS data than that seen from the 

project data. As noted previously, the cracking and IRI from the HPMS data have larger 

variances than those observed from the project data. These factors are believed to be the cause 

of the difference in the good overall condition observed. 
62

Table 29. Comparison of HPMS 2014 and project data statistics. 

Element HPMS Project Data 

Mean Minimum Maximum Mean Minimum Maximum 

IRI 80 in./mile 6 in./mile 771 in./mile 72 in./mile 3 in./mile 959 in./mile

Rut Depth 0.12 in. 0 in. 1.77 in. 0.15 in. 0.0 in. 1.5 in.

Faulting 0.05 in. 0 in. 9 in. 0.04 in. 0 in. 0.6 in.

Cracking 4.7% 0% 100% 3.0% 0% 100% 

The maximum values shown in table 29 illustrate that there are some unusual values contained in 

the HPMS data set. For example, the 9-inch value for faulting was observed in a State where 

other faulting values greater than 1 inch were observed. It appears that this particular State was 

reporting values in an incorrect unit, possibly tenths of inches. The large IRI value observed in 

the 2014 HPMS data is actually less than the largest value observed in the project data. These 

IRI values were reviewed against the images and physical features within the pavement surface 

were observable to explain these larger values. Similarly, the maximum values from the 2014 

HPMS data for rut depth and cracking are similar to the maximum values observed in the project 

data set. 

Three States were selected for further review of the differences in the HPMS and project data –

Washington, New Hampshire, and Kentucky. The condition metrics were compared between 

these two data sets for each of these States, which represent three conditions of comparability 

between the overall conditions. Washington performance compares well between the two data 

sets. New Hampshire shows a poor comparison between the overall conditions from the two 

data sets. Kentucky has a fair comparability between the two data sets. This type of comparison 

will be covered in more detail in the next chapter. 

Table 30 provides the results of this comparison for all three States. The condition metrics 

compare best for the State where the overall conditions compare well. The condition metrics do 

not compare well for the State where the overall condition compares poorly. The State with the 

fair overall condition comparison fell in the middle of the comparisons for these three States. 

The faulting did not compare well for either Washington or Kentucky. New Hampshire did not 

have concrete pavement to use for the comparison. 

One additional aspect to the comparison of HPMS and the project data considered was the 

location of bridges. The data collection included marking bridge locations as part of that effort. 

Therefore a sample of data was used to compare the locations of the bridges marked by the 

project team and the location of the bridges identified within the HPMS. 

The sample used for this comparison included I-10 in Alabama, Arizona, and California as well 

as I-15 in Arizona. A paired t-test was used to compare both the start location and the length of 

the bridges identified within both data sets. Table 31 presents the results of these comparisons. 

These comparisons indicate that there is a significant difference in the starting location of the 

bridges identified between the two data sets. Further, the project data identified 274 bridges 

within the sample route selected for this comparison while the HPMS data identified 586 bridges 

in these routes. It is likely that the large difference observed is due to the identification of box 
63

culverts and other small structures as bridges within the HPMS data. State DOTs are better able 

to track these smaller locations, which are not observable when driving the route. 

Table 30. Comparison of the condition metrics by state.

State Condition Metric HPMS 2014 Project Data Statistically 

Mean Significant Standard 

Deviation Mean Standard 

Deviation 

WA IRI 84 47 86 49 No

Rut Depth 0.26 0.13 0.27 0.15 Yes 

Percent Cracking 4 10 4 10 No

Faulting 0.04 0.07 0.06 0.04 Yes 

NH IRI 52 16 44 26 Yes 

Rut Depth 0.18 0.03 0.10 0.09 Yes 

Percent Cracking 11 8 1 1 Yes 

KY IRI 58 32 61 40 Yes 

Rut Depth 0.09 0.05 0.09 0.04 No

Percent Cracking 8 12 4 13 Yes 

Faulting 0.10 0.14 0.04 0.02 Yes 

Table 31. Comparison of bridge location and length.

Element Mean Difference Statistical 

Significance 

Correlation 

Coefficient 

Starting Milepost 0.132 mile Yes 0.99999 

Bridge Length 0.004 mile No 0.94

4.6. IMPROVEMENTS TO DATA COLLECTION AND REPORTING 

The third objective of the project is to recommend improvements to data collection and reporting 

necessary to make HPMS unbiased or improve precision of the data. The following sections 

discuss various aspects of the collected data to identify potential improvements in the data 

collection and reporting required by the NPRM. 

4.6.1 Direction and Lane 

As noted previously, approximately 500 miles of data were collected in the adjacent lane and in 

the opposing direction. These data were used to compare the condition metrics in these lanes to 

identify the need for data collection in additional lanes. 

Data comparisons were made using the data accumulated to 0.1-mile intervals. Table 32

presents the results of these comparisons. The results show that there is little difference in the 

average values between the data in these other locations and the primary route data. Additionally, 

the table illustrates that the condition in the adjacent lane is better on average than that estimated 

for the primary lane. 
64

Table 32. Comparison of adjacent lane and opposing direction with primary lane. 

Element Primary 

Average 

Adjacent 

Average 

Opposing 

Average 

Statistical 

Significance 

IRI 68 in/mile 66 in/mile 66 in/mile Yes, Yes 

Rut Depth 0.14 in. 0.11 in. 0.14 in. Yes, Yes 

Faulting 0.07 in. 0.05 in. 0.07 in. Yes, Yes 

AC HPMS 

Cracking 2.3% 1.6% 2.4% Yes, No 

AC % WP 4.9% 3.5% 5.2% Yes, No 

AC NPRM 

Under 2.5% 1.7% 2.6% Yes, No 

AC NPRM 

Over 2.6% 1.8% 2.6% Yes, No 

AC NPRM 

Step 2.5% 1.8% 2.6% Yes, No 

PCC 

Cracking 11.7% 7.0% 13.6% Yes, No 

The other aspect to the data in the opposing direction to consider is the comparison of the surface 

type. If the surface type is different in the opposing direction from the primary, it may still be 

important to collect data in both directions as the primary direction condition may not be 

expected to represent the condition in both directions. The surface type was observed to be 

different for approximately 3 percent of the 552 miles of data collected in the opposing direction. 

Based on this analysis, the data collected in the primary direction in the outside lane is sufficient 

to provide an estimate of condition of the interstate system. 

4.6.2 Section Length 

The next aspect of the data collection reviewed is the section length used for the reporting length. 

The current version of the rule requires that data be reported at 0.1-mile intervals. The first step 

in this analysis was to use a power spectral density analysis to determine if the content of the 

signal identified any particular required section length. In order to perform this analysis, two 

sample sections were used for the analysis. The first was an asphalt-surfaced section in Arizona 

on I-10 from milepost 320 to milepost 356 and a jointed concrete section on I-90 in Wisconsin 

from milepost 61.32 to milepost 68.54. Figure 20 thru Figure 23 provide the power spectral 

density for the IRI and rut depth on the asphalt section and the IRI and faulting on the jointed 

concrete section, respectively. 

The power spectral density plots demonstrate that most of the content is contained in the smaller 

wavelengths for IRI and rut depth. Faulting appears to be fairly random. These plots suggest 

that smaller section lengths are necessary to distinguish the areas of good and poor condition 

from areas of fair condition. 
65

Figure 20. Graph. Power spectral density curve for IRI on asphalt section.

Figure 21. Graph. Power spectral density curve for rut depth on asphalt section. 
66

Figure 22. Graph. Power spectral density curve for IRI on jointed concrete section. 

Figure 23. Graph. Power spectral density curve for faulting on jointed concrete section. 

A further review of the impact of the section length on the results was performed using the 

section identified in Arizona. For this section, the impact of the section length was evaluated by 

accumulating the data at different lengths including 0.01-mile, 0.05-mile, 0.1-mile, 0.5-mile, and 
67

1-mile. Figure 24 through figure 26 provide the samples of the results of the varying levels of 

accumulation for IRI, rut depth, and percent cracking on the asphalt pavement section. A full set 

of figures illustrating these comparisons are provided in appendix C. 

The figures confirm the observation made from the power spectral density plots. The blue line 

that shows up in the middle of the graph representing the 1-mile section length illustrates how 

the maximum and minimum values are averaged out with a longer section length. Generally, the 

smaller the section length the simpler it becomes to distinguish between areas of good and poor 

condition from areas of fair condition. 

The other item to consider in determining the appropriate section length is the size of the 

database. Smaller sections will increase the size of the database and subsequently the difficulty 

in transmittal and analysis of the data. Therefore, the optimal section length is a tradeoff 

between identifying specific locations of good and poor condition and the size of the database. 

Analyses of IRI, percent cracking and rutting suggest that longer section lengths tend to mask 

areas of good and poor condition. Selection of section length is a matter of balancing the need to 

identify specific areas of good and poor condition with the increase in database size that comes 

with smaller sections. The 0.1-mile section length appears to be a good balance between these 

two needs. 

Figure 24. Graph. Impact of section length on average IRI – 0.1 vs 0.01-mile. 
68

Figure 25. Graph. Impact of section length on rut depth – 0.1 vs 0.5-mile.

Figure 26. Graph. Impact of section length on percent cracking.
69

4.6.3 Sampling of the Interstate Highway System 

The next question about the data collection effort reviewed was the possibility of sampling the 

system to determine condition. The database was used to estimate the number of samples 

required to estimate the mean and standard deviation of the population represented by the 

database to a given level of confidence and acceptable error. Three confidence levels were used 

in this investigation: 90 percent, 95 percent, and 99 percent. Three acceptable error levels were 

also used: 1 percent, 5 percent, and 10 percent. Table 33 presents the results for the full data set. 

The mileage shown in the table identifies the mileage of the available data used to estimate the 

required sample size. This full data set indicates that a minimal sample is required to estimate the 

condition of the network based solely on IRI with a sampling requirement of 18 percent for a 99 

percent confidence limit and a 1 percent error level; however, estimating the condition of the 

network based on cracking will require a more substantial sample. Cracking on CRCP sections 

shows a sampling requirement of 97 percent to achieve a 95 percent confidence level with a 5 

percent acceptable error level. 

Table 33. Sampling requirements at the national level.

Element Confidence 

Level 

Error Level 

1% 5% 10% 

IRI 

(8,484 miles) 

90% 8% 0.4% 0.1% 

95% 11% 1% 0.1% 

99% 18% 1% 0.2% 

Rut Depth 

(6,630 miles) 

90% 16% 1% 0.2% 

95% 21% 1% 0.3% 

99% 31% 2% 0.5% 

Faulting 

(1,261 miles) 

90% 67% 7% 2% 

95% 74% 10% 3% 

99% 83% 16% 5% 

AC Cracking 

(6,630 miles) 

90% 72% 9% 3% 

95% 78% 13% 4% 

99% 86% 20% 6% 

CRCP Cracking 

(421 miles) 

90% 100% 97% 90% 

95% 100% 98% 93% 

99% 100% 99% 96% 

JPCC Cracking 

(1,262 miles) 

90% 91% 28% 9% 

95% 93% 35% 12% 

99% 96% 49% 19% 

The sample collected for this project represents an 18 percent sample of the overall IHS across 

the nation. Chapter 3 illustrates the efforts taken to develop a routing that encompassed a wide 

range of traffic, climate, and construction or maintenance practices used across the contiguous 

US. Table 33 illustrates that at a 95 percent level of confidence, this sample can be expected to 

represent average conditions across the contiguous US for IRI, rutting, and faulting. Because 

cracking was considered by surface type, the level of sampling identified must be reviewed by 

surface type. The project data set represents a 28 percent sample of the asphalt surfaced portion 

of the IHS, a 38 percent sample of the CRCP portion of the IHS, and a 22 percent sample of the 
70

JPCC segments of the IHS. These percentages are based on records from 2014 HPMS providing 

the surface type. These percentages suggest that the project data sample is not sufficient to fully 

capture the condition of cracking on CRCP and JPCC pavements. The sampling of the asphalt 

surfaced pavements was sufficient to capture cracking condition. 

Using the larger sample may result in some issues with the estimated sample requirements. The 

large size of the data set may mask some of the variability that is important to the FHWA to 

capture. Further, it is likely that data collected will be used to draw conclusions about the 

conditions within a particular State as opposed to only looking at the data at the national level. 

Therefore, the data were reviewed at the State level. Table 34 presents the minimum sampling 

level identified at the State level and table 35 presents the maximum sampling level identified at 

the State level. Table 36 presents the average of the sampling requirements at the State level. As 

with table 33, the mileages shown in the tables represent the mileage of the data set used for 

estimating the sampling requirements. 

In all cases, the sampling rate required for the IRI is the lowest of the condition metrics. The 

sampling required for cracking is the highest of the condition metrics. In general, the sampling 

requirements for cracking are well over 50 percent at the State level. These sampling rates 

suggest that a 100 percent sampling rate is required for data collection. 

