FOREWORD

Pavement performance analysis encompasses a variety of applications, including the prediction

ofindividual distresses, pavement design and rehabilitation, and pavement management. In all

ofthese applications, knowledge ofthe cumulative trafflc loads imposed on the pavement is a

crucial element ofthe analysis process, especially in developing load-related distress prediction

models. Because ofthe expensive and massive nature ofthe trafflc data collection process in the

field, it has been the responsibility ofthe participating agencies since the inception ofthe Long

Term Pavement Performance (LTPP) program. The result has been considerable variation in the

quality and quantity ofthe historical and monitoring traffic data, creating a need to estimate

cumulative traffic loads for the entire pavement lifespan using the available fragmented data.

This report provides a methodology for obtaining cumulative traffic load spectra applicable for

all LTPP sites, and demonstrates this methodology using 12 case studies. This report will be of

interest to engineers involved in pavement design, pavement performance evaluation and

prediction, and pavement maintenance and rehabilitation.

~r{au\ Tong, P.E.

Director

Office ofInfrastructure

Research and Development

NOTICE

This document is disseminated under the sponsorship ofthe Department ofTransportation in the

interest ofinformation exchange. The United States Government assumes no liability for its

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

The United States Government does not endorse products or manufacturers. Trade and

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

object ofthe document.

1. Report No.

FHWA-RD-00-054

2. Government Accession No.

Technical Report Documentation Page

3. Recipient's Catalog No.

4. Title and Subtitle

ESTIMATING CUMULATIVE TRAFFIC LOADS, Final Report for

Phase 1

7. Author(s)

Jerry J. Hajek and Olga I. Selezneva

9. Performing Organization Name and Address

ERES Consultants, Inc.

9030 Red Branch Road, Suite 210

Columbia, Maryland 21045

12. Sponsoring Agency Name and Address

Office ofInfrastructure Research and Development

Federal Highway Administration

6300 Georgetown Pike

McLean, Virginia 22101-2296

5. Report Date

Julv 2000

6. Performing Organization Code

8. Performing Organization Report No.

10. Work Unit No. (TRAIS)

C6B

11. Contract or Grant No.

DTFH61-96-C-00003

13. Type of Report and Period Covered

Final Report

Sept. 1998 to Aug. 1999

14. Sponsoring Agency Code

15. Supplementary Notes

Contracting Officer's Technical Representative (COTR): Cheryl Allen Richter, HRDI

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

16. Abstract

The knowledge oftraffic loads is a prerequisite for the pavement analysis process, especially for the development ofload-related distress

prediction models. Furthermore, the emerging mechanistically based pavement performance models and pavement design methods (such

as the anticipated 2002 Pavement Design Guide) require the knowledge of the load spectra acting on the pavement during its lifespan.

This report describes a procedure for obtaining axle load spectra for Long Term Pavement Performance (LTPP) sections. The procedure

has been demonstrated and evaluated by applying it to 12 LTPP sections for which different amounts of monitoring traffic data were

available. Typically, for the majority of LTPP sections, there are only a few years for which the traffic load data were collected by

automated equipment in the mid-1990s. To obtain axle load spectra for all in-service years, traffic loads must be projected: backcasted

(forthe years before the installation oftraffic monitoring equipment) and forecasted (for the years afterthe automated equipment no longer

provides data).

This report also contains a review ofthe evolution of motor carrier technology (in terms of economical and political changes, regulatory

changes in vehicle weights and dimensions, and engineering changes) and its impact on traffic loads, and a proposal to develop and use

new summary statistical measures and tools for the management of load spectra. Major findings and recommendations include:

I. Traffic load projections for the majority of LTPP sites are feasible.

2. The accuracy and reliability ofthe traffic projections for many ofthe LTPP sites may not be as high as originally anticipated.

3. The involvement of State highway agencies (SHAs) in the traffic projection process is crucial.

4. The traffic projection process is very challenging and labor-intensive. The main reason for the labor-intensive process is the need to

carry out extensive quality assurance activities to resolve inconsistencies in the reported traffic data.

5. The quality assurance process would greatly benefit from developing a knowledge base or a catalog documenting a typical range of

traffic load variables. The process would also benefit from the availability ofsummary measures for traffic loads that are independent

of pavement variables.

6. Traffic modeling and forecasting is a highly cost-effective way to extend limited sampling data and compensate for the lack of data.

To obtain payback on the large investment in the traffic data collection effort by SHAs, it is necessary to allocate sufficient resources

to utilize fully the available traffic data through the traffic prediction process.

7. It is recommended to proceed with carrying out traffic load projections for the selected LTPP sites (Phase 2 study).

8. Phase 2 study, and any future projection oftraffic loads, should include the input from the representatives of SHAs.

17. Key Words

Asphalt pavements, concrete pavements, LTPP, traffic.

18. Distribution Statement

No restrictions. This document is available to the

public through the National Technical Information

Service, Springfield, VA 22161.

19. Security Classification (of this report)

Unclassified

20. Security Classification (of this page) 21. No. of Pages

Unclassified

22. Price

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

APPROXIMATE CONVERSIONS TO SI UNITS APPROXIMATE CONVERSIONS FROM SI UNITS

Symbol When You Know MUltiply By To Find Symbol Symbol When You Know Multiply By To Find Symbol

LENGTH LENGTH

in inches 25.4 millimeters mm mm millimeters 0.039 inches in

ft feet 0.305 meters m m meters 3.28 feet ft

yd yards 0.914 meters m m meters 1.09 yards yd

mi miles 1.61 kilometers km km kilometers 0.621 miles mi

AREA AREA

in2 square inches 645.2 square millimeters mm2 mm2 square millimeters 0.0016 square inches in2

ft2 square feet 0.093 square meters m2 m2 square meters 10.764 square feet ft2

ydZ square yards 0.836 square meters m2 m2 square meters 1.195 square yards ydZ

ac acres 0.405 hectares ha ha hectares 2.47 acres ac

mi2 square miles 2.59 square kilometers km2 km2 square kilometers 0.386 square miles mi2

VOLUME VOLUME

fI oz fluidounces 29.57 milliliters mL mL milliliters 0.034 fluidounces fI oz

gal gallons 3.785 liters L L liters 0.264 gallons gal

.... III ft3 cubic feet 0.028 cubic meters m3 m3 cubic meters 35.71 cubic feet ft3 .... yd' cubic yards 0.765 m3 m3 cubic meters 1.307 cubic yards yd3 cubic meters

NOTE: Volumes greater than 1000 I shall be shown in m3.

MASS MASS

oz ounces 28.35 grams g g grams 0.035 ounces oz

Ib pounds 0.454 kilograms kg kg kilograms 2.202 pounds Ib

T short tons (2000 Ib) 0.907 megagrams Mg Mg megagrams 1.103 short tons (2000 Ib) T

(or "metric ton") (or "to) (or "t") (or "metric ton")

TEMPERATURE (exact) TEMPERATURE (exact)

of Fahrenheit 5(F-32)/9 Celcius DC DC Celcius 1.8C + 32 Fahrenheit of

temperature or (F-32)11.8 temperature temperature temperature

ILLUMINATION ILLUMINATION

fc foot-candles 10.76 lux Ix Ix lux 0.0929 foot-candles fc

fI foot-Lamberts 3.426 candelalm2 cdlm2 cdlm2 candelalm2 0.2919 foot-Lamberts fI

FORCE and PRESSURE or STRESS FORCE and PRESSURE or STRESS

Ibf poundforce 4.45 newtons N N newtons 0.225 poundforce Ibf

Ibflin2 poundforce per 6.89 kilopascals kPa kPa kilopascals 0.145 poundforce per Ibflin2

square inch square inch

• 51 is the symbol for the International System of Units. Appropriate (Revised September 1993)

rounding should be made to comply with Section 4 of ASTM E380.
TABLE OF CONTENTS

Section

CHAPTER 1. INTRODUCTION 1

BACKGROUND 1

NEED FOR PROJECTING TRAFFIC LOADS 2

STUDY OBJECTIVES 2

TRAFFIC DATA CONCERNS 3

REPORT ORGANIZATION 3

CHAPTER 2. CHARACTERISTICS OF LTPP TRAFFIC DATA 7

LTPPTRAFFICDATASTRUCTURE 7

CHARACTERISTICS OF LTPP TEST SECTIONS 10

CHARACTERISTICS OF LTPP TRAFFIC DATA 10

COMPARISON OF TRENDS IN HISTORICAL AND MONITORING DATA 19

CONCERNS REGARDING QUALITY OF TRAFFIC DATA 19

CHAPTER 3. METHODOLOGY FOR PROJECTING TRAFFIC LOADS 23

EVOLUTION OF MOTOR CARRIER TECHNOLOGY 25

REQUIREMENTS FOR TRAFFIC LOAD PROJECTION METHODOLOGY 29

PROJECTION CATEGORIES 30

DESCRIPTION OF TRAFFIC LOAD PROJECTION METHODOLOGY 31

PROJECTION OF TRUCK TECHNOLOGY 32

CHAPTER 4. PROCEDURE FOR CONDUCTING CASE STUDIES

AND TRAFFIC PROJECTIONS 35

ORGANIZATION OF THE PROCEDURE 35

QUALITY ASSURANCE ISSUES AND TRAFFIC PROJECTIONS 35

IMPORTANCE OF LOCAL INVOLVEMENT 36

STEP-BY-STEP PROCEDURE FOR CARRYING OUT CASE STUDIES 36

STEPS 1 THROUGH 6 - APPLICABLE TO ALL CATEGORIES 36

STEP 7 FOR CATEGORY 1 AND 2 PROJECTIONS 43

STEP 7 FOR CATEGORY 3 PROJECTIONS 48

STEP 7 FOR CATEGORY 4 PROJECTIONS 53

STEPS 8 THROUGH 12 - APPLICABLE TO ALL CATEGORIES 54

CHAPTER 5. CASE STUDIES AND SENSITIVITY ANALYSES 63

SELECTION OF SITES FOR CASE STUDIES 63

ORGANIZATION OF CHAPTER 5 65

SUMMARY OF CASE STUDIES FOR CATEGORY 1 65

DESCRIPTION OF CASE STUDIES FOR CATEGORY 1 66

SUMMARY OF CASE STUDIES FOR CATEGORY 2 89

DESCRIPTION OF CASE STUDIES FOR CATEGORY 2 89

SUMMARY OF CASE STUDIES FOR CATEGORY 3 107

DESCRIPTION OF CASE STUDIES FOR CATEGORY 3 108

DESCRIPTION OF CASE STUDIES FOR CATEGORY 4 124

11l
TABLE OF CONTENTS (Continued)

Section

EFFECT OF QUANTITY OF MONITORING DATA 130

BRIEF SUMMARY 136

CHAPTER 6. STATISTICAL MEASURES AND TOOLS FOR

MANAGING TRAFFIC LOADS 141

THE NEED FOR SUMMARY TRAFFIC LOAD STATISTICS 141

LOAD SPECTRA COEFFICIENTS 144

GENERAL AXLE LOAD EQUIVALENCY FACTORS 145

LTPP TRAFFIC LOADING GUIDE 147

CHAPTER 7. COMPUTATION AND STORAGE OF PROJECTED TRAFFIC DATA 149

COMPUTATION OF ANNUAL GROWTH FACTORS 151

COMPUTATION OF BASE ANNUAL AXLE LOAD SPECTRA 152

COMPUTATION OF BASE ANNUAL AXLE LOAD SPECTRA BY VEHICLE

CLASS 153

COMPUTATION OF BASE VEHICLE DISTRIBUTION BY CLASS 154

COMPUTATION OF AXLE PER VEHICLE CLASS FACTORS 155

COMPUTATION OF MONTHLY DISTRIBUTION FACTORS 156

COMPUTATION OF PROJECTED ANNUAL AXLE LOAD SPECTRA 156

SUMMARY OF TRAFFIC PROJECTION PROCESS 157

CHAPTER 8. SUMMARY AND RECOMMENDATIONS 159

SUMMARY , , . 159

CONCLUSIONS 160

RECOMMENDATIONS 162

REFERENCES 165

APPENDIX A - COMPUTATIONAL PROCEDURE FOR AGGREGATION

OFMONTHLYTRAFFlCDATA 167

APPENDIX B - TYPICAL AXLE LOAD SPECTRA USED FOR CATEGORY 3 AND 4

PREDICTIONS 169

Reproduced from a best available copy.

PROTECTED UNDER INTERNATIONAL COPYRIGHT

ALL RIGHTS RESERVED

NATIONAL TECHNICAL INFORMATION SERVICE

U.S. DEPARTMENT OF COMMERCE

IV
LIST OF FIGURES

Figure Page

1 Comparison of trends for average historical and monitoring ESALs per day 4

2 LTPP traffic data levels 8

3 Flow of LTPP traffic data 9

4 Daily traffic data for aggregate load levels for Class 9 vehicles for Site 26-1001 -

tandem-axle loads, 0-142 kN (0-31,999Ib), 1996 13

5 Axle load distributions by day of the week for Class 9 vehicles for Site 12-4000 -

tandem-axle loads, 0-142 kN (0-31,999Ib), 1991 to 1996 15

6 Weekday/weekend axle load counts for Class 9 vehicles for Site 29-4036 - single-axle

loads, 0-75.6 kN (0-16,9991b), 1992 to 1996 15

7 Distribution of single-axle loads for the NAR and NCR States/Provinces 16

8 Distribution of tandem-axle loads for the NAR and NCR States/Provinces 16

9 Distribution of tridem-axle loads for the NAR and NCR States/Provinces 17

10 Cumulative distribution of single-axle loads for the NAR and NCR States/

Provinces 17

11 Cumulative distribution of tandem-axle loads for the NAR and NCR States/

Provinces 18

12 Cumulative distribution of tridem-axle loads for the NAR and NCR States/

Provinces 18

13 Annual Projection Data Sheet for Site 05-3059 20

14 Percent increase in vehicle-miles traveled on rural Interstates 24

15 Location of site-related sections for Site 04-1002 39

16 Annual growth trends for Site 05-3059 41

17 Comparison of normalized annual load spectra for Site 05-3059 45

18 Load spectra coefficients for Site 05-3059 46

19 Comparison of normalized average and 1996 annual load spectra for Site 05-3059 49

20 Truck distribution by class for Site 37-3807 51

21 Comparison of projected, historical, and monitoring ESALs for Site 05-3059 58

22 Comparison of monthly traffic variations for Site 12-4000 59

23 Comparison of average monthly load spectra for Site 12-4000 60

24 Comparison of monthly ESALs per day variation for Site 12-4000 61

25 Annual Projection Data Sheet for Site 12-4000 67

26 Comparison of truck AADT volumes for Site 12-4000 68

27 Comparison of annual load spectra for Site 12-4000 70

28 Base annual load spectrum for average of 1992 through 1994 and 1996 data for

Site 12-4000 71

29 Truck distribution by class for Site 12-4000 72

30 Comparison of ESAL volumes for Site 12-4000 74

31 Annual Projection Data Sheet for Site 27-0501 76

32 Comparison of truck AADT volumes for Site 27-0501 77

33 Comparison of annual load spectra for Site 27-0501 78

34 Comparison of ESAL volumes for Site 27-0501 79

v
LIST OF FIGURES (ConUnued)

Figure

35 Comparison of monthly traffic variations for Site 27-0501 80

36 Annual Projection Data Sheet for Site 29-4036 82

37 Comparison of truck AADT volumes for Site 29-4036 83

38 Comparison of load spectra for Site 29-4036 84

39 Truck distribution by class for Site 29-4036 85

40 Comparison of ESAL volumes for Site 29-4036 86

41 Comparison of monthly traffic variations for Site 29-4036 87

42 Comparison of annual load spectra for Site 29-4036 88

43 Annual Projection Data Sheet for Site 04-1002 91

44 Comparison of truck AADT volumes for Site 04-1002 93

45 Load spectra used for computation of the base spectra for Site 04-1002 94

46 Load spectra for Site 04-1002 for 1993 and base spectra 95

47 Annual truck count distribution by class for Sites 04-1002, 04-1024, and 04-1025 96

48 Comparison of annual ESAL projections for Site 04-1002 97

49 Annual Projection Data Sheet for Site 29-5483 98

50 Projected total truck volumes for Site 29-5483 100

51 Load spectra for Site 29-5483 for all monitoring years 101

52 Load spectra for Site 29-5483 for 1992/1993 102

53 Load spectra for Site 29-5483 for monitoring years 1994 through 1997 103

54 Comparison of average load spectra based on the years 1992/1993 and 1994

through 1997 for Site 29-5483 104

55 Annual truck count distribution by class for 1992/1993 and 1994 through 1997

for Site 29-5483 105

56 Annual ESAL projections for Site 29-5483 106

57 Annual Projection Data Sheet for Site 37-3807 109

58 Comparison of truck AADT volumes for Site 37-3807 110

59 Truck distribution by class for Site 37-3807 111

60 Base load spectra for Site 37-3807 112

61 Comparison of projected and historical ESALs for Site 37-3807 113

62 Annual Projection Data Sheet for Site 48-5287 114

63 Comparison of truck AADT volumes for Site 48-5287 115

64 Truck distribution by class for selected years for Site 48-5287 116

65 Base load spectra for Site 48-5287 117

66 Projected and historical ESALs for Site 48-5287 118

67 Annual Projection Data Sheet for Site 55-3012 119

68 Comparison of truck AADT volumes for Site 55-3012 120

69 Truck distribution by class for Site 55-3012 121

70 Base load spectra for Site 55-3012 122

71 Projected and historical ESALs for Site 55-3012 123

72 Annual Projection Data Sheet for Site 01-6019 125

73 Comparison of truck AADT volumes for Site 01-6019 126

VI
LIST OF FIGURES (Continued)

Figure Page

74 Base truck volume distribution by class for Site 01-6019 127

75 Base load spectra for Site 01-6019 128

76 Comparison of projected and historical ESALs for Site 01-6019 129

77 Base load spectra for Site 04-1002 131

78 Annual Projection Data Sheet for Site 17-5423 132

79 Comparison of truck AADT volumes for Site 17-5423 133

80 Comparison of truck distribution curves for Arizona and Illinois rural Interstates 134

81 Comparison of projected and historical ESALs for Site 17-5423 135

82 Historical and projected truck AADT volumes for Site 29-4036 for Category 4 137

83 Comparison of 1983 load spectra for Site 29-4036 138

84 Comparison of 1995 load spectra for Site 29-4036 140

85 Comparison of ESAL volumes for Site 29-4036 140

86 Number of average ESALs per five-axle tractor-semitrailer on rural Interstates 143

87 Comparison of differences between AASHTO LEF and GAF for single axles 146

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

data 149

89 Summary of the proposed computed parameters tables 151

90 Flowchart for computation of the annual traffic growth factors 152

91 Flowchart for computation of the base annual axle load spectra 153

92 Flowchart for computation of the base annual load spectra by vehicle class 154

93 Flowchart for computation of the base annual vehicle distribution by class 155

94 Flowchart for computation of the axle-per-truck factors 156

95 Flowchart for computation of projected annual axle load spectra 157

96 Overall outline of traffic projection process 158

97 Load spectra for Class 4 vehicles 170

98 Load spectra for Class 5 vehicles 171

99 Load spectra for Class 6 vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172

100 Load spectra for Class 7 vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173

101 Load spectra for Class 8 vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

102 Load spectra for Class 9 vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

103 Load spectra for Class 10 vehicles 176

104 Load spectra for Class 11 vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177

105 Load spectra for Class 12 vehicles 178

106 Load spectra for Class 13 vehicles 179

107 Comparison of axle load spectra for Class 6 vehicles 180

108 Comparison of axle load spectra for Class 9 vehicles 181

109 Comparison of axle load spectra for Class 10 vehicles 182

110 Normalized load spectra for Class 4 vehicles 183

111 Normalized load spectra for Class 5 vehicles 184

112 Normalized load spectra for Class 6 vehicles 185

113 Normalized load spectra for Class 7 vehicles 186

vii
LIST OF FIGURES (Continued)

Figure Page

114 Normalized load spectra for Class 8 vehicles 187

115 Normalized load spectra for Class 9 vehicles 188

116 Normalized load spectra for Class 10 vehicles 189

117 Normalized load spectra for Class 11 vehicles 190

118 Normalized load spectra for Class 12 vehicles 191

119 Normalized load spectra for Class 13 vehicles 192

viii
LIST OF TABLES

1 Comparison of historical and monitoring ESAL trends for North Atlantic and

North Central Region sites 5

2 LTPP CTDB data types 9

3 Distribution of LTPP test sections by functional classification 11

4 Distribution by vehicle class at LTPP sites 12

5 FHWA vehicle types 13

6 Data availability codes 38

7 Extent of monitoring data for Site 05-3095 39

8 Vehicle class distribution for Site 37-3807 50

9 Estimated vehicle class distribution for Site 17-5423 54

10 Annual projection factors for Site 05-3059 55

11 Cumulative ESALs computation sheet for Site 05-3059 57

12 Computation of average ESALs per day 62

13 Description of LTPP sites used for the case studies 64

14 Monitoring data available for Site 12-4000 66

15 Comparison of truck AADT volumes for Site 12-4000 68

16 Comparison of ESAL volumes for Site 12-4000 74

17 Monitoring data available for Site 27-0501 '" 75

18 Comparison of truck AADT volumes for Site 27-0501 77

19 Comparison of ESAL volumes for Site 27-0501 79

20 Monitoring data available for Site 29-4036 81

21 Comparison of truck AADT volumes for Site 29-4036 83

22 Comparison of ESAL volumes for Site 29-4036 86

23 Monitoring data available for Site 04-1002 90

24 Comparison of 1993 data for nearby sites 92

25 Comparison of truck AADT volumes for Site 04-1002 93

26 Cumulative ESALs computation sheet for Site 04-1002 97

27 Monitoring data available for Site 29-5483 98

28 Comparison of truck AADT volumes for Site 29-5483 100

29 Cumulative ESALs computation sheet for Site 29-5483 106

30 Comparison of truck AADT volumes for Site 37-3807 110

31 Cumulative ESALs computation sheet for Site 37-3807 113

32 Comparison of truck AADT volumes for Site 48-5287 115

33 Cumulative ESALs computation sheet for Site 37-3807 118

34 Comparison of truck AADT volumes for Site 55-3012 120

35 Cumulative ESALs computation sheet for Site 55-3012 123

36 Comparison of truck AADT volumes for Site 01-6019 126

37 Cumulative ESALs computation sheet for Site 01-6019 129

38 Comparison of truck AADT volumes for Site 17-5423 133

39 Cumulative ESALs computation sheet for Site 17-5423 135

ix
LIST OF TABLES (Continued)

40 Monitored and historical truck AADT volumes for Site 29-4036 for Categories

1,3, and 4 137

41 Comparison of ESAL volumes for Site 29-4036 140

42 Average ESALs per FHWA vehicle class 9 (predominantly five-axle tractorsemitrailer), urban Interstates 142

43 Influence of pavement-related variables on AASHTO LEFs 143

44 Factors considered for inclusion in the proposed LTPP Traffic Loading Guide 148

45 Description of the projected categories 150

x
CHAPTER 1. INTRODUCTION

BACKGROUND

Traffic data are recognized as a key data element or feature for the analysis of pavement

performance. Pavement performance analysis can vary from short-term pavement evaluation to

long-term pavement performance prediction, and it encompasses a variety of applications,

including the prediction of individual distresses, pavement design and rehabilitation, and

pavement management. In all these applications, knowledge of the cumulative traffic loads

imposed on the pavement is a crucial part of the analysis process, especially in developing loadrelated distress prediction models.

At the inception of the Long Term Pavement Performance (LTPP) program, traffic

information was recognized as the most difficult and complicated data item to be collected. This

is because of the expensive and massive nature of the traffic data collection and analysis process,

and because the collection of traffic data in the field has been the responsibility of the

participating agencies, whereas other field data, such as roughness and deflections, have been

collected directly by the Strategic Highway Research Program (SHRP). Over the years, the

participating agencies received a series of guidelines on how traffic data should be collected and

reported to LTPP. Examples of the most recent guidelines are given in references 1 and 2.

Most of the traffic data collection in the field is done automatically. Automatic Traffic

Recorders (ATR) collect traffic volumes, Automatic Vehicle Classifiers (AVC) collect the

vehicle volumes by vehicle category, and weigh-in-motion (WIM) scales collect axle weight

data. The traffic data collected in the field are sent to LTPP, in the format specified in the

Traffic Monitoring Guide [3], for processing and storage in the Central Traffic Database

(CTDB). Selected aggregated traffic data are also stored in the LTPP Information Management

System (IMS) [4].

There are three basic categories of traffic data stored in the CTDB: historical, monitoring,

and supporting. Historical data were estimated by State highway agencies (SHAs) for the LTPP

sections prior to the installation of traffic monitoring equipment. Historical data estimates

include total Annual Average Daily Traffic (AADT) volumes, total AADT truck volumes, and

total annual equivalent single-axle loads (ESALs) for each year from the time of roadway

opening to traffic to the beginning of LTPP traffic monitoring. In some cases, very little

supporting data were available, and the historical data are "best estimates." In all cases, the

methodologies used by the SHAs to obtain the historical estimates have been recorded [4]. The

years from the time the site was opened to traffic to the time the site was assumed by the LTPP

experiment are referred to as historical years.

Monitoring data have been submitted by the SHAs for the years since the LTPP

experiment began. Monitoring data fall into two categories: measured and estimated.

Monitoring measured data are obtained by actual field measurements using ATR, AVC, and

WIM equipment, whereas estimated monitoring data are estimated by SHAs for the years for

which measured monitoring data are not available. The years from the time a site was assumed

by the LTPP to present are referred to as monitoring years.

1
Supporting data describe conditions under which historical and monitoring data were

collected. Supporting data may also include calibration results obtained for WIM scales.

NEED FOR PROJECTING TRAFFIC LOADS

The quality and quantity of historical and monitoring traffic data vary considerably. As a

rule, historical data do not contain axle load data, and many LTPP sites have many years for

which monitoring measured data--either WIM data or both AVC data and WIM data-are not

available. Since the knowledge of cumulative axle loads is a crucial part of the pavement

performance analysis process, the cumulative traffic loads for the entire pavement lifespan need

to be estimated using the available fragmented historical and monitoring traffic data.

Furthermore, because emerging mechanistically based pavement performance models and

pavement design methods (such as the anticipated 2002 Pavement Design Guide) require

knowledge of load spectra (rather than only ESALs), the cumulative traffic loads need to be

estimated in terms of load spectra. A load spectrum is the distribution of the number of axles by

load ranges for different axle configurations (single, tandem, tridem). In the CTDB, load spectra

are also given separately for different vehicle classes.

The original name of this project, Traffic Backcasting Study, implies that traffic

monitoring data are projected backwards. Closer examination of the available monitoring data

revealed that, in order to obtain cumulative traffic loads for the entire in-service life of pavement

sections, monitoring data have to be not only backcasted, but also interpolated (for the interim

monitoring years without monitoring data) and forecasted (for years after the automated

equipment no longer provides data). Consequently, rather than backcasting, the process of

obtaining traffic loads for the entire lifespan of the pavement sites is referred to herein as

projecting.

STUDY OBJECTIVES

To obtain load spectrum data for each year that an LTPP section has been in service, a

study has been sponsored by the Federal Highway Administration (FHWA) to develop a

procedure for projecting traffic loads and to apply the procedure to LTPP sections. The study

has been divided into two phases. In Phase 1, a traffic projection methodology is to be

developed. In Phase 2, based on FHWA's approval, the methodology is to be used to obtain

cumulative annual load spectra for specific sections. The projected annual load spectra are to be

stored in computed parameter tables.

The objectives of the Phase 1 study are to develop, document, demonstrate, and evaluate

a methodology for obtaining cumulative traffic load spectra applicable for all LTPP sites. The

demonstration and the evaluation of the methodology is to be documented by realistic case

studies.

2
TRAFFIC DATA CONCERNS

Until now, historical trends in traffic data stored in CTDB have not been systematically

evaluated. An example of such an evaluation, in terms of annual ESALs, is given in figure 1.

Figure 1 shows the relationship between historical ESALs (estimated by SHAs) and

monitoring ESALs (measured by automated equipment) for three selected sites. The top part of

the figure shows an example of a section (OPS test site 01-1021) with a consistent and expected

trend between the historical estimates of ESALs and the ESALs calculated using measured

monitored traffic data. The middle part of figure 1 is an example of a section (OPS site 05-3048)

exhibiting significant differences between the historical ESALs and the monitoring ESALs. The

bottom part of figure 1 is an example of a section (OPS test site 01-1019) with conflicting trends

between historical and monitoring ESALs (growth versus decline). The alarming drop in

ESALs reported for this section in 1996 invites further examination.

The trends in data presented in figure 1 are rather typical. A rough comparison of

historical and monitoring trends in ESALs for all sites in the North Atlantic and North Central

Regions, summarized in table 1, indicates that the historical and monitoring ESAL trends do not

compare well and that the trends in the historical and monitoring ESALs vary considerably. It

should also be noted that no annual historical or monitoring ESALs were available for 27 sites.

The subject of trends in ESALs was presented in this introductory chapter to indicate

from the beginning that the process of projecting traffic volumes, relying on the available

historical and monitoring data, is challenging and requires the judgment and involvement of the

SHAs.

REPORT ORGANIZATION

This report documents the results of Phase 1 of the FHWA study. It builds on the Interim

Report [5] and its three appendixes. This report contains a step-by-step methodology for

projecting traffic load spectra suitable for all LTPP sections and demonstrates this methodology

using 12 case studies.

Chapter 1 contains the introductory material. Chapter 2 describes basic characteristics of

the LTPP traffic data base and its contents. The proposed traffic projection methodology is

presented in chapter 3. A step-by-step procedure for conducting case studies, which is also

essentially the procedure recommended for projecting traffic loads for the rest of the LTPP

sections, is presented in chapter 4. Chapter 5 contains detailed description of the 12 case studies

used to demonstrate and evaluate the proposed prediction methodology. Chapter 6 describes

statistical measures and tools for managing traffic loads, including the proposed LTPP Pavement

Loading Ouide. The summary and recommendations are presented in chapter 7.

3
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...... ...... ...... ...... ...... ...... ...... ...... ...... ......

Year

-a-Historical _Monitored

Figure 1. Comparison of trends for average historical and monitoring ESALs per day.

4
Table 1. Comparison of historical and monitoring ESAL trends for North Atlantic

and North Central Region sites.

Number of Monitoring Trend Historical Trend

Sections Type Type Comments

2 Decreasing Fluctuating Historical and Monitoring are on comparable scale

I Decreasing Increasing Historical and Monitoring are on comparable scale

2 Decreasing Increasing Historical much higher than Monitoring

I Decreasing Increasing Monitoring much higher than Historical

2 Fluctuating Decreasing Historical and Monitoring are on comparable scale

I Fluctuating Decreasing Monitoring much higher than Historical

8 Fluctuating Fluctuating Historical and Monitoring are on comparable scale

2 Fluctuating Fluctuating Historical much higher than Monitoring

I Fluctuating Fluctuating Monitoring much higher than Historical

11 Fluctuating Increasing Historical and Monitoring are on comparable scale

7 Fluctuating Increasing Historical much higher than Monitoring

2 Fluctuating Stable Historical and Monitoring are on comparable scale

I Fluctuating Stable Monitoring much higher than Historical

2 Increasing Fluctuating Historical much higher than Monitoring

I Increasing Increasing Historical and Monitoring are on comparable scale

5 Increasing Increasing Historical much higher than Monitoring

I Increasing Stable Historical and Monitoring are on comparable scale

I Stable Increasing Historical and Monitoring are on comparable scale

4 Stable Increasing Historical much higher than Monitoring

2 Stable Stable Historical much higher than Monitoring

I One observation Fluctuating Historical much higher than Monitoring

I One observation Increasing Historical much higher than Monitoring

27 No ESALs No ESALs No ESAL graph available

5

CHAPTER 2. CHARACTERISTICS OF LTPP TRAFFIC DATA

The traffic data collection and analysis effort has proven to be one of the most

challenging aspects of the LTPP program. Reliable traffic data, and especially axle load

spectrum data, are a necessity if the LTPP program is to provide useful products that will result

in cost-effective durable pavements. Under SHRP, it was assumed that the participating highway

agencies would be able to easily accomplish traffic data collection at each test section using the

available technology. However, the high cost of installing, maintaining, and operating AVC and

WIM equipment has resulted in a smaller amount of monitoring data, and at fewer sites, than

perhaps originally anticipated.

LTPP data were used previously for a number of traffic studies, such as to evaluate the

influence of the sampling rates on the precision of traffic estimates [6] or to study seasonal

variation in traffic volumes [7]. However, no systematic utilization of traffic data involving the

estimation of cumulative traffic loads has been attempted before. The estimation of cumulative

loads utilizes trends in historical and monitoring data and is also a powerful quality assurance

tool.

LTPP TRAFFIC DATA STRUCTURE

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

The LTPP CTDB stores traffic data on five levels, as outlined in table 2 and shown in

figure 2. Levels I through 4 store only measured monitoring data, whereas Level 5 stores

historical and supporting data. Briefly, Level I data features annual axle load spectra for all

vehicle classes combined. Level 2 data features annual axle load spectra for individual vehicle

classes. (Vehicle classes used by LTPP are identical to the FHWA vehicle classes given in

reference 3.) The annual axle load spectra on Level 2 are given separately for 10 vehicle classes,

4 through 13 inclusive. Level 3 data feature daily axle load spectra for individual vehicle classes.

Up to 365 records for each vehicle class may appear on Level 3 for each LTPP test section and

for each monitoring year. Thus, Level 3 data files can be very large and cannot be easily

manipulated. Level 4 contains data in the form submitted by the SHAs. Level 5 contains

supporting data.