Table 34. Minimum sampling requirements at the State level. 

Element Confidence 

Level 

Error Level 

1% 5% 10% 

IRI 

(880 miles) 

90% 39% 3% 1% 

95% 48% 4% 1% 

99% 61% 6% 2% 

Rut Depth 

(628 miles) 

90% 50% 4% 1% 

95% 58% 5% 1% 

99% 71% 9% 2% 

Faulting 

(159 miles) 

90% 88% 23% 7% 

95% 91% 30% 10% 

99% 95% 42% 15% 

AC Cracking 

(165 miles) 

90% 96% 51% 20% 

95% 97% 59% 27% 

99% 98% 72% 39% 

CRCP Cracking 

(19 miles) 

90% 99% 88% 64% 

95% 100% 91% 72% 

99% 100% 95% 81% 

JPCC Cracking 

(120 miles) 

90% 97% 55% 23% 

95% 98% 63% 30% 

99% 99% 75% 42% 
71

Table 35. Maximum sampling requirements at the State level. 

Element Confidence 

Level 

Error Level 

1% 5% 10% 

IRI 

(24 miles) 

90% 97% 53% 22% 

95% 98% 61% 29% 

99% 99% 74% 41% 

Rut Depth 

(22 miles) 

90% 98% 63% 30% 

95% 99% 71% 38% 

99% 99% 81% 51% 

Faulting 

(4 miles) 

90% 100% 91% 70% 

95% 100% 93% 77% 

99% 100% 98% 86% 

AC Cracking 

(14 miles) 

90% 100% 99% 93% 

95% 100% 99% 95% 

99% 100% 99% 97% 

CRCP Cracking 

(1 mile) 

90% 100% 100% 100% 

95% 100% 100% 100% 

99% 100% 100% 100% 

JPCC Cracking 

(4 miles) 

90% 100% 100% 100% 

95% 100% 100% 100% 

99% 100% 100% 100% 

Table 36. Average sampling requirements at the State level. 

Element Confidence 

Level 

Error Level 

1% 5% 10% 

IRI 

90% 77% 17% 5% 

95% 82% 21% 7% 

99% 88% 31% 12% 

Rut Depth 

90% 80% 19% 6% 

95% 85% 24% 8% 

99% 91% 34% 13% 

Faulting 

90% 96% 59% 33% 

95% 97% 66% 39% 

99% 99% 75% 50% 

AC Cracking 

90% 99% 79% 53% 

95% 99% 83% 60% 

99% 100% 89% 71% 

CRCP Cracking 

90% 100% 97% 90% 

95% 100% 98% 92% 

99% 100% 99% 95% 

JPCC Cracking 

90% 100% 89% 72% 

95% 100% 92% 77% 

99% 100% 95% 84% 
72

4.6.4 Inclusion of Bridges 

One additional item of interest was the impact of the inclusion of bridges within the data 

collection. The only condition metric collected on the bridge decks was IRI. This comparison 

involved comparing the overall average of the IRI with and without the bridge deck IRI using a 

t-test. The t-test was significant although the difference in the average IRI values was very 

small. The difference in the conditions due to the inclusion of the bridges in the IRI was also 

very small. Table 37 provides the results of the comparison illustrating that there is very little 

difference between the two approaches for evaluating pavement condition. 

Table 37. Comparison of data with and without bridges. 

Element With Bridges Without Bridges 

Average IRI 72 in./mile 69 in./mile

Percent Good 62% 63% 

Percent Fair 37% 36% 

Percent Poor 1% 1% 

4.6.5 Equipment Type 

Faulting 

Two types of faulting data were collected. The first was collected using the LCMS sensor which 

allows for a visual verification of the joint location. The three dimensional nature of this sensor 

allows for measuring the faulting at each identified joint. The joint locations were manually 

reviewed as part of data collection to obtain faulting from the LCMS sensor. 

The second method for faulting collection was using the inertial profiler data. These data were 

used in accordance with AASHTO Designation R36-13, “Evaluating Faulting of Concrete 

Pavements” to estimate the faulting of each joint. (14) 

A paired t-test was used to compare the two data sets at the 0.01-mile level. Table 38 provides 

the results of the comparison. The paired t-test was statistically significant. The difference in 

the faulting values also resulted in a small change in the overall condition for the network. The 

inertial profiler faulting displayed more variability than the LCMS faulting. 

As part of the investigation into the differences in equipment, differences in the number of joints 

were observed between the two methods. These are presented in Table 38. The database also 

contains the number of fault measurements within each 0.01-mile section. Comparisons were 

made between the fault count and the joint count for each 0.01-mile section. The average 

number of joints within each 0.01-mile section is 3.6. The differences in all of the data sets are 

statistically significant; however, the LCMS fault count is much closer to the joint count with an 

average difference of 0.2 joints per 0.01-mile section. Table 38 illustrates that although 

statistically the faulting changes based on the type of sensor used for calculation, due to the 

nature of the condition evaluation, these differences are diminished by the categorical nature of 

that process. 
73

Table 38. Comparison of faulting from LCMS and RSP. 

Element LCMS Fault RSP Fault 

Average Fault 0.04 inch 0.02 inch 

Standard Deviation Fault 0.05 inch 0.06 inch 

Average Number of Faults 3.4 faults 0.4 faults 

Average Number of Joints 3.6 joints 

Percent Good 62% 64% 

Percent Fair 37% 35% 

Percent Poor 1% 1% 

Rutting 

Two types of rutting were considered as part of the data collection. The first is the rut depth 

based on the transverse profile as collected from the LCMS sensor. The second is an estimate of 

the rut depth that would have been measured using a 5-point laser system. Table 39 provides the 

results of this comparison performed using the 0.01-mile interval data. 

Table 39. Comparison of rutting from LCMS and 5-point profile. 

Element LCMS Rut 5-point Rut 

Average Rut Depth 0.15 inch 0.09 inch 

Standard Deviation Rut 0.10 inch 0.12 inch 

Percent Good 62% 64% 

Percent Fair 37% 35% 

Percent Poor 1% 1% 

A paired t-test was used to compare the two data sets. The t-test was statistically significant and 

shows a large difference in the average values. Further, the standard deviations show that the 

values for the 5-point vary over a wider range than the rut depth determined from the LCMS 

transverse profile. The difference in the overall condition are due primarily to the negative rut 

depths observed in the 5-point rut depth values. As with the faulting, table 39 illustrates that 

though there are differences in the data based on the sensor type, the impact of these differences 

is diminished by the categorical nature of the condition evaluation. 

Cracking Definition 

As noted previously, several definitions for percent cracking were explored for the asphalt 

surfaces. These definitions have been provided in chapter 3 of this document, but are repeated

here to ease understanding of the terms used in this section. 

 AC HPMS – Total area of wheelpath cracking divided by the total lane area 

 AC Percent Wheelpath – Total area of wheelpath cracking divided by the area of the 

wheelpath 
74

 NPRM Under – Area of affected wheelpath cracking plus the area of transverse cracking 

(transverse cracking length multiplied by 1 ft). The length of transverse cracking is 

reduced by the amount of the transverse cracking within the wheelpath. Total crack area 

is divided by the total lane area. This definition is illustrated in Figure 7. 

 NPRM Over – Area of affected wheelpath cracking plus the area of transverse cracking 

(transverse cracking length multiplied by 1 ft) divided by the total lane area. This 

definition is illustrated in Figure 7. 

 NPRM Step – The area of transverse cracking is reduced in a graduated manner based on 

the area of wheelpath cracking. Where the percentage of wheelpath cracking is greater 

than 80 percent of the total wheelpath area, the transverse cracking length is reduced by 

the amount of transverse cracking within the wheelpath. The full transverse cracking 

length is used where the percent of wheelpath cracking is less than 20. If the percent of 

wheelpath cracking is between 20 and 40 percent, 75 percent of the transverse crack 

length within the wheelpath is used. If the percent of wheelpath cracking is between 40 

and 60 percent, 50 percent of the transverse crack length within the wheelpath is used. 

Finally, if the percent of wheelpath cracking is between 60 and 80 percent, 25 percent of 

the transverse crack length within the wheelpath is used. The total crack area is divided 

by the total lane area. This definition is illustrated in Figure 7. 

Comparisons were made between the results associated with these various definitions of 

cracking. Table 40 provides the results of these comparisons. A paired t-test was used to 

identify the statistical significance of the differences observed. 

Table 40. Comparison of definitions of cracking on AC pavements. 

Element AC HPMS AC % WP NPRM 

Under NPRM Over NPRM Step 

Average 1.8% 4.1% 2.1% 2.2% 2.2% 

Standard Deviation 5.7% 12.1% 5.9% 5.9% 5.9% 

Percent Good 64% 61% 63% 63% 63% 

Percent Fair 35% 38% 36% 36% 36% 

Percent Poor 1% 1% 1% 1% 1% 

The results show that even though the differences were found to be statistically significant, there 

is very little difference in the average values with the exception of the percent wheelpath 

cracking. This parameter is based on a smaller area of the lane as opposed to the total lane area 

so the size of this value is larger (by approximately twice) than the others and has a larger 

variability associated with it. 

Table 41 presents the correlation coefficients between the various definitions. These correlations 

illustrate that the data are very highly correlated and represent the same aspect of the pavement 

section. Based on these data, the impact of the definition is limited on the resulting overall 

condition.
75

Table 41. Correlations between cracking definitions.

AC % WP NPRM Under NPRM Over NPRM Step 

AC HPMS 100% 100% 99% 99% 

AC % WP 99% 99% 99% 

NPRM Under 100% 100% 

NPRM Over 100% 

4.7. SUMMARY 

The data analysis covered a series of efforts to address questions posed by the FHWA. The data 

collected indicate that 63 percent of the network is in good condition and 1 percent is in poor 

condition. The HPMS data indicate that 47 percent of the network is in good condition and 2 

percent is in poor condition. Comparisons with the condition metrics indicate that there is no 

significant bias between the two data sets. 

The primary difference in the two data sets is primarily due to differences in variability. The 

comparison of the collected data with the quality control data, Infrastructure Health data, and 

LTPP data suggest that the data collected for this project are of good quality. Further these 

comparisons suggest that attention to quality of the data is more important than the frequency of 

data collection. This observation is supported by the comparisons with the LTPP data which 

were not statistically significant even though the difference in time was as much as 5 years for 

comparisons with IRI, percent cracking, and faulting and larger for comparisons with rut depth. 

Data collection included sufficient data to review the need to collect data in multiple lanes and 

directions. Comparisons with these data suggest that there are not significant differences in data 

collected in different directions of travel where the surface type was the same. The surface type 

was the same for 97 percent of the mileage where data was collected in both directions. Data 

collected in adjacent lanes suggested that the differences were not significant and the condition 

reported for the outside lane is worse than the adjacent lane. These analyses indicate that data 

collection may be limited to one lane and one direction of travel. 

Analyses were performed to identify the appropriate section length and sampling rate of the 

network. In particular, analyses of IRI, percent cracking and rutting suggest that longer section 

lengths tend to mask areas of good and poor condition. Selection of section length is a matter of 

balancing the need to identify specific areas of good and poor condition with the increase in 

database size that comes with smaller sections. The 0.1-mile section length appears to be a good 

balance between these two needs. 

A study of the sampling rate suggests that if all review of the data would be performed at the 

national level, less than 100 percent of the Interstate system could be assessed. Further, the size 

of the full data set may mask some of the variability in the data. Sampling requirements at the 

State level suggest that a 100 percent sample is required. 

Analyses indicate that there is little difference in the overall condition when the ride quality 

measured on the bridges is included. Further, the average IRI changed very little when the 
76

bridges were included. The bridges had very little impact on the network level ride quality 

analysis. For ease of collection, our recommendation is to collect ride quality on bridges. 

The faulting protocols allow for use of several different types of devices. The faulting data 

collected for this project included data from both an inertial profiler and from the LCMS sensor. 

The faulting from the LCMS sensor appears to be more reliable than that from the inertial 

profiler. The inertial profiler occasionally missed joints so it was difficult to make sure that all 

zero faulting was included in the average values. Additionally, the inertial profiler occasionally 

identified a crack as a joint. 

As with the faulting, the protocols designated for rutting allow the use of at least two different 

devices. Rut depths were collected using the LCMS sensor and a 5-point rut depth was 

estimated. Comparisons between these two data sets indicated that the 5-point rut depth is not a 

reliable measure of rutting on pavements. This method of data collection yields negative rut 

depths and may miss the maximum rut depth due to vehicle wander. However, the differences in 

the two methods of data collection exhibited by the overall condition is limited since the data are 

being categorized. 

The various definitions of cracking made very little difference in the overall condition. The 

various definitions resulted in very similar values of cracking with one exception, percent 

wheelpath cracking. The percent wheelpath definition provides the percentage of the wheelpath 

with cracking on the basis of the area of the wheelpaths. Therefore the percent wheelpath 

cracking was approximately twice the value for other types of cracking.
77

CHAPTER 5. SUPPLEMENTAL ANALYSIS 

Subsequent to the completion of the planned analyses, which were presented in the previous 

chapter, a series of supplemental analyses were conducted at the request of FHWA. These 

analyses were pursued to answer specific questions raised related to the impact of the threshold 

values for each condition metric, the impact of the section length on the assessed condition 

metrics, and direct comparisons of the HPMS and project data. This chapter presents the results 

of these analyses. 

5.1. THRESHOLD ANALYSIS 

An analysis of particular interest to the FHWA involved reviewing the impact of alternate 

threshold levels on the individual condition metrics, overall pavement condition and proposed 

performance measures. Table 42 provides a comparison of the thresholds indicated in the 

NPRM to the alternate thresholds of interest to the FHWA. 

Table 42. Comparison of NPRM and revised thresholds. 

Condition Metric Performance Level Threshold

NPRM Alternate 

IRI 

Good <95 <95

Fair 95 – 170 95 – 170 

Poor 

>170: Areas with a 

population less than 

1,000,000

>220: Areas with a 

population of at least 

1,000,000

>170

Percent Cracking, AC 

Good <5% <5% 

Fair 5 – 10% 5 – 20% 

Poor >10% >20% 

Percent Cracking, 

CRCP

Good <5% <5% 

Fair 5 – 10% 5 – 10% 

Poor >10% >10% 

Percent Cracking, 

JPCC 

Good <5% <5% 

Fair 5 – 10% 5 – 15% 

Poor >10% >15% 

Rutting 

Good <0.20 <0.20

Fair 0.20 – 0.40 0.20 – 0.40

Poor >0.40 >0.40

Faulting 

Good <0.05 <0.10

Fair 0.05-0.15 0.10 – 0.15

Poor >0.15 >0.15 
78

The alternate thresholds result in 62.0% of the pavement in good condition and 0.7% with poor 

condition. These values are slightly different from the performance identified with the threshold 

levels indicated in the NPRM. There are, however, some differences in the percentages at the 

individual condition levels. Figure 27 illustrates the comparison of the percentages for the full 

data set and each condition metric. 

The largest differences are seen in the faulting metric, with the percentage of good condition 

changing from 79.1 percent to 93.8 percent when moving from the NPRM thresholds to the 

alternate ones. The percent cracking also showed a fairly significant change in the percentage of 

poor segments, moving from 7.9 percent to 4.3 percent. 

Figure 27. Chart. Comparison of condition metrics. 

Table 43 provides a comparison by State of the performance measures as determined using both 

sets of thresholds. Comparisons were also made for each section to determine if the performance 

changed for each individual section. Differences were observed for approximately 177 miles of 

the total 8,627 miles of routine data collection. Those States that show a difference in 

percentages of good, fair and poor overall condition between the two sets of threshold values 

generally have at least 1 percent of the mileage in that State that shows a difference in 

comparison results for individual sections. 
79

Table 43. Comparison of performance measures from project data using different 

thresholds by State. 

State Mileage, 

miles 

NRPM Thresholds Alternate Thresholds 

% Good % Fair % Poor % Good % Fair % Poor 

All 8,250 63.0 36.0 1.0 62.0 37.3 0.7

Alabama 58 78.8 20.7 0.5 77.2 22.8 0.0

Arizona 418 69.0 30.8 0.2 68.5 31.4 0.1

California 356 59.1 40.1 0.8 57.7 41.2 1.1

Colorado 430 45.3 52.4 2.3 44.3 54.4 1.2

Connecticut 102 55.6 43.2 1.2 55.2 43.9 0.9

Delaware 22 62.7 36.8 0.5 60.5 39.5 0.0

Florida 356 60.4 37.3 2.3 58.8 39.7 1.5

Georgia 109 58.9 40.4 0.7 57.9 41.7 0.4

Idaho 67 35.7 60.7 3.6 34.5 63.1 2.4

Illinois 123 77.9 21.9 0.2 75.9 23.8 0.3

Indiana 308 64.5 35.2 0.3 63.6 36.0 0.4

Kansas 419 86.5 13.4 0.1 84.8 15.1 0.1

Kentucky 298 75.9 23.8 0.3 74.0 25.8 0.2

Louisiana 218 65.9 30.7 3.4 64.0 33.0 3.0

Maine 289 36.5 61.9 1.6 36.1 63.2 0.7

Maryland 97 81.4 18.6 0.0 78.5 21.4 0.1

Massachusetts 85 83.8 16.1 0.1 81.9 18.0 0.1

Minnesota 252 58.0 41.9 0.1 58.4 41.6 0.0

Missouri 247 79.2 20.6 0.2 78.5 21.4 0.1

Mississippi 71 69.7 29.6 0.7 68.1 31.6 0.3

Montana 49 49.4 49.1 1.6 49.0 50.3 0.7

Nevada 124 93.0 7.0 0.0 92.9 7.0 0.1

New Hampshire 14 98.5 1.5 0.0 98.5 1.5 0.0

New Jersey 90 41.8 57.0 1.2 39.0 60.0 1.0

New Mexico 164 53.4 45.6 1.0 53.0 46.4 0.6

New York 198 70.1 29.5 0.4 69.3 30.1 0.6

North Carolina 181 59.7 40.2 0.1 59.0 40.9 0.1

Pennsylvania 254 51.1 47.7 1.2 50.8 48.1 1.1

Rhode Island 43 82.2 17.3 0.5 80.3 19.7 0.0

South Carolina 196 68.4 30.3 1.3 68.4 30.6 1.0

South Dakota 396 83.2 16.7 0.1 83.1 16.8 0.1

Tennessee 119 90.5 9.3 0.2 89.4 10.4 0.2

Texas 862 54.3 45.3 0.4 53.0 46.7 0.3

Utah 232 78.6 21.3 0.1 80.8 19.2 0.0

Virginia 174 58.7 39.2 2.1 56.6 41.5 1.9

Washington 285 31.4 61.6 7.0 31.8 63.7 4.5

West Virginia 159 75.6 24.0 0.4 73.5 26.4 0.1

Wisconsin 185 44.5 54.7 0.8 44.1 55.4 0.5

Wyoming 202 47.2 52.4 0.4 48.3 51.5 0.2
80

The same comparison was performed for the data obtained from the HPMS database. The data 

used for this analysis were based on HPMS sample data. Figure 28 provides a comparison of 

these results, while Table 44 provides a comparison of the results by State for data from the 

HPMS database. 

Generally, the differences in the performance measures by State for the HPMS data are similar to 

those observed in the project data with one exception. The percent poor for Colorado differed by 

10 percentage points. In looking at the individual condition metrics, the cracking and IRI both 

showed changes in condition, but neither had a difference of 10 percent. A direct comparison at 

each location where data were available revealed that approximately 11 percent of the distance 

had a change in condition based on the alternate threshold levels. 

Figure 28. Chart. Comparison of condition metrics for HPMS data. 

In the HPMS data, the State of Delaware showed an increase in the percentage of poor condition. 

The mileage from this State is small, plus a significant portion of the mileage is located within 

the Philadelphia urban area. This is important because pavement condition in this area, in terms 

of IRI, contains sufficient data within the 170 in/mile to 220 in/mile range to cause the decrease 

in performance from fair to poor. 

Overall, the threshold analysis results suggest that very little difference is observed as a result of 

the change in the threshold levels. Larger differences may be observed when reviewing smaller 

subsets of the data, as was the case with the changes in the State data. 
81

Table 44. Comparison of HPMS 2014 performance using different thresholds by State. 

State Mileage, 

miles 

NRPM Thresholds Alternate Thresholds 

% Good % Fair % Poor % Good % Fair % Poor 

All 23,680 47.1 51.2 1.7 46.7 52.3 1.0

Alabama 353 25.0 74.3 0.7 24.7 74.5 0.8

Arizona 1,069 56.9 42.8 0.3 56.9 42.9 0.2

California 336 52.8 45.6 1.6 46.1 51.2 2.7

Colorado 519 23.3 61.5 15.2 23.2 71.8 5.0

Connecticut 146 55.0 45.0 0.0 53.2 46.6 0.2

Delaware 18 35.9 64.1 0.0 35.9 61.1 3.0

Florida 404 58.9 41.0 0.1 58.9 41.1 0.0

Georgia 355 26.2 71.9 1.9 25.9 73.0 1.1

Idaho 611 37.3 61.6 1.1 37.2 61.4 1.4

Illinois 528 0.1 99.9 0.0 0.1 99.9 0.0

Indiana 968 71.8 28.2 0.0 69.8 30.1 0.1

Kansas 336 60.8 37.4 1.8 61.4 37.5 1.1

Kentucky 626 53.5 46.3 0.2 53.4 46.4 0.2

Louisiana 738 30.5 68.1 1.4 29.6 66.6 3.8

Maine 366 43.9 56.1 0.0 43.9 56.1 0.0

Maryland 186 70.7 29.3 0.0 70.2 29.8 0.0

Massachusetts*0 #NA #NA #NA #NA #NA #NA 

Minnesota 906 58.9 39.7 1.4 58.0 41.5 0.5

Missouri 541 76.4 23.6 0.0 75.9 24.1 0.0

Mississippi 420 70.1 29.3 0.6 69.5 30.0 0.5

Montana 335 68.6 29.6 1.8 68.4 30.9 0.7

Nevada 267 62.4 37.1 0.5 62.4 37.5 0.1

New Hampshire 244 0.0 99.3 0.7 0.0 99.5 0.5

New Jersey 133 61.5 38.1 0.4 61.3 38.3 0.4

New Mexico 449 30.4 69.5 0.1 30.3 69.6 0.1

New York 223 0.0 93.7 6.3 0.0 97.4 2.6

North Carolina 439 60.2 39.8 0.0 60.0 39.8 0.2

Pennsylvania 1803 72.0 27.9 0.1 71.6 28.2 0.2

Rhode Island 49 51.6 48.4 0.0 51.1 48.9 0.0

South Carolina 347 17.4 82.6 0.0 17.4 82.6 0.0

South Dakota 673 38.2 61.7 0.1 42.2 57.7 0.1

Tennessee 277 42.7 56.7 0.6 42.6 56.7 0.7

Texas 1093 54.4 44.8 0.8 54.0 45.6 0.4

Utah 931 49.0 48.6 2.4 49.0 50.0 1.0

Virginia 1098 39.7 58.6 1.7 39.5 59.9 0.6

Washington 745 34.5 61.6 3.9 35.9 61.6 2.5

West Virginia 545 66.9 30.0 3.1 66.7 33.3 0.0

Wisconsin 622 28.8 70.6 0.6 28.8 70.4 0.8

Wyoming 166 32.7 64.9 2.4 29.3 66.4 4.3

*For Massachusetts, #NA indicates that the pavement distress data was not available at the time of this report 
82

5.2. IMPACT OF SECTION LENGTH 

In a more extensive review of the section length (as compared to that presented in the previous 

chapter), the case study data, as identified in Section 4.6.2, were used to review the impact of 

data summarized to 0.2-mile intervals, 0.3-mile intervals, and 0.4-mile intervals. All 

comparisons were to the data accumulated to 0.1-mile sections as required by the NPRM. The 

graphs shown in figure 29 through figure 31 illustrate the impact of the section length on the 

average pavement condition values – percent cracking, IRI, and rut depth, respectively. The 

complete set of section length-pavement condition figures is provided in appendix C. 

Figure 29 through figure 31 clearly show that there is a definite reduction in the peaks with the 

longer section lengths. This finding further confirms that the longer section lengths tend to 

diminish the ability to discern areas of good and poor condition. To further illustrate this point, 

figure 32 through figure 34 were prepared to show the change in the standard deviation with the 

increasing section length for percent cracking, IRI, and rut depth, respectively; specifically, data 

presented in figure 32 through figure 34 are based on section lengths of 0.01 mile, 0.05 mile, 0.1 

mile, 0.2 mile, 0.3 mile, 0.4 mile, 0.5 mile, and 1 mile. 

Figure 29. Graph. Impact of section length on percent cracking. 
83

Figure 30. Graph. Impact of section length on IRI.

Figure 31. Graph. Impact of section length on rut depth. 
84

Figure 32. Graph. Change in standard deviation of percent cracking by section length. 

Figure 33. Graph. Change in standard deviation of IRI by section length. 
85

Figure 34. Graph. Change in standard deviation of rut depth by section length. 

Figure 32 through figure 34 show that the standard deviation decreases with longer section 

lengths. In turn, the decrease in standard deviation indicates that the maximum values are 

reduced and the minimum values are increased with the longer section lengths. IRI is the 

pavement condition metric most impacted by the change in section length, but rut depth and 

cracking also are impacted by the reduced section length. 