The LTPP IMS contains Level I data, including the monitoring load spectra for all

vehicle classes combined and segregated by axle type (single, tandem, tridem, and quads), as

well as annual ESALs.

Monitoring traffic data collected by SHAs are sent to Regional Coordination Office

Contractors (RCOC) in electronic format as individual vehicle records (raw data). After

processing by RCOC, data are stored in the CTDB or in the IMS where they can be requested by

the users. The flow of the traffic data between the SHAs and the users is illustrated in figure 3.

7
ac Procedures

/

Characteristics S~e f (SNID~)

/

ATR,AVC,and

WIM Data

LTPP CTOS

Level 1

Level 2

Level 3

QC Procedures

Level 4

LTPP IMS

Traffle Tables

Aggregation Level

Annual Summaries Combined

Over All Classes

Annual Summaries by Vehicle

Class

Daily Summaries byVehicle

Class. ESALs first calculated

here.

Raw Volume, Vehicle Class,

and Weight Records

/ Supporting

Data LevelS

Figure 2. LIPP traffic data levels.

8

Sheets 1·13; Includes Traffic

Estimates by SHAs
Updates Technical Assistance

Contractors

Traffic

Researchers

Data ,--------------,

Data Requested

Raw

Data

RCOC

Level 1 & Data IL.- --. ,-- L.- --, Available Matrix

Revision

Requests

,-- ----, Levels 1, 2,

3 4 & 5

,D~ta nLTPP Traffic I------+~ -e Database

CJ~D~~~ I

~~~~~~~ '----.---. _ Updates L-- ----l

./ 0000000

Pavement

Researchers

States

- LTPPIMS

o Data ./II ,.,. - Requested L- ----l

Figure 3. Flow of LTPP traffic data.

Table 2. LTPP CTDB data types.

Data Type Description of Subtypes Available

Levell Annual axle loads, ESALs for all vehicles, and other summary statistics

Level 2 Annual axle loads, ESALs by vehicle class, and other summary statistics

Volume (daily traffic volumes by vehicle class)

Level 3 Vehicle class (daily traffic volumes by vehicle class)

Weight (ESALs and weight ranges by vehicle class)

Volume (hourly traffic volumes by lane; 13-card")

Level 4

Vehicle class (hourly volumes by vehicle class; 14-card")

Weight (individual truck weight records; 17-card")

Vehicle class data submittal forms

Level 5

Weight data submittal forms

Historical data sheets (Sheets 1-9) plus monitoring estimates (Sheet 10)

Note: Levels 3 and 4 have three distinct data types: volume, vehicle classification, and weight.

Likewise, Level 5 is composed of three different types. Record layouts will therefore vary.

9
CHARACTERISTICS OF LTPP TEST SECTIONS

The distribution of LTPP test sections by highway functional classification is given in

table 3, based on IMS data available as of fall 1998. LTPP sections located on rural Interstates

and rural principal arterial highways comprise 32.2 percent and 42.7 percent of the sections,

respectively. Altogether, 86.1 percent of all LTPP sites are located on rural highways. There are

9.3 percent of the sections located on urban principal arterial roadways (Interstate and other).

The vehicle distribution, by FHWA vehicle class and for each highway functional

classification, is given in table 4. This table is based on limited data as of early 1997, but is

considered representative. For truck traffic, vehicle class 9 (five-axle single trailer trucks) is the

most frequent vehicle type for all highway classes, with the exception of urban major collectors

and rural major collectors, where vehicle class 5 predominates. Class 5 (two-axle, six-tire singleunit trucks) is the second most frequent vehicle type. The FHWA vehicle truck classes are

defined in table 5.

CHARACTERISTICS OF LTPP TRAFFIC DATA

LTPP traffic data have been used previously by several studies to characterize trends in

the data [7], analyze the consequences of alternative sampling trends [6], and to assess their

potential for developing models for predicting axle load distributions [8]. These studies were

reviewed to obtain a better understanding of the LTPP data characteristics.

A study on seasonal patterns of vehicle volumes [7] concluded that, on a national level,

both rural and urban sites exhibit a general pattern of lower volumes during the winter months

and higher volumes from April through September. A similar pattern was reported by Sharma

and Leng [9], who examined seasonal travel patterns for 52 sites in Minnesota. Hallenbeck [7]

reported that on a national level, both rural and urban sites exhibit a general pattern of lower car

and truck volumes during the winter months and higher volumes from April through September.

Kim et al. [8] used LTPP data for 21 sites located on Interstate highways in the North Central

Region (NCR) to develop axle load distributions for mechanistic pavement design. Data analysis

showed that, for Interstate highways within the LTPP NCR, the distribution of axle load spectra

was stable from 1991 through 1994.

The LTPP traffic data exhibit pronounced variations in truck volume, depending on the

day of the week. For example, figure 4 shows the variation in the total number of tandem-axle

loads (in the range of 0 to 142.4 kN [0 to 31,999 lb] for vehicle class 9 in 1996) for Site

26-1001. For the majority of the sections evaluated in this study, it was found that weekday

truck traffic is significantly higher than weekend truck traffic. However, it is also possible that

some sites exhibit the opposite patterns.

10
Table 3. Distribution of LTPP test sections by functional classification.

By General Pavement Studies (GPS) Section

LTPP Total,

Code Description GPS-l GPS-2 GPS-3 GPS-4 GPS-5 GPS-6 GPS-7 GPS·9 Total %

1 Rural Interstate 54 27 59 21 41 60 41 17 320 32.2

2 Rural Principal Arterial 123 88 50 24 20 97 19 4 425 42.7

6 Rural Minor Arterial 34 23 6 4 1 16 8 0 92 9.2

7 Rural Major Collector 8 2 1 0 2 4 0 0 17 1.7

8 Rural Minor Collector 2 1 0 0 0 0 0 0 3 0.3

9 Rural Local Collector 0 0 0 0 0 0 0 0 0 0.0

11 Urban Interstate 5 0 11 15 19 4 9 4 67 6.7

12 Urban Principal Arterial 5 5 2 1 3 5 2 3 26 2.6

14 Urban Minor Arterial 15 4 7 5 0 5 0 1 37 3.7

16 Urban Major Collector 2 3 0 0 0 1 0 0 6 0.6

17 Urban Minor Collector 1 0 0 0 0 0 0 0 1 0.1

19 Urban Local Collector 0 0 0 0 0 0 0 0 0 0.0

Unknown 0 0 0 0 0 0 0 1 1 0.1

Total 249 153 136 70 86 192 79 30 995 100

By Specific Pavement Studies (SPS) Section

LTPP Total,

Code Description SPS-l SPS-2 SPS-3 SPS-4 SPS-5 SPS-6 SPS-7 SPS-8 SPS-9 Total %

1 Rural Interstate 2 6 0 0 9 6 3 0 10 36 23.7

2 Rural Principal Arterial 14 4 0 0 8 5 0 1 15 47 30.9

6 Rural Minor Arterial 0 0 0 0 0 0 0 3 0 3 2.0

7 Rural Major Collector 0 0 0 0 0 0 0 1 0 1 0.7

8 Rural Minor Collector 0 0 0 0 0 0 0 6 0 6 3.9

9 Rural Local Collector 0 0 0 0 0 0 0 0 0 0 0.0

11 Urban Interstate 0 0 0 0 0 0 0 0 2 2 1.3

12 Urban Principal Arterial 0 2 0 0 0 0 0 0 0 2 1.3

14 Urban Minor Arterial 0 0 0 0 0 0 1 0 0 1 0.7

16 Urban Major Collector 0 0 0 0 0 0 0 0 0 0 0.0

17 Urban Minor Collector 0 0 0 0 0 0 0 1 0 1 0.7

19 Urban Local Collector 0 0 0 0 0 0 0 0 0 0 0.0

Unknown 2 0 13 6 19 1 0 2 10 53 34.9

Total 18 12 13 6 36 12 4 14 37 152 100

11
Table 4. Distribution by vehicle class at LTPP sites.

Vehicle Percentage

Functional

Class Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Class 7

Ftural Interstate 0.2 63.1 12.3 0.3 2.3 0.8 0.1

Ftural Principal Arterial 0.2 72.1 16.0 0.3 2.5 0.8 0.1

Ftural Minor Arterial 0.1 72.3 19.1 0.3 2.2 0.7 0.1

Ftural Major Collector 0.0 74.5 18.2 0.4 3.7 0.6 0.0

Urban Interstate 0.2 75.7 15.7 0.2 1.6 0.8 0.0

Urban Principal Arterial 0.2 71.3 17.4 0.2 2.5 0.6 0.3

Urban Minor Arterial 0.1 74.2 16.2 0.4 2.0 0.7 0.1

Urban Major Collector 0.0 74.5 19.9 0.1 1.9 0.4 0.0

Vehicle Percentage

Functional

Class Class 8 Class 9 Class 10 Class 11 Class 12 Class 13

Ftural Interstate 1.8 16.8 0.3 1.5 0.2 0.3

Ftural Principal Arterial 1.5 5.6 0.4 0.2 0.1 0.2

Ftural Minor Arterial 1.6 3.5 0.1 0.1 0.0 0.0

Ftural Major Collector 0.9 1.5 0.0 0.0 0.0 0.0

Urban Interstate 1.1 3.9 0.2 0.2 0.1 0.6

Urban Principal Arterial 1.4 3.9 0.6 0.3 0.4 0.8

Urban Minor Arterial 1.1 4.5 0.2 0.3 0.1 0.2

Urban Major Collector 1.2 1.3 0.3 0.0 0.1 0.2

Note: FHWA vehicle class types are given in table 5.

12
Table 5. FHWA vehicle types.

Vehicle Class Description

Class 1 Motorcycles

Class 2 Passenger cars

Class 3 Other 2-axle, 4-tire single-unit vehicles

Class 4 Buses

Class 5 2-axle, 6-tire single-unit trucks

Class 6 3-axle single-unit trucks

Class 7 4+-axle single-unit trucks

Class 8 4-axle or fewer single trailer trucks

Class 9 5-axle single trailer trucks

Class 10 6+-axle single trailer trucks

Class 11 5-axle or fewer multi-trailer trucks

Class 12 6-axle multi-trailer trucks

Class 13 7+-axle multi-trailer trucks

.. .... ..- . ... • • iiI'. •• ..... ... ....... ,'. . ,,-.\ .. . ...-

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Dates Monitored

Figure 4. Daily traffic data for aggregate load levels for Class 9 vehicles for

Site 26-1001 - tandem-axle loads, 0-142 kN (O-31,999Ib), 1996.

13
Considering the large variation in daily truck volumes, and the differences between

weekday and weekend traffic, it was decided to aggregate by month all weekday and all weekend

traffic to assess the potential of using aggregated data for traffic projection. As such, for each

month, the average weekday and the average weekend aggregated axle load counts were

determined. This is illustrated in figure 5, which shows separately the monthly variations in

tandem-axle loads (in the range of 0 to 142.4 kN [0 to 31,999 Ib] for vehicle class 9, between 91

and 96 for Site 12-4000), and in figure 6, which shows the monthly variations in single-axle

loads (in the range of 0 to 75.6 kN [0 to 16,999Ib] for vehicle class 9, between 92 and 96 for Site

29-4036).

In addition to the review of the previous studies, we have also conducted an extensive

evaluation of LTPP traffic data to obtain insights regarding data characteristics such as amount of

data, trends and ranges, seasonal variation, variation between sites, potential of vehicle class 9

for prediction of axle load spectra, assessment of data inconsistencies, and so on. Most of the

results of these exploratory analyses were presented in the interim report and the accompanying

appendixes [5]. The assessment included evaluation of load distribution characteristics,

including:

• Daily traffic variation between weekdays and weekends.

• Distribution by vehicle class and axle load ranges.

• Distribution by month and year.

• Axle load distribution for all vehicle classes.

• Trend analysis for Class 9 vehicles, including weekday and weekend traffic

variation.

In the following, only selected characteristics of LTPP data are presented to illustrate the

basic data characteristics.

The average normalized distribution of monitored axle loads for 18 States and Provinces

of the North Atlantic (NAR) and North Central Regions (NCR) is given in figures 7 through 9.

These data are averages for all years from 1991 to 1997 for which the data were available.

Corresponding plots in terms of cumulative percentage of load applications are given in figures

10 through 12. Both single- and tandem-axle load distributions typically exhibit bi-modal

shapes. Also, both single- and tandem-axle loads exhibit a similar pattern, with the exception of

data obtained for Rhode Island. The loading pattern for tridem axles is much more diverse than

that obtained for single and tandem axles. This can be best seen by comparing the variation in

plots presented in figure 12 with the plots in figures 10 and 11. Overall, even though the results

presented in figures 7 through 12 are State or Provincial averages for all available years (1991

through 1997), the differences between the States appear to be significant.

14
70 ....-------------------,------------ ,

l!) l!) l!) l!) l!) (0 (0 (0 (0 (0

0) 0) 0) 0) 0) 0) 0) 0) 0) 0) qJ 0) 0)

0, t5 u ,6 .!,. C: 0, >- ,6 ci "5 6. >- ::::l Q) Q) C. ::::l ::::l 0 Q) Q) 0

« 0 0 LL « .., « z LL « .., 00 z

Month-Year

• Sunday

X Wednesday

+ Saturday

---Linear (Wednesday)

- - - Linear (Saturday)

• Monday

:t: Thursday

-Linear (Monday)

---Linear (Thursday)

- Linear (Sunday)

l:J. Tuesday

• Friday

- - - Linear (Tuesday)

----Linear (Friday)

Figure 5. Axle load distributions by day of the week for Class 9 vehicles for

Site 12-4000 - tandem-axle loads, 0-142.4 kN (0-31,999Ib), 1991 to 1996.

1400 -y--------------------.-----'-------------..,

1200 +------------------------------------1

400 h-~,...."....JOI;QIi~=O-~-=----'=---------------------____I

200 +----nn==---------------------------------I

O-h---,-.,..._r-r-.,___,.~r_T"..,......,___,__r_r_,_.,___,.~r_T"..,......,r_r__r_r_,_.,.....,.~r_T"...,.....,r_r__r_r_,_.,.....,._r_,......,.._,_r_r_..,....,_r_.,..._r_,__r_I

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ C-!. b>6.~:> 0 '=.0 >.C:~ 6>6.~ :> (:; c.c t!.. t!.. >'c-!' C»6.~:> cJ c.o t!.. t!.. >-C...!.. C»6.~ ;> 0 c.o t!.. t!.. >'c"'!" 6>6.~:> 0 ::::l~::::lQ)ouoQ)roQ)~«c.ro::::l~::::lQ)ouoQ)roQ)~«c.ro::::l~::::lQ)ouoQ)roQ)~«c.ro::::l~::::lQ)ouoQ)roQ)~«c.ro::::l~::::lQ)ouoQ)

.., «00 ZO"'LL~ :::.., «00 ZO"'LL~ :::.., «00 ZO"'LL~ :::.., «00 ZO"'LL~ :::.., «00 ZO

Month-Year

• Weekdays --0-- Weekends ---Linear (Weekdays) - - - Linear (Weekends)

Figure 6. Weekday/weekend axle load counts for Class 9 vehicles for

Site 29-4036 - single-axle loads, 0-75.6 kN (0-16,999Ib), 1992 to 1996.

15
25%,-----------------------~-------------__,

iii ... ~~'""I" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\I

"'" CD ex> 0 C\I

"'" CD ex> 0 C\I"'" CD ex> 0 ..... ..... C\I C\I C\I C\I C\I C')

0% -t---,..--,...---.-----,--..,.---,.....:--,..---.--.:;;;",..:::....:

o

~20%+---+------9-------------------------------1

o

l15% +_----:d---\-------:.,.(---\:-------t""'--'~r_---------------------__1

'C

III

o

.....I

'0 10% -t----'.;--\----...-Hr----;

C

Q)

a.. 5% +---i~~

Single-Axle Loads, Ib

--+-CT

--+--MO

~VA

_._IL

-NJ

····;i/···········NF

•• i!r • -IN

--OH

ON

~KY

---<>--PA

+ PE

~MI

--O-RI

-PQ

--e-MN

--t:r-VT

-SK

Figure 7. Distribution of single-axle loads for the NAR and NCR StateslProvinces.

14% -,----------------------------------------,

12%

u,

C

0

tii 10%

.2

a.

0- « 8% 'C

III

0

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Tandem-Axle Loads, Ib

--+-CT _._IL .. -i:J:. ·IN ~KY ~MI --e-MN

--+--MO -NJ -OH ---<>--PA --O-RI --t:r-VT

~VA NF ON + PE -PQ _.,,,_.-SK

Figure 8. Distribution of tandem-axle loads for the NAR and NCR StateslProvinces.

16
30%

25% UJ

c:

0

.2 20%

a.

a.

«

"C

0

a:l 15% ..J

'0

+"

c:

Ol 10%

Ol

a..

5%

0%

0 20000 40000 60000 80000 10000C

Tridem-Axle Loads, Ib

-+-CT _____ IL •• -l:r - -IN ~KY --,-MI --..-MN

--+--MO -NJ -OH ~PA ---O--RI --i:r-VT

--.-VA NF ON + PE -PO -'@-·-SK

Figure 9. Distribution of tridem-axle loads for the NAR and NCR States/Provinces.

100%

c: 90% 0

.2 80%

a.

a.

« 70% "C

a:l

..J

0 60%

'0 50% 'E

Ol

Ol 40%

a..

>

Ol 30%

"'5 20%

E

:::l

u 10%

0% 0

8 8 0 0 § § 0 0

8 8 0 0 0 8

C\I V co co C\I v co ,.... ,.... ,.... 8 8 §

Single-Axle Loads, Ib

8o

co

C\I

o

8

C\I

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o

8

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NF

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-OH

."".",' ,,",ON

~KY

--<>--PA

+ PE

--.-MI

---O--RI

-po

Figure 10. Cumulative distribution of single-axle loads for the NAR and NCR States/Provinces.

17
100%

E 90% 0

.2 80%

0.

Co

« 70%

"0

al

0 60% ...J

15 50% 'E

Q)

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Q)

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iii

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0% 0 o

g

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g

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g

o g

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Tandem-Axle Loads, Ib

~CT --ll-IL - - -t:r - -IN ~KY --.-MI -.-MN

-f--tvlO -NJ ·'"··"~·--·OH ~PA --D-RI --l1-VT

---*-VA NF,.{)}w-ON + PE - PO -,,_.-SK

Figure 11. Cumulative distribution of tandem-axle loads for the NAR and NCR

States/Provinces.

10%

0% 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t co C\l CD 0 '<t co C\l CD 0 '<t co C\l CD 0 '<t co C\l CD 0 '<t co C\l CD 0 .... C\l C\l C\l C') C') '<t '<t '<t LO LO CD CD CD I'- I'- co co co Ol Ol 0

.... Triple-Axle Loads, Ib

70% +-------------..6~'*::,;.i!I1'S;~~__f_~"_7L--___r~~-----------~

20%+--..."

40% +-----..",~.;#1J_____:;;::7'!&

60% L-------~~I2,:,:Jt~~Q~L---------__J

80% +----------------,~~_AI<_,))~~'_/_"--_.7':~--___=7"'_"----------___i

100% -,---------------------,=---=

E 90% +---------------__~!._"".......F o

.2

0.

Co «

"0

al

.3

15'E 50% +--------=:711.,..,"'- Q)

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0-

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.~

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u

~CT --ll-IL

-f--tvlO -NJ

---*-VA NF

- - -l!r - -IN

OH

ON

~KY

~PA

+ PE

--.-MI

--D-RI

-PO

Figure 12. Cumulative distribution of tridem-axle loads for the NAR and NCR States/Provinces.

18
COMPARISON OF TRENDS IN HISTORICAL AND MONITORING DATA

Historical data provided by SHAs are annual data: AADT volumes, annual truck

volumes, and annual ESALs. Thus, to compare historical and measured monitoring data, the

monitoring data must also be in terms of annual data. To facilitate this comparison, the research

team developed a set of Annual Traffic Data Projection Sheets. These sheets summarize, in one

place, all applicable historical and monitoring data for a given section, including:

• AADT volumes.

• AADT truck volumes (for FHWA vehicle classes 4 through 13 inclusive).

• Annual average daily number of ESALs.

• Average number of ESALs per truck, "truck factor" (third item in this list divided

by the second item).

The bottom part of the sheet shows annual percent change since the section was open to

traffic. Step-by-step instructions for developing the Annual Traffic Data Projection Sheet are

given in chapter 4, step 4. Figure 13 show an example of the Annual Traffic Data Projection

Sheet for Site 05-3059.

CONCERNS REGARDING QUALITY OF TRAFFIC DATA

The following concerns regarding quality of traffic data are based on the review of

available LTPP traffic data documentation and on the results of data analysis conducted during

the course of this study.

Sampling Errors - Annual load spectra given in Level I are reported even if they are

based on only I day of measured monitoring data. In this case, to obtain annual estimates

from 1 day of WIM data, the measured data are adjusted by volume and class distribution

estimates. The sampling errors can be estimated based on work reported by Hallenbeck

[6].

Calibration ofAVC and WIM Equipment - WIM scale calibration concerns have not yet

been studied in sufficient detail. It has been shown that even a relatively small drift in the

response of scale sensors or relatively small calibration errors can result in significant

changes in the axle weights. The research team had no opportunity to examine WIM or

AVC calibration results, and it appears that these results are generally not available. It is

disturbing that the CTDB contains only data related to single, tandem, and tridem axles.

No quadruple axles have been reported, even though such axles regularly occur, for

example, in Michigan and Ontario. This indicates that AVC equipment may not be

functioning properly.

19
o'" .I>- 0 o 0

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MDT Truck Volume

MDT Total Volume

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1977

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-< 1987 (1)

1988

1989

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1992

1993

1994

1995

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Average ESALs per Day

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1997

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1977

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% Change Since Date of Opening to

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1998
Unavailability ofMeasured Monitoring Data - Typically, only 2 or 3 years of measured

monitoring data are available for the majority of LTPP sites. Several sites are missing

WIM data and some sites are missing both WIM and Ave data.

Compatibility Between Historical and Monitoring Data - The compatibility between

historical and monitoring data was discussed in terms of ESALs in chapter 1. Overall,

there are many perceived discrepancies between historical and monitoring data, and also

within both the historical and the monitoring data.

21

CHAPTER 3. METHODOLOGY FOR PROJECTING TRAFFIC LOADS

Forecasting of traffic loads for pavement design is a common pavement engineering

activity, and most SHAs have developed practices and procedures to do so in terms of the

traditional ESALs [10-13]. However, producing traffic load projections (backcasting,

interpolating, and extrapolating) for LTPP sections is difficult because of the large number of

sites involved, the location of the sites across the United States and Canada, and the need to

project axle load spectra (not only the traditional ESALs).

Generally, forecasting of traffic loads for pavement design involves two types of

projections:

• Forecasting of traffic volumes and loads.

• Forecasting of truck technology through which the projected traffic volumes and

loads are applied to the pavement. For example, at present, the predominant

application of truck loads is through air suspension and wheels equipped with

radial tires.

Projection of traffic loads and volumes can be accomplished using network assignment

models or corridor assignment models. The network assignment models utilize a variety of

procedures, such as origin-and-destination studies; economic input-output models; and the

traditional transportation planning process consisting of trip generation, distribution, and

assignment. These procedures can provide balanced forecasts over a large area based on macroeconomic trends.

The corridor assignment models for projecting traffic volumes and loads utilize a set of

projection factors, usually called growth factors, which are applied to the specific traffic

characteristics that need to be projected, such as traffic volumes or truck factors (ESALs per

truck). Different growth factors can be used for different vehicle classes. For long-term

projection, usually only one global growth factor is used, and this factor is applied to the total

truck volume. Growth factors can be based on region-wide or corridor-specific trends, on the

combination of regional and corridor-specific trends, and, if available data exist, on historical

trends in vehicle volumes and loads.

Because there are relatively few and disconnected LTPP sections in any specific region,

the corridor assignment models are more suitable for projecting traffic loads for LTPP sites than

. the area-wide network assignment models.

The examination of data shows that for the majority of sections, some historical and

monitoring data trends exist. Even though these trends are often conflicting, they provide some

guidance. The historically based trends can be verified or supported by region-wide trends. For

example, FHWA maintains a web site, www.fhwa.dot.gov/ohim/ohinstat.htm. which provides

vehicle travel statistics (e.g., miles of travel) segregated by State, year, FHWA highway

functional class, and vehicle type (e.g., passenger cars, heavy single-unit trucks, and combination

trucks). Data from this site were used to produce figure 14, which compares the growth in

Preceding Page Blank 23
vehicle-miles for passenger cars with that for combination trucks on rural Interstates. Some

observations based on figure 14 include the following:

• Between 1982 and 1994, the growth in passenger car and combination-truck travel

was similar and averaged about 5 percent per year.

• After 1994, the rate of growth for combination trucks increased to more than 10

percent per year, whereas the rate of growth for passenger cars remained at 5

percent.

• For the 32.3 percent of the LTPP sites located on rural Interstates, we can expect a

substantial growth in truck traffic during the period of 1981 to 1997.

100

90

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0) 0) 0) 0) 0) 0) 0) 0) 0) 0) Cl) 0) 0) 0) 0) 0) 0) ,.... ,.... ,... ,... ,... ,.... ,...

Year

-4-Passenger cars Combination trucks

Figure 14. Percent increase in vehicle-miles traveled on rural Interstates.

A number of indicators have been used to establish a growth rate for truck traffic, such as

ton-miles of transported freight, total shipment value, diesel fuel usage, truck production volume,

and truck registration numbers. On the macro level, long-term growth in gross domestic product

(GDP) was also used. However, each of these indicators has flaws as an overall indicator of

truck traffic growth. For example, there are several reasons for truck traffic to be increasing at a

greater rate than the economy as a whole. Manufacturing industries have been restructured so

that a typical manufacturing plant no longer makes the finished product from the raw materials.

Many small plants now take in the raw materials and make components, which may then be

assembled into larger components at another plant before ultimately becoming a part of the

24
finished product. The prime example of this type of restructuring is the automobile industry.

This process requires just-in-time delivery that is suppCJrted by truck transportation.

The projection of truck technology will be described at the end of this chapter, after

discussing the evolution of motor carrier technology and the proposed traffic-prediction

procedure for LTPP sections.

EVOLUTION OF MOTOR CARRIER TECHNOLOGY

This section reviews the salient changes in motor carrier technology, referred to as "truck

technology," that occurred during the LTPP traffic data projection period. This review is

important for several reasons. First, the review underscores the difference between projecting

traffic volumes and loads and projecting truck technology. It is through truck technology that the

projected traffic volumes and loads are applied to the pavement. Second, some changes in truck

technology may affect pavement performance, and their existence should be understood. Third,

the review should provide insight into possible errors in traffic load projections intended for

pavement performance modeling, if the ongoing changes in truck technology are ignored. And

finally, the review should provide insight for interpreting observed historical changes in vehicle

class volume and axle load.

During the lifespan ofLTPP sections, truck technology in the United States and Canada

underwent extensive changes in three areas:

• Economical and political changes.

• Regulatory changes in vehicle weights and dimensions.

• Engineering changes.

The main changes in these three areas and their connection to the prediction of traffic

loads and, ultimately, to pavement performance modeling are briefly discussed below.

Economical and Political Changes

Economical and political changes include changes in taxation, insurance requirements,

and, most significantly, deregulation. Deregulation can alter two fundamental aspects of

operation in the motor carrier industry-who can compete and what prices can be charged. The

deregulation process started in the late 1970's, and the last vestige of trucking regulation-that

maintained by the States over transportation wholly within their boundaries-was overturned by a

Federal preemption in 1994, becoming effective in 1995 [13]. The result of these changes is

more intensive competition between motor carriers (and between transportation modes), leading

to the increase of truck traffic and better utilization of vehicle plants. Deregulation may have

contributed to the accelerated growth in vehicle-miles of travel on rural Interstates after 1994, as

shown in figure 14.

Better utilization of vehicle plants leads to the use of customized truck configurations for

"full-load" trucks (these trucks usually transport one commodity, such as portland cement or

logs, and operate either fully loaded or unloaded) and to better utilization of "less-than-full-Ioad"

25
trucks (trucks used for pick-up and delivery of general cargo). The consequence of these

changes, together with the widespread use of wireless communication technology, has been a

trend toward increased payload, truck axle weights, and ESALs per truck.

Regulatory Changes in Vehicle Weights and Dimensions

The changes in vehicle weights and regulations have been occurring on both the Federal

and the State levels.

Changes on the Federal Level

The maximum gross vehicle weight (GVW) of 356 kN (80,000 Ib), together with the

single-axle weight limit of 89 kN (20,000 lb) and tandem-axle weight limit of 151 kN (34,000

Ib), was instituted on the Interstate system by provisions of the Surface Transportation

Assistance Act (STAA) of 1982. However, from the viewpoint of the States, these were

"minimum/maximum" weights they were expected to be accommodated throughout the Interstate

system. In practice, the STAA only resulted in a weight increase on the Interstate system in a

few States.

The introduction of double trailers to the Interstate system was significant in the eastern

States, but these now represent only about 3 percent of all large trucks. Voluntary extension by

States of the Federal weight and dimension limits for State roads probably had a much more

significant effect. The States were free to increase the weights, particularly the GVW of vehicles

operating on highways in their jurisdiction, and to maintain higher pre-existing loads allowed by

regulation or permit (the so-called "grandfather" rights).

With time, about 25 States permitted the operation of a variety of vehicles, including a

combination of truck tractor and two or more trailers or semitrailers with a GVW exceeding 356

kN (80,000 Ib). This type of vehicle was defined as a longer combination vehicle (LCV) in the

1991 Intermodal Surface Transportation Efficiency Act (ISTEA). In 1992, under the authority of

ISTEA, Federal regulation §23 CFR 658.5 "froze" the values of weights and dimensions of

LCVs at the values that were in effect on June 1, 1991 (July 6, 1991 for Alaska). The frozen

values became the new maximum vehicle weights and dimensions applicable to the Interstate

system for this class of vehicle. ISTEA froze States' rights to change their regulations and

permits for LCVs, and the 1998 Transportation Efficiency Act for the 21st Century (TEA-21)

maintained the LCV freeze. States continue to be able to interpret grandfather rights for straight

trucks and tractor-semitrailers, but generally have been reluctant to do so without some kind of

Federal agreement.

Changes on the State Level

The vehicle weight and dimension regulations on the State level have been evolving for

many years. Consequently, State laws covering vehicle weights and dimensions are complex and

differ from State to State, or even from county to county (e.g., in New York State) or between

roads within a State (Interstate, toll, and State highways). States also use a permit system that

allows permanent or semi-permanent exemptions from the regulations. Thus, there are eight

26
axle group combinations in Michigan, four axle group combinations in Washington State, and

liftable axles in these States and many others.

In terms of grandfather rights on Interstates, nine States allow single-axle loads greater

than 89 kN (20,000 lb), eight allow tandem-axle loads greater than 151 kN (34,000 lb), and three

allow GVW greater than 356 kN (80,000 lb). At least 19 States also allow some kind of other

vehicles and loads at gross weights greater than 356 kN (80,000 Ib), some on Interstates under

permit or regulation, and some only on other roads. Sometimes the allowance depends on the

cargo.

Some specific examples of State regulations include:

• Maine allows 445 kN (100,000 Ib) on six axles for raw forest products and

constructions materials.

• New Hampshire allows 441 kN (99,000 Ib) on six axles, plus 5 percent tolerance,

and other conditions that are not known.

• New York has several permit systems. One for dump and garbage trucks allows

weights well over the bridge formula (New York's "R permits" for dump trucks

and garbage vehicles are restricted to some State roads), another allows six- and

seven-axle tractor-semitrailers up to about 467 kN (105,000 Ib), and a local

system in six down-State counties allows up to 552 kN (124,000 Ib) on six axles.

• Massachusetts, Rhode Island, New Jersey, North Carolina, South Carolina, and

perhaps others allow weights well over the bridge formula (up to 50 percent) for

special hauling vehicles (SHVs) such as dump trucks and garbage trucks.

• Kentucky has special rules on some roads for coal haulers.

• Michigan allows up to 730 kN (164,000 Ib) on 11 axles, either a tractorsemitrailer or a double trailer (less weight for fewer axles).

• Ohio allows Michigan trucks at Michigan weights by permit on State roads for

access to the Ohio and Indiana Turnpikes.

• Montana allows B-trains (longer combination vehicles) at Canadian weights

(about 614 kN [138,000 1b]) on some routes.

• Washington allows B-trains at 467 kN (105,000 lb).

• Eastern seaboard States (at least some of Maryland, Delaware, Rhode Island,

Pennsylvania, New Jersey, New York, Massachusetts, and New Hampshire) have

made international containers eligible for overweight permits by declaring the

container to be an indivisible load. The vehicle may still have to conform to

Bridge Formula B, but the permit system has required and popularized use of

tridems on container chassis in the last 2 to 3 years.

• Weights may go up by 10 percent in winter in Minnesota.

The evolution of truck weights and dimensions is likely to continue, with most of the

changes happening on State roads off the national network. The changes are prompted by

competition (within the industry and by other modes), increasing international trade and greater

use of containers (many International Standards Organization [ISO] containers cannot be legally

carried by trucks limited to 356 kN [80,000 Ib] GVW), and the increasing impact of the North

American Free Trade Agreement (NAFTA) truck traffic (trucks coming to the United States from

27
Canada have a high proportion of tridem axles). Under NAFTA, the United States' borders are

to open January 1,2000 to Canadian and Mexican trucks (and vice versa). The situation in

Canada is analogous: Provinces agreed to allow operation of trucks meeting common

requirements in all Provinces, but are free to allow vehicle weights and dimensions above the

minimum requirements as they wish.

Obviously, the changes in regulations over time will differ between LTPP sites in

different States. They are also likely to differ over time between the sites in the same State

because of route-specific permits, or because certain commodities that are given preferential

treatment primarily use certain routes. For example, log trucks are typically seen only on roads

leading to mills. In summary, because of the diversity in the vehicle weight and dimension

regulations, LTPP sites have been exposed to a variety of different and changing vehicle streams.

Engineering Changes

The following engineering changes in truck technology have occurred over the lifespan of

the LTPP experiment in essentially a uniform manner at all LTPP sites.

• Nearly exclusive use ofradial tires. While it was reported in 1988 that 74 percent

of all truck tires were radials [10], today that number is 100 percent. Radial tires,

compared to bias ply tires, operate at a higher tire pressures, and their imprint is

more sharply defined.

• Increased use ofair suspension. Presently, about 80 percent of all new truck

tractors and about 60 to 70 percent of all new semitrailers are equipped with air

suspension. A well-maintained air suspension is believed to result in lower

dynamic pavement loads compared to the traditional spring-leaf suspension.

However, air suspensions result in a common trailer heave frequency regardless of

load. Thus, the switch to air suspension may result in high pavement loads

always occurring at the same point past a crack or pothole when vehicles travel at

the same speed (the phenomenon of spatial repeatability). The spatial

concentration of loads may lead to accelerated localized pavement damage.

• Increase in the width oftrailers. STAA of 1982 increased the allowable width of

trailers from 2.44 to 2.59 m (96 to 102 in). (This is a regulatory change, but it

was included among the engineering changes because of its direct influence on

pavement performance.) The increased track width for trailers (the track width for

tractors stayed at 2.44 m [96 in]) means that the trailer tires run partly on the

pavement surface not used by the tractor, and vice versa. Because the lifespan of

trailers is about 20 years, the full impact of this change is just now coming into

force.

• A limited trend to use wide single tires (super singles). After their initial

appearance in the mid-1980s, the trend toward the use of wide single tires in

North America fizzled.

28
• Changes in axle groups. In several jurisdictions, the appearance of multiple-axle

groups (tridem and higher) occurred aft~r the mid-1970s.

REQUIREMENTS FOR TRAFFIC LOAD PROJECTION METHODOLOGY

The proposed methodology was developed to satisfy the following basic requirements.

Capability to Produce Required Traffic Load Projections - The basic requirement of the

projection methodology is to produce, for each in-service year of LTPP sections, an

annual axle load distribution spectrum. The annual axle load distribution spectrum

combines the annual contribution of all vehicles in classes 4 through 13, and is reported

separately for single, tandem, and tridem axles. Where warranted by available

monitoring data, monthly distribution factors are also required. It should be noted that

some sites already have monitoring annual spectra in IMS for at least some monitoring

years. For these years, traffic projections may not be required.

Capability ofPredicting Traffic Loadingsfor the Majority ofLTPP Sites - Despite the

variability in the quantity and quality of available historical and monitoring data, it is

important to have a traffic projection procedure that can accommodate virtually all sites.

Full Utilization and Compatibility With Available Data - The guiding principle of traffic

projections is the need to utilize fully all available monitoring data for pavement

performance prediction. The selection of the projection method must fit the available

monitoring data and achieve the best utilization. The methodology must be compatible

with the amount and the accuracy of available traffic data.

Transparency and Modularity - The procedure should be understandable and well

documented. If more data become available, or if certain steps in the prediction process

are improved in the future, it should be easy to incorporate these changes into the

procedure.

Utilization ofKnowledge ofSampling Procedures and Trends in Data - For example,

previous LTPP traffic studies assessed the relative importance of the classified truck