These data were then used to review the impact of the section length on the overall condition. 

Figure 35 provides the results of those calculations. These results do not indicate the anticipated 

trend of increasing percent fair. Although it is difficult to observe, the quantity of poor 

performance does decrease with increasing section length as might be expected. 

5.3. POINT-BY-POINT COMPARISON 

A more detailed, point-by-point comparison was conducted to review the differences, if any, 

between the project-gathered data in 2015 and the 2014 HPMS data. Towards this end, a series 

of maps were prepared to illustrate the condition by location for both the project data and the 

HPMS data. Subsequently, a series of maps were prepared to illustrate how the data compared at 

each location. 

 

Figure 36 through figure 38 illustrate the range of comparisons observed in the data. 

Figure 36 illustrates a State where the results compared well, figure 37 illustrates a State with 

poor comparability between the two data sets, and figure 38 illustrates a mixed result in the 

comparability between the two data sets. These figures are intended to demonstrate that for 

some States the performance determined from the HPMS and project data were very comparable, 

for some States the data do not compare well, and for some States the result is mixed. Table 45
86

summarize the results of the comparisons for each of the 39 States where both data sets were 

available. 

Figure 35. Performance comparison by section length.

It is important to emphasize that the HPMS data used in this analysis, as has been the case for all 

analyses discussed in this report, were the sample data. Therefore, the column identified as “No 

HPMS Data” does not indicate where States are failing to report data, but rather, it is an 

indication of how much sampling is being conducted on the Interstate(s) included in the data 

collection for this project.

A range of conditions were observed in this comparison with some of the States showing good 

comparability between the two data sets and some showing poor comparability between States. 
87

Figure 36. Map. Example of good point-by-point comparison.
88

Figure 37. Map. Example of poor point-by-point comparison. 
89

Figure 38. Map. Example of mixed point-by-point comparison. 
90

Table 45. Mileage by comparison category. 

State Same 

Performance 

Different 

Performance 

No HPMS 

Data 

No Project 

Data 

No 

Data 

Total 

Mileage 

Alabama 15% 32% 41% 0% 12% 66

Arizona 63% 24% 12% 1% 0% 421 

California 9% 2% 86% 0% 3% 369 

Colorado 24% 24% 47% 3% 2% 450 

Connecticut 20% 9% 63% 2% 6% 112 

Delaware 28% 24% 43% 1% 4% 23

Florida 20% 7% 71% 0% 2% 361 

Georgia 7% 5% 85% 0% 3% 112 

Idaho 67% 22% 0% 10% 1% 74

Illinois 8% 28% 59% 1% 4% 128 

Indiana 65% 17% 10% 7% 1% 333 

Kansas 25% 7% 67% 0% 1% 424 

Kentucky 36% 19% 36% 4% 5% 329 

Louisiana 40% 29% 11% 6% 14% 274 

Maine 77% 17% 0% 5% 1% 303 

Maryland 21% 5% 63% 4% 7% 108 

Massachusetts* 0% 0% 93% 0% 7% 91

Minnesota 69% 22% 0% 9% 0% 276 

Mississippi 68% 16% 8% 7% 1% 77

Missouri 25% 8% 66% 0% 1% 251 

Montana 1% 14% 78% 0% 6% 50

Nevada 59% 11% 30% 0% 0% 124 

New Hampshire 1% 43% 41% 4% 11% 16

New Jersey 22% 8% 62% 2% 6% 99

New Mexico 25% 28% 47% 0% 0% 164 

New York 5% 7% 84% 0% 4% 206 

North Carolina 26% 6% 68% 0% 0% 181 

Pennsylvania 65% 24% 0% 7% 4% 284 

Rhode Island 35% 29% 35% 0% 1% 43

South Carolina 16% 16% 67% 0% 1% 199 

South Dakota 55% 40% 1% 4% 0% 413 

Tennessee 23% 5% 68% 2% 2% 122 

Texas 24% 16% 58% 0% 2% 881 

Utah 73% 27% 0% 0% 0% 232 

Virginia 65% 32% 1% 1% 1% 178 

Washington 83% 12% 1% 2% 2% 297 

West Virginia 59% 39% 1% 1% 0% 160 

Wisconsin 32% 21% 46% 0% 1% 187 

Wyoming 6% 2% 88% 0% 4% 209 

*For Massachusetts, #NA indicates that the pavement distress data was not available at the time of this report.
91

5.4. SUMMARY 

Supplemental analyses were performed at the request of FHWA to further investigate specific 

issues related to the data requirements presented in the NPRM and how the project data compare 

to the HPMS. 

The following conclusions were drawn from these analyses: 

 Revision of the threshold values has a limited impact on the resulting overall condition. 

The overall condition identified from the individual condition metrics is impacted, but the 

combination of the metric-based condition into the overall condition results in very little 

change. 

 Any change in section length to a value longer than 0.1-mile will result in loss of detail in 

terms of identifying specific areas of good and poor performance. As noted in chapter 4, 

the appropriate section length for reporting of condition is a balance of the size of the 

database being requested and the detail of the information obtained. 

 When comparing the data from the HPMS database with the data collected for the 

project, no significant biases were observed. Some States provided data that were more 

comparable to the project data than others. 

93

CHAPTER 6. CONCLUSIONS AND RECOMMENDATIONS 

This chapter provides the conclusions and recommendations resulting from the project. The 

project was conducted to meet three objectives as follows: 

 Collect an unbiased baseline data for statistically significant sample of the entire IHS and 

produce a report indicating pavement condition on the IHS nationally and in each State 

where data were collected. 

 Determine if HPMS is an unbiased representation of pavement condition on the IHS. 

 Recommend improvements to HPMS data collection and reporting necessary to either 

make HPMS unbiased or improve its precision, in regard to performance management 

and FHWA's use of HPMS data, which in turn will enable responses to questions such as: 

o Is two-way data collection necessary? 

o Does data need to be collected in more than one lane in a direction? 

o What is the optimum HPMS section length? 

o Do all distress items require full extent reporting or is sampling adequate? 

o Are protocols proposed by FHWA adequate for collecting and reporting distress 

or do they need improvement? 

In order to meet these objects, approximately 10,000 miles of data were collected on the IHS 

which consists of almost 20 percent of the IHS. Data were collected on nine interstates in 39 

States. Of the mileage collected, approximately 8,624 miles were collected in the primary 

direction of travel in the primary lane. Approximately 500 miles were collected in the adjacent 

lane, approximately 500 miles were collected in the opposing direction, and repeat data 

collection was performed on approximately 500 miles. 

The following statements provide the conclusions observed from this study: 

 62 percent of the IHS is in good condition and 1 percent is in poor condition. 

 No significant bias was observed between the HPMS and the IS Condition data collected 

for this project. 

 Attention to data quality has more impact on the data and subsequent conclusions than 

frequency of data collection. 

 Data collected in opposing directions are very similar and the surface was the same for 

approximately 97 percent of the distance over which data were collected in both 

directions of travel. 
94

 Data collected in the adjacent lane are very similar to those collected in the primary 

(outside) lane of travel. Although overall, the condition metrics indicate that the adjacent 

lane is in slightly better condition than the primary lane. 

 Selection of the section length is a tradeoff between detail of information and database 

size. With longer section lengths, the IRI, cracking, and rutting condition metrics will 

tend toward a fair condition resulting in more of the network appearing as fair condition. 

With shorter section lengths, the database will become larger and more difficult to 

handle. 

 Further analysis of section length confirms that even a small change in section length can 

reduce the impact of small areas of good and poor condition. 

 Review of statistically-based sampling requirements suggests that for conclusions drawn 

only on data at the national level, a 50 percent or smaller sample may be acceptable for 

data collection. For conclusions drawn on State-level data sets, sampling will not provide 

sufficient data to estimate condition. 

 Ride quality data collected on the bridge decks made very little difference on the overall 

condition. Other analyses indicated that it is very difficult for the data collection crew to 

identify where all bridge decks are especially since HPMS identifies pavement over box 

culverts as a bridge. These observations suggest that third party data collectors should not 

be relied upon for exclusion of data on bridges. 

 A comparison between faulting data collected using the LCMS system and the inertial 

profiler indicates that these two systems do not collect the same data. Review of the 

number of faults identified by these two systems suggests that the LCMS provides a more 

reliable evaluation of the average faulting in a section as the inertial profiler may not 

accurately identify all joints within a section. 

 Rut depth values were provided from both the LCMS sensor and an estimated 5-point rut 

depth. Comparisons between these two values illustrates that the LCMS sensor provides 

a more reliable rut depth as the 5-point value may result in a negative rut depth. 

 Several different definitions of percent cracking on asphalt pavements were reviewed 

using the data collected. Percent wheelpath cracking was the only definition which used 

some basis other than full section area by using only the area of the wheelpaths. The 

percent wheelpath cracking was approximately twice the value of the other definitions of 

percent cracking and resulted in a small difference in the percent good overall condition 

with no difference in the percent poor overall condition. 

 Changes in threshold values investigated have limited impact on the performance 

measures developed from the data for the national data. Some of the measures at the 

State level are impacted by these changes in the threshold values. Of note is that 

removing the provisions for urban populations related to IRI may increase the percentage 

of fair and poor performance more than the increase in good performance by the increase 

in the threshold values for the faulting and percent cracking. 
95

 Comparisons of the good/fair/poor condition at the location level further illustrate the 

variability of the data in the HPMS database. The HPMS and project data compare well 

for some States and for others the data do not compare well. In some cases, the sampling 

rate of the data used by the State for submittal to the HPMS is clearly visible. In other 

cases the use of a reporting interval larger than the 0.1-mile interval required by the 

NPRM. 

Based on the analyses conducted the following recommendations are made for data collection: 

 Pavement metrics 

o A data collection quality control plan should be provided by each State for data 

submitted for the HPMS.

o Condition data should be collected for the full extent of the outside lane in the 

primary direction of travel to estimate the performance of the network. 

o Condition data reported for segments of 0.1-mile in length provides a good 

tradeoff between detail of information and database size. 

o Ride quality data should be collected on bridge decks. 

 The data collection effort was not arranged to directly validate the protocols; however, 

several recommendations may be made with regard to the protocols used. 

o Rut depth data collected using a 5-point system should not be used for estimating 

the performance of asphalt surfaced pavements. 

o Improvements in collection of faulting data will be required. 

o Clarification in the data collection guidelines should identify the preference for 

sealed cracks. 

o Protocols should identify how to address the quantification of cracking in areas 

where transverse cracks cross areas of wheelpath cracking. 

 The following recommendations are for future research to be pursued: 

o Improvements are required for measurement of faulting. Further review should be 

undertaken of automated methods for collection of faulting data and how to 

collect reliable and repeatable data. 

97

REFERENCES 

1. Federal Register Volume 80, Issue 2. 80 FR 325 – National 

Performance Management Measures; Assessing Pavement Condition for the National 

Highway Performance Program and Bridge Condition for the National Highway 

Performance Program. January 5, 2015. 

2. NCHRP. 2013. Increasing Consistency in the Highway 

Performance Monitoring System for Pavement Reporting, Final Report. National 

Cooperative Highway Research Board (NCHRP). Washington, DC. 

3. Guerre, J., J. Groeger, S. Van Hecke, A. Simpson, G. Rada, and B. Visintine. 2012. 

Improving FHWA’s Ability to Assess Highway Infrastructure Health - Pilot Study 

Report. FHWA-HIF-12-049. Federal Highway Administration, Washington, DC. 

4. Guerre, J., J. Groeger, S. Van Hecke, and A. Simpson. 2011. Improving FHWA’s 

Ability to Assess Highway Infrastructure Health – National Meeting Report. Federal 

Highway Administration, Washington, DC. 

5. Simpson, A., G. Rada, B. Visintine, and J. Groeger. 2013a. Improving FHWA’s Ability 

to Assess Highway Infrastructure Health Pilot Study Report Addendum - Rutting Bias 

Investigation. FHWA-HIF-13-037. Federal Highway Administration, Washington, DC. 

6. Simpson, A., G. Rada, B. Visintine, J. Groeger, and J. Guerre. 2013b. Improving 

FHWA’s Ability to Assess Highway Infrastructure Health: Development of Next 

Generation Pavement Performance Measures. FHWA-HIF-13-042. Federal Highway 

Administration, Washington, DC. 

7. Van Hecke, S., and K.Ebright-McKeehan. 2012. “Memorandum to J. Groeger, AMEC, 

HPMS Interstate Pavement Condition Data and Tri-State Study Area Identification.” 

Cambridge Systematics, Chicago, IL. 

8. Van Hecke, S., Alam, M., Ebright-McKeehan, K., Visintine, B. and Groeger, J. 2013. 

Improving FHWA’s Ability to Assess Highway Infrastructure Health: Assessing the 

Health of Two Corridors Using the Pavement Health Track (PHT) Tool. Draft report. 

Federal Highway Administration, Washington, DC. 

9. AASHTO. Standard Practice for Collecting the Transverse Pavement Profile, AASHTO 

Designation PP 70-14. American Association of State Highway and Transportation 

Officials, 2014. 

10. Federal Highway Administration. 2014. Highway Performance Monitoring System - 

Field Manual. http://www.fhwa.dot.gov/policyinformation/hpms/fieldmanual/. Federal 

Highway Administration, Washington, DC. 
98

11. AASHTO. Standard Practice for Quantifying Roughness of Pavements, AASHTO 

Designation R 43-13. American Association of State Highway and Transportation 

Officials, 2013. 

12. AASHTO. Standard Practice for Determining Pavement Deformation Parameters and 

Cross-Slope from Collected Transverse Profiles, AASHTO Designation PP 69-14. 

American Association of State Highway and Transportation Officials, 2014. 

13. AASHTO. Standard Practice for Determining Rut Depth in Pavements, AASHTO 

Designation R 48-10. American Association of State Highway and Transportation 

Officials, 2013. 

14. AASHTO. Standard Practice for Evaluating Faulting of Concrete Pavements, AASHTO 

Designation R 36-13. American Association of State Highway and Transportation 

Officials, 2013. 

15. AASHTO. Standard Practice for Quantifying Cracks in Asphalt Pavement Surfaces, 

AASHTO Designation R 55-10. American Association of State Highway and 

Transportation Officials, 2013. 

16. AASHTO. Standard Practice for Quantifying Cracks in Asphalt Pavement Surfaces from 

Collected Images Utilizing Automated Methods, AASHTO Designation PP 67-14. 

American Association of State Highway and Transportation Officials, 2014. 

17. AASHTO. Standard Practice for Collecting Images of Pavement Surfaces for Distress 

Detection, AASHTO Designation PP 68-10. American Association of State Highway 

and Transportation Officials, 2010. 

18. Miller, J.S. and W.Y. Bellinger. 2003. Distress Identification Manual for the Long-Term 

Pavement Performance Program (Fourth Revised Edition). Report No. FHWA-RD-03-

031. Federal Highway Administration, Washington, DC. 

19. Pierce, L.M., McGovern, G. and Zimmerman, K.A. 2013. Practical Guide for Quality 

Management of Pavement Condition Data Collection, Draft Report, Federal Highway 

Administration, Washington, DC. 

20. AASHTO. Standard Specification for Inertial Profiler, AASHTO Designation M 328-14. 

American Association of State Highway and Transportation Officials, 2014. 

21. Visintine, B., G.R. Rada, and T. Thompson. 2014. “Memorandum to J. Jiang, FHWA, 

Creation of HPMS Datasets for LTPP Test Sections.” AMEC. Beltsville, MD, March 26, 

2014.

22. ASTM E1703 / E1703M-10, Standard Test Method for Measuring Rut-Depth of 

Pavement Surfaces Using a Straightedge, ASTM International, West Conshohocken, PA, 

2015, www.astm.org.
99

23. FHWA. 2014. LTPP Directive D-61, “Translation of LTPP Cracking Data for National 

Applications.” Washington, DC.

24. Lehman, E.L., 1998, Elements of Large-Sample Theory, Springer-Verlag, New York. 

101

APPENDIX A. DATA STORAGE AND QUALITY REVIEW PLAN 

The objective of this document is to provide the storage and review process to be used for the 

data collected as part of the Interstate Pavement Condition Sampling project. The document 

provides a layout of the project database to be used for storage of the project data. The 

document also provides a step-by-step listing of the quality review to be conducted on the data as 

they are submitted. 

1. Set up database 

Database to be set up in MS Access to provide primary storage for data to be used in the 

project analyses. This database is a project deliverable. The database has the table 

structure as indicated below. 

a. ID table – basic identification data for every 0.01-mile segment for which data are 

collected 

i. State (Character, 2) 

ii. Route (Character, 3) 

iii. Begin Milepost (Numeric, 4,2) 

iv. End Milepost (Numeric, 4,2) 

v. Latitude (Numeric, 3,5) 

vi. Longitude (Numeric, 3,5) 

vii. Elevation (Numeric, 4,2) 

viii. Date of data collection (Date – mm/dd/yyyy hh:mm:ss) 

ix. Lane of data collection (Numeric, 1,0) – 1 indicated for primary outside 

lane for data collection, 2 for adjacent lane 

x. Direction of travel (Character, 1 – N,S,E,W) 

xi. QC Data (Character, 1 – Y or N) 

xii. Climate zone (Character, 3 – WF, WNF, DF, DNF) 

xiii. Surface (Numeric, 1,0) 

xiv. Urban/Rural – from HPMS data (Numeric, 5,0) 

xv. Driver (Character, 3) 

xvi. Operator (Character, 3) 

xvii. Air Temperature (Numeric, 3,1) 

xviii. Pavement Surface Temperature (Numeric, 3,1) 

xix. Speed of Data Collection (Numeric 2,1) 

xx. Vehicle ID (Character, 6) 

xxi. Comments 

b. Event table – identifies location of bridges, construction, and pavement change 

i. State (Character, 2) 

ii. Route (Character, 3) 

iii. Date of data collection (Date – dd/mm/yyyy) 

iv. Milepost (Numeric, 3,2) 
102

v. Event (Note field) – field should identify bridge begin/end, construction 

begin/end, pavement change 

c. 0.01-mile IRI – provides IRI data at the 0.01-mile increment for primary route 

data collection 

i. State (Character, 2) 

ii. Route (Character, 3) 

iii. Date of data collection (Date – dd/mm/yyyy) 

iv. Begin milepost (Numeric, 3,2) 

v. End milepost (Numeric, 3,2) 

vi. Left IRI (Numeric, 3,0) 

vii. Right IRI (Numeric, 3,0) 

viii. Avg IRI (Numeric, 3,0) 

ix. Processor (Character, 3) 

x. QC Reviewer (Character, 3) – in all cases, this is the subcontractor 

personnel in charge of reviewing the data prior to submission 

xi. QA Reviewer (Character, 3) 

d. 0.01-mile Rutting – provides rutting data at the 0.01-mile increment for primary 

route data collection 

i. State (Character, 2) 

ii. Route (Character, 3) 

iii. Date of data collection (Date – dd/mm/yyyy) 

iv. Begin milepost (Numeric, 3,2) 

v. End milepost (Numeric, 3,2) 

vi. Left Rut Depth (Numeric, 1,2) 

vii. Right Rut Depth (Numeric 1,2) 

viii. Avg Rut Depth (Numeric 1,2)

ix. Processor (Character, 3) 

x. QC Reviewer (Character, 3) 

xi. QA Reviewer (Character, 3) 

e. 0.01-mile Faulting – provides faulting data at the 0.01-mile increment for primary 

route data collection. Faults measured as 0 inch will be recorded as 0 inch. 

i. State (Character, 2) 

ii. Route (Character, 3) 

iii. Date of data collection (Date – dd/mm/yyyy) 

iv. Begin milepost (Numeric, 3,2) 

v. End milepost (Numeric 3,2) 

vi. Average Fault (Numeric 1,2) 

vii. Standard Deviation Fault (Numeric 1,2) 

viii. Minimum Fault (Numeric 1,2) 

ix. Maximum Fault (Numeric 1,2) 

x. Processor (Character, 3) 

xi. QC Reviewer (Character, 3) 

xii. QA Reviewer (Character, 3) 
103

f. 0.01-mile Percent Cracking – provides percent cracking at the 0.01-mile 

increment for primary route data collection 

i. State (Character 2) 

ii. Route (Character, 3) 

iii. Date of data collection (Date – dd/mm/yyyy) 

iv. Begin milepost (Numeric 3,2) 

v. End milepost (Numeric 3,2) 

vi. Percent Cracking HPMS (Numeric 3,0) 

vii. Percent Cracking Wheelpath (Numeric 3,0 – AC only) 

viii. Processor (Character, 3) 

ix. QC Reviewer (Character, 3) 

x. QA Reviewer (Character, 3) 

g. Condition (0.1-mile intervals) – provides the condition metrics and the 

performance management measures at the 0.1-mile increment for performance 

management measures. 

i. State (Character, 2) 

ii. Route (Character, 3) 

iii. Date of data collection (Date – dd/mm/yyyy) 

iv. Begin milepost (Numeric 3,2) 

v. End milepost (Numeric 3,2) 

vi. Avg IRI (Numeric 3,0) 

vii. Percent Cracking Wheelpath (Numeric 3,0) 

viii. Avg Rutting (Numeric 1,2) 

ix. Avg Faulting (Numeric 1,2) 

x. IRI Condition (Character, 1 – G/F/P) 

xi. Cracking Condition (Character, 1 – G/F/P)

xii. Rut Condition (Character, 1 – G/F/P) 

xiii. Fault Condition (Character, 1 – G/F/P) 

xiv. Overall Condition (Character, 1 – G/F/P) 

xv. Note for Missing Data (Note) Field will contain explanations/reasons for 

data that are missing – e.g., missing data due to bridge, construction event, 

data quality issue or other reason. 

h. 0.01-mile IRI – Additional IRI data collected at 0.01-mile increments as part of 

adjacent lane, opposing direction, or quality control 

i. State (Character, 2) 

ii. Route (Character, 3) 

iii. Date of data collection (Date – dd/mm/yyyy) 

iv. Lane of data collection (Numeric, 1,0) – 1 indicated for primary outside 

lane for data collection, 2 for adjacent lane 

v. Direction (Character, 1 – N,S,E,W) 

vi. QC (Y or N) 

vii. Begin milepost (Numeric 3,2) 

viii. End milepost (Numeric 3,2) 
104

ix. Left IRI (Numeric 3,0) 

x. Right IRI (Numeric 3,0) 

xi. Avg IRI (Numeric 3,0) 

xii. Processor (Character, 3) 

xiii. QC Reviewer (Character, 3) 

xiv. QA Reviewer (Character, 3) 

i. 0.1-mile IRI – Additional IRI data collected at 0.1-mile increments as part of 

adjacent lane, opposing direction or quality control. Note that primary data 

collection IRI at 0.1-mile intervals will be stored in the condition table above. 

i. State (Character, 2) 

ii. Route (Character, 3) 

iii. Date of data collection (Date – dd/mm/yyyy) 

iv. Lane (Character, 1 – P = primary, A = adjacent, O = opposing direction 

primary lane) 

v. Direction (Character, 1 – N,S,E,W) 

vi. QC (Y or N) 

vii. Begin milepost (Numeric 3,2) 

viii. End milepost (Numeric 3,2) 

ix. Left IRI (Numeric 3,0) 

x. Right IRI (Numeric 3,0) 

xi. Avg IRI (Numeric 3,0) 

xii. Processor (Character, 3) 

xiii. QC Reviewer (Character, 3) 

xiv. QA Reviewer (Character, 3) 

j. 0.01-mile Rutting – Additional rut data collected at 0.01-mile increments as part 

of adjacent lane, opposing direction or quality control 

i. State (Character, 2) 

ii. Route (Character, 3) 

iii. Date of data collection (Date – dd/mm/yyyy) 

iv. Lane (Character, 1 – P = primary, A = adjacent, O = opposing direction 

primary lane) 

v. Direction (Character, 1 – N,S,E,W) 

vi. QC (Y or N) 

vii. Begin milepost (Numeric 3,2) 

viii. End milepost (Numeric 3,2) 

ix. Left Rut Depth (Numeric 1,2) 

x. Right Rut Depth (Numeric 1,2) 

xi. Avg Rut Depth (Numeric 1,2) 

xii. Processor (Character, 3) 

xiii. QC Reviewer (Character, 3) 

xiv. QA Reviewer (Character, 3) 

k. 0.01-mile Faulting – Additional faulting data collected at 0.01-mile increments as 

part of adjacent lane, opposing direction or quality control 
105

i. State (Character, 2) 

ii. Route (Character, 3) 

iii. Date of data collection (Date – dd/mm/yyyy) 

iv. Lane (Character, 1 – P = primary, A = adjacent, O = opposing direction 

primary lane) 

v. Direction (Character, 1 – N,S,E,W) 

vi. QC (Y or N) 

vii. Begin milepost (Numeric 3,2) 

viii. End milepost (Numeric 3,2) 

ix. Average Fault (Numeric 1,2)

x. Standard Deviation Fault (Numeric 1,2) 

xi. Minimum Fault (Numeric 1,2) 

xii. Maximum Fault (Numeric 1,2) 

xiii. Processor (Character, 3) 

xiv. QC Reviewer (Character, 3) 

xv. QA Reviewer (Character, 3) 

l. 0.01-mile Percent Cracking – Additional cracking data collected at 0.01-mile 

increments as part of adjacent lane, opposing direction or quality control 

i. State (Character, 2) 

ii. Route (Character, 3) 

iii. Date of data collection (Date – dd/mm/yyyy) 

iv. Lane (Character, 1 – P = primary, A = adjacent, O = opposing direction 

primary lane) 

v. Direction (Character, 1 – N,S,E,W) 

vi. QC (Y or N) 

vii. Begin milepost (Numeric 3,2) 

viii. End milepost (Numeric 3,2) 

ix. Percent Cracking HPMS (Numeric 3,0) 

x. Percent Cracking Wheelpath (Numeric 3,0 – AC only) 

xi. Processor (Character, 3) 

xii. QC Reviewer (Character, 3) 

xiii. QA Reviewer (Character, 3) 