volumes and WIM data for the precision of ESAL estimates, and they quantified the

importance of site-specific WIM data for estimating traffic loads [6].

Capability ofPredicting Seasonality - The term seasonality refers to temporal variations

in traffic loading data on an hourly, daily, weekly, monthly, yearly, or other periodic

basis. Any requirements for predicting seasonal changes in traffic loads should be

dictated by the pavement performance modeling needs. For the purposes of supporting

the development of pavement performance prediction models for the 2002 Pavement

Design Guide, it appears that only two seasonality periods need to be considered for

LTPP pavement performance modeling: day-night variation and monthly variation. Daynight variation (or hourly variation) was not addressed by the current prediction

procedure; however, the projection of monthly variation was addressed. In addition to

the current requirements stemming from the ongoing work on the 2002 Pavement Design

29
Guide, other seasonal changes in the traffic loads may need to be projected in the future

(for example, weekly traffic load variation associated with the spring thaw period). To

provide projections of hourly or weekly variation in traffic loads would require extensive

processing of Level 4 CTDB data, raw data submitted by SHAs, and the development of

specific projection procedures.

PROJECTION CATEGORIES

Because of the large differences in the quantity of traffic monitoring data available for the

LTPP sections, different traffic load projection methods need to be developed for different

sections. Based on the assessment of available measured monitoring data, four categories of

traffic projections were established:

Category 1 is intended to capture all LTPP sections that have sufficient AVC and WIM

data to enable projection of monthly variation in traffic loadings. The definition of

Category 1 was established in the interim report [5] by consensus between FHWA and

the Data Analysis Technical Support (DATS) contractor as follows: at least 210 days of

AVC data for at least 2 years, a minimum of 1 weekday and 1 weekend of WIM data per

quarter, and the availability of AVC and WIM data for at least 2 years in a 5-year period.

Category 2 is intended to represent sections with both AVC and WIM data; however,

compared with Category 1, the amount of data is insufficient for projection of monthly

variation in traffic loadings. It is expected that the majority of sites in this category will

meet the recommended minimum data collection requirements for General Pavement

Studies (GPS) sites (continuous AVC data plus 2 days of WIM data per year) [1].

Category 3 represents LTPP sections with solid AVC monitoring data, but virtually no

site-specific WIM monitoring data.

Category 4 represents LTPP sections with virtually no reliable AVC and WIM measured

monitoring data.

The following distribution of the LTPP sites into the four projection categories is

tentative and is given only to provide a preliminary indication of the projection needs. The

reason for the crude estimate is that: (l) the mere presence of AVC and WIM monitoring data

(as shown in an IMS summary table) does not guarantee the actual availability of monitoring

data for projection; and (2) the existence of other factors that influence the selection of the

projection category, such as site-specificity of monitoring data and the availability of regional

vehicle classification and weight data, was not evaluated.

Number of Sections per Category Total

Number of

1 2 3 4 Sections

100 550 200 100 950

30
DESCRIPTION OF TRAFFIC LOAD PROJECTION METHODOLOGY

The traffic load projection methodology was established by taking into account the

requirements for the methodology discussed in the previous section, particularly the quantity and

quality of available historical and monitoring data. A corridor assignment model using a global

growth factor was selected as the main projection tool. The growth factors are used as

multipliers to project a representative annual load spectrum (called base annual load spectrum)

where necessary. Monthly spectra are obtained by dividing the annual load spectra into 12 parts

using monthly distribution factors.

The following description of the traffic load projection methodology is intended to

provide an overview of the methodology. A detailed, step-by-step description of the traffic load

projection methodology, including examples, is provided in chapter 4.

1. Development of Annual Traffic Data Projection Sheet and Establishment of

Annual Traffic Growth Factors. The growth factors are based on the historical

and monitoring traffic data, and on the quantity and quality of AVC and WIM

measurements used to obtain the data. Site-specific annual growth factors are

determined for each in-service year of LTPP sections.

2. Selection ofthe Base Annual Load Spectrum. The base annual spectrum is a

representative spectrum used to calculate annual load spectra for years without the

annual monitoring spectra.

For Categories 1 and 2, the available annual monitoring spectra are used to

determine and select the base annual load spectrum.

For Category 3, the base annual load spectrum is obtained by combining

the available site-specific vehicle class distribution with the best available

(or typical) axle load spectra for individual vehicle classes.

For Category 4, the base annual load spectrum is obtained by combining

the typical vehicle distributions with the typical axle load spectra for

individual vehicle classes.

3. Calculation ofLoad Spectra for All In-Service Years. The base annual load

spectrum is calculated for all in-service years. This is done by multiplying the

base annual load spectrum by the appropriate annual traffic growth factors.

4. Calculation ofMonthly Load Spectra. For Category 1, it is necessary to produce

monthly monitoring vehicle volumes and load spectra. At present, traffic data

stored in CTDB are available only on a daily or an annual basis. To aggregate the

data on a monthly basis requires extensive programming and data processing, and

this work is now carried out elsewhere by LTPP. The traffic projection

methodology assumes that the required monthly axle load spectra will be

produced as part of future CTDB activities. Nevertheless, to illustrate and assess

the feasibility of projecting monthly load spectra, we have developed monthly

load spectra (based on monthly AVC and WIM data) for three sections in

Category 1. The development of the monthly monitoring load spectra was a

31
significant and labor-intensive undertaking requiring extensive programming and

processing of Level 3 data. The procedure developed and used to obtain monthly

spectra is documented in appendix A.

Note: Following the principle of full utilization of available traffic data, the

projection of monthly variation of classified truck volumes should also be

considered for Category 2 sites, and perhaps even for Category 3 sites, with

extensive AVC data spanning several years.

5. Development ofMonthly Traffic Data Projection Sheet. The monthly projection

sheet and its accompanying monthly distribution factors use a concept similar to

the one that was used for the development of the Annual Traffic Data Projection

Sheet and its annual growth factors. However, there are two changes: the traffic

data are presented on a monthly basis, and only measured monitoring data are

used to establish the monthly distribution factors (annual growth factors are based

on both historical and monitoring data).

6. Development ofMonthly Distribution Factors. The Monthly Distribution Factors

are used to distribute the annual axle load spectrum into 12 monthly spectra. The

monthly factors are developed only if a visible and logical pattern in the monthly

vehicle load distribution is present for 2 or more years.

PROJECTION OF TRUCK TECHNOLOGY

Evolution of truck technology resulted in the economical, political, regulatory, and

engineering changes described previously. In this section, the option of projecting these changes,

as part of traffic load projections, is discussed.

Economical and Political Changes

Changes in the economical and political sphere can be accommodated within the

framework of annual growth factors.

Regulatory Changes in Vehicle Weights and Dimensions

When projecting load spectra by multiplying the base annual load spectra by an annual

growth factor, it is implied that only traffic volumes are changing, while the technical

characteristics of trucks remain unchanged. However, during the lifespan of LTPP sites, truck

characteristics may have also changed because the vehicle weights and dimension regulations

have changed, and because of the engineering changes described previously. Consequently, for

example, when backcasting annual load spectra from 1995 to 1978 (by multiplying the 1995 load

spectrum by an annual growth factor), the 1995 spectrum will contain load spectra for vehicles,

axle groups, or loads that did not exist at some earlier time. For example, a portion of the 1995

spectrum that includes tridem axles will be projected to 1978 and will be assumed to exist in that

year. However, it is possible that no tridem axles existed in 1978 on that particular section, and

if they did exist, they probably were not equipped with air suspension and radial tires.

32
Appropriate procedures and methods exist to project (backcast or forecast) traffic streams

resulting from regulatory changes. These procedures have been developed to quantify the impact

of proposed regulatory changes in truck weights and dimensions on the highway infrastructure.

Typically, they are based on iterative estimations of the change in the truck fleet from one year to

the next, considering one-for-one vehicle replacement, replacement of one configuration by

others due to regulatory changes, and the growth rate [15].

However, when pondering the option of projecting regulatory changes, consideration

must be given to the accuracy of the underlying traffic monitoring data and the amount of work

involved in developing models for projecting regulatory changes. The regulatory changes may

differ even between LTPP sites in the same jurisdiction (e.g., Interstates versus State highways).

Consequently, many specific models would be needed, and the development of these models

would require close cooperation with local regulatory agencies. Given the requirement for sitespecific models and the uncertainties in projecting traffic volumes, the projection of regulatory

changes for LTPP sites is not recommended at present.

Engineering Changes

Projecting engineering changes in truck technology is relatively simple because the

engineering changes have occurred essentially uniformly at all LTPP sites. The need for

projecting engineering changes should be dictated by pavement performance modeling

requirements, particularly when it comes to modeling specific pavement distresses. In the

absence of specific directions, engineering changes were not projected.

33

CHAPTER 4. PROCEDURE FOR CONDUCTING CASE STUDIES

AND TRAFFIC PROJECTIONS

ORGANIZATION OF THE PROCEDURE

The description of the method for conducting case studies is organized as a step-by-step

procedure and covers the projections in all four projection categories. Typically, each step is first

described in terms of its objectives and the procedures involved, and the description is followed

by an example. To distinguish the description of the step from its example, the examples are

typed in italics.

With the exception of steps 7 and 12, all steps are common, with slight variations, to all

four categories.

Even though the step-by-step procedure presented herein was developed for case studies,

it is expected that a similar procedure will be used for the subsequent work involving routine

projection of traffic loads for all LTPP sites. The present procedure is labor-intensive,

particularly the steps dealing with quality assurance issues.

QUALITY ASSURANCE ISSUES AND TRAFFIC PROJECTIONS

Previous quality assurance activities conducted by LTPP on traffic data were constrained

in several ways.

• Because of the large quantities of traffic data, it was necessary to automate the

quality assurance of data being collected by WIM and AVC equipment.

• The quality assurance employed previously can be characterized as an automated

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

detailed quality assurance process [16].

• Quality assurance did not consider trends in monitoring data. Data obtained for

each day, or year, were evaluated independently. Thus, for example, ESALs

obtained for 1992 were not compared with ESALs obtained for the previous or

subsequent years.

• The relationships and trends between historical and monitoring traffic data were

not systematically evaluated. Thus, for example, historical total truck volume

reported for 1992 was not compared with monitored total truck volume obtained

for 1993.

Quality control and assurance activities conducted by the participating States and

Provinces responsible for collection of traffic data are believed to vary considerably, and are

usually not documented in the CTDB.

Preceding Page Blank 35
In summary, to ensure the basic integrity of the data used for projections, it is also

necessary to carry out the basic quality assurance review of traffic data. Consequently, quality

assurance review is an integral part of conducting case studies. Furthermore, because of many

traffic data problems and inconsistencies encountered during the completion of the case studies,

the largest part of the procedure for conducting case studies deals with quality assurance issues.

IMPORTANCE OF LOCAL INVOLVEMENT

It is highly desirable and recommended to carry out quality assurance reviews, as well as

traffic projections, in cooperation with representatives from the participating States and

Provinces. These representatives know local traffic patterns and are usually in the best position

to estimate past growth rates; historical, current, and expected traffic volumes; composition; and

loads. However, this was not done for the case studies presented herein.

STEP-BY-STEP PROCEDURE FOR CARRYING OUT CASE STUDIES

STEPS 1 THROUGH 6 - APPLICABLE TO ALL CATEGORIES

Step 1. Describe Site Characteristics.

The description of site characteristics related to the transportation function of the highway

can enable the analyst to form an opinion about the expected traffic volumes, composition, and

loads. The description should include highway number; number of lanes; functional type of the

highway using FHWAILTPP classification; specific description of the highway function (e.g.,

farm to market route, recreational, or commuter route), if known; and general location of the

highway. Background characteristics of sites are also available on Data Sheet 1, CTDB Level 5.

However, access to Level 5 data is very time-consuming. The location of the site can be

obtained from IMS. It is also advisable to note the location of other LTPP sites in the vicinity

that can serve as surrogates for projections using site-related monitoring data.

Example for Site 05-3095:

Site 05-3095 is located on Interstate 540, afour-Iane urban principal arterial highway in

Arkansas, connecting Fort Smith, population 40,000, with Interstate 40. Fort Smith is

roughly equidistant between Little Rock and Oklahoma City and is about 16 km (10

miles) from Interstate 40.

Step 2. Outline Vehicle Weight and Dimension Regulations Applicable to the Site.

The objective of the outline is to provide the analyst with insight regarding possible

causes for atypical vehicle weights and load spectra. The outline should address vehicle weight

and dimension regulations (Federal and State, including special permit regulations) that represent

a significant departure from Federal Regulation §23 CFR 658.

Very few, if any, LTPP sites are located on highways subjected to heavy atypical truck

traffic, such as log-haul roads in Mississippi, coal-haul roads in Kentucky and West Virginia, or

the New York State Thruway. Also, there are probably very few, if any, LTPP sites subjected to

36
light atypical truck traffic, such as the Garden State Parkway (New Jersey). However, many sites

are located on highways where trucks with gross vehicle weights exceeding 356 kN (80,000 lb)

are allowed without a permit. These sites are in the jurisdictions that allow longer combination

vehicles (LCV); in the States and Provinces with tailor-made vehicle weight and dimension

regulations (such as Michigan, Rhode Island, and Ontario); and on roads often subjected to

special permit vehicles, such as routes near northeastern ports used to haul ISO containers rated

at 30 metric tons. LCV were grandfathered in June 1991 under the provisions of the Surface

Transportation Assistance Act of 1982 in approximately 25 States, including Arizona, New

York, Ohio, and Utah. An overview of developments in truck technology is outlined in chapter

3.

For traffic loading projections in Categories 1 and 2, the outline of vehicle weight and

dimension regulations provides useful insight for quality assurance purposes. For projections in

Categories 3 and 4, the outline is particularly important because these categories rely on regional

or site-related vehicle class distributions and on regional load spectra, respectively.

Consequently, when choosing a representative vehicle class distribution, or a representative set

of load spectra distributions, it is advisable to ascertain the expected truck regulatory

environment.

Example:

Levare not allowed at Site 05-3095. State and special permit regulations are unknown

and are difficult to establish without contacting local representatives.

Step 3. Describe Quality and Quantity of Available Monitoring Data.

The following sources should be assessed to obtain information on the quality and

quantity of the available monitoring data.

• Equipment location codes in the IMS [4]. These codes indicate the location of the

AVC and WIM equipment in relation to the test site. The locations of the AVC

and WIM equipment are coded separately as:

Site-specific (S) - The equipment is located at or nearby the test section

and accurately measures the traffic that crosses the test section.

Site-related (R) - The equipment is located on the same roadway as the

test section, but a major truck traffic generator is located between the test

section and the equipment.

Other (0) - The equipment is located on another roadway.

Additional information on location identification codes can be found in reference

4. Limited review of the equipment location codes in IMS indicates that these

codes are consistently reported.

37
• Data availability codes in IMS. These codes indicate the amount and basic

characteristics of monitoring data. Data. avaihibility codes are shown in table 6

[4].

Table 6. Data availability codes.

Data

Code Interpretation

9 Continuous WIM meeting the American Society for Testing and Materials

(ASTM) standard.

8 Continuous WIM not meeting the ASTM standard.

7 Continuous AVC with portable WIM for all seasons.

6 Continuous AVC with some seasonal WIM.

5 Continuous AVC with limited WIM.

4 Continuous AVC with no WIM.

3 Continuous Automatic Traffic Recorders (ATR) with limited - but seasonal -

vehicle classification and WIM data.

2 Limited - but seasonal - vehicle classification and WIM data.

I Limited vehicle classification and truck weight data (short counts).

0 Data collected on different roadway.

A review of the data availability codes in the IMS indicates that these codes are

consistently reported.

• Assessment ofthe amount and monthly distribution ofdays with available sitespecific AVC and WIM data. Several IMS tables need to be examined to obtain

this information because the main IMS table, which provides a summary of

annual data (TRF_MONITOR_BASIC_INFO), does not consistently contain data

for all monitoring years.

• AVC and WIM calibration data. The records pertaining to the results of the

calibration of AVC and WIM equipment are stored in CTDB Level 5. These

records can be valuable in interpreting data with perceived idiosyncrasies.

However, information on AVC and WIM equipment calibration is often

unavailable, and it is very labor-intensive to obtain and analyze calibration data.

Example for Site 05-3095:

Data availability code is 6. No information on calibration ofAVe and WIM equipment is

available. The extent ofmonitoring data is summarized in table 7.

38
Table 7. Extent of monitoring data for Site 05-3095.

Monitoring Year

Data Type 1992 1993 1994 1995 1996 Total

AVe Days 19 81 201 32 172 505

Month 3 3 9 3 ? 18+

WIM Days 20 37 20 11 17 105

Month 3 5 3 1 ? 12+

Notes: Month = Number ofmonthsfor which some ofthe AVe and WIM data are

available.

? = Data could not be obtained.

• Availability ofsite-related data. If there are no (or limited) site-specific Ave and

WIM data, it is necessary to investigate the availability of site-related Ave and

WIM data. The location of the nearby LTPP sites, obtained in step 1, can be a

starting point.

Example:

Refer to figure 15, prepared as part ofthe case study for Site 04-1002, which provides a

sketch ofsite-related sections. The data from site-related sections were usedfor traffic

projections.

"-

~sVega .........~.

'''..

"".."

....... ,"."

Figure 15. Location of site-related sections for Site 04-1002.

39
Step 4. Develop Annual Traffic Data Projection Sheet.

The Annual Traffic Data Projection Sheet is used to summarize and compare trends in the

values of all traffic variables available for the LTPP lane that are expressed as annual values and

are available for both historical and monitoring years.

In general, the following annual traffic variables are available:

• AADT volumes.

• AADT truck volumes (for FHWA vehicle classes 4 through 13 inclusive).

• Annual average daily number of ESAL.

• Average number of ESAL per truck, "truck factor" (third item in this list divided

by the second item).

However, for sites belonging to Categories 3 and 4, ESAL monitoring data are not

available. It should also be noted that AADT volumes are frequently missing from the IMS

monitoring information table.

To facilitate the assessment of changes in the annual variables (first through fourth

bulleted items above), the bottom part of the Annual Traffic Data Projection Sheet shows annual

percent change since the site was opened to traffic.

For years with missing annual summary data in table TRF_MONITOR_BASIC_INFO

(see step 3, third bullet), it is necessary to calculate the average truck factors (fourth bulleted item

above) from the truck factors available for individual vehicle classes. This is done by calculating

a weighted average:

TF

i=13 TF. * truck. L...t i=4 I I

total trucks

(1)

where: TF

i

TFi

trucki

total trucks

= Average annual truck factor.

= FHWA vehicle classes 4 through 13.

= Average annual truck factor for vehicle class i.

= Average annual number of vehicles in Class i.

= Average annual total of vehicles in Classes 4 through 13.

Note: The above calculation of missing average truck factors provides useful trend data,

but may not always be necessary.

An example ofan Annual Traffic Data Projection Sheet is shown infigure 13.

40
1400

1200

1000 Q)

E

::l

(5

> 800

0

::l

.... 600

0

400

200

--Ir- Historical Truck Volume

___Projected Truck Volume (2% growth)

......Monitoring Truck Volume

-+-Projected Truck Volume (5% growth)

Figure 16. Annual growth trends for Site 05-3059.

Step 5. Develop Annual Traffic Data Projection Model.

Step 5.1. Systematically and Critically Review Data Plotted on the Annual Traffic Data

Projection Sheet.

The objective is to identify common trends in annual traffic data and to provide

diagnostic assessment of the data. The diagnostic assessment of data is the basic quality

assurance activity to ascertain the reliability of the relationship between monitoring data obtained

for different years and between historical and monitoring data. The assessment should identify

data problems, abnormalities and idiosyncrasies, and their possible causes. In addition to

considering annual traffic data given on the Annual Traffic Data Projection Sheet (figure 13),

quality and quantity of underlying monitoring data (obtained in step 3) should also be taken into

account.

Examplefor Site 05-3059, shown infigure 13:

• Considering historical data, there was a steady growth in AADT and truck

volume between 1977 and 1989. In 1990, there was a sudden jump. Steady

growth in AADT resumed after 1990. In 1996, however, there was a sudden drop

in truck volume based on historical data.

• Monitoring AADT and truck volumes, as well as annual ESALs, are flat or are

declining.

41
• Monitoring truckfactors, equal to about 1, appear to be highfor an urban

freeway, but are acceptable considering the rigid pavement on this site.

• The estimated 1996 truckfactor (2.9) is apparently incorrect. (For other years,

monitoring truckfactors were close to 1.)

In some cases, particularly where truck factors seem to be problematic or "out of line," it

may be necessary to also evaluate annual vehicle spectra to ascertain probable reasons for the

atypical values and to decide what importance should be placed on problematic data when

making projections. For further discussion, refer to step 7.1 (Categories 1 and 2) and step 7.2

(Category 3).

Step 5.2. Formulate Annual Traffic Data Projection Model and Summarize Reasons for Its

Selection.

The Annual Traffic Data Projection Model is used to project annual load spectra for all

years (between the year the site was opened to traffic and the terminal year). Thus, the years for

which the projections are particularly required include years with historical data, monitoring

years with missing or evidently problematic (monitoring) data, and years after the monitoring

period. Although no annual predictions are required for years with acceptable annual monitored

spectra, the projections may be done for consistency. For the purposes of the Phase I study,

1997 was selected as the terminal year, because this is the last year with reported traffic data in

the current IMS release.

Ideally, the Annual Traffic Data Projection Model should be based mainly on monitoring

annual average daily ESALs because:

• Monitoring data, which are based on actual measurements, should be more

reliable than historical data, which are usually based on estimates.

• ESALs combine both vehicle classification volumes and axle loads.

Also, ideally, the model should be based on regression analysis of trends in monitoring

ESAL data.

However, in practice, monitoring annual load spectra may be available for only 1 or 2

years, or may be missing entirely. Thus, it is necessary to consider all trends in annual traffic

data presented on the Annual Traffic Data Projection Sheet and to consider their associated

quality and quantity. For example, the values of variables based on many months of monitoring

data may be assigned more weight than the values based on only a few days of monitoring data.

Example for Site 05-3059:

Based on the combined evidence provided by historical monitoring data and assessed in

step 5.1 (AADT and truck volumes, annual ESALs that do not exhibit growth, and

historical truck volume data exhibiting about a 4-percent annual growth rate during the

period of 1978 through 1989, as shown in figure 15), the use ofa linear projection model

corresponding to a simple 2-percent annual growth rate is recommended.

42
The projection was based on AADT truck volumes by superimposing the 2-percent annual

growth rate trend line on the monitored AADT truck volumes, as illustrated in figure 16.

Because ofthe uncertainty involved in developing the Annual Traffic Data Projection

Model, the consequences ofusing a different annual growth rate-a 5-percent annual

growth rate-will also be evaluated.

Step 6. Select Category for Projecting Traffic Loads.

The selection of the projection method must fit the available AVC and WIM monitoring

data in order to achieve the best utilization. The four basic projection categories were described

in chapter 3. The selection of the projection category should be based on:

• Available AVC and WIM data in terms of quantity of data. There are many

possible combinations of available AVC and WIM data.

• Quality and reliability of monitoring data.

• Site-specificity of monitoring data.

• Availability of applicable regional vehicle classification and weight data.

Consequently, the selection of the load spectra projection method requires judgment. For

example, the mere existence of AVC and WIM monitoring data does not guarantee the use of

Category 2 predictions.

Example for Site 05-3059:

Category 2 projections were selected because ofthe existence ofsite-specific AVC and

WIM data that passed the quality assurance review based on annual trends ofhistorical

and monitoring data. The amount ofAVC and WIM data is insufficientfor Category 1

projections.

STEP 7 FOR CATEGORY 1 AND 2 PROJECTIONS

Step 7. Develop Base Annual Load Spectrum To Be Used for Projection.

In this step, references are often made to comparing and evaluating data, and to

identifying inconsistencies in data. The data in this case are annual load spectra obtained for

different years and sites. To effectively assess this type of data, it is necessary to develop

guidelines enabling the analyst to judge the expected values. For example, what is the expected

or acceptable load spectrum for two-axle six-tire trucks? What is the expected average truck

factor for a rural Interstate? What is the expected annual load spectrum for urban arterial roads?

To provide answers to such questions in a systematic way, the development of a set of typical

values of traffic loading data is proposed.

The need to have a set of typical traffic loading values for comparison and traffic data

management purposes is the basic idea behind the proposal presented in chapter 6 to develop and

use summary statistical measures for comparing and evaluating axle load distributions and

cumulative axle loadings, and the proposal to develop a pavement traffic loading guide.

43
Step 7.1. Obtain and Critically Assess the Annual Load Spectra for All Available Years.

Critical assessment and review of the spectra is basically a quality assurance exercise to

ensure that annual load spectra, used for the subsequent projections, are sound. Four mutually

complementary procedures are suggested. The procedures (l through 4) are listed in the order of

increasing detail and complexity. The use, and the extent of the use, of these procedures will

depend on the quality and the amount of monitoring data and on the match between monitoring

and historical data.

1. Develop normalized annual load spectra for single, tandem, and tridem axles for

all monitoring years and examine them against each other and against the typical

load spectra for differences and possible inconsistencies.

Example:

Refer to figure 17. The results suggest that, in spite ofdifferences in spectra obtainedfor

the five different years, all spectra appear to be valid.

2. Calculate and critically assess load spectrum coefficients (LSC) for single,

tandem, and tridem axles (LSCt , LSC2, LSC3). The need for LSCs and their

function is described in chapter 6. The calculation is given in equation 2.

LSC

~:l [( mid-10~ range;) ]3.8 ,

L 1-1 18000 ,

load-range counti

* L total count

(2)

where: LSC =

1 =

mid-load range i =

load-range counti = L =

Load spectrum coefficient used to compare normalized load spectra.

Number of load ranges (the numbers differ for single, tandem, and

tridem axles).

Average load range in pounds for load range i, e.g., if the load range is

6,000 to 6,999, use 6,500.

Number of axles in load range i.

1 for single axles, 2 for tandem, and 3 for tridem. (L converts the effect

of tandem and tridem axles into that for single axles.)

Because LSCs characterize the entire spectrum using one number, LSCs can be

used to compare load spectra obtained for different years by comparing a few

numbers. They can also be used to compare site-specific spectra with typical

spectra. The calculation of LSCs may not be necessary if a visual comparison of

spectra clearly indicates similarity between spectra obtained for different years.

44
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Axle Loads, Ib

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,...

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,...

20% ..·.,..·"···.·····.·.·,,,·.·.·.·.,.·.· ·..· ~.i.~gl~A~? ·.·.w ·.·..· ·•·.·•·.·•.·.••.·.·.·.·.·.·.·.·.·.·."·...•..•...•..-·.·.·.,,· w ..

18% 1

16% I

14% +----.1.....:------------------------------------1

[!l 12% -t-=-~J~~;1l\_------------------------___; 10% -{-=.::j~~..4.tl'1i1I&r----------------------------'

'0tf!. 8% t~L~.fl~~~~~~""*--------------------ii ..".

60{, 1< Elf -:I ~_

4% +-....:.~--------- -,::::a." • .---------------------------l

0%2% t~=:=~=;=~=:;=~~~~i;O(~;;;~;;:;;;;;;;;;;:;;;;;~~=1 o 000 0 0 o 0 0 0 0 o 0 0 0 0 C\J "<t <D co 0

Tandem Axle

12%

6%

4%

2%

0% 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 "<t co C\J <D 0 "<t co C\J <D 0 "<t co C\J <D 0 "<t co C\J <D 0 "<t IX)

C\J C\J C\J C'l C'l "<t "<t "<t U'} U'} <D <D <D r-- r-- IX) IX) IX)

Axle Loads, Ib

...........................................................................................................................................................................................................................................................................................................................................................................................................................................:

:

10% +---~'1"Iiiif'io,---------------------------------------;J

8%+-~;---,lJF--Ul:'lt'------------------------------------:

'0

TridemAxle

.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.........•.....•.•.•.•.•.•.•.•.•.•.•...•.•.•.•.•.•." .•.•.•.•.•.•.•.•.•.•.•...............•.•.•." w ·········""···1

0%

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 <D C\I <Xl "<t 0 (0 C\J <Xl "<t 0 (0 <Xl "<t 0 <D C\I C') C') "<t "<t L{) (0 (0 "- co Ol Ol

Axle Loads, Ib

--+-1992 _1993 --tr-1994 --*-1995 -'-1996 -eJ-AVERAGE

16%

14%

12%

<n

CD 10%

8%

'0

0 6%

4%

2%

1 1b =0.00445 kN

Figure 17. Comparison of normalized annual load spectra for Site 05-3059.

45
Example for Site 05-3059:

Load spectra coefficients were calculatedfor si1}gle, tandem, and tridem axlesfor all

monitoring years (1992 through 1996) and were plotted in figure 18. Data in figure 18

indicate that there are relatively small differences between load spectra obtainedfor

individual monitoring years. The exception are LSCsfor tridem axles, which declined

from 1.60 in 1992 to 1.00 in 1998.

3. Develop normalized annual load spectra for vehicle classes 4 through 13 for

selected years and examine them for possible inconsistencies. Compare sitespecific spectra with expected spectra. The proposed LTPP Traffic Loading

Guide (chapter 6) will contain representative load spectra. Emphasis should be

placed on Class 9 vehicles.

Example:

The calculation ofannual load spectra for individual load classes requires processing of

raw Level 2 data, a time-consuming process. Because total annual spectra were judged

to be reasonable, spectra for individual vehicles were not calculatedfor this case study.

An example ofnormalized load spectra for individual vehicles is given in appendix B.

0.20 -r------.==========+::::::::::=="""-.-.,,~---=.=======---i

0.00 +------,------,-------,-------,-------;

C\J

0>

0>

.....

-.-Single

C')

0>

0>

.....

-.:t

0>

0>

.....

Year

--",*"" Tandem

l!l

0>

0>

.....

--'-Tridem

<.0

0>

0>

.....

Figure 18. Load spectra coefficients for Site 05-3059.

4. Calculate and assess load spectra coefficients for vehicle classes 4 through 13,

according to equation 2.

46
5. Calculate annual gross vehicle weight distributions of Class 9 vehicles for

selected years and assess their properties.

The validity of the weight data obtained by WIM scales can be effectively

assessed using the GVW distribution of five-axle single-trailer trucks (five-axle

vehicles consisting of two units, one of which is a tractor or straight truck power

unit). In jurisdictions that limit GVW to 356 kN (80,000 Ib), up to 80 percent of

pavement traffic loads are imposed by this vehicle type. Specific procedures have

been developed to use the GVW distribution of these trucks as a quality assurance

calibration check for WIM scales [16, 17]. The five-axle single-trailer trucks are

Class 9 vehicles. Another frequently used check includes the relationship

between the weight of steering axles and the GVW of Class 9 vehicles.

Example:

The calculation ofGVW distribution ofClass 9 vehicles requires retrieval ofdata from

Level 4 ofCTDB and customized processing ofdata. The interpretation ofthe results

requires judgment and knowledge oflocal conditions. The use ofGVWfor the

assessment ofWIM data has not been demonstrated by a case study. The feasibility and

usefulness ofthe procedure are well documented in references 16 and 17.

Concluding Remarks

Quality assurance of annual traffic load monitoring spectra is a demanding, laborintensive task. However, this task is essential, and it is also cost-effective. For example, in a

situation where there are two annual monitoring vehicle spectra (for two consecutive years) that

differ significantly to the point that one is probably invalid, it is possible, through the procedures

described above, to ascertain the validity of the spectra and to produce valid traffic projections.

The resources required to do so (through systematic analysis of available data) pale in

comparison to the resource that have been spent or that would have to be spent to collect traffic

data in the field.

Step 7.2. Determine/Select Base Annual Load Spectrum.

Base annual load spectrum is used for projecting traffic loads for years without

monitoring load spectra. A review of more than 150 LTPP sections indicates that there are

usually about 1 to 4 monitoring years, whereas the total number of in-service years is about 10 to

25.

The selection of the annual load spectra should consider the differences in the spectra

obtained in step 7.1 and the quality and quantity of underlying monitoring data determined in

step 3. Particular attention should be paid to trends in average annual ESALs (middle part of

figure 13).

Typically, an average annual load spectrum for all monitoring years will be used.

However, if one of the available annual spectra is clearly superior to the other spectra (for

47
example, because of quality and quantity of AVC and WIM data), the sole use of this spectrum

may be warranted. In an ideal situation, if several (say, five or more) annual load spectra all fit

well into the Annual Traffic Data Projection Model, it is advisable to use the first and the last

annual load spectra as the base annual load spectrum. That is, the first available annual load