2. Data received for completed State-route combination. 

Data was received from the data collection vendor at the completion of collection on each 

State-route combination. Data have been submitted on a USB drive and the original USB 

drive containing each submittal was maintained until data collection was completed and 

the data received were reviewed. Each submittal received a review in accordance with 

the following steps. 

a. Data files scanned for viruses 

b. Data stored on network under Data Collection Task in project file 

c. Original data file to be maintained under the data collection task 
106

d. Copy of data then placed under Data Analysis Task in project file as working file 

e. Original USB hard drive maintained until all data collection complete and has 

been received 

3. Data were reviewed for quality and consistency. 

These checks performed on the data will be a review of the data against itself. These 

basic checks will be completed using the data as received. Portions of the checks will be 

automated over the course of the project, but initially checks will be conducted manually 

or with the use of features in Excel. 

a. Initial checks 

i. Data is for State-route combination identified in submittal 

ii. QC, adjacent lane, and opposing direction data labeled 

iii. Data submittal contains all required elements 

1. Data needed to complete database 

2. Check that all required data elements were included in the data set 

iv. ROW images provided 

v. Identify if rain was falling in general vicinity of data collection vehicle 

using website such as www.wunderground.com which stores historic 

weather information 

vi. Data collection vendor equipment daily and weekly checks 

vii. Once these items verified, log data submittal showing date of submittal. 

Use log form as attached. 

b. Check following for completeness 

i. Roughness 

ii. Percent cracking 

iii. Faulting for all records with a surface type of 3 or 4 

iv. Rutting for all records with a surface type of 2 or 5 

v. Surface type 

vi. Location information 

vii. Event data 

c. Check for range 

i. Roughness – 40 to 250 in/mile

ii. Percent Cracking – less than 100% 

iii. Rutting – less than 1 inch

iv. Faulting – less than 1 inch 

v. Surface type – no unpaved surfaces. Surface type should be 2, 3, or 5

vi. Air Temperature – 40 to 100°F 

vii. Pavement Surface Temperature – 20 to 130°F 

viii. Speed – 40 to 65 mph 

d. Data consistency 

i. Faulting data should not be provided on a surface type of 2 or 5 
107

ii. Compare slab length to surface type. Typically, a slab length of 25 ft or 

greater will be a JRCP (Surface type 4). The data collection vendor will 

be instructed to default to surface type 3 (JPCP) if unsure whether surface 

is 3 or 4. 

iii. Rutting data should not be provided on a surface type of 3 or 5 

iv. Difference in rut depth between wheelpath values at the same location ≥ 

0.25 inch. (Based on LTPP data which have average difference less than 

0.1 inch and a standard deviation of approximately 0.1 inch.) 

v. Both values of percent cracking complete for surface types of 2 

vi. Have record for every 0.01-mile increment 

vii. Have record for IRI calculated at 0.1-mile increments 

viii. Difference in IRI between wheelpath values at the same location ≥ 50 

in/mile (As with rut depth, based on LTPP data which have average 

difference of 13 in/mile and standard deviation in difference of 16 in/mile) 

ix. For every begin event marker, there is an ending event marker 

x. Same data collection vehicle used for all data in a single submittal 

xi. Data collection vehicle matches prior data collection vehicle, unless 

communication provided indicating otherwise 

e. Review ROW images 

i. Are images clear? Review images from various times of day (morning, 

noon, and late afternoon). 

1. Determine the total mileage collected for the day. Divide this 

mileage by 4. 

2. Review images for 5 miles at each of the following: 

a. First mileage of the day 

b. At a quarter of the mileage collected 

c. At half of the mileage collected 

d. At three-quarters of the mileage collected 

e. Last 5 miles of the day 

ii. Compare 5% of event markers to ROW images – do they match? 

iii. Compare areas of cracking with ROW images – can cracks be observed? 

Complete this check for a minimum of 10 locations. Lack of visible 

cracking does not necessarily indicate a problem with the data but this 

check is to be used in concert with the other checks to review quality of 

submittal. 

f. Review downward/pavement images 

i. Are images clear? 

ii. Compare marked images to quantity reported for minimum 10 locations –

are these consistent? 

g. Flag data failing any of the checks identified under items a through f above. 

i. Any data failing these checks, notify project team (Rada, Groeger, 

Simpson, Visintine) 
108

ii. Team will make decision regarding flagged data, i.e., data failing checks 

in items a through f above. 

1. If data appears to be outlying record, data will be maintained and 

stored in database 

2. If data appears to be part of persistent issue (more than 4 records 

flagged for a day for the same reason), subcontractor will be 

notified. 

3. If more than 10 records flagged for same issue, a stop work order 

will be issued to subcontractor to sort out problem and correct any 

equipment issues. 

h. In instances where QC data are provided. 

i. Compare QC data to original data 

ii. Identify segments that differ by more than 5% 

iii. Share list of segments with project team (Rada, Groeger, Simpson, 

Visintine) 

iv. If more than 10% of repeat data are flagged, stop work order will be 

issued to subcontractor for them to investigate potential equipment issue. 

4. After review, data passing QC review will be filtered into project database as identified 

under item 1. 

5. After data has been added to database, perform the following additional QC reviews 

based on consistency comparisons with other data sets. 

a. Compare to I-90 pilot study data 

i. Data collected along I-90 through SD, MN, and WI 

ii. Perform one-to-one comparison with data collected on I-90 corridor for 

Infrastructure Health project 

iii. Flag data based on any of the following deviations: 

1. IRI increase > 10 in/mile per year or decrease > 5 in/mile 

2. Rut depth increase > 0.1 inch/year or decrease > 0.05 inch 

3. Faulting increase > 0.08 in/year or decrease > 0.04 inch 

4. Percent cracking > 10% / year or decrease > 5% 

b. LTPP Data 

i. Identify segments along data collection route matching LTPP test sections 

ii. Compare data from that segment to most recent LTPP performance data 

for matching test section 

6. Once all reviews have been completed, the QA Reviewer will add their initials to the 

database tables in that field. 

The compilation of these records represents the project database used to perform the analyses 

required to accomplish project objectives. 
109

Table 46. Data submittal log. 

State Route Data Received Review 

Completed 

Date Notify 

Data 

Collection 

Vendor 

Date 

Resubmittal, 

if needed 

WA I90 

ID I90 

WY I90 

SD I90 

MN I90 

WI I90 

AZ I15 

NV I15 

CA I15 

CA I10 

AZ I10 

NM I10 

TX I10 

LA I10 

MS I10 

AL I10 

FL I10 

UT I70 

CO I70 

KS I70 

MO I70 

GA I95 

SC I95 

NC I95 

VA I95 

MD I95 

DE I95 

PA I95 

NJ I95 

NY I95 

CT I95 

RI I95 

MA I95 

NH I95 

ME I95 

PA I81 

NY I81 

WV I79 
110

State Route Data Received Review 

Completed 

Date Notify 

Data 

Collection 

Vendor 

Date 

Resubmittal, 

if needed 

KY I64 

IN I64 

IL I64 

TN I65 

KY I65 

IN I65 
111

APPENDIX B. PROJECT DATABASE DATA DICTIONARY 

The IHS pavement condition sampling database consists of eight tables. These tables are detailed 

here. At the top of each table, the table name (in bold) from the database file is provided, and the 

table name is followed by a brief description of the table contents. The table then describes each 

of the data elements within the table, including attribute, data type, description and notes. 

Table 47 is cracking data for asphalt concrete pavements at 0.01-mile interval.

Table 47. 0.01mile_Crack_AC. 

Attribute Data Type Description Notes 

Session_Name Text 14 Collected file name used by data 

collection subcontractor 

SSRNMP###DL 

SS – State 

RN – Route Number 

### - Milepost at 

beginning of the file 

D – Direction 

L – Lane 

Suffix QC added if 

QC data 

State Text 2 2 character postal code for State 

Route_Number Text 3 I for Interstate followed by twodigit route number 

Direction Text 1 N = North 

S = South 

E = East 

W = West 

Lane Number 1 1 = Primary Lane 

2 = Adjacent Lane 

Begin_Milepost Number 8,4 Milepost of summary section start Miles 

End_Milepost Number 8,4 Milepost of summary section end Miles 

Section_Length Number 8,4 Driven distance in the summary 

section 

Miles 

Begin_Latitude Number 

12,8 

GPS location of summary section 

start 

Decimal Degrees 

WGS84 

Begin_Longitude Number 

12,8 

GPS location of summary section 

start 

Decimal Degrees 

WGS84 

Begin_Elevation Number 5,2 GPS location of summary section 

start 

Height above 

Ellipsoid in Feet –

WGS84 

End_Latitude Number 

12,8 

GPS location of summary section 

end 

Decimal Degrees –

WGS84 

End_Longitude Number 

12,8 

GPS location of summary section 

end 

Decimal Degrees –

WGS84 

End_Elevation Number 5,2 GPS location of summary section 

end 

High above Ellipsoid 

in Feet – WGS84 

QC_Data Text 1 Y = repeat quality control 

collection segment 

N = not quality control collection 

segment 

Vehicle_ID Text 3 ID of collection vehicle 

Driver Text 3 Initials of driver 
112

Attribute Data Type Description Notes 

Operator Text 3 Initials of operator 

Speed Number 2 Speed of vehicle at summary 

section start 

MPH 

Collection_Date Date Date of data collection MM/DD/YYYY 

Collection_Time Time Time of data collection HH:MM:SS 

Air_Temperature Number 3 Ambient air temperature Degrees Fahrenheit 

Surface_Temperature Number 3 Temperature of pavement surface Degrees Fahrenheit 

Surface_Type Text 4 Surface Type of pavement AC = asphalt 

concrete 

CRCP = continuously 

reinforced concrete 

pavement 

JPCP = jointed 

concrete pavement 

Bridge_Flag Text 4 True = Bridge deck located within 

the summary section 

Blank = No bridge deck within 

the summary section 

Lane_Deviation_Flag Text 4 True = Segment contains a lane 

deviation 

Blank = no deviation contained 

within the segment 

Construction_Flag Text 4 True = Segment contains 

construction 

Blank = no construction 

contained within the segment 

Percent_Cracking_HPMS Number 3,1 Area of affected wheelpath 

divided by area of lane 

% 

Percent_Cracking_Wheelpath Number 3,1 Area of affected wheelpath 

divided by the total area of the 

wheelpath 

% 

Percent_Cracking_NPRM_Under Number 3,1 Area of affected wheelpath plus 

area of transverse cracking not in 

the wheelpath divided by area of 

lane 

% 

Percent_Cracking_NPRM_Over Number 3,1 Area of affected wheelpath plus 

area of transverse cracking 

divided by area of lane 

% 

Percent_Cracking_NPRM_Step Number 3,1 Area of affected wheelpath plus 

area of transverse cracking not in 

the wheelpath with addition of 

transverse cracking in the 

wheelpath based on percentage of 

wheelpath cracking (none if more 

than 80% wheelpath, 25% if 

between 60 and 80% wheelpath, 

50% if between 40 and 60% 

wheelpath, 75% if between 20 

and 40% wheelpath, and 100% if 

less than 20% wheelpath) divided 

by area of lane 

% 

Length Number 3,1 Length of segment considered in 

accumulating traffic data 

Feet 
113

Attribute Data Type Description Notes 

Lane_Width Number 4,2 Average width of lane for the 

segment of cracking data 

Feet 

Num_Transverse Number 3,2 Transverse cracking length 

divided by lane width 

Wheelpath_Length Number 5,2 Length of affected wheelpath Feet 

Affected_Wheelpath_Area Number 5 Area of affected wheelpath Square Feet 

Wheelpath_Area Number 5 Area of wheelpath Square Feet 

Fatigue_Area Number 5 Area of fatigue present in entire 

lane 

Square Feet 

Transverse_Crack_Length Number 5 Length of transverse cracks in 

segment 

Feet 

Lane_Area Number 5 Area of lane in segment Square Feet 
114

Table 48 is cracking data for continuously-reinforced concrete pavement and jointed concrete 

pavement for 0.01-mile segments.

Table 48. 0.01mile_Crack_PCC. 