spectrum can be used for backcasting, and the last annual load spectrum can be used for

forecasting. The use of two base annual spectra will increase the effort required to complete step

9.

The base annual load spectrum is assigned to the base year. The base year is typically the

average year for the years used in the calculation of the base spectrum.

Example for Site 05-3059:

There are differences in the amount ofavailable monitoring data. According to table 7,

the best AVC year is 1994 (with 201 monitoring days), and the best WIM year is 1993

(with 37 monitoring days). The best combined year appears to be 1996 (with 172 AVC

days and 17 WIM days). Because ofthe variation in the annual spectra shown infigure

17, use ofthe average annual spectrum for the years 1992 through 1996 as the base

spectrum for projection is recommended.

Considering the uncertainty involved in determining the base annual spectrum, the

consequences ofusing a different annual spectrum were investigated. Specifically, in

addition to using the average 1992 through 1996 spectrum, the 1996 annual load

spectrum alone was also used. The 1996 spectrum was selected because the shape ofthis

spectrum appears to differ more than other spectra from the average 1992 through 1996

spectra, as shown in figure 19 and as indicated by load spectra coefficients shown in

figure 18.

STEP 7 FOR CATEGORY 3 PROJECTIONS

Step 7. Develop Base Annual Load Spectrum To Be Used for Projection.

Step 7.1. Obtain and Critically Assess the Annual Vehicle Volume Distributions for

Classes 4 Through 13 for All Available Years.

Annual vehicle volumes are available in CTDB Level 2 as processed data.

Step 7.2. Determine/Select Base Annual Volume Distribution and Base Total Annual

Truck Volume.

Typically, the base annual volume distribution will be calculated as an average of the

available monitored distributions. Similarly, as in the process of selecting annual load spectrum

48
16%

14%

12%

! 10%

8% '5

'#- 6%

4%

2%

SingleAJde

:c.....P~,

0% 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\J '<t <D co 0 C\J '<t <D co 0 C\J '<t <D co 0 C\J '<t <D co 0 C\J '<t C\J C\J C\J C\J C\J C') C') C') C') C') '<t '<t '<t

Axle Loads, Ib

Tandem Axle

.•.•.•.•.•.•.•.•.•.•.•.•.•...•.•.•.•.•.•.•.•.•.•.•.•.•·•·•·•.·.·.·.·.·.·.·.·.·.·.·.·.·.·,,·.w.·.·.·.·.·.·.·.·.·.·.·.·••·.·.w.·.·.·.·.w.·.·.·.·.·.·.·.·.·.·.·.·.·.·•.•.•.•.•.•.•.•.•.•.......•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•...........•.•.•.•.•.•.•.•.•.•.•.•.•·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.·.·•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•...•...•.•.......•.•·.·.·.·.·.1

8%+---*=------1=-----l~----------------------------------i

4% -l-----~~~~...,...---------------

6% -t--D"'------""=:--------------------------------;

10%

12%

2% -I-----------------'~"r_L....~----------------------;

0% 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

'<t co C\J <0 0 '<t co C\J <0 0 '<t co C\J <0 0 '<t co C\I <0 0 '<t co .... C\I C\J C\I C') C') '<t '<t '<t LO LO <0 <0 <0 I'- I'- co co co

Axle Loads, Ib

Tridem Axle ..........................w w w ·.· "".·.·.·.·.·.·.·"".· w" · ·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.· ·.·.w.·.....................•...•...•.•.•.•...•.....•................................w••••••••••••••••••••••••••••.•.••w.·.·.·"".·."".·.·.1

0%

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 <D C\I co 0 <D C\I co '<I' 0 <D C\I co '<I' 0 <D C') C') '<t '<I' U) <D <D r-- r-- co 0> 0>

Axle Loads, Ib

14%

12%

10%

! 8%

'0 6%

4%

2%

~AVERAGE

1 lb =0.00445 k.N

Figure 19. Comparison of normalized average and 1996 annual load spectra for Site 05-3059.

49
in Categories 1 and 2, if one of the available annual volume distributions is clearly superior to

the other volume distributions, the sole use of this volume distribution may be warranted. Also,

in an ideal situation with many years of high-quality AVC data, the use of the first and the last

annual volume distributions as base volume distributions is recommended. In this case, the first

annual volume distribution will be used for backcasting, and the last annual volume distribution

will be used for forecasting.

The establishment of the base total annual truck volume (for vehicle classes 4 through 13)

follows the procedure for establishing the base annual volume distribution described above.

Example for Site 37-3807:

The base annual volume distribution was obtained as an average distributionfor the

years 1993 through 1995. The results are summarized in table 8 andfigure 20. Figure

20 also shows the presence ofClass 14 vehicles. In general, Class 14 vehicles are

vehicles that could not be classified into the 13 vehicle classes and include both cars and

trucks.

Table 8 Vehicle class distribution for Site 37-3807

Percentage of Vehicles for Vehicle Classes 4 Through 13 Total

Year Truck

4 5 6 7 8 9 10 11 12 13 Volume

1993 0.86 4.62 6.16 0.29 8.75 62.4 0.42 4.55 1.58 0.06 290,600

1994 1.07 5.71 6.68 0.31 9.23 63.8 0.51 4.01 1.74 0.10 376,300

1995 0.80 13.11 6.09 0.67 8.26 60.6 0.53 5.22 1.60 0.17 319,700

Average 0.92 7.79 6.34 0.42 8.77 62.4 0.49 4.56 1.65 0.12 328,900

Step 7.3. Obtain/Select Load Spectra for Vehicle Classes 4 Through 13.

Load spectra for vehicle classes 4 through 13 were expected to be obtained by sitespecific WIM measurements. However, because the load spectra are not available for Category 3

predictions, they need to be estimated.

The following possible sources of surrogate representative load spectra for vehicle classes

are listed in the order of preference.

1. Site-related load spectra.

2. Regional load spectra obtained for:

- Similar site(s) in the same jurisdiction, or

- Site(s) with a similar functional classification as the site in question.

3. Typical load spectra.

For comparison and calculation purposes, it is preferable to obtain normalized load

spectra rather than actual spectra. As proposed in chapter 6, the development of a Pavement

Loading Guide will facilitate judicious selection of representative load spectra.

50
300000 -,----------------------------------,

250000 -t-------------------------------1

200000

en

"E

:J

0

0 150000 .lit.

0

:J

100000

50000 -t----------------jJ-----t------------l

2 3 4 5 6 7 8 9 10 11 12 13 14 15

70 --------

60

50

f/l

.lit. 40 0

:J

...-

0 30

."g0

20

10

0

2 3 4 5 6 7 8 9 10 11 12 13 14 15

-+-1993 --.-1994

Truck Class

---l:r- 1995 ~Average

Figure 20. Truck distribution by class for Site 37-3807.

51
Example for Site 37-3807:

For the purposes ofthis case study, the typical.load spectra for the 10 truck classes,

given in appendix B, were used.

Step 7.4. Obtain Total Annual Load Spectra for Monitoring Years.

Two approaches should be considered for calculating annual monitoring year spectra:

• Use of monitored AVC volumes obtained for specific years - Typically, annual

load spectra for monitoring years (the years for which classified vehicle volumes

were obtained by AVC equipment), should be obtained by multiplying, for each

vehicle class, the normalized load spectra (obtained in step 7.3) by the appropriate

vehicle volumes (the volumes obtained by AVC equipment for the particular

monitoring year).

• Use of base annual volume distribution - If annual vehicle volume distributions

(obtained and evaluated in step 7.2) vary in quality and quantity, or the volume

distributions are available for a few years only, it may be advantageous to use the

base annual volume distribution for the calculation of annual load spectra for all

monitoring years. It should be recalled that the selection of base annual volume

distribution takes into account the quality and quantity of AVC data obtained for

different monitoring years, and that base annual volume distribution can average

the differences between annual distributions. Consequently, this approach may

predominate.

Calculation of annual load spectra for a given monitoring year is summarized by

equations 3 through 5.

(3)

(4)

(5)

where: s = Load spectrum for single axles (vector containing the number of single

axles belonging to each of the pre-defined load categories for single axles).

Sj = Vector containing normalized load spectra for class i vehicles.

aj = Single-axle coefficient (number of single axles per vehicle) for vehicle

class i. (Axle coefficients are required to transfer normalized vehicle load

spectra into actual load spectra for a given number of vehicles.)

Vj = Percentage of vehicles of class i for a given monitoring year.

V = Annual volume of vehicles in Classes 4 through 13 for a given monitoring

year.

52
d, t = Load spectrum for tandem and tridem axles, respectively.

bi

, ci = Tandem- and tridem-axle coefficients, respectively.

Example for Site 37-3807:

Average annual vehicle volume distribution (for the years 1993 through 1995, i.e., the

base volume distribution) was used. It was assumed that the use ofthe average volume

distributions will produce robust spectrafor the projections. The base volume

distributions were multiplied by the typical load spectra (appendix B) in accordance with

equations 3 through 5. Annual vehicle volume distributionsfor the years 1993 through

1995 are shown infigure 20.

Step 7.5. Calculate Base Annual Load Spectrum.

The calculation of the base annual spectrum (the spectrum used to project traffic loads for

years without monitoring spectra) is similar to the calculation of monitoring year spectra given

by equations 3 through 5. However, instead of the option of using monitoring year-specific Vi

and V, base values of these variables, established in step 7.2, are always used.

Example for Site 37-3807:

The calculation was identical to that described in step 7.4.

STEP 7 FOR CATEGORY 4 PROJECTIONS

Step 7. Develop Base Annual Load Spectrum.

It is assumed that there are no significant Ave and WIM data. However, it is assumed

that truck volumes were reliably estimated by ATRs or by other means.

Step 7.1. Obtain/Select Representative Vehicle Volume Distribution and Load Spectra for

Vehicle Classes 4 Through 13.

The order of preference for the selection of annual vehicle volume distributions and

spectra is:

1. Site-related vehicle distribution.

2. Regional vehicle distribution obtained for:

- Similar site(s) in the same jurisdiction, or

- Site(s) with a similar functional classification as the site in question.

3. Typical load spectra.

53
Step 7.2. Estimate Annual Volumes for Vehicle Classes 4 Through 13.

Vehicle volume distributions selected in step 7.1 will be typically normalized

distributions. To obtain actual base vehicle volume distributions, the normalized distribution are

multiplied by the site-specific total truck volumes.

Example for Site 17-5423:

1997 was considered the base year, with a total AADT truck volume of 1,934. The results

are summarized in table 9.

Table 9. Estimated vehicle class distribution for Site 17-5423.

Distribution of Vehicles for Vehicle Classes 4 Through 13 Total

Vehicle Truck

Distribution 4 5 6 7 8 9 10 11 12 13 Volume

Representative

Normalized 2.19 7.11 3.61 0.89 4.50 76.0 1.40 2.95 0.55 0.77 100%

Distribution, %

Base

Distribution 42 138 70 17 87 1,470 27 57 11 15 1,934

Step 7.3. Calculate Base Annual Load Spectra.

Calculation of annual load spectra is based on equations 3 through 5. However, instead

of using monitoring year-specific Vi and V, base values of these variables, established in steps 7.1

and 7.2, are used.

Example for Site 17-5423:

A representative vehicle distribution was selected using currently available data.

Representative load spectra given in appendix B were used. The above selections were

made mainly to demonstrate the feasibility ofcomputations. It is possible that a detailed

review ofavailable data would permit the use ofregional-based vehicle classifications

and load spectra or that the availability ofthe LTPP Pavement Loading Guide will

facilitate the selection ofbetterfitting typical load classifications and load spectra.

STEPS 8 THROUGH 12 - APPLICABLE TO ALL CATEGORIES

Step 8. Develop Annual Projection Factors.

The Annual Traffic Data Projection Model is used to calculate annual traffic projection

factors. Traffic projection factors are multipliers used to project annual traffic load spectra from

the base annual spectrum. For Category 1 and 2 projections, annual projection factors for

monitoring years may not be required because these spectra are accepted as reported in the

CTDB.

54
Example for Site 05-3095:

The base annual load spectrum is assigned to 1994. Using a simple 2-percent growth

rate, the annual projection factors for the years 1978 through 1997 are shown in table

10.

Table 10. Annual projection factors for Site 05-3059.

Year Annual Projection Factor

1978 0.68

1979 0.70

1980 0.72

• •

• •

• •

1996 1.04

1997 1.06

Step 9. Calculate Annual Load Spectra for All In-Service Years.

Annual load spectra for all in-service years are obtained by multiplying all axle counts

(for all load ranges of single, tandem, and tridem axles) of the base annual spectrum by the

annual projection factors.

For quality assurance purposes, and to facilitate communication with pavement

professionals, it is recommended to calculate the resulting ESALs for all historical and

monitoring years and cumulatively for all years.

Example:

The cumulative ESAL computation sheetfor Site 05-3059 is given in table 11.

Step 10. Provide Recommendations for Enhancement.

Summarize what should be done to improve the accuracy and reliability of traffic

loadings for the site. Consideration should be given to both field traffic data collection and the

traffic data projection process.

Example for Site 05-3059:

Traffic monitoring on this site should continue in order to verify the recent trend of

decreasing ESALs. The following specific issues should be discussed with a

representative ofthe Arkansas DOT:

• Assumed annual growth rate of2 percent.

• Estimated truckfactorfor 1996, which appears to be high.

55
• The reason for the sudden increase in theestimated truck volumes reported

for 1990 through 1992.

Step 11. Conduct Sensitivity Analysis.

Sensitivity analyses have been conducted selectively as part of the Phase 1 study. The

objective was to identify key assumptions made during the projection process, particularly the

assumptions made where other defendable alternatives existed, and to evaluate and compare the

consequences of these alternative assumptions.

Example for Site 05-3059:

It was assumed that truck traffic was growing at a simple growth rate of2 percent. This

assumption was based mainly on judgment-a higher growth rate is a possibility. Also,

as discussed in step 7.2 for Categories 1 and 2, different base spectra can be selected. In

summary, the following three cases were evaluated:

• Base Case: Average annual load spectra and 2-percent growthfactor.

• Sensitivity 1: Average annual load spectra and 5-percent growth factor.

• Sensitivity 2: 1996 annual load spectra and 2-percent growth factor.

In each case, annual load spectra for all years between 1978 and 1997 were calculated

together with the cumulative annual load spectrum (from 1992 through 1997) and the

corresponding cumulative ESALs. The results, summarized in table 11 andfigure 21,

indicate that the use ofalternative load spectra can have an influence on the change in

cumulative ESALs (from 3.64 to 3.07 million) comparable to the change in growth rate

from 2 percent to 5 percent (from 3.64 to 2.82 million). The higher cumulative ESALs

obtainedfor the 2-percent growth rate (compared to the 5-percent rate) are caused by

applying the 2-percent growth rate to higher initial truck volumes.

Step 12. Monthly Variation.

Step 12.1. Develop Monthly Traffic Data Projection Sheet and Critically Review Available

Monthly Data.

The Monthly Traffic Data Projection Sheet is used to summarize and compare monthly

trends in total truck volumes, ESALs, and ESALs per truck. In addition to comparing monthly

trends in total truck volume, it is also possible to compare monthly trends for individual vehicle

classes. This type of comparison was done by Hallenbeck [7], who recommended the

aggregation of the 10 individual truck classes into a few major classes to obtain more definite

monthly trends. However, based on case study analysis, even the aggregation of all trucks into

one class may not reveal systematic, repeatable monthly trends.

56
Table 11. Cumulative ESALs computation sheet for Site 05-3059.

Historical Monitoring

ESALs per ESALs per Base

Year day day Case Sensitivity 1 Sensitivity 2

1978 244 390 115 301

1979 249 401 143 310

1980 255 413 172 320

1981 260 424 201 329

1982 266 435 229 338

1983 271 447 258 348

1984 277 458 287 357

1985 285 470 315 367

1986 293 481 344 376

1987 304 493 372 385

1988 310 504 401 395

1989 334 516 430 404

1990 1052 527 458 414

1991 1175 539 487 423

1992 1219 682 682 682 682

1993 616 616 616 616

1994 540 540 540 540

1995 557 557 557 557

1996 470 470 470 470

1997 607 659 479

Cumulative ESALs for Base Case =3,639,245

Cumulative ESALs for Sensitivity 1 =2,823,552

Cumulative ESALs for Sensitivity 2 =3,069,581

57
1400

1200

mel 1000

Q;

0-

-I

m 800

<t:

(/)

w 600 Q)

OJ

Q) 400

200

0

I'-- eo 0> 0 .-- C\J C') '<t U) to I'-- eo 0> 0 .-- C\J C') '<t U) to I'-- eo I'-- I'-- I'-- eo eo eo eo eo eo eo eo eo eo 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> .-- .-- .-- .-- .-- .--

Year

_ Monitoring ESALs per day

-+- PFOJECTED5%_ESALJ-ER_DAY

-+- Hstorical ESALs per day

PFOJECTED2%_ESALYtFLDAY

-*- FmJECTED2%_1996_ESAL_PER_DAY

Figure 21. Comparison of projected, historical, and monitoring ESALs for Site 05-3059.

Example for Site 12-4000:

The Monthly Traffic Data Projection Sheet infigure 22 shows no discernable monthly

patternfor the total truck volumes and indicates a weak trend toward higher loads

(ESALs per day) during warmer months. Figure 23 compares average monthly load

spectra and is an example ofthe work done to ascertain the reliability ofmonthly load

spectra obtainedfor different years.

58
9000

8000

G> 7000 E::;,

6000 ;g

..l<: 5000 0

2

I- 4000 >.

::c 3000 C

0 2000 ::2

1000

- _ ?'o

8 10 12 14

Month

-0-1994

4 6

~1993

2

0-1----...,.----...,....----,------,..-----,----,-----1

o

~1991

2 3 4 5 6 7 8 9 10 11 12

350 .·.·,.·.·.·.·.·.·.·.·.·.·.w.·"w.w"·.,,,,""w.·.·.·.·.·.·.·.·.·,,·.·,,·.,·.w.·,,·.·.·.·.·.·.·.w.·.·.w.·.·.·.w.w.w.w, " " ·.·.·.·.·.·.·.·.m·.'·.'·.·,.~.·.·.·.·.,·.·,,·.·.·.·.·.· ".....•..w w m wi

300 i

250 11_-:;?4~~~~~~~~;;a.::::::;;~~~~~~~l-:x~~.........o::::=--i~ Cl

<0 200 ¥-~----------------------'~-----""'.........=.,.....'""'i5 a.

150 +-----------------------------,~::::---; «

100 +--------------------------.~------:.f

50 F~~~==+====~-------lb-~:::::::::::j7=-----------l

O-+--........,...--,.......-.....,----r--...,--.......,..--"T"'"'""--,...--r----,----'!

1

Month

-0-1994 -6-1995 --1996

,.l

---- ./

2 3 4 5 6 7 8 9 10 11 12

Month

1.8 ··.··.·.··· ·····ww·.··········.·····.·.·.·.·.·.· ·.· ··••··.·.·······.w·..···.··.··.·.·.····.·.·.·.·.·.·.·.···············l

1.6 t-----------:::::;;a;;;;;:;;:::::jj:::::::;:=-==liiiic------jl "- ---".., - - ........... n. l

4; ~:~1 ·V~~~~=~~./'2~y~S~~~~~~~~"'~'~2./~h-~~~:~-----'~--o.~; a. 0.8

0.6 +--------------------------:::;;;i""--..,..;;;a,

w 0.4 1E==~::::::_----------"'F:;:::::::::;;;;:_-______::7=-------j

0.2 +--------====-=~""*'=~~-----=-=::::::::=",~------j

O+-----,.--...,.------,---,----,---,---r----,---,----,-----I

1

-1992 -0-1994 ---tr-1995 -1996

Figure 22. Comparison of monthly traffic variations for Site 12-4000.

59
Single Axle

---- - -- 0 0 0 0 0 o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 co 0 C\J '<t <0 co 0 C\J '<t <0 co 0 C\J '<t C\J C\J C\J C\J C\J C') C') C') C') C') '<t '<t '<t

Axle Loads, Ib

Tandem Axle

o

o

o

<0

o

o o

o

o

C\J

...

o

o

o

o

...

1800 "' "',,"' ·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·..·.·.m.·.·" ·.· ·.." · · "'..1

1600 +-------::it~---------------------------~

1400 +----ft----------------------------------;

-E1 1200 +----f-I-+-\---------------------------~ ::l 1\.-....

8 1~~ 1::::::::::::::::::::.~~ []~~:::::~JD"O.n~~~:::~~::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::=j ! 600 -j----II'-f-I'-\;~ ....""-----'0"'dlii\------------------__!

400 -1---I-+II-l:J"Q-----~Q_::!.........-------------------_1

2~ t=I!Hj~/~/L~=:==::~=l:i:::::LlL~~!!~iii~M:;:;;;;;;:;;;~;;~;;;:;;=;=:j

o § § § C\J '<t co

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t co C\J <0 0 '<t co C\J <0 0 '<t co C\J <0 0 '<t co C\J <0 0 '<t co ... C\J C\J C\J C') C') '<t '<t '<t U') U') <0 <0 <0 r--. r--. co co co

Axle Loads, Ib

Tridem Axle

450 , " "' , ,:

400 +------_,.----------------------------------\~

350 +------J--+------------------------------""'~

II 300 +------h..----~---------------------------___j

::+----0-""'=---\------.......,.-----------------41

< 150 +----~---_l"1I""""=Ll,:,.....,k£:Y"''--~~-------------------__i

100+---~~----------~.r----------------------l

50 +---t:::I~------------~l:H:l:IIf[J:llIo:__-------------___j

O+--,.........-,-......,.---r---r........,-,--..,.......-,............,...:~__r_~~~ ............a_.l.....,..~ o

80 ·.·.·.·.·,w.·.·.·.·.·.·.·.·.·.·.WN.·.·.·,w.·.·.·.·.·.·.·.•.•.•.•.•.•.•.•.....•.•.•.•.•.•.•.•.•.""."".•.•.•.•.•.•.•.•.•.·,.·.·.·."".·.·.·.·.·.w.·.·.·.· •.•.•.•.•.•.• ·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.·.·.·.·.·.· ·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.w.·.·.·.·.·.·.·.·.·.·.•.•.""m...•.•."j

70 -1--------------------+-------------------:

60 +-------------------+-\------------------:

50 -I------------------f-\-------------------;

40 +------------------1'----+------------------;

30 -1------------------+--+-----------------:;

20 +-------------------;d---......-----------------:

10 +-------------~r=7':ip.o=-=~;;;Fi-\-~-----------__;

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 <0 C\J co '<t 0 <0 C\J co '<t 0 <0 C\J co '<t 0 <0 C\J co ... C\J C') C') '<t '<t U') <0 <0 r--. r--. co C) C) 0 0... Axle Loads, Ib

~January --+-August

1 lb =0.00445 kN

Figure 23. Comparison of average monthly load spectra for Site 12-4000.

60
Step 12.2. Develop Monthly Distribution Factors and Summarize Reasons for Their

Selection.

Monthly adjustment factors are used to distribute the annual load spectra into 12 months.

Based on data evaluated so far, it is recommended that only one set of monthly distribution

factors be used for all projection years.

Example for Site 12-4000:

Monthly ESALs were usedfor the development ofthe monthly distributionfactors. The

monthly ESALs obtainedfor 1992,1994, and 1995 were averaged (figure 24) and used to

obtain monthly distribution factors (table 12). Considering the data variability on which

the monthly distribution factors are based and the discrepancies between the historical

and monitoring trends in traffic data (refer to the case study for Site 12-4000 in chapter

5), the monthly distribution factors given in table 12 should be accepted with caution.

350

300 +---------------....__-----------------l

iU'2 200rf-r-----------------------'-----~!lIli=;;;;;;iiII~ .... Q)

a.

;J, '" 150 +---------------------------------l

ill • 100 +---------------------------------;

50 -1-------------------------------<

2 3 4 5 6 7 8 9 10 11 12

O+----,..--..,...----,.--....,----,,....---,---...--"'""'T""--,....---.,...----!

1

Month

--1992 -+-1994 -+-1995 ~AVERAGE

Figure 24. Comparison of monthly ESALs per day variation for Site 12-4000.

61
Table 12. Computation of average ESALs per day.

Monthly Distribution

Month 1992 1994 1995 Average Factors, %

1 210 168 208 195 6.8

2 246 236 255 246 8.5

3 274 279 265 273 9.5

4 232 262 288 261 9.1

5 237 252 266 252 8.8

6 273 249 241 254 8.8

7 294 232 232 252 8.8

8 282 280 236 266 9.3

9 258 212 251 240 8.4

10 219 265 - 242 8.4

11 187 239 136 188 6.5

12 214 196 - 205 7.1

62
CHAPTER 5. CASE STUDIES AND SENSITIVITY ANALYSES

This chapter describes 12 case studies and the results of sensitivity analyses conducted as

part of the case studies. The objective of the case studies was to verify and document the

feasibility of the procedure for projecting traffic loads and to identify possible site-specific

challenges and data problems. The sensitivity analyses address the following topics:

• Effect ofKey Assumptions - Exploratory analyses were carried out by evaluating

the influence of key assumptions used for the projection of traffic loads, such as

growth rates, the selection of base annual traffic load spectra, and vehicle

distribution by class, during the course of the case studies. In particular, such

analysis was carried out for case studies and situations where the key assumptions

were based on doubtful data.

• Reliability ofHistorical Traffic Loading Estimates - While the historical and

monitoring data were compared systematically during the course of carrying out

case studies, and the major discrepancies between historical and monitoring data

were assessed, it was not possible to resolve all the differences between these two

types of data, or to decide which data type is more accurate for a specific set of

circumstances. To do so would require the involvement of SHAs supplying

historical and monitoring data.

• Effect of Quantity ofMonitoring Data - In addition to the sensitivity analyses

done in the course of carrying out case studies, a separate analysis was done to

assess the potential effect of the amount of monitoring data on the precision of the

projected traffic load estimates.

SELECTION OF SITES FOR CASE STUDIES

According to the work plan, three case studies were carried out for each projection

category. The LTPP sites for the case studies were selected to represent different geographical

areas, functional load categories, traffic volumes, and data availability. The main objective in

selecting case study sites was to obtain a representative set of sites so that generalizations could

be made, based on the case studies, regarding the required effort and expected outcome of traffic

load projections for the rest of the LTPP sites.

The selected LTPP sites used for the case studies are listed in table 13. The sites are

located in 10 States and on 5 functional classes of highways. The number of lanes ranges from

two to six, and daily ESALs per LTPP lane range from 75 to 3,000.

63
Table 13. Description of LTPP sites used for the case studies.

LTPP Functional No. of Daily

Category Site State Class Lanes ESALs1 Comments

1 12-4000 Florida Rural Principal 4 140 0=8.2

Arterial

27-0501 Minnesota Rural Principal 4 170 SPS site

Arterial SN=7.8

29-4036 Missouri Urban Interstate 4 2,200 Also in Category 4

0=9.8

2 04-1002 Arizona Rural Interstate 4 3,000 Uncertain historical

growth

SN=4.7

05-3059 Arkansas Urban Interstate 4 800 0=9.4

29-5483 Missouri Rural Principal 2 400 SN= 2.5

Arterial

3 37-3807 North Carolina Rural Principal 4 320 Good match of

Arterial monitoring and

historical data

48-5287 Texas Urban Interstate 6 700 SN = 2.5

55-3012 Wisconsin Rural Minor 2 75 0=7.1

Arterial

4 01-6019 Alabama Rural Interstate 4 1,070 SN=2.5

04-1002 Arizona Rural Interstate 4 3,000 Historical or 2,800

projected.

Also in Category 2

17-5423 Illinois Rural Interstate 4 980 SN= 2.5

lAverage daily (monitoring or projected) ESALs in the LTPP lane in 1997.

SN = Structural Number

D = PCC slab thickness in inches (1 in = 25.4 mm)

64
ORGANIZATION OF CHAPTER 5

The description of case studies is organized into four parts, each part describing one

projection category. Each part is introduced by a summary description of the three case studies

done for each category, and the summary description is followed by the description of the

individual case studies. The description of case studies is illustrated by tables and figures and is

accompanied by a brief commentary to point out salient features, insights, or data problems. A

detailed description of each step involved in conducting the case studies is presented in chapter 4.

Not all analyses, tables, and figures done in the course of carrying out case studies are presented

in this report. This was done to keep the focus on the main issues and maintain the interest of the

reader. The last section describes the sensitivity analysis regarding the effect of the quantity of

the monitoring data on the precision of the traffic projections.

SUMMARY OF CASE STUDIES FOR CATEGORY 1

Category 1 sites include LTPP sites that have sufficient AVC and WIM data to enable the

projection of monthly variation in traffic loadings.

Site 12-4000

Site 12-4000 is on a four-lane rural principal arterial highway in Florida. It is a prime

example of why the traffic load prediction process requires the involvement of SHAs. The main

challenge is that monitoring ESALs reported during the period between 1992 and 1997 have

been declining, contrary to expectations, and are much lower than historical ESALs reported by

the SHA. For example, historical truck volume for 1990 is five times higher than monitoring

truck volume for 1991. The monthly variation in traffic loads suggests that a pattern of higher

monthly loads during months 2 through 10, roughly in accordance with expected general trends

outlined in chapter 2, is probable. This is the only site (out of three) for which a pattern of

monthly variation in loads emerges from monitoring data.

Site 27-0501

Site 27-0501 is on a four-lane rural principal arterial highway in Minnesota. This is an

example of an LTPP site with good agreement between historical and monitoring data, at least in

terms of total truck volume. However, this site has only 2 years of monitoring data. It is also an

example of a site with good quality WIM data. The axle load spectra for the individual truck

classes obtained for this site were selected as typical base spectra for Category 3 and 4

projections. The hypothesis that there is a historical pattern in the monthly variation of traffic

loads is not supported by available monitoring data. Truck factors (average ESALs per truck)

seem to be higher during winter months, as' would be expected in Minnesota, which allows 10

percent higher axle loads during winter. However, this conclusion needs to be verified by

representatives of the SHA, since a similar result may be caused by calibration drift during

winter months.

65
Site 29·4036

Site 29-4036 is on a four-lane Interstate in Missouri. It shows the best agreement

between historical and monitoring data among the 12 case studies presented herein. Because of

this agreement, and the extensive monitoring data following an exponential trend, annual growth

factors were based on a regression line utilizing both historical and monitoring truck volumes.

There is considerable variation in axle load spectra, which is usually not expected to occur on

Interstate highways. No discernable pattern in monthly load variation was observed. Monthly

variation in monitoring ESALs per truck for some years was more than 100 percent-another

unexpected result. In view of some questionable WIM data, Site 29-4036 is another strong

example of the need to involve SHAs in the traffic prediction process.

DESCRIPTION OF CASE STUDIES FOR CATEGORY 1

Site 12·4000

Site 12-4000 is located on a four-lane highway on a rural principal arterial highway in

Florida. Extensive, site-specific AVC and WIM monitoring data are available for this site, as

shown in table 14.

Table 14. Monitoring data available for Site 12-4000.

Monitoring Year

Data Type 1991 1992 1993 1994 1995 1996 1997 Total

Days 126 287 348 316 213 352 0 1642

AVC

Month 6 12 11 12 9 12 ? 62

Days 0 293 259 330 228 227 28 1365

WIM

Month 0 12 12 12 10 11 ? 57

Notes: Month = Number of months for which Ave and WIM data are available.

? = Data could not be obtained.

The available annual traffic data are summarized in figure 25. The historical and

monitoring data trends are conflicting-historical trends indicate a substantial increase in truck

volumes and loads, while the trend for monitoring truck volume data is declining. Particularly

disturbing is the decline in ESALs per truck between 1992 and 1997. As discussed in chapter 3,

because of the increased competitiveness and advances in wireless communications, an opposite

trend was expected. The annual growth factor was estimated by assuming: (l) a total truck

volume that was between historical and monitoring volumes, and (2) a 3-percent simple growth

rate, as shown in figure 26 and table 15.

66
% Change Since Date of Opening to Traffic

c

.(

,~

,

[

[

J........

> ~~ i> V 0

c:< 0 r---.... ("I> r-c

I 4

I

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t r-.... j-q

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AADT Truck Volume

I\) .I>- Ol 00 a I\)

a a a a a a a a a a a a a 1972

1973

1974

1975

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1961

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1964

-< 1985

CD 1986

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1968

1989

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1991

1992

1993

1994

1995

1996

1997

1998

1999

a I\) W .I>- 01 Ol ..... 00 a a a a a a a a a a a a a a a a a a a a a a a a

AADT Total Volume

o ...... f\) f\.) w u, Ut <.n

ESALs per Truck

a

Average ESALs per Day

I\) .r::.. 0> en a f\) J:i,. 0> o a 0 0 a 0 a 0 o a a 0 0 0 0 a a 1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1963

1964

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m 1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

o

o

o

f\) -1:10 0> CD o 0 0 0 0 0 000 0 0 0 0

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

-< 1985

m1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

::s

::s

tHH

s:::

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'"d ! tHtI

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Ul

~~(: (:?;:; ("\l (tI .- ..- -

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(t t: ~,fll "CD

_ ?lii~~;:' N ... .., .-+ <: ,..,

I agog g.~ " '< "'" _. ":l o tn!!..:;

o

o
1200

1000 +---------------------...----------------1

600 +---------------+----*------------1

(])

§ 800 -t-----------------~="'I~_+----------~

-a:l

b

<Xl Ol Ol Ol Ol Ol R

Ol Ol Ol Ol gOl 00Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol mOl Ol Ol Ol

O-t-..,.--.---r---,-,...--r-....,--,---,...---,-,...-.,.-.-,-.,............,.......,.--,r--.,--,--,-......,........,.............,-.,--,-.,......-i

f2 :e Ol Ol Ol Ol ,.... ,....

-.-Monitoring Truck Volume

_ Projected Truck Volume (3% growth)

Historical Truck Volume

Figure 26. Comparison of truck AADT volumes for Site 12-4000.

Ta e bl 15 Companson 0 f truekAADT vo urnes for S'tIe 124000 -

Growth Projected Truck Monitoring Truck Historical Truck

Year Factor Volume, 3% Growth Volume Volume

1974 0.59 200 253

1975 0.61 206 366

1976 0.63 212 341

1977 0.64 218 364

1978 0.66 224 392

1979 0.68 230 374

1980 0.70 236 396

1981 0.72 242 392

1982 0.73 248 406

1983 0.75 254 417

1984 0.77 260 440

1985 0.79 266 434

1986 0.80 272 731

1987 0.82 278 809

1988 0.84 284 828

1989 0.86 290 582

1990 0.88 296 969

1991 0.89 163

1992 0.91 171

1993 0.93 176

1994 0.95 181 197

1995 0.96 226

1996 0.98 161

1997 1.00 150

68
A comparison of annual axle load spectra obtained for the years 1992 through 1997 and

the summaries in figure 27 indicates a considerable variation in the annual spectra. This

observation is based on the variation in annual load spectra observed for other LTPP sites (which

will be presented for the subsequent case studies). Specifically, the 1995 and 1997 annual load

spectra, particularly for tridem and tandem axles, appear to be outliers. For this reason, the base

annual load spectrum was obtained by averaging annual load spectra for 1992, 1993, 1994, and

1996 only. The resulting base annual load spectrum is shown in figure 28. The axle load

spectrum for tridem axles has the primary peak at 240 kN (54,000 lb). This is clearly on the high

side. A mitigating consideration is the relatively low number of occurrences. Perhaps more

disturbing is the tertiary peak for the tandem axle at 205 kN (46,000 lb), with many axle counts

exceeding 500. The reasons for these overloads should be clarified with the SHA.

The comparison of the annual vehicle class distribution, shown in figure 29, also

indicates considerable variation. Again, data for 1995 and 1997 are considered to be outliers. It

is also interesting to note that few, if any, trucks have more than five axles (vehicle classes 11,

12, and 13, shown in figure 15). Thus, the reason for the overloaded tandem and tridem axles

cannot be explained by the presence of larger combination trucks. However, Florida allows a

truck tractor and two trailer units up to 32.3 m (l06 ft) long.

Note: In order to obtain a better understanding ofthe variation between total annual

spectra observedfor all three sites usedfor case studies in Category 1, and to obtain typical axle

load spectra requiredfor Category 3 and 4 projections, we have developed and compared

annual axle load spectra for each truck category (vehicle classes 4 through 13). The resulting

load spectra are presented in appendix B. Load spectra in appendix B are the average spectra

for all monitoring years, reported separately for the 3 sites and the 10 truck classes. The results

indicate thatfor Site 29-4036, some trucks were clearly misclassified:

• Two-axle, six-tire vehicles (Class 5) do not have tandem axles, contrary to data

reported in figure 98 in appendix B.

• Four-axle tractor-semitrailers (Class 8) do not have tridem axles, contrary to

data reported infigure 101 in appendix B.

• Six-axle tractor-semitrailers (Class 10) should, in general, have tridem axles,

contrary to data reported in figure 103 in appendix B.

• Five-axle, three-unit trucks (Class 11) should not have tridem axles, contrary to

data reported in figure 104 in appendix B.

Axle load spectrafor Site 12-4000 did not show major discrepancies. However, a closer

examination revealed a few minor inconsistencies, such as:

• Three-axle single-unit trucks (Class 6) show atypically high tandem-axle loads

(figure 99 in appendix B).

• Five-axle tractor-semitrailers (Class 9) do not exhibit the typical bimodal

distribution oftandem-axle weights (figure 105 in appendix B).

• Six-axle, two-unit trucks (Class 10) should have tridem axles (figure 106 in

appendix B).

69
Single Axle

.·.·.·.·.v.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·,.·.w.·.·.·.·.·,.·.·.·.·.·.·.·.·.·.·.·.·.v.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.·.•.•.•.........•.•.•.•.•.............•..--.....•................w.'".•.•.•.•.•.w

i

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

C\I -:t CO CO 0 C\I -:t CO CO 0 C\I -:t CO CO 0 C\I -:t CO CO 0 C\I -:t .... .... .... .... .... C\I C\I C\I C\I C\I C") C") C") C") C") -:t -:t -:t

Axle Loads, Ib

Tandem Axle

20000

III

C 15000 ::J

0

() 10000

.i!

5000

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -.:t <Xl C\J <0 0 -.:t <Xl C\J <0 0 -.:t <Xl C\J <0 0 -.:t <Xl C\J <0 0 -.:t <Xl C\J C\J C\J C') C') -.:t -.:t -.:t U) U) <0 <0 <0 I'- I'- <Xl <Xl <Xl

Axle Loads, Ib

Tridem Axle

2000 +.-.---1