Attribute Data Type Description Notes 

Session_Name Text 14 Collected file name used by data 

collection subcontractor 

SSRNMP###DL 

SS – State 

RN – Route Number 

### - Milepost at 

beginning of the file 

D – Direction 

L – Lane 

Suffix QC added if 

QC data 

State Text 2 2 character postal code for State 

Route_Number Text 3 I for Interstate followed by twodigit route number 

Direction Text 1 N = North 

S = South 

E = East 

W = West 

Lane Number 1 1 = Primary Lane 

2 = Adjacent Lane 

Begin_Milepost Number 8,4 Milepost of summary section start Miles 

End_Milepost Number 8,4 Milepost of summary section end Miles 

Section_Length Number 8,4 Driven distance in the summary 

section 

Miles 

Begin_Latitude Number 12,8 GPS location of summary section 

start 

Decimal Degrees 

WGS84 

Begin_Longitude Number 12,8 GPS location of summary section 

start 

Decimal Degrees 

WGS84 

Begin_Elevation Number 5,2 GPS location of summary section 

start 

Height above Ellipsoid 

in Feet – WGS84 

End_Latitude Number 12,8 GPS location of summary section 

end 

Decimal Degrees –

WGS84 

End_Longitude Number 12,8 GPS location of summary section 

end 

Decimal Degrees –

WGS84 

End_Elevation Number 5,2 GPS location of summary section 

end 

High above Ellipsoid 

in Feet – WGS84 

QC_Data Text 1 Y = repeat quality control 

collection segment 

N = not quality control collection 

segment 

Vehicle_ID Text 3 ID of collection vehicle 

Driver Text 3 Initials of driver 

Operator Text 3 Initials of operator 

Speed Number 2 Speed of vehicle at summary 

section start 

MPH 

Collection_Date Date Date of data collection MM/DD/YYYY 

Collection_Time Time Time of data collection HH:MM:SS 

Air_Temperature Number 3 Ambient air temperature Degrees Fahrenheit 

Surface_Temperature Number 3 Temperature of pavement surface Degrees Fahrenheit 
115

Attribute Data Type Description Notes 

Surface_Type Text 4 Surface Type of pavement AC = asphalt concrete 

CRCP = continuously 

reinforced concrete 

pavement 

JPCP = jointed 

concrete pavement 

Bridge_Flag Text 4 True = Bridge deck located within 

summary section 

Blank = No bridge deck within 

the summary section 

Lane_Deviation_Flag Text 4 True = Segment contains a lane 

deviation 

Blank = no deviation contained 

within the segment 

Construction_Flag Text 4 True = Segment contains 

construction 

Blank = no construction contained 

within the segment 

HPMS_Cracking_Percent Number 3 CRCP – Area of punchouts and 

patching divided by area of lane 

JPCP – Number of cracked slabs 

divided by total number of slabs 

% 

Transverse_Cracked_Slab_Count Number 3 Total count of transversely 

cracked slabs in segment 

Count 

Longitudinal_Cracked_Slab_Count Number 3 Total count of longitudinally 

cracked slabs in segment 

Count 

Combination_Cracked_Slab_Count Number 3 Total count of slabs containing 

both longitudinal and transverse 

cracks in segment 

Count 

Joint_Count Number 2 Total count of joints in segment Count 

Punchout_Area Number 5 Area of all punchouts in segment Square Feet 

Patching_Area Number 5 Area of all patches in segment Square Feet 

Lane_Area Number 5 Area of lane in segment Square Feet 
116

Table 49 is faulting data collected for 0.01-mile segments of jointed concrete surfaces. 

Table 49. 0.01mile_Fault. 

Attribute Data Type Description Notes 

Session_Name Text 14 Collected file name used by data 

collection subcontractor 

SSRNMP###DL 

SS – State 

RN – Route Number 

### - Milepost at 

beginning of the file 

D – Direction 

L – Lane 

Suffix QC added if QC 

data 

State Text 2 2 character postal code for State 

Route_Number Text 3 I for Interstate followed by twodigit route number 

Direction Text 1 N = North 

S = South 

E = East 

W = West 

Lane Number 1 1 = Primary Lane 

2 = Adjacent Lane 

Begin_Milepost Number 8,4 Milepost of summary section start Miles 

End_Milepost Number 8,4 Milepost of summary section end Miles 

Section_Length Number 8,4 Driven distance in the summary 

section 

Miles 

Begin_Latitude Number 12,8 GPS location of summary section 

start 

Decimal Degrees 

WGS84 

Begin_Longitude Number 12,8 GPS location of summary section 

start 

Decimal Degrees 

WGS84 

Begin_Elevation Number 5,2 GPS location of summary section 

start 

Height above Ellipsoid 

in Feet – WGS84 

End_Latitude Number 12,8 GPS location of summary section 

end 

Decimal Degrees –

WGS84 

End_Longitude Number 12,8 GPS location of summary section 

end 

Decimal Degrees –

WGS84 

End_Elevation Number 5,2 GPS location of summary section 

end 

High above Ellipsoid 

in Feet – WGS84 

QC_Data Text 1 Y = repeat quality control 

collection segment 

N = not quality control collection 

segment 

Vehicle_ID Text 3 ID of collection vehicle 

Driver Text 3 Initials of driver 

Operator Text 3 Initials of operator 

Speed Number 2 Speed of vehicle at summary 

section start 

MPH 

Collection_Date Date Date of data collection MM/DD/YYYY 

Collection_Time Time Time of data collection HH:MM:SS 

Air_Temperature Number 3 Ambient air temperature Degrees Fahrenheit 

Surface_Temperature Number 3 Temperature of pavement surface Degrees Fahrenheit 
117

Attribute Data Type Description Notes 

Surface_Type Text 4 Surface Type of pavement AC = asphalt concrete 

CRCP = continuously 

reinforced concrete 

pavement 

JPCP = jointed 

concrete pavement 

Bridge_Flag Yes/No Yes = Bridge deck located within 

summary section 

No = No bridge deck within the 

summary section 

Lane_Deviation_Flag Text 4 True = Segment contains a lane 

deviation 

Blank = no deviation contained 

within the segment 

Construction_Flag Text 4 True = Segment contains 

construction 

Blank = no construction 

contained within the segment 

LCMS_Faulting_Average Number 4,2 Average fault height derived from 

LCMS adjusted to account for 

undetected joints 

Inches 

LCMS_Fault_Count Number 2 Number of faults detected by 

LCMS 

Count 

LCMS_Faulting_Standard_Deviation Number 4,2 Standard deviation of faults 

measured with LCMS and added 

zero values 

Inches 

LCMS_Faulting_Minimum Number 4,2 Minimum fault height included in 

average from LCMS including 

added zero values 

Inches 

LCMS_Faulting_Maximum Number 4,2 Maximum fault height included in 

average from LCMS 

Inches 

RSP_Faulting_Average Number 4,2 Average fault height derived from 

RSP adjusted to account for 

undetected joints 

Inches 

RSP_Fault_Count Number 2 Number of faults detected by RSP Count 

RSP_Faulting_Standard_Deviation Number 4,2 Standard deviation of faults 

measured with RSP and added 

zero values 

Inches 

RSP_Faulting_Minimum Number 4,2 Minimum fault height included in 

average from RSP including 

added zero values 

Inches 

RSP_Faulting_Maximum Number 4,2 Maximum fault height included in 

average from RSP 

Inches 

Joint_Count Number 2 Number of joints detected by 

LCMS supplemented by manual 

detection 

Count 
118

Table 50 is IRI data for the 0.01-mile segment. These data were collected on all surface types. 

Table 50. 0.01mile_IRI. 

Attribute Data Type Description Notes 

Session_Name Text 14 Collected file name used by data collection 

subcontractor 

SSRNMP###DL 

SS – State 

RN – Route Number 

### - Milepost at 

beginning of the file 

D – Direction 

L – Lane 

Suffix QC added if QC 

data 

State Text 2 2 character postal code for State 

Route_Number Text 3 I for Interstate followed by two-digit route 

number 

Direction Text 1 N = North 

S = South 

E = East 

W = West 

Lane Number 1 1 = Primary Lane 

2 = Adjacent Lane 

Begin_Milepost Number 8,4 Milepost of summary section start Miles 

End_Milepost Number 8,4 Milepost of summary section end Miles 

Section_Length Number 8,4 Driven distance in the summary section Miles 

Begin_Latitude Number 12,8 GPS location of summary section start Decimal Degrees WGS84 

Begin_Longitude Number 12,8 GPS location of summary section start Decimal Degrees WGS84 

Begin_Elevation Number 5,2 GPS location of summary section start Height above Ellipsoid in 

Feet – WGS84 

End_Latitude Number 12,8 GPS location of summary section end Decimal Degrees –

WGS84 

End_Longitude Number 12,8 GPS location of summary section end Decimal Degrees –

WGS84 

End_Elevation Number 5,2 GPS location of summary section end High above Ellipsoid in 

Feet – WGS84 

QC_Data Text 1 Y = repeat quality control collection 

segment 

N = not quality control collection segment 

Vehicle_ID Text 3 ID of collection vehicle 

Driver Text 3 Initials of driver 

Operator Text 3 Initials of operator 

Speed Number 2 Speed of vehicle at summary section start MPH 

Collection_Date Date Date of data collection MM/DD/YYYY 

Collection_Time Time Time of data collection HH:MM:SS 

Air_Temperature Number 3 Ambient air temperature Degrees Fahrenheit 

Surface_Temperature Number 3 Temperature of pavement surface Degrees Fahrenheit 

Surface_Type Text 4 Surface Type of pavement AC = asphalt concrete 

CRCP = continuously 

reinforced concrete 

pavement 

JPCP = jointed concrete 

pavement 
119

Attribute Data Type Description Notes 

Bridge_Flag Yes/No Yes = Bridge deck located within summary 

section 

No = No bridge deck within the summary 

section 

Lane_Deviation_Flag Text 4 True = Segment contains a lane deviation 

Blank = no deviation contained within the 

segment 

Construction_Flag Text 4 True = Segment contains construction 

Blank = no construction contained within 

the segment 

IRI Number 3 Average IRI in/mile 

IRI_Left Number 3 Left wheelpath IRI in/mile 

IRI_Right Number 3 Right wheelpath IRI in/mile 
120

Table 51 is rut depth data collected for 0.01-mile segments with asphalt concrete surfaces. 

Table 51. 0.01mile_Rut. 

Attribute Data Type Description Notes 

Session_Name Text 14 Collected file name used by data collection 

subcontractor 

SSRNMP###DL 

SS – State 

RN – Route Number 

### - Milepost at 

beginning of the file 

D – Direction 

L – Lane 

Suffix QC added if QC 

data 

State Text 2 2 character postal code for State 

Route_Number Text 3 I for Interstate followed by two-digit route 

number 

Direction Text 1 N = North 

S = South 

E = East 

W = West 

Lane Number 1 1 = Primary Lane 

2 = Adjacent Lane 

Begin_Milepost Number 8,4 Milepost of summary section start Miles 

End_Milepost Number 8,4 Milepost of summary section end Miles 

Section_Length Number 8,4 Driven distance in the summary section Miles 

Begin_Latitude Number 12,8 GPS location of summary section start Decimal Degrees WGS84 

Begin_Longitude Number 12,8 GPS location of summary section start Decimal Degrees WGS84 

Begin_Elevation Number 5,2 GPS location of summary section start Height above Ellipsoid in 

Feet – WGS84 

End_Latitude Number 12,8 GPS location of summary section end Decimal Degrees –

WGS84 

End_Longitude Number 12,8 GPS location of summary section end Decimal Degrees –

WGS84 

End_Elevation Number 5,2 GPS location of summary section end High above Ellipsoid in 

Feet – WGS84 

QC_Data Text 1 Y = repeat quality control collection 

segment 

N = not quality control collection segment 

Vehicle_ID Text 3 ID of collection vehicle 

Driver Text 3 Initials of driver 

Operator Text 3 Initials of operator 

Speed Number 2 Speed of vehicle at summary section start MPH 

Collection_Date Date Date of data collection MM/DD/YYYY 

Collection_Time Time Time of data collection HH:MM:SS 

Air_Temperature Number 3 Ambient air temperature Degrees Fahrenheit 

Surface_Temperature Number 3 Temperature of pavement surface Degrees Fahrenheit 

Surface_Type Text 4 Surface Type of pavement AC = asphalt concrete 

CRCP = continuously 

reinforced concrete 

pavement 

JPCP = jointed concrete 

pavement 
121

Attribute Data Type Description Notes 

Bridge_Flag Yes/No Yes = Bridge deck located within summary 

section 

No = No bridge deck within the summary 

section 

Lane_Deviation_Flag Text 4 True = Segment contains a lane deviation 

Blank = no deviation contained within the 

segment 

Construction_Flag Text 4 True = Segment contains construction 

Blank = no construction contained within 

the segment 

Rutting_Average Number 4,2 Average of left and right rut depth values Inches 

Rutting_Left Number 4,2 Left wheelpath rut depth Inches 

Rutting_Right Number 4,2 Right wheelpath rut depth Inches 

5pt_Rutting_Average Number 4,2 Average of left and right rut depths based 

on 5-pt simulation 

Inches 

5pt_Rutting_Left Number 4,2 Left wheelpath rut depth based on 5-pt 

simulation 

Inches 

5pt_Rutting_Right Number 4,2 Right wheelpath rut depth based on 5-pt 

simulation 

Inches 

Table 52 is location of features impacting data collection.