~~~~-~~l

4000 -l-------,..~_...,J-----+-""*-------------------------<!

3000 +-------,..=-J~;;;;::r;iil"I:=-----;...,..l'--------------------!.

7000

6000

5000

700

600

500

300

200

100

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -.:t <Xl C\J <0 0 -.:t <Xl C\J <0 0 -.:t <Xl C\J <0 0 -.:t <Xl C\J <0 0 -.:t <Xl C\J <0 0 -.:t <Xl C\J C\J C\J C') C') -.:t -.:t -.:t U) U) <0 <0 <0 I'- I'- <Xl <Xl <Xl Ol Ol 0 0 0

Axle Loads, Ib

1!J

§ 400 +-----------------t----=:r-------------------: 8

Q)

~1992 .......... 1993 -<>-1994 .......1995 __1996 """-1997 -D-Average

1 Ib =0.00445 kN

Figure 27. Comparison of annual load spectra for Site 12-4000.

70
Single Axle

g g g g

o 0 0 0

o 0 0 0

g g g g

g;j g

16000 ·..·.·..·."'.·.·.·.·..·.·.·.·..·"·,,·.WO,,,·.,,.wo·.·,,·."wo·.·"".".·"·.·"WO·"'.·.·.·.·.·..·•·•·•·••·.·.·.,,••·•·•·.·•......... "...•....•.•.•.•...•.............•.....•.•.".",.•.•.•.•.•.•.""".." •...........•..........." .•."".•."'".....wowo·.·.·.·1

14000 +------I1'""tJ---[]f----------------------'

12000 +-----,-r----"\\,---------------------:

10000 -I--__=-~-p+------L.j\L-----------------------'

8 8000 +------.f.\----F-=------\---------------------:

6000 +----l--\+F-D---o-\.-----------------

4000 +-_-+__u ,--}-,r-- ---'

2000 J:J--".I L..Jn....

o

o 0 g g goo 0 0 ° g0!?ooo

g f6 '" g

Axle Loads, Ib

Tandem Axle

4000 .---....- .---..- ...- .--- ...- ·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.· ·.·.---.·.··············...-·············...-····'··1

3500

3000 +-------'F-~.---------[::J.:=,.--------------------'~

-E 2500 ! ::s 8 2000 -l---+---~LJot:II------=4L--+------------------i

1500 +----F--------='--------'\-------------------i

1000 +---I-------------lb-------------------l

500 -J--J.---------~:b~~Jn----------_J

O+-=:.-.,..-.r--r--....---.---,.--r--r---.---r--..,....~~IQcIOOODOrOQ[],OO_~ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t IX> C\l (l) 0 '<t IX> C\l (l) 0 '<t IX> C\l (l) 0 '<t IX> g! (l) 0 '<t IX> C\l C\l C\l CO) CO) '<t '<t '<t to to (l) (l) (l) I'- IX> IX> IX>

Axle Loads, Ib

TridemAxle

~~~ ·.·.·.·.·.·.·.·...-.·.w.·.·.·.··.···.·.·.·.· ···········.....................•...................•.............~;:.~ ·······,,···.······················.·.·.·.·.·w.---.·.·.·.·"1.

160

i!J 140 +--------------IP---~-\----------------'! § 120 +- .,.j,'-- \+- ---i1

8 100 +- --I-P ...... hf-- ---.,! Ql 7 \ ! x 80 -1-------------1------\----------------.;

<l: 60 +- ---_lC~----_o_\.----------___;1

..A,'""\ ! 40 +----,.....o-o-a--.-..I:F---u."o-o-d=-=~------\:;-tJ...---------j

20 +---[]""""'--=..u:~=-------==-==-=-------"'h_.,----_----------i

O+-~{_-r--.,...___r-...,_.......,r__--.,....-.,...___r-...,___,,......::::::;:I:l=O~~D_[],{Jf_OrD_IDr-___l

o o

o

o

(l)

o

o

o

C\l

o

f6° o

o

o

o

o

o

g

o

o

o

(l)

CO)

o

o

o

o

g o

o

to

o

o

o

o

(l)

o

o

o

(l)

(l)

o

o oo

Axle Loads, Ib

1 lb =0.00445 kN

Figure 28. Base annual load spectrum for average of 1992 through 1994 and 1996

data for Site 12-4000.

71
50000

45000

40000

35000

III

C 30000 :l

0

() 25000

.2

.c

Q) 20000 >

15000

10000

5000

0

0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Vehicle Class

60

50

40

III

.:&.

0

:l

30

'0

<f<

20

10

0

0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Vehicle Class

--+-1991

-+--1995 --.-1996

~1993

-0-1997

--*-1994

-o-Average

Figure 29. Truck distribution by class for Site 12-4000.

72
Axle load spectra for individual vehicle typesfor Site 27-0501 appeared to be problemfree. For this reason, these spectra were selected as base spectrafor Category 3 and 4

predictions.

For quality control purposes, the projected annual load spectra were converted into

ESALs and are given in table 16 and figure 30. The total ESALs between 1974 and 1997 is

about 1.7 million. This number is unusually low for a rural Interstate with a rigid pavement.

Again, local involvement is essential for determining the causes of such discrepancies.

The projection of monthly variation in traffic loads for this site was previously discussed

in chapter 4, step 12.

In summary, in spite of the extensive AVe and WIM data available for this site, the

overall traffic load projection is weak and unconvincing. The discrepancy between historical

data and conventional expectations on one side, and the results of the monitoring data on the

other, cannot be resolved without local involvement.

Site 27-0501

Site 27-0501 is located on a four-lane rural principal arterial road in Minnesota.

Available Ave and WIM monitoring data are summarized in table 17.

The Annual Traffic Data Projection Sheet (figure 31) shows a reasonable correspondence

between historical and monitoring truck volumes. The 1991 AADT and truck volume show an

unexpected spike. However, the annual 1991 data are based on only 8 days of AVe data, and

1991 data were not used in the projection. The correspondence between historical and

monitoring ESALs is poor. The historical assumption of 0.7 ESALs per truck is not supported

by monitoring data. The annual growth factors were estimated by fitting a 7.3-percent simple

growth trend to the historical and monitoring truck volumes (figure 32 and table 18). The base

annual axle load spectrum was obtained by averaging 1992 and 1994 spectra, which are quite

similar (figure 33). Figure 34 and table 19 summarize traffic projection results in terms of

ESALs.

Monthly data were obtained from Level 3 eTDB through the data extraction process

described in appendix A. Data given in the Monthly Traffic Data Projection Sheet (figure 35) do

not show any historical trend, with the exception of the ESALs per truck ratio, which appears to

be higher during winter months.

In summary, we are comfortable with traffic projections at this site. The difference

between historical and monitoring ESALs per truck should be brought to the attention of the

SHAs.

73
1200 +----------------p.___=----'li\:--/+---------.;

1000 / 0

en 800 +---------------_-.r1.-.n--+-------------------;

600 ~-----:r];:::;n::::::oo-o- ... _o_--o...-e-.,,[J=:::I:~~~----------""i"I---j Cl

; 400

200 +--->F~==R'~~e--9E-llt-M-*"'*"-*-*-M~......~~*=K--'l:::~~::::II~~ O-l-_,_.....,..--,-.,...-_,_.....,..-,-.,...-"'"'r"--r---r-,....-..,--,-~-r--..,..--r-.....,..__r__.,__,_.....,..___,r-i

C\J

f"-.

Ol

....

_ Monitoring ESALs per day Historical ESALs per day ~Projected ESALs per day

Figure 30. Comparison of ESAL volumes for Site 12-4000.

Table 16. Comparison of ESAL volumes for Site 12-4000.

Year Monitoring ESALs per Day Historical ESALs per Day Projected ESALs per Day

1974 397 154

1975 575 159

1976 534 164

1977 570 168

1978 614 173

1979 586 177

1980 622 182

1981 616 187

1982 636 191

1983 655 196

1984 690 201

1985 682 205

1986 1148 210

1987 1271 215

1988 1301 219

1989 915 224

1990 228

1991 233

1992 257 257

1993 273 273

1994 258 258

1995 256 256

1996 193 193

1997 138 138

Cumulative ESALs =1,774,159

74
Table 17. Monitoring data available for Site 27-0501.

Monitoring Year

Data Type 1991 1992 1993 1994 Total

Days 8 358 ? 351 717

AVe

Month ? 12 ? 12 24

Days 0 349 ? 353 702

WIM

Month ? 12 ? 12 24

Notes: Month = Number of months for which AVC and WIM data are available.

? = Data could not be obtained.

75
% Change Since Date of Opening to Traffic

\

>

"0I»I )I V

c:

•••

•

11t

( N)

~t

CD0

::>

-<

'"

Co

'"

()

.......~. •

000 ...... N (,) c.n

m '""'"

0 000

o 0 0 0 0 0 a a

MDT Truck Volume

o c.n -. ......N I\,)WW o a c.n 0 01 0 (J1

a

o 0 0 0 a a a

0 000 0 a 0 0

MDT Total Volume

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

-< 1984 CII

9l 1985

o0

CD

o 0 0 0 0 0 0 . ",w~CnO>

ESALs per Truck

Average ESALs per Day

-. -.l> I\,) I\,)W c.n 0 c.n 0 c.n 0

o0a0 000

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

-< 1984 CII

9l 1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

• I.

-. I'\) I'\) (,.)

00000

tn0 c.n0 c.n0

0

0

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

-< 1984 CII

9l 1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

.....

(J'Q

t::

(aw

tHH .....

t::

eo

"tl

...,

..g. 0

(")

..... .....

tHH -:J 0

0\

0

fa

$:l)

CZl

i:J'" s- o0

.....

0'

ii ...,

CZl

..... rfJ .., ft " " <: <: .. tr1trJ2. 2.= N r.t.:l tn c c ;

-:JI tfl

F:F:~ UJ"

"

~;o...

0 'g'g~~t"' U\ .., 2 =d 0

..... .el e: ..., tn 2.;";
600 T·································.--,,···················.·.·.·"·.·.·.·.·.·.·.·.·.·.·.·.·.·.w ,,·.·,,..·.·.·.·.·.·.·.·.·.·.·.w.w.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.· ·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·m.·.~.~~w····.·.·.·.·.·.·.·.·.·.·.·.,..~ -.·.·.· w.;,

500

Q)

E::l 400

is

::l 300 ...

lI200

100 t------------------------------i

.......- Monitoring Truck Volume

--e- Projected Truck Volume (7.3% growth)

Historical Truck Volume

Figure 32. Comparison of truck AADT volumes for Site 27-0501.

Table 18. Comparison of truck AADT volumes for Site 27-0501.

Growth Projected Truck Monitoring Truck Historical Truck

Year Factor Volume, 7.3% Growth Volume Volume

1974 0.41 190 180

1975 0.44 204 190

1976 0.46 218 200

1977 0.49 232 280

1978 0.52 246 360

1979 0.55 259 350

1980 0.58 273 335

1981 0.61 287 340

1982 0.64 301 345

1983 0.67 315 340

1984 0.70 329 340

1985 0.73 343 315

1986 0.76 357 300

1987 0.79 371 280

1988 0.82 385 265

1989 0.85 399 285

1990 0.88 413

1991 0.91 573

1992 0.94 441

1993 0.97 454

1994 1.00 469

1995 1.03 482

1996 1.06 496

1997 1.09 510

77
Single Axle

n

I:L. C

, l

"bod III

- 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\J "<t lD OJ 0 C\J "<t lD ..-

Tandem Axle

30000 .•.•............,..•.•.............•..,.,., ,......•,.,.,..•....................,..•.•...................._._ ,.,., ,.~~ .._._._......•.._,....•......................,.,.,.,.,.,.,., ,.,.,., , ,.,.,.,.,.,.,.,.,., ,,, ,........•.....•...•...•.•..............................j

i 25000 +------:;;:::JU-------------------------------:

$3 20000 -1-~~'"+__fEj~l\_-----------------------------;

c

::l 8 15000 +-~'-----I.,/----1I,Jr_----------------------------l

<D

10000 -t-------l'"

5000 +-----------"'-'Io'=--~'t.k-------------------

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...,. IX) C\l co 0 ...,. IX) C\l co 0 ...,. IX) C\l co 0 ...,. IX) co 0 ...,. IX)

C\l C\l C\l (') (') ...,. ...,. ...,. l!) l!) co co co I'- IX) IX) IX)

Axle Loads, Ib

Tridem Axle

o

o

o

IX)

o

...

o

o

o

co

0)

o

o

o

-D-Average

o 00 8

l8

..•...._._ ,.~ _._._._ , ,•...•.....•...•.•.•.•.•.•.•.•.•.•.•.•.•.........•.•.•.•.•.•.•.•, ,•.•.•..........,., ,•..,., ,.,.,•.•.•.•.•.•....,•.•.•.•.•......,..•.•.•.•....,.,., , ,.,., ,.,., "::

l

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 co C\l IX) ...,. 0 co C\l IX) ...,. 0 ... ... C\l (') (') ...,. ...,. l!) co

Axle Loads, Ib

--+-1992 _1994

2500

2000

E1500 ::l

0

0

1000

500

1 lb =0.00445 kN

Figure 33. Comparison of annual load spectra for Site 27-0501.

78
300

250

...............................................................................................................•..................................................····································1

50

0

C\I C') "<t ll) CD ..... co Ol 0 ,.... C\I C') "<t ll) CD ..... co Ol 0 ,.... C\I C') -a; ll) CD ..... ..... ..... ..... ..... ..... ..... ..... ..... co co co co co co co co co co Ol Ol Ol Ol Ol Ol Ol

,....

Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol ,.... ,.... ,.... ,.... ,.... ,.... ,.... ,.... ,.... ,.... ,....

Year

200 t------rf-----------l..b..,G"i""----.~---------+

150 t-------;;::(------ ~~__~C-.-.:...>r=--)_E_~_4;

!!!

Q)

100 T--=::~;=:;::~~~~+-='s:::::....:..:....---------_·---_-Il ..i:

_ fvbn~oring ESALs per cay -0- Historical ESALs per Day A"ojected ESALs per cay

Figure 34. Comparison of ESAL volumes for Site 27-0501.

Table 19. Comparison of ESAL volumes for Site 27-0501.

Year Monitoring ESALs per Day Historical ESALs per Day Projected ESALs per Day

1974 123 64

1975 132 69

1976 137 73

1977 192 78

1978 247 83

1979 241 87

1980 230 92

1981 233 97

1982 236 101

1983 233 106

1984 233 111

1985 216 115

1986 205 120

1987 192 125

1988 181 129

1989 195 134

1990 139

1991 143

1992 141 141

1993 153

1994 174 174

1995 162

1996 167

1997 172

Cumulative ESALs =1,034,452

79
... -- --

........................." " " " : " \

--- --

16000

14000 (])

E 12000 :::J

0

> 10000 ..>:::

0

:::J 8000

6000 .s::

C 4000 0

::!: 2000

0

2 3 4 5 6 7 8 9 10 11 12

Month

250 .·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.·.·.·.·.·.·.·.·.· ·.·.·.·.·.·ow.w.·.·.·.·.·.·.·.·ow.·.·.·.w_.·.w.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.wow.·.·.·.·.·.·.·.·.·.wow.__.........................•...•.........................·.·.·.·.·.".·.·.·.·.·.·.·.wow.__.·.·.·.·.· •.·.·.·.·.· ·.1

200 +-----.....--------------------------!(

150 k~....====!~~;;::;;;;;;~~-__:III=::~;_-------.::~-..;:----.J. coo <i! 100 +----------------------------'

(/)

w 50 +- -i

6 7 98 10 11 12

Month

2 3 4 5

O-!----r---..,.--,...--,......--,..--I""""""-.,.--....,.---..,.--.,.---i

1

:::: V~~-===~""~~"~"""~"~····~··I

g 0.40 ~~ -=~~D:::::::~~=~-~::::::::~=~0::::::::=J"1.~==:::D.....,.... -l

0.30 -1- '...- .......t---__---=::!~!!t::==~::::::::::~==:::11:::::::=:~

_ ~~_ _" oo

...J

0.20 +---------------------------~

w

0.10 -1--------------------------:

0.00 +---,---,,--.,---.--.---....,---,..--,----r----..----1

2 3 4 5 6 7 8 9 10 11 12

Month

_1992 -0-1994

Figure 35. Comparison of monthly traffic variations for Site 27-0501.

80
Case Study 29-4036

Site 29-4036 is located on a four-lane urban Interstate in Missouri. There is very good

agreement between historical and monitoring data (figure 36), and monitoring data are supported

by extensive AVC and WIM records (table 20). The trend in the annual truck volumes was

utilized to develop an exponential function for the calculation of annual growth factors (figure 37

and table 21).

Table 20. Monitoring data available for Site 29-4036.

Monitoring Year

Data Type 1990 1991 1992 1993 1994 1995 1996 1997 Total

Days 4 3 36 337 331 316 298 352 1,677

AVC

Month 1 1 2 12 12 11 ? ? 39

Days 0 0 124 343 149 73 296 352 1,337

WIM

Month 0 0 6 12 5 4 ? ? 27

Notes: Month = Number of months for which AVC and WIM data are available.

? = Data could not be obtained.

The five annual monitoring axle load spectra (for 1992 through 1996) are compared in

figure 38. Compared to the two annual spectra obtained for the previous case study (figure 33),

there is considerable variation between the spectra. The 1995 spectrum is clearly an outlier, and

as discussed in connection with Site 12-4000, data in appendix B indicate that some vehicles

were misclassified. When interpreting the variation in the annual monitoring axle load spectra,

the note included in the description of the case study for Site 12-4000, discussing load spectra for

individual vehicle classes, should also be considered. Figure 39 shows that a large percentage of

trucks (10 to 20 percent) were not classified and were reported as Class 14. Usually, unclassified

vehicles are distributed proportionally across all vehicle classes and are not reported separately in

the CTDB. Base annual spectrum was obtained by averaging annual spectra for 1992, 1993,

1994, 1996, and 1997.

Annual ESALs are summarized in table 22 and figure 40. The cumulative number of

ESALs for the 15-year period between 1983 and 1997 is about 7.2 million. Site 29-4036 was

also used to assess the effect of the quantity of the monitoring data on the precision of traffic load

projections discussed at the end of this chapter.

Monthly data summarized in figure 41 are inconclusive for developing monthly

distribution factors for the annual load spectra, particularly when considering other data,

including monthly spectra given in figure 42. Monthly data were obtained from Level 3 CTDB

through the data extraction process described in appendix A. The large differences between the

August 1995 and October 1995 load spectra appear to be unjustified.

81
2000

1800

1600

1400 :::J

1200

tl 1000

2

f- 800 fo 600

400

200

o

/ '0 .&.. /

cf /~

/ /

/ r -4

....--0 /

0-

.N"

8000

7000

6000 Q)

5000 o

4000 i

'0

3000

2000

1000

o N M ..,. 10 co I"- eo (l) 0 c;; N M ..,. 10 co I"- eo eo eo eo eo eo eo eo (l) (l) (l) (l) (l) (l) (l)

(l) (l) (l) (l) (l) (l) (l) (l) (l) (l) (l) (l) (l) (l) (l) (l)

.... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Year

3000 2.5

~2500 2 0 .><:

~2000 0

:::J

rn 1.5 ....I :u 1500 c. w rn

1000 ....I «

f.l:! en 0.5 Q) w 500

0 0

N M ..,. It) co I"- eo Cl 0 .... N M ..,. It) co I"- eo eo eo eo eo eo eo eo Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl Cl .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Year

f.l:!

f- 1200

B

01

'2 1000 c:

Q)

c.

0 800

-

0

600

0

2l 400 c:

i:i5

Q) 200 01

c:

(ll

.s::; 0

t) N ("') '<t LO co l"- eo m 0 .... N ("') '<t LO co eft eo eo eo eo eo eo eo eo m m m m m m m m m m m m m m m m m m m m m m .... .... .... .... .... .... .... .... .... .... Year

Monitoring data Historical data All data are for LTPP lane

• --0--- AADT volume, all vehicles

... --f:r-- AADT volume, all trucks

• --0- Average ESALs per day

• --<>-- Average ESALs per truck

Figure 36. Annual Traffic Data Projection Sheet for Site 29-4036.

82
y = 2E-110eO.1302x -

- Jt7

R2 = 0.8995 .... / f-----

/~

.//

r- -/I.

/ ------ A ___

N'

2000

1800

1600

1400

> 1200

g 1000

.= 800

l:i 600

400

200

o

C\J

co

(j)

orC'? -.::t

co co

(j) (j)

or- orLO <0

co co

(j) (j)

or- orr--. co

co co

(j) (j)

or- or-

(j) 0

co (j) (j) (j)

orYear

or- C\J (j) (j)

(j) (j)

orC'? -.::t

(j) (j)

(j) (j)

or- orLO <0

(j) (j)

(j) (j)

or- orr--. co

(j) (j)

(j) (j)

or- or-

-.-Monitoring Truck Volume

- Projected Truck Volume

-tr-Historical Truck Volume

Figure 37. Comparison of truck AADT volumes for Site 29-4036.

Table 21. Comparison of truck AADT volumes for Site 29-4036.

Growth Projected Truck Monitoring Truck Historical Truck

Year Factor Volume Volume Volume

1983 0.21 289 242

1984 0.24 330 266

1985 0.27 375 295

1986 0.31 428 535

1987 0.35 487 932

1988 0.40 555 954

1989 0.46 632 848

1990 0.52 720 826

1991 0.59 820 831

1992 0.68 934 868

1993 0.77 1064 919

1994 0.88 1212 1430

1995 1.00 1380 1317

1996 1.14 1572 1700

1997 1.30 1791 1856

83
Single Axle

100000 ·.·.w.·.·.·.·.·.·.·.·,,·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·N.W.·.·.·.·.·.·.·.·.·.·.·.·.W.·.·.·.·.·.·.·.W.W.·.·,,·.·.·.·.·.·.·.·NN.·.·.·.·.·.·.·.· ·.·.·.·.·.·.·.·.·.·.·.W.W.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.· ·.·.·.·.W.·.·

80000 +-----\-------------------------------~!

60000 +---\---hl-= !

40000 -t--f~~; i

20000

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\l '<t <D II) 0 C\l '<t <D II) 0 C\l '<t <D II) 0 C\l '<t <D II) 0 C\l '<t ,.... ,.... ,.... C\l C\l C\l C\l C\l C') C') C') C') C') '<t '<t '<t

Axle Loads, Ib

Tandem Axle

..................................................................................................................................................................................................................................................................................................................................................................................

10000

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -.;t co C\J <0 0 -.;t co C\J <0 0 -.;t co C\J <0 0 -.;t co C\J <0 0 -.;t co C\J C\J C\J

'" '" -.;t -.;t -.;t L!) L!) <0 <0 <0 r-.. r-.. co co co

Axle Loads, Ib

60000

50000

(/) 40000 E

::l

0

() 30000 (I)

20000

TridemAxle

W"1IIIL

.... • - - -..... -- 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 o 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -.;t co C\J <0 0 -.;t co C\J <0 0 -.;t co C\J <0 0 -.;t co C\J <0 0 -.;t co C\J <0 0 -.;t co C\J C\J C\J

'" '" -.;t -.;t -.;t L!) L!) <0 <0 <0 r-.. r-.. co co co OJ OJ 0 0 0

Axle Loads, Ib

~~~ m .•.•.•.•.••.•.•.•.•••.••.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•·•·•·••••·•••·.·.·.".· ·•·•·•·••·••·•·•·•••·•·•••·.m " .•·.m·•••·•·•·•·••·•·••••·•·•·•·••·•·•·•·•·••·•••••••·••..•!

1400 +--.... iIt.~k\-\-----ft-,-------------------------'i %! 1200 +--..--'..----+/t----------------------------i 8 1000 +-----"""\.""'\\,------+-I+----------------------------i

.9! 800 +------\,.Jl.,,~-__;;... ~I-+----------------------------i

600 +--~"~_-I-F-!!!!-=ln,.-----11\---------------------<

200

400o1=~~=~"-.~~~~--~I!~~-~~--~-~~~~~-~~!~~i~Q~5a~~~=3 o

-"-1993 ~1994 ~Average

1 lb =0.00445 leN

Figure 38. Comparison of load spectra for Site 29-4036.

84
350000 .,..----------------------------..,

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Vehicle Class

o -I-...,....---r-"--,--~~~~~~--1~~ ~~~l---! o 1

50000 +---------u.;.~---~~--~:__-____,r__-_I_f_j~,-\-\--~

250000 +----------------tHft-----------~

300000 +--------------------tl:-------------~

100000 +-----------------+---\II--------I/-"*-\\--~

.$

§ 200000 +--------------------ffl~I__-------_r_--~ o

()

g 150000 +-----------------.,---,---tll--------ff+\---~

t=

80%

70%

60%

en 50% u

::J

....

I- 40%

-0

#. 30%

20%

10%

0%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Truck Class

-+-1990

""'-1995

_1991

---1996

-I!:r-1992

-1-1997

-0- 1993 --*""" 1994

~AVERAGE

Figure 39. Truck distribution by class for Site 29-4036.

85
3000

2500

0

...

CD 2000 a.

lI)

...J

c{ 1500 en

UJ

CD

CI 1000 e

CD

500

0

C\J C') "<t l{) CD I'-- <Xl (j) 0 ,.... C\J C') "<t l{) CD I'-- <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) (j) ,.... ,.... ,.... ,.... ,....

Year

_ M:>nitoring ESALs per Day --i.:r Hstorical ESALs per Day

Figure 40. Comparison of ESAL volumes for Site 29-4036.

Table 22. Comparison of ESAL volumes for Site 29-4036.

Year Monitoring ESALs per Day Historical ESALs per Day Projected ESALs per Day

1983 570 449

1984 625 511

1985 693 582

1986 1258 663

1987 2192 756

1988 2244 861

1989 1995 980

1990 1117

1991 1272

1992 1824

1993 1698

1994 2102

1995 2603

1996 2109

1997 2203

Cumulative ESALs =7,201,762

86
50000 (])

E

:::l 40000

"0

>

30000 0

2

f- 20000 >-

£;

c

0 10000

::2

W~~............... .", -'

--~-: ~~........ -/ - - ...-.... -- ---

o

1 2 3 4 5 6 7 8 9 10 11 12

Month

-1993 -+-1994 -'-1995 -K-1996 -1997

" - / A

• .,,/' ..J~ .... -/ .......... -- -...-..

- -.....

7fI:"

~ j

6000

>- 5000 (1l

0 4000

CD

0.. 3000 (J)

.....I

« 2000 en

ill 1000

o

1 2 3 4 5 6 7

Month

8 9 10 11 12

-1992 -+-1993 -'-1994 -K-1995 -1996

6 7 8 9 10 11 12

Month

2 3 4 5

4

3.5

3

2.5 -f-----:or--------.,.,;£------+---ll<-------------'

2 .....==!~~~:__~~---____jLJ~~-----­

1.5 +----------:~~~~~:::::::::",-il~~~~--=~~;=j

1+-="""'""'_-----IC~-----------~+-....;;~::::__------,

0.5 -f-------------------------=-=------'

O-f-----,---.,.---,---,..----,----r--.,....---r---,----,-----j

1

-1993 -+-1994 -'-1995 ~1996

Figure 41. Comparison of monthly traffic variations for Site 29-4036.

87
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\l V (0 co 0 C\l V (0 co 0 C\l V (0 co 0 C\l V (0 co 0 C\l V .... .... .... .... C\l C\l C\l C\l C\l (') (') (') (') (') v v v

Axle Loads, Ib

Tandem Axle

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t co C\I CD 0 '<t co C\I CD 0 '<t co C\I CD 0 '<t co C\I CD 0 '<t co ... ... C\I C\I C\I (') (') '<t '<t '<t U') U') CD CD (0 "- "- co co co

Axle Loads, Ib

Tridem Axle

Single Axle

:~~~ •.•.•.•.•.••.•.•.•.••.•.•.••••.•.•.•.••.••.•.•.••.•.•.•·..· ,·.·.·.·.wow.wo •••·•·•·•·•·•·•·•·•·•·•·•·•·•·•·•· , •.•.•.•::

-E 4000 +------------::ilf-.3\--------------------------l ::J

8 3000 t-----:::i~~~=-~~------------------- Q)

2000 +---~"-----_1L--='r--.....----------------------i

1000 -l--__-L-------,~--~-----lI~-----------------___l

o -!-Or=*T~~__r_~~~~~~lo*e...,..~~~r*_Ar1.....~_1 o

:~~ ·••· wow••·•·•·•·•·•·•·•·•·•·•·•·•·•·•·•·•·•·•·•·•·•· !:

3000 +-_--=~:__-------~L-----!~--------------------;

-E!

::J 2500 -t-------'''lr----:T'=-'*:::-:---..~---...--------------------1

8 2000 +---,/-~~~..:~~--~lr-----------------i

1500 +----#--------......r---~-------------------'

1000 -t------.I-----------=..r------'''''c:-----------------f

500 +-----,~-------------4to.,__--~----------------f

o-!-l5r--r---r--.,...--r---.-,.......-r--r--r-.::;!!~~9iM...... ....~t__r__f

o

30 .·.".·.w.·.·.·.·.·.·.·.w.wnn.·.w.w.·.".·.·.w.w.w.w.w.w~.w~.·.·.w. ·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.wm.•·.·.·.·.·.·.·.·.·n.·.·.·.·.·.·.·.·.·.·.·.w.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.·.w.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.· w.w ,,~.,,-"'.•....."'.w·········l

25 +----,j~------------------------------..;

-E!20+--~r------------------------------------;

::J 8 15 +------\,....-------------------------------'

10 -t----\-------------------------------:

5+---"""'".\-----.,II::=*=_-------r------;---.----------------'

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t co C\I (0 0 '<t co C\I (0 0 '<t co C\I CD 0 '<t co C\I CD 0 '<t co C\I CD 0 '<t co ... ... C\I C\I C\I (') (') '<t '<t '<t U') U') CD CD CD "- "- co co co Ol Ol 0 0 0 ... Axle Loads, Ib

-+-Aug-1995 -.Ir-Oct-1995

1 Ib =0.00445 kN

Figure 42. Comparison of annual load spectra for Site 29-4036.

88
In summary, the extensive AVC and WIM monitoring data, and a matching trend for

historical and monitoring truck volumes, provide some degree of confidence for traffic

projections. It is also evident that AVC and WIM equipment were not always calibrated.

SUMMARY OF CASE STUDIES FOR CATEGORY 2

Category 2 sites include LTPP sites that have AVC and WIM monitoring data.

Site 04-1002

Site 04-1002 is located on Interstate 40, about 72 km (45 mi) west of Flagstaff, Arizona.

This site was selected primarily to illustrate the potential benefits of using site-related monitoring

data. For sensitivity analysis purposes, traffic projections for this site were done twice-as a

Category 2 site (utilizing site-related WIM monitoring data) and as a Category 4 site

(disregarding site-related WIM data). The results show the importance of locating nearby traffic

monitoring sites and using site-related or surrogate data when warranted.

Site 05-3059

Site 05-3059 was used to illustrate the step-by-step procedure for conducting case studies

in chapter 4 and is not discussed again in this chapter.

Site 29-5483

Site 29-5483 is located on a two-lane rural principal arterial highway about 32 km (20

mi) northeast of Kansas City, Missouri. This case study serves as an example of problems

created by unreliable WIM data and is also used to illustrate the effect of different growth rates

on traffic projections.

DESCRIPTION OF CASE STUDIES FOR CATEGORY 2

Site 04·1002

The location of Site 04-1002 is shown in figure 15. It is a rural, remote location on

Interstate 40 about 72 km (45 mi) west of Flagstaff, Arizona. West of the site on Interstate 40

are six other LTPP sites (shown as dark squares in figure 15). There are no large truck traffic

destinations along this stretch of 1-40.

The available AVC and WIM monitoring data for this site, and the two closest adjacent

sites (04-1024 and 04-1025) are summarized in table 23. The Annual Traffic Data Projection

Sheet (figure 43) shows monitoring truck volumes to be about 60 percent lower than the

historical truck volumes. Load spectra available for this section also appear to be questionable.

In view of the close proximity to other LTPP sites, we have investigated the possibility of using

their monitoring traffic data for traffic projections at this site.

89
Table 23. Monitoring data available for Site 04-1002.

Parameter 1993 1994 1995 1996

Site No. 1002 1024 1025 1002 1024 1024 1025 Total

1025 1024

Days 92 45 157 175 229 204 185 94 269 1,450

AVe

Month 4 2 7 ? ? ? 7 4 ? 24

Days 16 16 40 0 0 22 0 0 0 94

WIM

Month 1 1 2 ? ? ? 0 0 ? 4

Notes: Month = Number of months for which Ave and WIM data are available.

? = Data could not be obtained.

90
2500 5000

4500

2000 4000 CD

E 3500 ::J ::J 0

> 1500 3000 g 0 2500 ]!j 2 0 I- 1000 2000 :: I0 1500 :::

500 1000

500

0 0 m a N M '<t It') CD r-- <Xl m a N M '<t It') CD r-- r-- <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl m m m m m m m m m m m m m m m m m m m m m m m m m m

Year

4500

4000

>- III 3500 0

3000 aU>

-' 2500

en

w 2000 Ql

Cl 1500 l!!

Ql

> 1000

500

0

,.., .../\ ~A....L\. DC: -y \

.-.d \ •

,..,--0-- \ J"1 • rrb..

4.5

4

3.5

3 g

.=

2.58-

2 U>

1.5 en

w

1

0.5

o

Figure 43. Annual Projection Data Sheet for Site 04-1002.

91
The evaluation of historical and monitoring truck volume and ESAL data given in table

24 indicates that the monitoring truck volumes obtained for Site 04-1002 are about half of those

reported for the nearby sites. Considering the absence of major truck destinations, the truck

volumes on all the sites listed in table 24 should be roughly equal.

Table 24. Comparison of 1993 data for nearby sites.

1993 Traffic Data in LTPP Lane

LTPP

Site AADT All AADT Trucks, AADT Trucks, ESALsper ESALsper

Vehicles Historical Monitoring Day Truck

04-1002 4,100 1,250 750 2,050 2.9

04-1021 4,300 1,600 N/A N/A N/A

04-1022 4,300 1,600 N/A N/A N/A

04-1024 4,000 1,550 1,650 2,550 1.5

04-1025 4,100 1,500 1,500 2,450 1.5

04-1062 4,000 1,500 1,600 3,000 1.9

04-1065 4,000 1,500 1,650 1,500 1.0

N/A - Not Available

Annual growth factors were established by fitting a trend line with a 7.6-percent simple

growth rate through historical traffic volumes (figure 44 and table 25). After the assessment of

annual load spectra for the nearby sites, the base spectrum was obtained as an average of the

1993 load spectrum for Site 04-1024, and 1993 and 1994 load spectra for Site 04-1025 (figure

45). The difference in the 1993 annual load spectrum obtained for Site 04-1002 (only one year

of WIM data was available for this section) and the base annual spectrum used for projection is

shown in figure 46. The significantly lower monitoring truck volumes for the section (table 24)

are reflected in the significantly lower axle counts (figure 46). Figure 47 shows that the

normalized vehicle volume distribution (bottom half of figure 47) was similar on all nearby sites.

Projected annual load spectra were summarized using ESALs, and the result is presented

in figure 48 and table 26. Figure 48 and table 26 also show the results of ESAL projections

using the Category 4 approach. These results will be discussed in a subsequent section dealing

with Category 4 projections.

In summary, by utilizing data from nearby sections, it was possible to project traffic data

with increased confidence.

Site 29-5483

Site 29-5483 is located on a rural principal arterial road in Missouri. Available AVC and

WIM data are shown in table 27.

92
2500

<D 2000

E

:::l

0

> 1500

..x:

u

:::l

...

l- 1000

I0

500 --

-

0

0> 0 ,.... C\I cry

""" LO <0 ,..... to 0> 0 ,.... C\I cry

""'" LO <0 ,..... ,..... to to to to to to to to to to 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> ,.... ,.... ,.... ,.... ,.... ,.... ,.... ,.... ,.... ,....

0> ,....

0> 0> 0> 0> 0> 0> 0> 0> 0> ,.... ,.... ,.... ,.... ,.... ,.... ,.... ,....

Year

......... Monitoring Truck Volume --A--Historical Truck Volume _Projected Truck Volume (7.59% growth)

Figure 44. Comparison of truck AADT volumes for Site 04-1002.

Table 25. Comparison of truck AADT volumes for Site 04-1002.

Growth Projected Truck Monitoring Truck Historical Truck

Year Factor, 7.6% Volume, 7.6% Growth Volume Volume

1980 0.48 589 600

1981 0.52 634 700

1982 0.56 679 700

1983 0.60 724 700

1984 0.63 768 800

1985 0.67 813 900

1986 0.71 858 1000

1987 0.74 903 1100

1988 0.78 947 1000

1989 0.82 992 1100

1990 0.85 1037 940

1991 0.89 1082 1150

1992 0.93 1126 1200

1993 0.96 1171 748

1994 1.00 1216 827 1300

1995 1.04 1261 1300

1996 1.07 1305 1400

1997 1.11 1350 2100

Note: Blank spaces mean that the information was not reported in the IMS database.

93
Single Axle

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'" '" '" '" '" oo:t oo:t oo:t

Axle Loads, Ib

Tandem Axle

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140000 +---------+-\b---------------------------'

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is 100000 -l-------0A~~~a-----------------------.J t) 80000 -l-------...L----.lIIIIIb-------------------------<

60000 +--------Ir-J ,,__.-- ---.;

40000 +-_-----1l:l ....._--.__

IJ ---a.......'=---------------------< Q.. .,..", b:&-

200000 i=!~5 -.-.'E~,~~~!~~....~=::=:===~~~!ili;;;;;;:;;;;;;;;;;;;;:j .- - - - - - - - - - - -- o

80000

60000

40000

20000

0

a a a 0 a 0 a a a 0 0 a a a a a a a a 0 a a a a a 0 a a a a a 0 a a a a a a a a a a a a a a a a a a a a a a a a a a a a a oo:t <Xl C\J CD a oo:t <Xl C\J CD a oo:t <Xl C\J CD a oo:t <Xl C\J CD a C\J C\J C\J

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Axle Loads, Ib

140000 ·············1

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:~~~ ·.·.·.·.,·.,·.·.·.·.·.·.·.w.w.·.·.·.·.·.·.·.·.·.·.·..· A.•.""."w"..wo•••~,••••••••••" •••••"'•••••••••••••••••••••••••••••••••••••, •••, •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••A·.·.·.·.·.·•.•·.·.·.w.w w.·.·.""·.·.·.·.:.i

3500

E 3000 -{------------I-\----:-:...... .'--------------------- ¥A i is 2500 -{-----------Hf---1\'-tI,-------------------------l

~~ rw /

" i

1500 -l-----------f-iH--I-'-A--\\------------------

1000 +- -I-I":....:'\\6-+-~k!\_\_---------------

5~ t=l;;;~~.ii;I~~~~~:/':;tl:L\~lIt'K.li.lHw;::;;;;:;~;=;::;~;::;;:;:;:;;j o

Axle Loads, Ib

--+--04-1025 (1993) ........04-1025 (1994) _04-1024 (1993) _AVERAGE

1 Ib =0.00445 kN

Figure 45. Load spectra used for computation of the base spectra for Site 04-1002.

94
Single Axle

140000

120000

100000 c::

:::l

0 80000

()

Q) 60000

40000

20000

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 N "<t CD eo 0 N "<t CD eo 0 N "<t CD eo 0 N "<t CD eo 0 N "<t ..... ..... ..... ..... ..... N N N N N (") (") (") (") (") "<t "<t "<t

Axle Loads, Ib

Tandem Axle

120000

100000

c:: 80000 - :::l

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() 60000 Q)

40000

20000

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 "<t eo N CD 0 "<t eo N CD 0 "<t eo N CD 0 "<t eo N CD 0 ..... ..... N N N (") (") "<t "<t "<t LO LO CD CD CD l"- I"- eo

Axle Loads, Ib

Tridem Axle

1800

1600

1400

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() 800 Q)

600

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.....

Axle Loads, Ib

--+-04-1002 (1993) -0-Base Spectrum

1 Ib =0.00445 kN

Figure 46. Load spectra for Site 04-1002 for 1993 and base spectra.

95
500000

450000

400000

350000 c

::J

0

() 300000

u

::J 250000 ...

I-

"'iii 200000 ::J

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c

« 150000

100000

50000

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"<t Ltl lD r-- !Xl Ol 0 .... N --- M

In In In In In In .... .... .... .... In In In In In In In In In In III III III III III III In In In In

(3 (3 (3 (3 (3 (3 III III III III

(3 (3 (3 (3

-+-04-1002 (1993) _04-1024 (1993) -+-04-1025 (1993) """'*""04-1025 (1994)

90

80

70

c 60 ::J

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III III III III III III In In In In

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-+-04-1002 (1993) _04-1024 (1993) -+-04-1025 (1993) """'*""04-1025 (1994) -e-AVERAGE

Figure 47. Annual truck count distribution by class for Sites 04-1002, 04-1024, and 04-1025.

96
4500 ...................................................................................................................................................................................................................................................................................................................................................................- - ......,. "'--1

! 4000 +----------------------,n---------------i

2000

1500

1000

~E ~E ~E )( )( )(

500 ~C tE )( t ~( ~E )( H ~E )E

)Eo ~E

0

0> 0 .... C\l (') '<t It) CD l'- co 0> 0 .... C\l (') '<t It) CD l'-

l'- co co co co co co co co co co 0> 0> 0> 0> 0> 0> 0>

0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0>

.... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... ... Year

35oo+--------------::;;ii"'c.-:=--------:~~----\-----------_j

j 2500 +----------c:;;J'.[F-------------\------....'7"'------i

w

t

4l

o 3000 +--------------7'I':....-----------'\-----------7Q

-0-Historical ESALs per Day _ Monitoring ESALs per Day

-'-Category 2 Projected ESALs per Day --*,-Category 4 Projected ESALs per Day

Figure 48. Comparison of annual ESAL projections for Site 04-1002.

Table 26. Cumulative ESALs computation sheet for Site 04-1002.

Historical ESALs Monitoring ESALs Category 2 Projected Category 4 Projected

Year per Day per Day ESALs per Day ESALs per Day

1980 2016 1209 330

1981 2170 1300 355

1982 2326 1392 380

1983 2326 1484 405

1984 2521 1576 430

1985 2792 1667 455

1986 3334 1759 480

1987 3526 1851 505

1988 3255 1943 530

1989 3488 2034 555

1990 3953 2126 580

1991 1959 2218 605

1992 2055 2310 630

1993 2104 2104 655

1994 2192 2493 680

1995 2740 2585 705

1996 2740 2677 730

1997 3014 2768 755

Cumulative ESALs for Category 2 =12,956,011

Cumulative ESALs for Category 4 =3,565,068

97
Table 27. Monitoring data available for Site 29-5483.

Monitoring Year

Data Type 1990 1991 1992 1993 1994 1995 1996 1997 Total

Days 4 0 0 311 278 297 363 348 1,601

AVe

Month 1 0 0 11 12 10 ? ? 34

Days 5 0 6 14 14 36 7 24 106

WIM

Month 1 0 2 2 2 5 ? ? 12

Notes: Month = Number of months for which AVC and WIM data are available.

? = Data could not be obtained.

Monitoring truck volumes shown in figure 49 have been increasing more rapidly than the

historical truck volumes. Because of this discrepancy, we have used two alternative growth

rates, as shown in figure 50 and table 28. The declining ESALs, in view of increasing truck

volumes, are caused by the precipitous drop in axle weights (figure 49). This site is yet another

example of the need to involve SHAs in the traffic projection process.

Figure 51 shows annual axle load spectra for all monitoring years. There is a large

unexpected variation in the spectra, considering that this site has a relatively high AADT of

about 4,000. The assessment of the spectra revealed that the annual spectra for this site formed

two clusters, one for 1992/1993 and the second for 1995 through 1997. The two sets of spectra

are shown in figures 52 and 53, and their averages are compared in figure 54. Spectra for

1992/1993, unlike 1995 through 1997 spectra, show a typical bi-modal distribution for tandem

axles, with the second peak at about 142 kN (32,000 lb). For this reason, the average of

1992/1993 spectra was selected as the base spectrum. Vehicle class distribution comparing

1992/1993 spectra with 1995 through 1997 spectra is shown in figure 55. The differences

observed between load spectra in figure 54 are also visible in figure 55.

The projected spectra were summarized using ESALs, and the results are given in figure

56 and table 29. The difference in growth rates (2.1 percent versus 9.9 percent) was significant

(5.5 million versus 4.3 million). There is a considerable difference (about 100 percent) between

projected ESALs and monitoring ESALs for 1992/1993, even though the base spectra were

obtained as an average of the 1992/1993 spectra. The computational audit of our procedure did

not reveal the cause of this discrepancy that was attributable to the traffic projection procedure

used. However, when we duplicated ESAL calculations done in the CTDB, the resulting ESALs

were exactly two times higher than those reported in the CTDB summary table. This leads us to

believe that ESALs reported in a CTDB monitoring data summary table and the ESALs

calculated using monitoring spectra given in the CTDB are conflicting.

98
MDT Truck Volume

a ....l,w C1Im a a a 0 0 0 o a 0 a a a a a a 0 a 0

MDT Total Volume c

C)

:>)

,~l .........

l~

•

to

1\~~ .k:

% Change Since Date of Opening to Traffic W w o a a a 0 a a 0 0 a a a 0

1971 1971

1972 1972

:!! 1973 1973 O'CI

s::::: 1974 1974 ""l :I

Cllg 1975 1975 +:-. ., \0 ~. 1976 1976

1~ 1~ ....... S"

::s 1978 1978 ::s

s::::: 1979 1979 Pl

1980 1980

~.ao

tttt 1981 1981 (") ::c

_. 1982 1982

::tog 1983 1983

g [~ 1984m1984

o 1985 1985

S" 1986 1986 Pl

en 1987 1987

g- :> :> ~:> 1988 1988

Cll "'"'- ...... !1!1 ;;: 1989 1989 JJ~""""~ Q ~: 1990 1990 '" '" = c: ., 1991

1991

(t 1992 1992

tv; ;;. 2" g. 1993 1993 \0 c:: _.

Vt '" 1i- 1994 1994

+:-. '" '" 00 1995 1995 W 1996 1996

1997 1997

Average ESALs per Day w m m

0 o a 0 a a 0 a a a 0 a a 0 a 0 a a a a a a , , , II ){

-...-...~

•

4

D

/

J

I

'r) a a a

0a """" ....l, ........

mm 00

ESALs per Truck

-. I\)w C1Im ...... a a a a a a a o a a a a a a a 1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984 Il>

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997
700 'N.W_~~"~,~WNoW.wNNA~W"oW,,",·,·,_.w.·A~,".·.,".,·.,,,,".·'.'W.·.·.'".w.w.·,.·'.·.wmOW.·.· N.·.·.·.·.·.·.· ·.·.·.·.·.·.·.·.·.w_.·.·.wN.·.~'·_.,·.·.·.·.·.W.·.wN.'".·.·.w.·.· .•.'" W'OW" W.w,.'".~~~ ,,_..

600 +--------------------------:--------------i'

m

500 +-------------------+---------~\____i o

> 300 400 t=:::;:;;~;:~~~~~~:t:::;;;~;:~~~~~~5 Ia 200 +-----------a==~~:..-----------------_____i

100

0

..... C\l ('I') "<t L{) <0 ...... co Ol 0 ..... C\l ('I') "<t L{) <0 ...... co Ol 0 ..... C\l ('I') "<t L{) <0 ...... ...... ...... ...... ...... ...... ...... ...... ...... ...... co co co co co co co co co co Ol Ol Ol Ol Ol Ol Ol Ol

Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... .....

Year

---6- Monitoring Truck Volume

_Projected Truck Volume (2.1% growth)

-tr-Historical Truck Volume

~Projected Truck Volume (9.9% growth)

Figure 50. Projected total truck volumes for Site 29-5483.

T ble 28 Ca ompanson 0 f truekAADT vo urnes for S'Ite 295483 -

Growth Factor, Projected Truck Growth Factor, Projected Truck Monitoring Truck Historical Truck

Year 2.1% Volume, 2.1% 9.9% Volume, 9.9% Volume Volume

1973 0.71 300 0.26 100 294

1974 0.72 306 0.29 115 295

1975 0.74 312 0.33 129 297

1976 0.75 319 0.37 144 299

1977 0.76 325 0.40 158 308

1978 0.78 331 0.44 173 349

1979 0.79 337 0.48 187 338

1980 0.81 344 0.52 202 355

1981 0.82 350 0.55 217 367

1982 0.84 356 0.59 231 337

1983 0.85 362 0.63 246 348

1984 0.87 369 0.66 260 289

1985 0.88 375 0.70 275 301

1986 0.90 381 0.74 290 316

1987 0.91 387 0.78 304 534

1988 0.93 394 0.81 319 540

1989 0.94 400 0.85 333 575

1990 0.96 406 0.89 348 260

1991 0.97 412 0.93 362 555

1992 333

1993 374

1994 1.01 431 1.04 406 425

1995 1.03 438 1.07 421 513

1996 1.04 444 1.11 435 393

1997 1.06 450 1.15 450 482

100
Single Axle

70000

60000

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5 40000 8

30000

20000

10000

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Axle Loads, Ib

Tandem Axle

21000 .w.·.·.·.·.·.··.··.··.·.·.·.··.·.·.··.·.·.·.·.····.·.··.·.·.·.·.··.·.·.·.·.··.·.··.·.w.·.·.·.·.w.··.·.·.·.·· ·····w·····

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15000

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12000

9000

6000

3000

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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 "l" 0:> C\J <0 0 "l" 0:> C\J <0 0 "l" 0:> C\J <0 0 "l" 0:> C\J C\J C\J C") C") "l" "l" "l" lj") lj") <0 <0 <0

Axle Loads, Ib

o 0 o 0 o 0 C\J <0 I'- I'-

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1000 +------1~----------::JU-----,.--~~---;~Jt------------------.;i

< 750

500 +--~o::__1.__---'r______..#-P.d~F-f--_Pl~~_rrlf_--------------;

25~ t=~~!5~*a~tta~~~z::2~~~e o

Axle Loads, Ib

--+-1990 ~1992 -0-1993 _1994 -6-1995 _1996 -+-1997 -o-AVEAAGE

1 Ib =0.00445 kN

Figure 51. Load spectra for Site 29-5483 for all monitoring years.

101
Single Axle

·.•.·.•.•.__.·.•.·.·.·.__.·.·.·.·.·.·.·.·.·.·.·.w.·.••.__.•...,,"'.__.·.·.·.·.·.·.·.·.·"'.·•.·.·.·.·.w.·.·.·.·.·.__.__.__.•.•.•.·.•.·.•.·.•.·.•.·.•.·.__.•·.w.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.·•.·.·.·.·.·.·.·.w.·.·.·.·.__.__.w...·.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.-..-.•.•.•.•.•"'".".•·.•..•"'.•-..-,.•.•.•1

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0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\I '<l" co 00 0 C\I '<l" co 00 0 C\I '<l" co 00 0 C\I '<l" co 00 0 C\I '<l" C\I C\I C\I C\I C\I cry cry cry cry cry '<l" '<l" '<l"

Axle Loads, Ib

Tandem Axle

30000

25000

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8 15000

10000

15000

12000

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c 9000 ::J

8

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x 6000 «

3000

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<l" 00 C\I co 0 '<l" a) C\I co 0 '<l" a) C\I co 0 '<l" 00 C\I co 0 C\I C\I C\I cry cry '<l" '<l" '<l" co co co co co r-.... r-.... a)

Axle Loads, Ib

Tridem Axle

.•.•...•...•.•.•....- ' '.- - '........•...•...•...•...•' ' '.' -.'............•...•.•.•...•...•...............•...•...•...•.•.•.•.•.•.•.•.•.•..- -•.....•.......•-.- - .

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Axle Loads, Ib

~1992 -0-1993 ~AVERAGE1992/1993

1250

1000

.l!l

c 750 ::J

8

OJ 500

250

1 Ib =0.00445 kN

Figure 52. Load spectra for Site 29-5483 for 1992/1993.

102
21000

18000

15000 (/)

'E::;, 12000 8

.i!! 9000

6000

3000

0 0

Single Axle

... -

'~ - .-

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (0 ex> 0 (\J '<t (0 ex> 0 gj '<t (0 ex> 0 (\J '<t (\J (\J (\J (\J C') C') C')

Axle Loads, Ib

Tandem Axle

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t ex> (\J (0 0 '<t ex> (\J (0 0 '<t ex> (\J (0 0 '<t ex> (0 0 (\J (\J (\J C') C') '<t '<t '<t LD LD (0 (0 (0 r-- ex>

Axle Loads, Ib

Tridem Axle

2250

2000

1750

23 1500 c

::;, 1250 8

Q) 1000

750

500

250

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (0 (\J ex> '<t 0 (0 ex> '<t 0 (0 ex> '<t 0 (0 (\J

(\J C') C') '<t LD (0 (0 r-- ex> Ol Ol 0

Axle Loads, Ib

_1994 .......... 1995 __1996 -+-1997 ~AVERAGE

1 Ib =0.00445 kN

Figure 53. Load spectra for Site 29-5483 for monitoring years 1994 through 1997.

103
Single Axle

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\l <0 00 0 C\l <0 00 0 gj <0 00 0 <0 00 0 C\l C\l C\l C\l C\l C'l C'l C'l C'l

Axle Loads, Ib

Tandem Axle

50000 .·.v."'w.·.·",·""""'wm"w.'·.w"w.w.·"·.·.·.·,.·.·.",,,,,,,,,,,,,,,"w.·.·.w.·.·.·.·.·.·.·.·.w.·.·.·.·.·.·.·.·.·.·.·.· ·.·.·.·.·.·.·.·m.w.w.·.w.,w.·.·.·.·.·.·.·.·.·."""w.· 'w ,,"", j

45000 +-...-~-------------------------------4

40000 +--\:-----------------------------------i

'" 35000 -I--\-\-----------------------------------i

§ 30000 -I---\""'\.....--:..... ~-_:....... ;r:;;,~-------------------------------1 8 25000 ~--~~~~'iE---------------------__i

l!l 20000 -1---~n..r---__'lp'_.::r;,___----------------------__1

15000 10000 ++=~=~:~.F~'jLL.~t~.....c.i~::r~,...;j'Cl~:\.~~I.\..~b~~=======================================~ ... ~~

500~ t=:;=:=~==;=~;=~~~~~.~H;:..H;::;;;;;;;;;;;;;:;;;;;;;;=~=:j o

15000

12000

i1

c:

::J 9000

8

6000

3000

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 C\l <0 0 00 <0 0 00 C\l <0 0 00 C\l <0 0 C\l C\l C\l C'l l{) l{) <0 <0 <0 "- "- 00

Axle Loads, Ib

Tridem Axle

1500 ........................................................- -.- - - - - -.-.- -.-.-.-.-.-.- -.-.-.-.-.-.- .

1250 +- ---;:~---------------------jl

750

500

250

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 <0 C\l 00 0 <0 C\l 00 0 <0 C\l 00 0 <0 C\l C'l C'l l{) <0 <0 "- "- 00 0> 0>

-E 1000 +-------------~---""'=---------------------!

::J

o

()

Gl

Axle Loads, Ib

~AVERAGE199~1993 -+-AVERAGE 1994-1997

1 1b =0.00445 leN

Figure 54. Comparison of average load spectra based on the years 1992/1993

and 1994 through 1997 for Site 29-5483.

104
80000

70000

60000

C'" 50000 ;:)

0

()

.><

t.l 40000 ;:)

t=

Cii

;:) 30000 c

c

<l:

20000

10000

0

'<t L{) <0 ..... <Xl

(J) (J) (J) (J) (J)

(J) (J) (J) (J) (J)

11l 11l 11l 11l 11l

() () () () ()

~1992

en 0 C\J C')

(J)

(J) (J) (J) (J) (J)

11l (J) (J) (J) (J)

U 11l 11l 11l 11l

U U U u

-0-1993

(J)

(J)

11l

U

(J)

(J)

11l

U

(J)

(J)

11l

U

--+-1997

Figure 55. Annual truck count distribution by class for 1992/1993 and 1994 through 1997

for Site 29-5483.

80000

70000

60000

C'" 50000 ;:)

0

()

.><

t.l 40000 ;:)

t=

Cii

;:) 30000 c

c

<l:

20000

10000

0

'<t L{) <0 ..... <Xl en 0

(J) (J) (J) (J) (J) (J)

(J) (J) (J) (J) (J) (J) (J)

11l 11l 11l 11l 11l 11l (J)

U U U U U U 11l

u

~1994 -..-1995 __._1996

105
····.·.·.·",·.·.·.·.·.·.·.w.·.·.·.·.·.·.·.·.·.·.·.·.·..............•.."'..•."."",..>-0-•••••>-0-.",••>-0- ",••••••••" ••••••••••">-0- >-0-••••••••••••••••••••••••••••"",·.·.·.·.·.w.·"w w.·.·.·.w.·.·.·.·.·""",·.·.·"" "",."..w ..""" ,,""',,>-o-."'...•w ,.,,,>-o- ,,>-o- .-w }

[]

0

r::. C') V It) <D r-- <Xl Cll 0 .... C\J C') V It) <D r-- <Xl Cll 0 .... C\J C') V It) <D r-- Cll Cll Cll

r-- Cll

r-- Cll

r-- r--

Cll Cll

r-- Cll

r-- r--

Cll <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl Cll Cll Cll Cll Cll Cll Cll Cll .... .... .... .... .... Cll Cll Cll Cll Cll Cll Cll Cll Cll Cll Cll Cll Cll Cll Cll Cll Cll Cll .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Year

1000

Q 800

j 600

w 400 Q)

Q) 200

Historical ESALs per Day

-.-PROJECTED_ESAL_PER_DAY_2.1 %

_ Monitoring ESALs per Day

PROJECTED_ESAL_PER_DAY_9.9%

Figure 56. Annual ESAL projections for Site 29-5483.

T bl 29 Ca e umuIat"lye ESALs com]: utahan sheet or S'lte 295483 -

Historical ESALs Monitoring ESALs Projected ESALs per Projected ESALs per

Year per Day per Day Day, 2.1 % Growth Day, 9.9% Growth

1973 460 505 183

1974 460 515 209

1975 463 526 236

1976 466 536 262

1977 482 547 289

1978 545 557 316

1979 526 568 342

1980 553 578 369

1981 573 589 396

1982 526 600 422

1983 542 610 449

1984 452 621 476

1985 471 631 502

1986 493 642 529

1987 833 652 555

1988 844 663 582

1989 896 673 609

1990 368 684 635

1991 866 694 662

1992 339 705 662

1993 380 715 662

1994 191 726 742

1995 214 736 768

1996 117 747 795

1997 99 757 822

Cumulative ESALs for 2.1 % Growth =5,758,452 Cumulative ESALs for 9.9% Growth =4,553,071

106
In summary, the most disturbing aspect and cause for concern is the steep (200 percent)

decline in monitored axle loads between 1993 and 1997. We hope this concern can be addressed

effectively by consulting the SHA.

SUMMARY OF CASE STUDIES FOR CATEGORY 3

Category 3 represents LTPP sections with solid AVC data but virtually no site-specific

WIM monitoring data. Consequently, in order to obtain the base annual spectrum, a

representative surrogate axle load spectrum for the 10 truck classes are required. As discussed

previously in connection with the evaluation of axle load spectra for Category 1 case study sites

(refer to the note for Case Study Site 12-4000), the load spectra for the individual truck classes

for all three Category 1 sections were evaluated and the set of load spectra from Site 27-0501

was selected to be used for Category 3 and 4 projections. The objective was to select a set of

spectra without obvious discrepancies. The selected Site 27-0501 spectra are given in appendix

B (figures 155 through 164 and 168 through 177). Admittedly, the use of site-related spectra for

Category 3 and 4 projections is preferable. However, the search for site-related spectra requires

extensive and time-consuming processing of Level 2 CTDB data. The selection of the most

suitable set of load spectra for individual vehicles would be greatly facilitated by developing a

catalog of candidate spectra.

Site 37-3807

Site 37-3807 is located on a rural principal arterial highway in North Carolina.

Interestingly, this section is not included in a table summarizing the availability of monitoring

data (TRE_MONITOR_BASIC_INFO IMS release 9.7, June 1999). However, a table

containing truck class distribution (TRE_MONITOR_VEHICLE_DISTRIB) contains monitoring

AC data for this site for 1993 through 1995. Otherwise, the section provides a typical example

of a Category 3 projection.