Table 52. Event_Table. 

Attribute Data Type Description Notes 

Session_Name Text 14 Collected file name used by data collection 

subcontractor 

SSRNMP###DL 

SS – State 

RN – Route Number 

### - Milepost at 

beginning of the file 

D – Direction 

L – Lane 

Suffix QC added if QC 

data 

Route Text 6 Route description SSRNDL 

SS – State

RN – Route Number

D – Direction

L - Lane

Begin_Milepost Number 8,4 Measured distance at start of feature Miles 

End_Milepost Number 8,4 Measured distance at end of feature Miles 

Feature_Type Text 14 Feature type that exists at referenced 

location 

Bridge 

Construction 

Lane Deviation 
122

Table 53 is location of changes in pavement type. 

Table 53. Pavement_Change.

Attribute Data Type Description Notes 

Session_Name Text 14 Collected file name used by data collection 

subcontractor 

SSRNMP###DL 

SS – State 

RN – Route Number 

### - Milepost at 

beginning of the file 

D – Direction 

L – Lane 

Suffix QC added if QC 

data 

State Text 2 2 character postal code for State 

Route_Number Text 3 I for Interstate followed by two-digit route 

number 

Direction Text 1 N = North 

S = South 

E = East 

W = West 

Lane Number 1 1 = Primary Lane 

2 = Adjacent Lane 

QC_Data Text 1 Y = repeat quality control collection 

segment 

N = not quality control collection segment 

Milepost Number 8,4 Measured distance at pavement change Miles 

Pavement Type Text 4 Pavement type that begins at referenced 

location 

AC

CRCP 

JPCP 

Table 54 is data accumulated to the 0.1-mile segment. Shorter segments are used where a 

pavement change occurs.

Table 54. Tenth_Mile_Data. 

Attribute Data Type Description Notes 

Session_Name Text 14 Collected file name used by data collection 

subcontractor 

SSRNMP###DL 

SS – State 

RN – Route Number 

### - Milepost at 

beginning of the file 

D – Direction 

L – Lane 

Suffix QC added if QC 

data 

State Text 2 2 character postal code for State 

Route_Number Text 3 I for Interstate followed by two-digit route 

number 

Direction Text 1 N = North 

S = South 

E = East 

W = West 

Lane Number 1 1 = Primary Lane 

2 = Adjacent Lane 

Begin_Milepost Number 8,4 Mile point of summary section start Miles 

End_Milepost Number 8,4 Mile point of summary section end Miles 
123

Attribute Data Type Description Notes 

Section_Length Number 8,4 Driven distance in the summary section Miles 

Begin_Latitude Number 12,8 GPS location of summary section start Decimal Degrees WGS84 

Begin_Longitude Number 12,8 GPS location of summary section start Decimal Degrees WGS84 

Begin_Elevation Number 5,2 GPS location of summary section start Height above Ellipsoid in 

Feet – WGS84 

End_Latitude Number 12,8 GPS location of summary section end Decimal Degrees –

WGS84 

End_Longitude Number 12,8 GPS location of summary section end Decimal Degrees –

WGS84 

End_Elevation Number 5,2 GPS location of summary section end High above Ellipsoid in 

Feet – WGS84 

Vehicle_ID Text 3 ID of collection vehicle 

Driver Text 3 Initials of driver 

Operator Text 3 Initials of operator 

Collection_Date Date Date of data collection MM/DD/YYYY 

Surface_Type Text 4 Surface type of pavement AC = asphalt concrete 

CRCP = continuously 

reinforced concrete 

pavement 

JPCP = jointed concrete 

pavement 

IRI with Bridge Number 4,1 IRI for the segment including data collected 

on bridge decks 

in/mile 

IRI No Bridge Number 4,1 IRI for the segment excluding data collected 

on bridge decks identified by the data 

collection crew 

in/mile 

Avg_Rutting Number 4,3 Average rut depth for the segment Inches 

Avg_5pt_Rut Number 4,3 Average rut depth for the segment based on 

5-point simulation 

Inches 

Avg_LCMS_Fault Number 4,3 Average fault for the segment derived from 

LCMS 

Inches 

Avg_RSP_Fault Number 4,3 Average fault for the segment derived from 

RSP 

Inches 

Percent Cracking 

HPMS PCC 

Number 3,1 Percent cracking for the segment 

CRCP – Area of punchouts and patching 

divided by area of lane 

JPCP – Number of cracked slabs divided by 

total number of slabs 

% 

Percent Cracking 

Over

Number 3,1 Area of affected wheelpath plus area of 

transverse cracking divided by area of lane 

% 

Percent Cracking 

Step 

Number 3,1 Area of affected wheelpath plus area of 

transverse cracking not in the wheelpath 

with addition of transverse cracking in the 

wheelpath based on percentage of 

wheelpath cracking (none if more than 80% 

wheelpath, 25% if between 60 and 80% 

wheelpath, 50% if between 40 and 60% 

wheelpath, 75% if between 20 and 40% 

wheelpath, and 100% if less than 20% 

wheelpath) divided by area of lane 

% 

Percent Cracking 

HPMS AC 

Number 3,1 Area of affected wheelpath divided by area 

of lane 

% 

Percent Cracking 

Under 

Number 3,1 Area of affected wheelpath plus area of 

transverse cracking not in the wheelpath 

% 
124

Attribute Data Type Description Notes 

divided by area of lane 

Percent Cracking 

%WP 

Number 3,1 Area of affected wheelpath divided by the 

total area of the wheelpath 

% 

IRI Perf Text 1 Pavement condition based solely on IRI 

including IRI collected on bridge decks 

G / F / P 

NB IRI Perf Text 1 Pavement condition based solely on IRI 

excluding IRI collected on bridge decks 

G / F / P 

NB IRI with no 

short segments Perf 

Text 1 Pavement condition based solely on IRI 

excluding IRI collected on bridge decks or 

for segments shorter than 0.1-mile 

G / F / P 

IRI Alt Perf Text 1 Pavement condition based solely on IRI 

including IRI collected on bridge decks 

using alternate thresholds 

G / F / P 

NB IRI Alt Perf Text 1 Pavement condition based solely on IRI 

excluding IRI collected on bridge decks 

using alternate thresholds 

G / F / P 

Rutting Perf Text 1 Pavement condition based solely on average 

rut depth 

G / F / P 

5pt Rut Perf Text 1 Pavement condition based solely on average 

rut depth from 5-point simulation 

G / F / P 

Faulting Perf Text 1 Pavement condition based solely on faulting 

derived from LCMS 

G / F / P 

RSP Fault Perf Text 1 Pavement condition based solely on faulting 

derived from RSP 

G / F / P 

Alt Faulting Perf Text 1 Pavement condition based solely on faulting 

derived from LCMS using alternate 

threshold values 

G / F / P 

PCC Crack Perf Text 1 Pavement condition based solely on percent 

cracking (CRCP and JPCP only) 

G / F / P 

Alt PCC Crack 

Perf 

Text 1 Pavement condition based solely on percent 

cracking (CRCP and JPCP only) using 

alternate threshold values 

G / F / P 

Over Cracking Perf Text 1 Pavement condition based solely on percent 

cracking over (AC only) 

G / F / P 

Alt Over Cracking 

Perf 

Text 1 Pavement condition based solely on percent 

cracking over (AC only) using alternate 

threshold values 

G / F / P 

Step Crack Perf Text 1 Pavement condition based solely on percent 

cracking step (AC only) 

G / F / P 

HPMS Perf Text 1 Pavement condition based solely on HPMS 

percent cracking (AC only) 

G / F / P 

Under Perf Text 1 Pavement condition based solely on percent 

cracking under (AC only) 

G / F / P 

%WP Perf Text 1 Pavement condition based solely on percent 

wheelpath cracking (AC only) 

G / F / P 

Performance Text 1 Pavement performance using IRI Perf, 

Rutting Perf, Faulting Perf, PCC Crack 

Perf, and Over Cracking Perf 

G / F / P 

Alt Performance Text 1 Pavement performance using IRI Alt Perf, 

Rutting Perf, Alt Faulting Perf, Alt PCC 

Crack Perf, and Alt Over Cracking Perf 

G / F / P 

No Bridge 

Performance 

Text 1 Pavement performance using NB IRI Perf, 

Rutting Perf, Faulting Perf, PCC Crack 

Perf, and Over Cracking Perf 

G / F / P 
125

Attribute Data Type Description Notes 

Alt No Bridge 

Performance 

Text 1 Pavement performance using NB IRI Alt 

Perf, Rutting Perf, Alt Faulting Perf, Alt 

PCC Crack Perf, and Alt Over Cracking 

Perf 

G / F / P 

Performance no 

short segments 

Text 1 Pavement performance using NB IRI with 

no short segments Perf, Rutting Perf, 

Faulting Perf, PCC Crack Perf, and Over 

Cracking Perf 

G / F / P 

Performance w 

RSP Fault 

Text 1 Pavement performance using IRI Perf, 

Rutting Perf, RSP Fault Perf, PCC Crack 

Perf, and Over Cracking Perf 

G / F / P 

Performance w 5pt 

rut 

Text 1 Pavement performance using IRI Perf, 5pt 

Rut Perf, Faulting Perf, PCC Crack Perf, 

and Over Cracking Perf 

G / F / P 

Performance with 

Step 

Text 1 Pavement performance using IRI Perf, 

Rutting Perf, Faulting Perf, PCC Crack 

Perf, and Step Crack Perf 

G / F / P 

Performance w 

HPMS 

Text 1 Pavement performance using IRI Perf, 

Rutting Perf, Faulting Perf, PCC Crack 

Perf, and HPMS Perf 

G / F / P 

Performance w 

Under 

Text 1 Pavement performance using IRI Perf, 

Rutting Perf, Faulting Perf, PCC Crack 

Perf, and Under Perf 

G / F / P 

Performance w 

%WP 

Text 1 Pavement performance using IRI Perf, 

Rutting Perf, Faulting Perf, PCC Crack 

Perf, and %WP Perf 

G / F / P 

127

APPENDIX C. SAMPLE LENGTH PLOTS 

Figure 39. Graph. Comparison of IRI at 1-mile section length to 0.1-mile section length.

Figure 40. Graph. Comparison of IRI at 0.5-mile section length to 0.1-mile section length.
128

Figure 41. Graph. Comparison of IRI at 0.05-mile section length to 0.1-mile section length.

Figure 42. Graph. Comparison of IRI at 0.01-mile section length to 0.1-mile section length.
129

Figure 43. Graph. Comparison of IRI at 0.4-mile section length to 0.1-mile section length. 

Figure 44. Graph. Comparison of IRI at 0.3-mile section length to 0.1-mile section length.
130

Figure 45. Graph. Comparison of IRI at 0.2-mile section length to 0.1-mile section length.

Figure 46. Graph. Comparison of rut depth at 1-mile section length to 0.1-mile section 

length.
131

Figure 47. Graph. Comparison of rut depth at 0.5-mile section length to 0.1-mile section 

length.

Figure 48. Graph. Comparison of rut depth at 0.05-mile section length to 0.1-mile section 

length.
132

Figure 49. Graph. Comparison of rut depth at 0.01-mile section length to 0.1-mile section 

length.

Figure 50. Graph. Comparison of rut depth at 0.4-mile section length to 0.1-mile section 

length.
133

Figure 51. Graph. Comparison of rut depth at 0.3-mile section length to 0.1-mile section 

length.

Figure 52. Graph. Comparison of rut depth at 0.2-mile section length to 0.1-mile section 

length.
134

Figure 53. Graph. Comparison of percent cracking at 1-mile section length to 0.1-mile 

section length.

Figure 54. Graph. Comparison of percent cracking at 0.5 mile section length to 0.1-mile 

section length.
135

Figure 55. Graph. Comparison of percent cracking at 0.05-mile section length to 0.1-mile 

section length.

Figure 56. Graph. Comparison of percent cracking at 0.01-mile section length to 0.1-mile 

section length.
136

Figure 57. Graph. Comparison of percent cracking at 0.4-mile section length to 0.1-mile 

section length.

Figure 58. Graph. Comparison of percent cracking at 0.3-mile section length to 0.1-mile 

section length.
137

Figure 59. Graph. Comparison of percent cracking at 0.2-mile section length to 0.1-mile 

section length.