Site 48-5287

Site 48-5287 is located on an urban Interstate in Texas. In one year during the

monitoring period, the truck volume dropped by more than 100 percent from its expected value.

Either the monitoring value is incorrect or the LTPP lane was closed for half of the year. Only

contact with the SHA can provide the answer. Otherwise, there is a good match between

historical and monitoring annual truck volumes.

Site 55·3012

Site 55-3012 is located on a rural minor arterial highway in Wisconsin. Considering the

low traffic volumes on this highway, there is a reasonable fit between the historical and

monitoring truck volumes. It is interesting to note that the historical truck volumes were

reported to be steadily increasing in a zig-zag pattern.

107
DESCRIPTION OF CASE STUDIES FOR CATEGORY 3

Site 37-3807

There were 161, 189, and 231 days of AVC monitoring data during 1993, 1994, and

1995, respectively. The Annual Traffic Data Projection Sheet is given in figure 57. Truck

volumes were projected using a trend line with a simple growth rate of 10.5 percent (figure 58).

The estimated monitoring truck volumes for 1996 and 1997 (shown in figures 57 and 58) were

disregarded. The resulting projected truck volumes are summarized in table 30. Truck volume

distributions for all three years (for which the AVC monitoring data were available) are very

similar (figure 59), and their average was used to develop the base annual spectrum (figure 60).

The base spectrum was obtained by combining the site-specific truck volumes with the typical

load spectra. The projected annual axle load spectra were summarized using ESALs, and the

result is shown in figure 61 and table 31.

In summary, the weak link in the prediction process for this North Carolina site is the use

of typical load spectra based on a Minnesota site. The proposed LTPP Loading Guide and a

consultation with the SHAs are necessary for the selection and use of more appropriate siterelated load spectra.

Site 48-5287

AVC monitoring data are available for eight consecutive years (1990 through 1997). The

average number of days for which these data are available is 51 per year, with a range of 27 to 82

days. Utilizing a very good agreement between the historical and monitoring truck volumes

(figure 62), an exponential growth trend line was used to project total truck volumes (figure 63

and table 32). As mentioned in the summary, there was an unexpected drop in truck volumes

obtained in 1996.

The 1996 truck volumes were not used for the projections. The base annual load

spectrum was obtained by averaging the truck volume distributions (figure 64) and combining

them with the typical load spectra. The result is shown in figure 65, whereas figure 66 and table

33 show the relationship between projected and historical (estimated) ESALs.

In summary, this site is a rather typical Category 3 site with its inherent limitations.

Site 55-3012

This Wisconsin site on a rural minor arterial highway has the following AVC monitoring

data: 1993 - 31 days, 1994 - 131 days, and 1996 - 173 days. There is also a record of AVC data

for 1991, but the data were disregarded because the reported average daily truck volume (11,316

on a two-lane highway) was obviously too high. It would be revealing to know why the

historical truck volumes were expected to have increased in a zig-zag pattern (figure 67). The

projection of truck volumes did not take this pattern into account and was based on the lowest

historical values that also fit the trend in the monitoring values (figure 68 and table 34).

108
1200 7000

1000 6000

Q)

E 5000 800

4000 0 600 19

3000 I- 400 I- Cl Cl

2000

200 1000

0 0

'" 0 N M "<t '" CD ..... co '" 0 ;;; N M "<t

'" CD ..... ..... co co <X> co co co co co co co '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" Year

1000

900

800

700

600

500

400

300

200

100

o

0

1\

I \

If \ J-i""

A II l:Y""

-=L ~'V.I

/ / "d ~// u0- .....

0---<

1.08

1.06

1.04

1.02 13

1

0.98 (/)

0.96 <i

0.94 ff3

0.92

0.9

0.88

'" 0 ;xi N M "<t '" CD ..... co '" 0 ;;; N M "<t

'" CD ..... ..... <X> co co co co co co co co '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" Year

0

IE 1000 l!!

I-

.9

Ol 800 c: 'c

Q)

a. 600

0

'0

400

Cl

fl 200 c:

in

Q) 0 Ol

c:

'"

.<:

0 -200

'" 0 ;xi N M "<t

'" CD ..... co '" 0 ;;; N M "<t '" CD ..... 0 ..... co co co co co <X> <X> co co '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" '" Year

Monitoring data Historical data All data are for LTPP lane

• ---0- AADT volume, all vehicles

... ----&- AADT volume, all trucks

• ----0- Average ESALs per day

• --<>-- Average ESALs per truck

Figure 57. Annual Projection Data Sheet for Site 37-3807.

109
1200

<J) 1000

E

:::l

0 800

>

u 600 :::l

....

I--

I-- 400

0

200

...................................................................................................................................................................................................................................................................................................................................................................................................................

0

0> 0 .... C\J (t) '<:t LO co r-... <Xl 0> 0 .... C\J (t) '<:t LO co r-... r-... <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... .... Year

--.l- Monitoring Truck Volume Historical Truck Volume

__ Projected Truck Volume (10.5% growth)

Figure 58. Comparison of truck AADT volumes for Site 37-3807.

Table 30. Comparison of truck AADT volumes for Site 37-3807.

Growth Factor, Projected Truck Monitoring Truck Historical Truck

Year 10.5% Volume, 10.5% Growth Volume Volume

1980 0.41 366 0

1981 0.46 404 214

1982 0.50 442 207

1983 0.54 480 460

1984 0.59 519 460

1985 0.63 557 440

1986 0.67 595 340

1987 0.72 633 640

1988 0.76 672 680

1989 0.80 710 640

1990 0.84 748 770

1991 0.89 786 822

1992 0.93 825 870

1993 0.97 863 796

1994 1.02 901 1031

1995 1.06 939 876

1996 1.10 977 316

1997 1.15 1016 352

110
300000 --r---------

250000 +----------------------------------1

200000

Ul

C

::::l

0

() 150000

0

::::l

100000

50000 -1-----------------1-----\----------------1

o 2 3 456 7 8 9 10 11 12 13 14 15 16

Vehicle Class

70%

60%

50%

Ul

0 40% ::::l

....

I-

-0 30%

0

20%

10%

0%

_..,,~""--

A

.A.. A

o 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Vehicle Class

-+-1993 _1994 -i!r-1995 -o-Average

Figure 59. Truck distribution by class for Site 37-3807.

111
Single Axle

1~~~~~ w --.---.-•.•.•....w ..::.........................•...•...•..•..•..•....--.-....................................................•.•.•.......... " , , ".--.-.•.......wn...•.........--.-ww.w···

t

80000 -I-------t-',+------------------------:

70000 ;------tl-\+-------------------------'

60000 ;------+-J-\t----------------------

50000 -I-----T.--~+------------------------\ 40000 -I----~/r----J\\-------------------------i 30000 -I-----.1.+---+-\-----------------------i

20000 +--.::---~~----'.'\-----------------------_____l -.......,.- \.. 10000 +----=-~'---------'II"'"4,..~T...~~-=-....-----.... -------------------f o-l-....,.......-r---"T-r--..,........-.-......,..........,.:~;b~.,....,..~ .........~~... ..........+_..,.............j o o

o 8

o

'<t

o

8co

o

8

to

o

8

o

......

o

o

o

'<t

8

o

co

......

8 0 0 0

o 888

gj

Axle Loads, Ib

Tandem Axle

o

o

o

co

(\J

o

o

o

to

(\J

o

o

o

o

C')

8o

co

C')

o

o

o

to

C')

o

o

o

o

o

o

.·w.· ·.·.•...•...__ --.- --.- · · --.-...............................................•...........--.-...•.......--.-...........•...................•...................................•...........•...................................•...•.......•...•...•.......•.·.__N.·.·.·.·.·.·.·.·.·.·.w.·.·.·w.·.·w.w.·.·.·'n.·.· __.-..__j

it J\

/ \! \

70000

60000

50000

l?

40000

30000

20000

10000

o o

o

o

'<t

8o

to

o

8

co

......

o

o

o

o

o

o

o

o o

o

o

co

C')

o

o

o

o

o o

o

o

to

'<t

o

o

o

o

o

o

co

U}

o

o

o

o

co

o

o

o

'<t

co

o

o

o

to

co

o

o

o

o

o

o

Ie

o

o

o

o

to

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 co (\J to '<t 0 co (\J to '<t 0 co (\J to '<t 0 co to ...... (\J C') C') '<t '<t U} co co I'- I'- to Ol Ol 0

......

Axle Loads, Ib

Axle Loads, Ib

Tridem Axle

1800 , ·..·.·.w..·.·.· ·..·.·.·.·.·.·.·.·.·.· ·.·.· ·.m·..·.·.·.·..·.""·..·.·.·..·.·.· ·.·..·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·..·..·.·.·.·.·.·.w.·.m·.·.·.·.·..·.·.·.·.·.·.·.·.·.·.· · w.·.·.·.·.·.·.·.w..·.·..,

1600 +---.,_-------------------------------l! 1400 +-_-\- -----jl

2 1200 +-__\-\- ----<;1

1000 -i---\.....\:------------------------------J!

.i1! 800 ;----'r--\--------------------------------l

600 ;----\-~-----------------------------1

400 ;-----".......... .------------------------------l

200 ;---------.:~:_:_--"""'"_........I10::_.,__-------------------_i ..............,-.r ...... O+--.,---r---.---:..,...::.-...:...,.----.---,----lEi"l~~Hh_4~..,... .........._+..............,.......+-+r--4 o

1 lb =0.00445 kN

Figure 60. Base load spectra for Site 37-3807.

112
1000

900

800

0 700

:ua. 600

« 500 rn

w

a> 400 Cl

a> 300

200

100

0

OJ 0 ,... C\J (') '<t III co ,... ex> OJ 0 m SJ (') '<t III co ,... ,... ex> ex> ex> ex> ex> ex> ex> ex> ex> ex> OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ OJ ,... ,... ,... ,... ,... ,... ,... ,... ,... ,... ,... ,... ,... ,... ,... ,... ,... ,... ,...

Year

Historical ESALs per Day -*- Projected ESALs per Day

Figure 61. Comparison of projected and historical ESALs for Site 37-3807.

Table 31. Cumulative ESALs computation sheet for Site 37-3807.

Year Historical ESALs per Day Projected ESALs per Day

1980 0 263

1981 200 290

1982 189 317

1983 422 345

1984 444 372

1985 436 400

1986 326 427

1987 682 455

1988 674 482

1989 641 510

1990 789 537

1991 844 564

1992 893 592

1993 619

1994 647

1995 674

1996 282 702

1997 315 729

Cumulative ESALs =3,257,433

113
% Change Since Date of Opening to Traffic

........ ...... I'\) I'\)

00000

0'1001a 01

a a a a a0

1972

::n 1973

:: 1974 "

n& 1~5

0'1N

S 1m .. ;I> 1977

1~8

g 1~9

e:.. 1980

.[ntttt

r

==

~::: 1~

Im~::: l~ I IIm

S' 1986

C/.) 1987

go 1988

1989 !:!l 6 6 Co 0' 1990 1-1 tr1 tTl £. E... = C/.)

_. :I>en en" :1>:3 :3" ..., 1991

t""1"tt J~ ,.('J :: 1992

('D "0 "0 e. e. 1:""1

+:- !l !l ;; :;- -3 1993 00 - c.. c: C1> ..., I § ~::: 1994 Ul. ;::-:;'" til (i'" = N • 1~ 00

1996

1997

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

Average ESALs per Day

I\,)W .f:lo 01m ""'-J Q) to

o0a0 a a 0 0a

a a aa0aaaaa

c, 0.....r-............

'1

\

J.....~

l4

~)

~I.,t.

<

1<

a0a 0 0 ...... ...... ...... m m

mm

ESALs per Truck

AADT Truck Volume

.., .I> (J) 00

a .., .I>

a a a a a a a a a a a a a a a 1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

-< 1984 CD

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

a .., .I> (J) 00 a a a a

a ..,

a

a a a a a

a a aa a a

a a

AADT Total Volume
·.·.·.·.,·.·.·.·.·.,·.·.·.·.·.·.·.·.·.mW.·.·.·.·.·.·.·.,·.·.w.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.-·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.,·.·.·.·.·.·.·.·.·.·.·•.......•...•...•...•.•.•.•.•.•.•.•.....•.•.•.•.•.•.•.•·.·.·.·.·.·.·.·.·.·.·.·.w.·.w.·.·.·.·.·.·.·.·.·.·.·.·.w.·.·.· w .-w•..."' 'w.--- "'''' 'l

y =2E_68eo.0816X >

R2 =0.8248 1000 i--L-----.J.---------------r-~~l--_____i

800 +-------------------------,,~~-------€

600 I-------------::~~;:F~!F------I

200 4 t==::;~~~:!~~~::~~==========j

1400

Q) 1200

E

;:,

(5

>

c..>

;:,

o

0

C\J C') '<t L() <0 I"- eo Q) 0 ..- C\I C') '<t L() <0 I"- eo Q) 0 ..- C\I C') '<t L() <0 l"-

I"- l"- I"- l"- I"- l"- I"- I"- eo eo eo eo eo eo eo eo eo eo Q) Q) Q) Q) Q) Q) Q) Q)

Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q) Q)

..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..- ..-

Year

-.-My-AADT_TRUCK_Combo

Projected Truck Volume

Historical Truck Volume

Figure 63. Comparison of truck AADT volumes for Site 48-5287.

Table 32. Comparison of truck AADT volumes for Site 48-5287.

Projected Truck Monitoring Truck Historical Truck

Year Growth Factor Volume Volume Volume

1974 0.21 188 102

1975 0.22 204 146

1976 0.24 221 263

1977 0.26 240 283

1978 0.28 260 335

1979 0.31 282 337

1980 0.34 306 335

1981 0.36 332 362

1982 0.39 361 374

1983 0.43 391 403

1984 0.46 425 501

1985 0.50 461 518

1986 0.55 500 554

1987 0.59 542 603

1988 0.64 588 567

1989 0.70 638 595

1990 0.76 693 694 686

1991 0.82 752 707 696

1992 0.89 815 753 757

1993 0.97 885 1073 872

1994 1.05 960 972

1995 1.14 1042 1022

1996 1.24 1130 386

1997 1.34 1226 1176

115
200000

180000

160000

140000

c: 120000 ::l

0

() 100000 <D

:§ 80000 <D

>

60000

40000

20000

0

I

I

f/

~\'"

'~~~

TV -"'L.

- o 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Vehicle Class

60%

50%

40% Ul

.l<:

0

:::J

t= 30%

'0

0

20%

10%

0%

0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Vehicle Class

~1990

---.-1995

-.-1991

-.-1997

-tr-1992

-0-- Average

~1994

Figure 64. Truck distribution by class for selected years for Site 48-5287.

116
Single Axle

o

o

o

o

'<t

o

o

o

co

C')

o

o o

o oo

8

C')

o

o

o

co

C\l

o

o

o

000 0

888 8

gj

Axle Loads, Ib

o

o

g o

o

o

'<t

8

o

C\l

o

o

o

o

80000 I

70000 I ,

60000 +--------,r--\--------------------------i;

50000 • \

" I \

1~~~~~ " -..." ;

Tandem Axle

:~~~~ ""'''"-...-..."'''''''" ••.:: "'''''',,-...,,'''',,.,,••,,•••••••••H 'j:

40000 +-- ---i¥'~\\----------------------------'::i J'{ I , j .11 35000 +---+/"""'V..-+-----'\--,-------------------------<:

§ 30000 +----J.L-~....>-........,,*-------------------------------{ 8 25000 -t-~----~r-----------------------------.....;

..9l 20000 +----------:....I=_---------------------------':

15000+-----------'l............... ---.........,,~-----------------~: 10000 +----------"-'I!:.--~,r---------------------;:

5000 +----------------'la-.....- ..... ----------------------,:

0+-...,.........,.-,.........,.....--r"-,.......-r-......,....--r......:!::;..,............. ......~~~~..... ............,--1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t co C\l lD 0 '<t co C\l lD 0 '<t co C\l lD 0 '<t co lD 0 '<t co C\l C\l C\l C') C') '<t '<t '<t to to lD lD lD I'- co co co

Axle Loads, Ib

Tridem Axle

\

\

\

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 co '<t 0 lD co I'- co m m 0

\

................................................................................................................................................................................................................................................................................·.·.·.·.·.·.w,,·.·.·.·.·.·.·.·.·.,·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w...·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·i:

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 lD C\l co '<t 0 lD C\l co '<t 0 lD C\l C') C') '<t '<t to lD lD

Axle Loads, Ib

3500

3000

.11 2500

c

::::J 2000 8

(]) 1500

1000

500

1 lb = 0.00445 kN

Figure 65. Base load spectra for Site 48-5287.

117
900 ·.·.·.·.·.·.·.v.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·,,·•.•...•...•...•.......•...•...•...•.......•.•.•.•.•.•.•,·.w,·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·•.........•.............................................•...............................•...................•·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.·.·.·.w.w.·.·.·.·.·.· w•.•.•.•.•.•v.w !

800 -f---------------::::----n.......-------------------i~ >- 700 -+-----------------I--...!.L----\-----------_..

(j; 600 -f---------------:"O-------\---------_.-=.=--- 0..

500 +-------=::;;rF;;;:rr.......----------l:,t---=:;;.o::...=..---! «

400 +-------rtL----------------w~!!.......,d----------'

300 -+------I-'-----------------.....~=-----------!

(\j

« 200 100 l==~~:;~~~:!~~~~:=:===============j O-+--,--,...---,-,...-...,.--,.--,............,.-,...--r--r---r----r-...,,.-....-....,.............--r-...,-.,............,......-r---r-...,---j

C\J C') '<t L{) <D r--. co CJ) 0 T""" C\J C') '<t L{) <D r--. co CJ) 0 T""" C\J C') '<t L{) <D r--.

r--. r--. r--. r--. r--. r--. r--. r--. co co co co co co co co co co CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ)

CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ) CJ)

T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T""" T"""

Year

_ Projected ESALs per Day ~Historical ESALs per Day

Figure 66. Projected and historical ESALs for Site 48-5287.

Table 33. Cumulative ESALs computation sheet for Site 37-3807.

Year Projected ESALs per Day Historical ESALs per Day

1974 106 85

1975 115 200

1976 125 359

1977 135 386

1978 147 458

1979 159 485

1980 173 482

1981 188 523

1982 204 562

1983 221 605

1984 240 770

1985 260 723

1986 282 795

1987 306 784

1988 332 578

1989 361 512

1990 391 356

1991 424 351

1992 461 373

1993 500 499

1994 542

1995 588

1996 638

1997 693

Cumulative ESALs =2,770,764

118
% Change Since Date of Opening to

Traffic

0. '" c; t;1 Average ESALs per Day MDT Truck Volume a a a 0 0 0 a a a a 0000000000 ...a. I\J UJ 01 m .................. 1975 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 "'" 0> 0> 0 I\) "'"

::: 1975 1975 0 0 0 0 0 0

:ntttt 1976 1976 1976 .r 1977 1977 1977

""l (1) !!;. 1978 1978 1978

0\ ., -...l 1979 1979 1979 » 1980 1980 1980 l tttt ::: :: ::: ~. 1984 1984 1984

S4.

_. ;;: 1985 1985 1985 ., .......::2 .. -< 1986 -< 1986 -< 1986 ro ro

\00 1987m1987 1987

1988 1988

p:>>>>>> 1988

en ;i;i>>= 1989 1989

::r OJ OJ ~:;- 1989

(1) .., .., .. 1990 1990

g. tT1 tT1 2. 2." 1990 '" "''' " ;;: 1991 0' g g 5' 1991 1991 ""l ~. • ... 1992

en 1l 1l 1992 1992

::;... 2" Er g. 1993 1993

'< i:f g:;, 1993 1994

Ul D: ;; 1994 1994

19~ 1~

o 1995 1996 ....... 1996

tv 1996 1997

1997

1997 1998

1998 +-fJ--+-.!.-+....L-j----J-+---4----1 1998 09999-.a.~~

0 oooog~~ - --

N.".O)CO N.". 0 0 0

ESALs per Truck MDT Total Volume
140 -r , '.' '.' " " ' '.'.'.'.' " ' , " , "" ,

120 4-----------..,.---~~----------------~

100 +-----------J"\------+---\--+---\--------.H.----------------]~--:

40 +---!~~------------------------------:

20 -1---------------------------------

w m 0 N M V w m 0 N M V w

w w w w w w w w w w m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m m

Year

---6-Historical Truck Volume -Ir-Monitoring Truck Volume

_Prqected Truck Volume (4.9% Growth)

Figure 68. Comparison of truck AADT volumes for Site 55-3012.

Table 34. Comparison of truck AADT volumes for Site 55-3012.

Growth Factor, Projected Truck Monitoring Truck Historical Truck

Year 4.9% Volume, 4.9% Growth Volume Volume

1977 0.53 44 72

1978 0.56 46 49

1979 0.58 48 76

1980 0.61 51 104

1981 0.64 53 70

1982 0.66 55 83

1983 0.69 57 118

1984 0.71 59 72

1985 0.74 61 111

1986 0.77 64 129

1987 0.79 66 89

1988 0.82 68 74

1989 0.84 70 110

1990 0.87 72 80

1991 0.90 74

1992 0.92 76

1993 0.95 79 66

1994 0.97 81 79

1995 1.00 83 0

1996 1.03 85 99

1997 1.05 87 88

120
14000

12000

10000

.l!l

c:

::3 8000 0

()

OJ

:§ 6000 .s:::.

OJ

> 4000

2000

0

0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

0.45

0.40

0.35

0.30

l/l

.:.:.

0 0.25 ::3

t=-

o 0.20

0

0.15

0.10

0.05

0.00

1'1

A~ In

\ j'A

\ J \'

t '~ 1

i ~p

L - -.....

2 3 4 5 6 7 8 9 10 11 12 13 14 15

__1993 -lr- 1994

Vehicle Class

-lIE-1996 -+-1997 ~Average

Figure 69. Truck distribution by class for Site 55-3012.

121
Single Axle

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\l '<t co co 0 C\l '<t co co 0 gj '<t co co 0 C\l '<t co co 0 C\l '<t C\l C\l C\l C\l C') C') C') C') C') '<t '<t '<t

Axle Loads, Ib

Tandem Axle

..

\ l'.

\ I \

, / ~~ \ ... '\............. ~~~ o

o

7000

6000

5000

.t2

c 4000

.i! 3000

2000

1000

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t co C\l co 0 '<t co C\l co 0 '<t co C\l co 0 '<t co g! co 0 '<t co C\l C\l C\l C') C') '<t '<t '<t L{) L{) co co co I'- co co co

Axle Loads, Ib

Tridem Axle

3500 · w •• • •.•• •.•••• •.•.•.••••••••••••.•.••••.•.•.•.•.••.•.••.••••••••••••.•••.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.••.•.•••.•.••••••••• " " .•.•.•.•.•.•.•.•.•.•.••••.•.•.•••••••• " •.••.••.••.•.••.•.

3000 -t-----""I'"-------------------------------"::

2500 -1--.......-F-~t_-------------------------i!:

I:~ ::==~==:=/\==~=./====:="==:=:="":==~=~....==....==..... ===:,===:.~=:=========================================================:1::

O+-.......,..-.,..-....,..-.,...........,....-,..---r--,..--,....~ M ..... .......... ...............~...,.................-.,..-~

o

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 co C\l co '<t 0 co co '<t 0 co g! co '<t 0 co co C\l C') C') '<t L{) co co I'- co Ol Ol 0

Axle Loads, Ib

400

350 +---\"r--------------------------------i:

300 -t---\-\--------------------------------;:

8 250 +---+\---------------------------------::

200 \

150 +----\..~--------------------------------:: 100 +-----~_3IIi..__----.....-...... --=----------------------;:

50 +-----~~~.....~~~E:..:.~_.:::::_--------------____i~ .~~ O+--r----,~___r-....,....:.-~-_,_-.,._....::!::r_ ...r+_...,.+_.....~~Hh_4~......_+~.........+r-___f o

1 lb = 0.00445 kN

Figure 70. Base load spectra for Site 55-3012.

122
160 ············· ····················1:

~1~ X I

120 -l---------------+--->t/\----------:I~.;

c.. jr\ 100 -!---------------I"'~n---_I_-_\_-------------: / \ i « 80 +--------~/-~\.....---O-:::t--~b.-------_-=-----=---

(]) 4060 i=::~;:~nk;;:~~n""~~rf~~~~~~~-:::::~~======r rr-- P "t:r

20 -t---~__U---------------------------i

0

<0 I"- eo 0> 0 ..... C\l C') -::t LO <0 I"- eo 0> 0 ..... C\l C') -::t LO <0 l"-

I"- l"- I"- I"- eo eo eo eo eo eo eo eo eo eo 0> 0> 0> 0> 0> 0> 0> 0>

0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... .....

Year

_ Projected ESALs per Day -0-Historical ESALs per Day

Figure 71. Projected and historical ESALs for Site 55-3012.

Table 35. Cumulative ESALs computation sheet for Site 55-3012.

Year Projected ESALs per Day Historical ESALs per Day

1977 38 33

1978 40 22

1979 42 36

1980 43 47

1981 45 33

1982 47 38

1983 49 52

1984 51 63

1985 53 99

1986 55 99

1987 56 68

1988 58 74

1989 60 137

1990 62 71

1991 64

1992 66

1993 67

1994 69

1995 71

1996 73

1997 75

Cumulative ESALs =432,146

123
Figure 69 shows that the truck volume distribution contains a high percentage of Class 5

vehicles (2-axle, 6-tire trucks), as would be expected on this type of highway (see table 4). The

base annual load spectra are given in figure 70. The projected and historical ESALs are

compared in figure 71 and table 35. According to figure 71, there is a good agreement between

the historical and projected ESALs. This result is not surprising, considering that the axle load

spectra used for this site were obtained from a rural principal arterial site in Minnesota (site

27-0501).

DESCRIPTION OF CASE STUDIES FOR CATEGORY 4

Category 4 represents LTPP sections with virtually no reliable monitoring measured

AVC and WIM data, but with historical and monitoring estimated truck volume data. Base load

annual spectra for Category 4 sites were obtained by combining site-related base truck

distributions with typical load spectra for the individual truck classes. Typical load spectra for

individual truck classes are given in appendix B; the spectra for Site 27-0501 were used for

Category 4 predictions.

Site 01-6019

Site 01-6019 is located on a rural Interstate in Alabama. There was no record of the

number of AVC days in the monitoring data summary table. However, there were data showing

truck volume distribution by class for 1995, but the reported volume was so low that it was

disregarded. The projection of truck volumes is documented in table 36 and figures 72 and 73.

The total truck volume in 1997 was estimated to be 1950 and was used as the starting point for

the projection. A linear regression line was fitted through historical truck AADT volumes.

The base normalized truck volume distribution shown in figure 74 was obtained by

averaging normalized truck distributions for sites located on rural Interstates in Alabama. Again,

to carry out Category 4 projections, it is necessary to have a traffic loading guide or catalog. The

base annual spectrum is shown in figure 75. Projected ESALs are summarized in figure 76 and

table 37. The projected ESALs are considerably lower than their historical ESALs. It is clear

that the selected base truck volume distribution (figure 74) and/or the typical load spectra for the

individual vehicles may not be appropriate.

Site 04-1002

Projection of traffic loading for site 04-1002 was done previously as a Category 2

projection using surrogate WIM data from nearby LTPP sections. Without the benefit of data

obtained from the nearby sites, this site may be considered suitable for Category 4 projections.

However, the main reason for carrying out this Category 4 projection was to evaluate the

consequences of using Category 4 projections rather than Category 2 projections. The Annual

Traffic Data Projection Sheet (figure 43) and the projection of annual truck volumes, shown in

figure 44 and table 25, were the same for both projection categories.

124
1400 6000

1200 5000

Ql 1000 Ql

E

::::J 4000

800 "'"0 3000 S 2

l- 600 ICl 2000 i 400

200 1000

0 0 a co C'I

'" '<t ll) <0 .... co 0> a c;; C'I

'" '<t ll) <0 .... co co co co co co co co co 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0>

Year

1200 0.801

>- 1000 0.8005

0'"

aJ 800 0.8 "'"0

c..

Vl

--l

<{ 600 0.7995 en

w Vl

--l Q) 400 0.799 <{ Cl en e? w Q)

200 0.7985

0 0.798 0 N M '<t ll) <0 .... 00 0> 0 C;; C'I M '<t ll) <0 .... 00 <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... ..... .....

Year

n

.-Q. ,..,/

! \.. F

/ V

/ ...Ii

~/ .............

-?& .~

- o

<Xl

0>

.....

.s 350 Cl

c: 300 'c

Q)

c.. 250

0

'0 200

2 0 150 "'IE 0",

~.= 100 c: 50 en

Q)

Cl 0 c:

'".c: -50

u

0 -100 N

<Xl

0>

.....

M

<Xl

0>

.....

It)

<Xl

0>

.....

<Xl

<Xl

0>

.....

0>

<Xl

0>

.....

o

0>

0>

.....

C'I

0>

0>

.....

Year

Monitoring data Historical data All data are for LTPP lane

• ---0-- AADT volume, all vehicles

... AADT volume, all trucks

• ---0-- Average ESALs per day

• ---<>-- Average ESALs per truck

Figure 72. Annual Projection Data Sheet for Site 01-6019.

125
500

•

0

0 T'"" C\l C') """ U') CD l"- eo 0> 0 T'"" C\l C')

""" U') CD l"-

eo eo eo eo eo eo eo eo eo eo 0> 0> 0> 0> 0> 0> 0> 0>

0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> 0> T'"" T'"" T'"" T'"" T'"" T'"" T'"" T'"" T'"" T'"" T'"" T'"" T'"" T'"" T'"" T'"" T'"" T'""

Year

1500 -l------------------:::-::::;;;;;olll"""'=-------~

1000 -1------f-=--9Il~~~--------------___i

2500

CD 2000 E

g

(.)

...

rro

--.-Monitoring Truck Volume Historical Truck Volume

_ Projected Truck Volume (10.8% growth)

Figure 73. Comparison of truck AADT volumes for Site 01-6019.

Table 36. Comparison of truck AADT volumes for Site 01-6019.

Growth Factor, Projected Truck Monitoring Truck Historical Truck

Year 10.8% Volume, 10.8% Growth Volume Volume

1981 0.37 717 621

1982 0.41 795 736

1983 0.45 872 803

1984 0.49 950 1184

1985 0.52 1027 1235

1986 0.56 1104 1004

1987 0.60 1182 1136

1988 0.64 1259 1202

1989 0.68 1337 1312

1990 0.72 1414

1991 0.76 1492

1992 0.80 1569

1993 0.84 1647

1994 0.88 1724

1995 0.92 1802 285

1996 0.96 1879

1997 1.00 1957

126
600000

500000

400000 en

1:

:::J

0

0 300000 Q)

.2

Q)

> 200000

100000

0

I,

I 1

I \

I

j

2 3 4 5 6 7 8 9 10 11 12 13 14 15

Vehicle Class

Figure 74. Base truck volume distribution by class for Site 01-6019.

127
Axle Counts

60000

90000

en

5"

(Q

<D

<D

.......................\1 .1

Axle Counts

44000

14000

10000

8000

12000

6000

I\) I\)

88

000oo 00 000

000

000

or-r-r-r-r--1

2000

4000

16000

38000

32000

28000

26000

36000

30000

34000

40000

42000

» 18000

(J) r 20000 g

.f} 22000

5'

24000

-i

gj

a.

(J) 3

(J)

.....

o I\) 0> o 0 0 0 o 0 0 0 o 0 0 0 o 0 0 0

0> en o 0 o 0 o 0 o 0

I\) o

0

o0

o0

o0

-~W"'-'-~'W"-'~W"""W~L~'L.-._t.~wL~-.J o

o r-r-r-r-rrr11

4000

8000

Axle Counts

12000

16000

52000

32000

36000

56000

24000

72000

60000

76000

28000

64000

20000

68000

88000

80000

84000

»

g. 40000 r

44000 CJ)

5' 48000

a:

<D3

(J)

I\) I\) (U (U

01 0010 010 01

o0 000 0 0

o

o 0 0 0 0 0 0

o I IIIIII!

6000

12000

18000

78000

54000

66000

72000

J+tf

24000 ]~' 30000

36000

42000

48000

84000

96000

108000

102000

c=

<Dr

(J)

z

0::

IIoat

VI

..-

0:::1

'J)

('U

--o

'J)

"0

('U

(') q

0'

en§"O o

..-I

0\ o

..-

\0

""I1

~.a

-.....l

VI

..-

tv

00
·.·.·.·.·.·.·.·.·.·.·.·.·.·.·..·..· ·.·.w.m·..·.·.·.· ·..· w..· · ·..·.· ·..w ·.·..·..·.· m·]i 1200

1000 +-------:::::;;;;::;;;[::J;-----r?,L:..-------------:-:-:::::;;R"........."""---

o

Qi 800 +-------+----l:F---------=...-~---------_____'<

Cl.

600 -!-------r~::::..=~---~~::::......:.-'------------------___j

w

Q)

Q)

400 +-----'l~~"----------------------------__1

200 -1----------------------------------1

to

Ol

Ol

....

C')

Ol

Ol

....

....

Ol

Ol

....

-+E-Projected ESALs per Day

.... C\I C') '<I' to CD I"- <Xl Ol 0 <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl <Xl Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol Ol .... .... .... .... .... .... .... .... .... .... Year

-0-Historical ESALs per Day

O-!---r----.--,...-"'"T""-...,--.,--.---r---.,--....,---r--,......--...,---,.--,--....,.---i

o

....

Figure 76. Comparison of projected and historical ESALs for Site 01-6019.

Table 37. Cumulative ESALs computation sheet for Site 01-6019.

Year Historical ESALs per Day Projected ESALs per Day

1981 496 390

1982 589 433

1983 641 475

1984 948 517

1985 989 559

1986 803 601

1987 910 644

1988 962 686

1989 1049 728

1990 770

1991 812

1992 854

1993 897

1994 939

1995 981

1996 1023

1997 1065

Cumulative ESALs =4,516,353

129
The base truck volume distribution used for the Category 4 projection was the average of

the 1993 and 1994 truck volume distributions (of the nearby sites located on the same stretch of

highway) shown in figure 47. The vehicle class-specific load spectra used were obtained from

Minnesota Site 27-0501. A comparison of base spectra used for Category 2 and 4 projections

carried out for this site is shown in figure 77. The resulting ESALs are compared with the

ESALs obtained previously using the Category 2 projection (in figure 48 and table 26). The

Category 4 ESALs are only a third of the Category 2 ESALs, as well as a third of the historical

ESALs. These results underscore the importance of utilizing surrogate or site-related data rather

than Category 4 projections.

Site 17-5423

Site 17-5423 is located on a rural Interstate in Illinois. This site was used to illustrate the

procedure for carrying out step 7 (see chapter 4) for Category 4 projections. The material already

provided in chapter 4 is supplemented by figure 78, which shows the Annual Traffic Data

Projection Sheet, and by figure 79 and table 38, which show the predicted truck volumes.

As part of the sensitivity analysis, the effect of using two alternative base truck volume

distributions was evaluated for this site. Both distributions are for rural Interstate sites, one in

Arizona and the other in Illinois. As shown in figure 80, both distributions are very similar.

However, the Arizona distribution has a higher proportion of Class 11 and 12 trucks, which is

translated into somewhat larger ESALs obtained for the Arizona alternative (table 39 and figure

81). It should be noted that the same axle load spectra were used for the calculation of base

annual load spectra and, thus, also for the calculation of ESALs for both alternatives. Overall,

the projected ESALs are less than half of the historical ESALs.

In summary, Category 4 projections require judicious selection of both base truck volume

distributions and base axle load spectra for individual truck classes. The base axle load spectra

used for Category 3 and 4 predictions have an overall ratio of ESALs per truck of less than 0.5

(figure 31). Thus, the use of this spectra has resulted in lower-than-expected loads.

EFFECT OF QUANTITY OF MONITORING DATA

Site 29-4036 was used to assess the effect of the amount of monitoring data on the

precision of the protected traffic loads. This site was already used as a case study to illustrate

Category 1 projections. The available AVC and WIM monitoring data for Site 29-4036 were

progressively reduced to fit Category 3 and 4. The reduced data were then used to carry out

.Category 3 and 4 projections for this site.

It is important to note that this work should be viewed as a proof-of-concept study done

within the framework of the traffic projection methodology developed in this report. Much more

realistic and comprehensive assessment of the influence of the amount of monitoring data on

traffic loads is described in reference 6.

130
Single Axle ................................................................................................................................................................................................................................................................................................................................................................................... 180000

160000

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120000

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Figure 77. Base load spectra for Site 04-1002.

131
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• -0-- AADT volume, all vehicles

... -fr-- AADT volume, all trucks

• --0- Average ESALs per day

• -<>--- Average ESALs per truck

Figure 78. Annual Projection Data Sheet for Site 17-5423.

132
-t--------------~------------------ilI

!

2500 ·.".·.w.·.·.·.·.·.·.·.·.·.·,.w.·.·.·.·.·.·.·.·.·.·.·.·.·NN.·.W.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.•...•....········································.·.···.·.·.·.w.•.w.•.•.•.•.•.•.•.•.•.·.•.·.·.·.·.·.·.·.·. mmm__mm_~-l

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--.- Monitoring Truck Volume -ir-Historical Truck Volume

---Projected Truck Volume (4.1 % growth)

Figure 79. Comparison of truck AADT volumes for Site 17-5423.

Table 38. Comparison of truck AADT volumes for Site 17-5423.

Growth Projected Truck

Year Factor, 4.1 % Volume, 4.1 % Growth Historical Truck Volume

1981 0.60 1169 1242

1982 0.63 1217 1242

1983 0.65 1265 1233

1984 0.68 1312 1260

1985 0.70 1360 1274

1986 0.73 1408 1368

1987 0.75 1456 1454

1988 0.78 1504 1530

1989 0.80 1552 1620

1990 0.83 1600 1710

1991 0.85 1647 1650

1992 0.88 1695 1620

1993 0.90 1743 1733

1994 0.93 1791

1995 0.95 1839

1996 0.98 1887

1997 1.00 1934

133
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2 3 4 5 6 7 8 9 10 11 12 13 14 15

Vehicle Class

Figure 80. Comparison of truck distribution curves for Arizona and Illinois rural Interstates.

134
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2000

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Year

;]-Historical ESALs per Day

Projected ESALs per Day-ILLINOIS

--tf-Projected ESALs per Day-ARIZONA

Figure 81. Comparison of projected and historical ESALs for Site 17-5423.

Table 39. Cumulative ESALs computation sheet for Site 17-5423.

Historical ESALs Projected ESALs per Projected ESALs per

Year per Day Day-Arizona Day-Illinois

1981 811 636 594

1982 1619 662 619

1983 1666 689 643

1984 1704 715 667

1985 1721 741 692

1986 1849 767 716

1987 1964 793 740

1988 2068 819 765

1989 2189 845 789

1990 2310 871 813

1991 2230 897 838

1992 2189 923 862

1993 2340 949 886

1994 975 911

1995 1001 935

1996 1027 959

1997 1053 984

Cumulative ESALs for Arizona =5,242,121

Cumulative ESALs for Illinois =4,895,220

135
The results of Category 1 predictions for Site 29-4036 are summarized in tables 21 and

22 and figures 36 through 42. Figure 82 shows the projection of truck volumes using only

historical truck volumes, as would be expected for Category 4 predictions. Table 40 provides a

comparison of truck volumes projected for Categories 1, 3, and 4. Figures 83 and 84 compare

the projected annual axle load spectra for 1983 and 1995, respectively. The results, in terms of

cumulative ESALs, are given in table 41 and figure 85. The ESALs for Category 3 and 4

projections are less than half of those for Category 1 projections. However, as discussed

previously, this was caused by the "light" base load spectra used for Category 3 and 4

projections.

BRIEF SUMMARY

The following observations are based on the 12 case studies described in this chapter:

• The proposed methodology can be used to project annual axle load spectra for all

LTPP sites.

• The involvement of SHAs in the predictive process is crucial. Many data

problems cannot be resolved without local involvement.

• Base axle load spectra for individual trucks, and base truck volume distrihution,

required for Category 3 and 4 projections, must be selected judiciously, and this

selection must be supported by a sound, well-organized, and well-documented

knowledge base. Therefore, the development of an LTPP Pavement Loading

Guide or a catalog is proposed.

• Sensitivity analysis can be used to quantify the effect of key assumptions made

during the prediction process.

• The majority of the data processing and engineering effort involved in the

projection process was spent on quality assurance issues.

• The quality assurance process would greatly benefit from developing a knowledge

base or a catalog documenting values and ranges of traffic variables. The process

would also benefit from the availability of summary measures for traffic loads that

are independent of pavement variables.

• Several data inconsistencies exist in the CTDB. For example, data in several

traffic monitoring tables are conflicting or missing; calculated ESALs reported in

the IMS appeared to conflict with ESALs calculated in the course of conducting

the case study for Site 29-5483.

136
2500

2000 Ql

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Year

-.-Projected Truck Volume

Figure 82. Historical and projected truck AADT volumes for Site 29-4036 for Category 4.

Table 40. Monitored and historical truck AADT volumes for Site 29-4036 for

Categories 1,3, and 4.

Monitored Truck Historical Truck Category 1 & 3 Truck Category 4 Truck

Year AADT AADT Volume Volume

1983 242 289 168

1984 266 330 305

1985 295 375 442

1986 535 428 579

1987 932 487 715

1988 954 555 852

1989 848 632 989

1990 826 720 1126

1991 831 820 1263

1992 868 934 1399

1993 919 1064 1536

1994 1430 1212 1673

1995 1317 1380 1810

1996 1700 1572 1947

1997 1856 1791 2084

137
Single Axle

....................-............•...•.........•.....•.•.•.•'•.•.•.........•..................-.- -....•.•.......•....-.-....•.•.•.•.•.•.....•...•.•.•.•.•.•........- -.- -......•.•.•.•....-..•.•.•.•........- -.- -.-..............•......-..•...•.......•..-.- :.

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Tandem Axle

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Axle Loads, Ib

Tridem Axle

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Axle Loads, Ib

........- Category 1 --0- Category 4

1 lb =0.00445 leN

Figure 83. Comparison of 1983 load spectra for Site 29-4036.

138
Axle Counts

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N M m 00 m 0 N M m 00

00 00 00 00 00 00 00 00 m m m m m m m m

m m m m m m m m m m m m m m m m VI

Year

_ Monitoring ESALs per Day

Category 1 ESALs per Day

_ Category 4 ESALs per Day

-C-Historical ESALs per Day

Category 3 ESALs per Day

Figure 85. Comparison of ESAL volumes for Site 29-4036.

Table 41. Comparison of ESAL volumes for Site 29-4036.

Monitoring ESALs Historical ESALs Category 1 Category 3 Category 4

Year per Day per Day ESALs per Day ESALs per Day ESALs per Day

1983 570 449 196 91

1984 625 511 223 165

1985 693 582 254 240

1986 1258 663 290 314

1987 2192 756 330 388

1988 2244 861 376 462

1989 1995 980 428 536

1990 1117 487 611

1991 1272 555 685

1992 1824 632 759

1993 1698 720 833

1994 2102 821 908

1995 2603 935 982

1996 2109 1065 1056

1997 2203 1213 1130

Note: Categories 1 and 3 have the same truck volume growth curve, but different base spectra.

Cumulative ESALs for Category 1 =7,201,762

Cumulative ESALs for Category 3 =3,111,247

Cumulative ESALs for Category 4 =3,343,464

140
CHAPTER 6. STATISTICAL MEASURES AND TOOLS FOR

MANAGING TRAFFIC LOADS

THE NEED FOR SUMMARY TRAFFIC LOAD STATISTICS

Pavement traffic loads consist of a large number of car and truck axles passing over the

pavement. Current vehicle monitoring equipment, such as WIM scales, can measure and record

axle weights and axle spacings for the majority of truck axles that pass over the pavement during

its lifespan and can produce millions of records. Consequently, the management of traffic loads,

including quality assurance, data storage, routine use of traffic load data, and forecasting of

traffic loads, requires the use of summary statistical measures. Traditionally, a summary

measure used was the number of ESALs as defined in the AASHTO Guide for the Design of

Pavement Structures [18].

It can be argued that, because we are moving toward mechanistic pavement design, we do

not need ESALs, but rather load spectra. The argument presented here is not against using load

spectra, but rather for the development of summary measures to facilitate working with load

spectra, and to support the use of spectra by providing tools for summarizing and comparing

them.

Working with load spectra is not an easy task. Considering the existence ofjust 3 axle

configurations (single, tandem, and tridem) and about 30 weight categories for each axle

configuration, the resulting load spectrum is represented by a matrix with 4 columns (a loadrange column and 3 axle configurations) and 30 rows (for the 30 weight categories). The 30 x 4

load spectrum matrix is difficult to assess in terms of overall size. Just for quality assurance

purposes, we need to summarize load spectra to ascertain the overall dimensions of the traffic

load. It should also be realized that the effect of traffic loads on pavement damage increases

exponentially with the size of the load. Consequently, conventional statistical measures, such as

a mean or a variance, are not related to the influence that these measures have on load-associated

pavement damage. For example, two spectra with the same mean may have a different effect on

pavement damage.

The AASHTO Guide mentioned above defines axle load equivalency factors (LEF),

which can be used to obtain ESALs. A LEF can be viewed as a pavement damage factor (or

index) assigned to each specific axle load. The size of the index is related to a standard load of

80 kN (18,000 lb). When the specific load occurs, its corresponding index materializes and

results in ESALs. In other words, the relationship between LEF and ESALs is similar to that

between a price list and a shopping bill. For example, if a single axle weighing 89 kN (20,000

lb) has a LEF of 1.5, the passage of 10 such axles (each weighing 89 kN [20,000 IbD results in

ESALs equal to 15.0. By repeating this procedure for other axle loads and summing the results,

it is possible to convert millions of different axle loads into one number-the number of ESALs.

Over the years, several different authors and agencies have proposed and used many

different LEFs. Even though the definition of LEF differs, the vast majority of the definitions

are based on a rational engineering basis. The most widely used LEFs are the AASHTO factors,

which depend on the following variables:

141
• Pavement type (flexible or rigid).

• Pavement thickness (structural number for flexible pavements, slab thickness for

rigid pavements).

• Axle configuration (single, tandem, or tridem). No LEFs are provided by

AASHTO for quadruple or higher axle groups.

• Pavement serviceability, initial serviceability, and serviceability at the end of the

pavement's lifespan in terms of present serviceability index.

Because ESALs based on AASHTO LEFs depend on pavement type, thickness, and

pavement serviceability, the comparison of traffic loads for different sections based on ESALs

(e.g., in terms of ESALs per truck or the total number of ESALs per year) is only approximate

and may lead to misunderstanding. For example, ESALs can change even though the traffic

stream does not change (e.g., between two intersections) just because of the change in the

pavement type or because the pavement was rehabilitated. The main practical advantage of

ESALs is their well-recognized relationship to pavement damage.

There is a need for a traffic load statistic (such as ESALs) that is independent of

pavement variables in the same way that AADT volumes are. Such a statistic would be

particularly useful for the management of traffic loads for the LTPP experiment because LTPP

sites encompass pavements of different types and thicknesses, and there is a need for a common

benchmark to evaluate, assess, and compare traffic loads across all sites.

To illustrate the advantages of developing and using LEFs that are independent of

pavement type and thickness, figure 86 is presented [19]. This figure shows average AASHTO

ESALs for five-axle tractor-semitrailers (vehicle class 9) on rural Interstates in several States.

The data suggest that there are considerable differences in ESALs for these vehicles between

States. However, it is uncertain to what degree these differences can also be attributed to the

influence of pavement-related variables (pavement type, thickness, and serviceability).

The influence of some of the pavement-related variables is illustrated using data taken

from reference 20 and presented in table 42. Data in table 42 show the variations in the average

AASHTO ESALs for five-axle tractor-semitrailers based on WIM data from Florida. Comparing

these variations with the ESAL variations given in figure 86, it appears that the variations in

ESALs per truck reported for different States are roughly equivalent to that for different

pavements.

Table 42. Average ESALs per FHWA vehicle class 9 (predominantly five-axle

tractor-semitrailer), urban Interstates.

Pavement Type Flexible Pavement Rigid Pavement

Thickness SN=3 SN=5 D=9 D= 12

ESALs/Class 9 1.05 0.99 1.53 1.62

Notes: SN = Structural number (a number related to the thickness of pavement layers and their materials).

D = Thickness of the PCC slab in inches.

142
MD NY PA NJ OH

Figure 86. Number of average ESALs per five-axle tractor-semitrailer on rural Interstates.

A typical variation in ESALs due to pavement-related variables, expressed as the

percentage of the ESAL value, is shown in table 43.

Table 43. Influence of pavement-related variables on AASHTO LEFs.

Percent Change in LEFs

Parameter

Flexible Pavement Rigid Pavement

Initial Serviceability ± 2 to 4% less than 1%

SNorD ± 5 to 10% ±5%

Terminal Serviceability 10 to 20% 4 to 8%

Pavement Type 0% for single axles, 40% for tandem axles, and 140% for tridem

axles

Note: Some of the entries in the table are based on reference 21. LEFs also depend on pavement distress used to

define serviceability. For AASHTO LEFs, serviceability is defined mainly in terms of roughness.

Because different pavement-related variables could have been used for the calculation of

ESALs presented in figure 86, it is not possible to attribute the differences in ESALs between the

States to traffic conditions only. This is a significant loss of information because, for quality

assurance and forecasting purposes, the knowledge of variation in the average ESALs for

different truck types is useful.

What is required is a summary traffic load statistic that: (1) is independent of pavement

variables, (2) is related to pavement damage, and (3) has a familiar meaning and magnitude.

With these needs in mind, two statistical measures are proposed in this chapter:

143
• Load Spectra Coefficients (LSCs) to summarize individual load spectra.

• General LEFs to summarize the load spectra of traffic streams.

LOAD SPECTRA COEFFICIENTS

The need to compare, assess, or evaluate axle load spectra is well recognized. Several

approaches have been proposed to do so, ranging from plotting the load distributions to using

non-parametric statistical tests. Some of these approaches are discussed below.

In addition to plotting the spectra, values of load spectra at different percentile levels can

also be calculated and compared. The Interim Report [5] describes the use of sigmoidal curve

parameters to compare spectra. When the cumulative percentage of the number of axles within

each weight group is plotted against the mid-range of the weight group, an S-shaped (sigmoidal)

curve is obtained, as illustrated in figures 7 through 9 (chapter 2). Statistically, this relationship

is called a cumulative density function. A sigmoidal curve is fitted to the data using non-linear

regression, providing two parameters that characterize the load spectrum. The sigmoidal curve is

of the form:

N = 100 * e -(;r

where: N = Cumulative percentage of the number of axles.

e = Number such as that in e = 1.

p,p = Curve parameters.

w = Mid-value of load range.

(6)

Each cumulative density function can be characterized by parameters p and p. The two

parameters are then evaluated to observe whether there are patterns in their change over time. It

was observed that these parameters provide a convenient way of describing the percentage

breakdown of the load spectra over time. This technique was used to assess load spectra in the

Interim Report [5]. However, a uniform increase in the spectra (or the uniform growth in the

number of axles) cannot be examined by using the two parameters.

Kim et al. [8] used the Kruskal-Wallis test to evaluate whether the differences in the axle

load distributions (single, tandem, and tridem axle distributions obtained as Statewide averages

for LTPP sections in the North Central Region) were statistically significant. Kim et al. also

used polynomial forms of regression equations to model the cumulative distribution of axle

loads.

Cunagin and Goff [12] used the Kolmogorov-Smirnov test to compare cumulative axle

load distribution functions obtained for several non-LTPP sites. They concluded that even the

sites that had similarly shaped gross vehicle weight distributions had significantly different load

spectra, in part due to the large data samples provided by WIM scales. This result highlights the

problem of comparing load spectra using parameters that are not related to pavement

performance prediction or pavement damage. For example, a statistically significant difference

144
between two cumulative load distributions ascertained using the Kolmogorov-Smimov test (or

the t-test comparing 85th-percentile differences between the two spectra) does not mean that the

two spectra have a significantly, or even practically, different influence on pavement

performance. It should be stressed that the tests of statistical significance depend largely on the

number of observations. Given high traffic volumes and the capabilities of WIM scales, the

number of observations available for the comparisons can be quite large, making the statistical

assertions of significance relatively frequent-but without any practical significance, such as the

linkage to pavement performance.

In conclusion, we need statistical measures that are related to the concept of pavement

damage and are independent of pavement-related variables. The LSC, introduced in chapter 4,

can fulfill this need. The LSCs for normalized load spectra were defined by equation 7:

LSC

~:l [( mw-toa: range,)] 3.8 ,

L 1-1 18000 ,

load-range count;

* L total count

(7)

load-range counti =

L =

where: LSC

I

mid-load range i

= Load spectrum coefficient used to compare normalized load

spectra.

= Number of load ranges (the numbers may differ for single, tandem,

and tridem axles).

= Average load range in pounds for load range i, e.g., if the load

range is 6,000 to 6,999, use 6,500.

Number of axles in load range i.

1 for single axles, 2 for tandem, and 3 for tridem. (L converts the

effect of tandem and tridem axles into that for single axles.)

Before discussing the features of the LSC, it is useful to define a companion summary

measure-a general axle load equivalency factor-and then discuss the two measures together.

GENERAL AXLE LOAD EQUIVALENCY FACTORS

The general axle load equivalency factors, or general axle factors (GAF), are defined by

equation 8:

where: W

(

W ) 3.8 GAF = 18,000

= Axle load of any type or spacing in pounds.

(8)

GAFs have a similar meaning and roughly the same magnitude as the AASHTO LEFs.

In fact, GAPs are virtually identical to AASHTO LEFs for single-axle loads for flexible

pavements with the structural number equal to 5 and the terminal serviceability index equal to

2.5. This is illustrated in figure 87, which shows that the biggest difference between the

145
AASHTO LEF and the GAF for the practical range of single-axle loads (4 to 160 kN [1,000 to

36,000 IbD is about 0.05, or about 2 percent of the LEF value.

0.06

u.. 0.05

"0

c: 0.04 CIl

u..

w 0.03 ...J

0

I- 0.02

:r:

en

0.01

c:

j 0.00

CIl

al -0.01

8

c: -0.02

l!!

:! -0.03 is

! \

j \ ! \

j \ -"..- •

,

/ \ /

JI' \ /

\J

-0.04

o 5 10 15 20 25 30 35 40

Single-axle load, in 1000 Ib

I lb = 0.00445 kN

Figure 87. Comparison of differences between AASHTO LEF and GAF for single axles.

The sum of the GAFs for all axles of a truck equals a general truck factor (GTF). If the

GTF were used in figure 86 (instead of the AASHTO-based truck factors), the differences

between States could have been attributed directly to loading conditions. The use of the GAF for

the calculation of the ESAL will yield the general ESAL (GESAL).

Like LSCs and GAFs, GESALs are also independent of pavement type and thickness,

level and type of pavement distress, and axle type. Any changes in LSCs and GAFs can be

attributed directly to changes in traffic loads.

LSCs and GAFs have the same exponent of 3.8. This results in a unique relationship

between LSCs and GAFs. For example, the following two calculations will yield identical

GESALs for an axle load spectrum:

Calculation 1 Using LSC:

• Transform the load spectrum into a normalized spectrum (by expressing the actual

axle counts as percentages of the total counts) and the corresponding total number

of axles. Note that a load spectrum is equal to its normalized form multiplied by

the total number of axles.

• Calculate the LSC for the normalized spectrum (using equation 7).

146
• Multiply the LSC by the corresponding number of axles to obtain the GESAL for

the spectrum.

Calculation 2 Using GAF (this calculation is identical to the ESAL calculation):

• Calculate the GAF for each load range of the axle load spectrum using equation 8.

• Multiply the GAF obtained for each load range by the number of axles for the

load range.

• Sum the results obtained for each load range to obtain the GESAL for the

spectrum.

In summary, the management of traffic loads can be greatly simplified using the proposed

LSCs and GAFs. The simplification would be particularly useful for quality assurance activities

and for projecting traffic loads for LTPP sections. Some of the consequences of using GAF for

pavement design are described in reference 22.

LTPP TRAFFIC LOADING GUIDE

As documented extensively in chapters 4 and 5, many traffic projection activities, such as

decisions regarding the acceptance of data (as part of quality assurance) and the selection of

traffic growth factors, require judgment. In particular, base axle load spectra for individual

trucks and base truck volume distribution, required for Category 3 and 4 projections, must be

selected judiciously. However, in the absence of guidelines for what is acceptable or expected,

and without the support and reliance on a well-organized and well-documented knowledge base,

the exercise ofjudgment is difficult. The lack of benchmark values can be overcome by the

development of a catalog of benchmark values of normalized load spectra, truck factors, vehicletype distributions, and so on.

Such a catalog or guide can be produced through an iterative process during the course of

Phase 2 work. As sites are assessed, applicable information can be assessed and synthesized for

inclusion into the Traffic Loading Guide. As work progresses, the collected information will

start to fulfill its quality assurance and traffic projection guiding roles. Subsequently, data can be

refined, presented in a user-friendly format, and supplemented by instructions for use.

A Pavement Traffic Loading Guide should include information on the following factors:

• Typical truck volume distributions for common highway functional types.

• Typical normalized axle load spectra (for vehicle classes 4 through 13) for

common highway function types. The normalized axle load spectra should be

accompanied by the corresponding number of axles and by the LSC.

• Typical ESALs (preferably GESALs) for the combination of vehicle classes and

functional types.

147
• Typical traffic load spectra, and the corresponding LSC, for common highway

functional types.

Table 44. Factors considered for inclusion in the proposed LTPP Traffic Loading Guide.

FHWA FHWA Vehicle Classes 4 Through 13 Classes 4 Through 13

Highway Combined

Functional Class 4 Classes 5, 6, 7 -7 13

Type

• Normalized Axle Load -7 • Normalized Truck

Spectra Volume Distribution

Rural • Axle per Vehicle Class -7 Same for the • Normalized Annual

Interstates • Load Spectra -7 other vehicle Load Spectra

Coefficients classes

• Typical ESALITruck -7

• •

• • Repeat for other functional types

• •

• • •

• • •

Urban • • •

Collector • • •

• • •

Even though the Guide has been conceived as an internal LTPP traffic projection tool,

and to a certain degree as a byproduct and the synthesis of traffic projections that will be done in

the course of Phase 2, it has the potential to become a useful product for pavement design

engineers.

148
CHAPTER 7. COMPUTATION AND STORAGE OF

PROJECTED TRAFFIC DATA

The projection of traffic loads requires a systematic approach to the process of analysis,

computation, and storage of the projected data. This chapter describes computational procedures

developed to generate projected annual axle load spectra for all in-service years of LTPP

sections, and outlines how the projected data will be archived within the IMS Traffic Module.

In addition to generating the projected annual axle load spectra, the projection process

will also yield a set of intermediate variables that are necessary to compute the projected load

spectra. It is proposed that both the projected annual axle load spectra and the intermediate

variables are stored in the IMS Traffic Module.

Figure 88 presents an overview of the existing IMS Traffic Module, consisting of

historical and monitoring traffic data, and the proposed addition of the projected traffic data to

the Traffic Module. Figure 88 also shows that the projected data will consist of the projected

axle load spectra and the intermediate data elements. The intermediate data elements carry

important information documenting how the projected axle load spectra were calculated. In

addition to the structured traffic data elements stored in the IMS, additional site-specific

documentation summarizing supporting data and underlying assumptions will be organized and

maintained in separate files. The intermediate data elements and the additional site-specific

documentation will enable future auditing of the projection process and/or the enhancement of

the projections, e.g., if additional data become available.

IMS Traffic Module

1rr--------------------------------'+r---------------------------~

Historical Data Monitoring Data Projected Data (Proposed)

Major Data Elements:

Total Volume

ESALs

Major Data Elements:

Axle Load Spectra

Volume bv Class

Total Volume

ESALs

..............................

Projected Annual

Axle Load Spectra

Intermediate

Data Elements

Figure 88. Overview of the IMS Traffic Module showing the proposed

addition of projected data.

149
As a result of the projection methodology that was developed in chapter 4 and applied to

case studies in chapter 5, a set of computed parameters tables was created to guide the

computation and storage of the projected axle load spectra data and the intermediate supporting

traffic projection data. This chapter also contains the description of the computed parameters

tables and the relationships between the variables in the tables. The following list represents a

set of the proposed computed parameters tables.

• Main Table:

- Projected Annual Axle Load Spectra

• Intermediate Tables:

- Annual Growth Factors

- Base Annual Axle Load Spectra

- Base Annual Axle Load Spectra by Vehicle Class

- Base Vehicle Distribution by Class

- Axle per Vehicle Class Factors

Depending on the availability of monthly monitoring data in the IMS database, an

additional intermediate table containing monthly distribution factors will be included in the set of

the computed parameters tables. Based on the availability and the amount of monitoring and

historical data, four different methods were developed and used to calculate projected load

spectra, as described in chapter 3. The summary of traffic data requirements for each projection

category is presented in table 45.

Table 45. Description of the projected categories.

Cate20ry No. Cate20ry Description

Projection method is based on site-specific truck volume (monitoring and

1 historical) and site-specific truck weight distribution (monitoring) data

available on a monthly basis.

Projection method is based on site-specific truck volume (monitoring and

2 historical) and site-specific truck weight distribution (monitoring) data

available on an annual basis.

Projection method is based on site-specific truck volume distribution by

3 vehicle class (monitoring and historical) and regional truck weight

distribution data.

Projection method is based on site-specific total truck volume (AADT) data,

4 regional truck volume distribution data by vehicle class, and regional truck

weight distribution data.

Figure 89 shows the proposed computed parameters tables and the hierarchy among the

tables. The basic computed parameters table is shown by heavy borderlines. A description of

the tables, as well as a description of the computational procedures required to obtain the tables

follows, and is structured under the following headings:

150
• Computation of Annual Growth Factors.

• Computation of Base Annual Axle Load Spectra.

• Computation of Base Annual Axle Load Spectra by Vehicle Class.

• Computation of Base Vehicle Distribution by Class.

• Computation of Axle per Vehicle Class Factors.

• Computation of Monthly Distribution Factors.

• Computation of Projected Annual Axle Load Spectra.

• Summary of the Traffic Projection Process.

Proposed Projected Traffic Tables

Main {

Table

Intermediate

Tables

Projected Annual Axle Load Spectra

Annual Axle Growth Factors

Base Annual Axle Load Spectra

Base Axle Load Spectra by Class

Base Vehicle Distribution by Vehicle Class

Axle per Vehicle Class Factors I " .

Monthly Distribution Factors I}

Applicable

for all

projection

categories

Specific for

projection

categories

3&4

Category

lonlv

Figure 89. Summary of the proposed computed parameters tables.

COMPUTATION OF ANNUAL GROWTH FACTORS

The Annual Growth Factors table contains annual traffic growth factors calculated for

each in-service year for the LTPP sites included in the table entitled, Projected Annual Axle

Load Spectra. These factors are used to compute projected annual axle load spectra.

Annual traffic growth factors are determined as a result of a comprehensive analysis of all

available site-specific monitoring and historical traffic data as outlined in chapter 4. The

computational procedure for the annual growth factors is shown in figure 90.

151
/ IMS Traffic Module 7' ..............................................................................................................!' ,

I Monitoring Traffic Volume Distribution I

Historical Traffic Data Monitoring Traffic Data

I Monitoring Axle Load Distribution I .................................. .........................................................................;..........··....................................·....·..··................·i·..........·····......................... .,r .,

Extract site-specific historical Extract site-specific monitoring Compute site-specific monitoring AADT

AADT volume and ESAL AADT volume and ESAL volume and ESAL information if IMS traffic

information information monitoring summary table is empty

I

,r

I Analyze monitoring and historical traffic data and develop traffic projection model I

.-

I Compute annual traffic projection coefficients calculated for each in-service year I

+

Annual Axle Growth Factors C

Figure 90. Flowchart for computation of the annual traffic growth factors.

COMPUTATION OF BASE ANNUAL AXLE LOAD SPECTRA

The table Base Annual Axle Load Spectra includes computed annual base load spectra

for single, tandem, tridem, and quadruple axle groups. The base annual load spectrum is a

typical load spectrum for each site that is included in the table Projected Annual Axle Load

Spectra. The base annuallo~d spectra are developed using guidelines given in chapter 4.

For the sites that fall into traffic projection Categories 1 and 2, the base axle load spectra

are computed using available monitoring annual axle load spectra from the IMS traffic module.

For the sites that fall into traffic projection Categories 3 and 4, the base spectra are computed

using class-specific normalized regional base spectra from the new proposed table Base Annual

Axle Load Spectra by Vehicle Class. In addition, knowledge of the base vehicle class

distribution (from the table Base Vehicle Distribution by Class) and axles per truck coefficients

(from the table Axles per Vehicle Class Factors) is required for the sites in traffic projection

Categories 3 and 4. The computational procedure for the base annual axle load spectra is shown

in figure 91.

152
r··-------·--···--------------·--·---·--·--·········· - _ __ _ _.. s.~!~~&~J_~.f.-l ,

i .

i I IMS Traffic Module .

r--------...l....-:~~~~~~~~~~~~:.1- ___.1

Traffic Monitoring Summary Monitoring Axle Load Distribution !

L.....-------r-----_---I !

Get number of days used to estimate

annual load spectra

Obtain annual load spectra for all

monitoring years

Select base spectra based on number of days used to estimate annual load spectra

and through comparison of the available annual load spectra for different years

Base Annual Axle Load Spectra

Categories 3 & 4

Axle per Vehicle Class Base Vehicle Base Annual Axle Load

Factors Distribution by Class Spectra by Class

J

Annual Number of Axles by Class = NormaIized Regional

Axles per Truck * Base Vehicle Volume by Class Base Spectra

Base Number

•

of Axles (all truck classes combined) = L(Norrnalized Number of Axles by Class * Annual Number of Axles by Class)

Base Annual Axle Load Spectra C

.................................................................................................................................................................................................................................................· · 1

I

!

i

I

I

Figure 91. Flowchart for computation of the base annual axle load spectra.

COMPUTATION OF BASE ANNUAL AXLE LOAD SPECTRA BY VEHICLE CLASS

The Base Annual Axle Load Spectra by Vehicle Class table contains computed

normalized and vehicle-class-specific base annual load spectra for LTPP sites falling under the

requirements for traffic projection Categories 3 and 4. The normalized spectra are given for 10

FHWA vehicle classes (vehicle classes 4 through 13), and for each class, the spectra are given

separately for single, tandem, tridem, and quadruple axle groups. The normalized vehicle-class153
specific base spectra are computed using the vehicle-class-specific monitoring spectra for the

sites that represent similar functional highway types and that are located in similar geographical

regions as outlined in chapter 4. The data source for this table is vehicle-class-specific load

spectra extracted from Level 2 LTPP CTDB data. The computational procedure for the

normalized class-specific base spectra is shown in figure 92.

CTDB Level 2 Data for Category 1 & 2 Sites

Obtain site-specific annual axle distribution by vehicle class by year for Category I & 2 Sites

Develop regional base annual axle distribution by vehicle class for Category 3 & 4 Sites

Normalize regional base annual axle distribution by vehicle class for Category 3 & 4 sites

Base Annual Axle Load Spectra by Vehicle Class

Figure 92. Flowchart for computation of the base annual load spectra by vehicle class.

COMPUTATION OF BASE VEHICLE DISTRIBUTION BY CLASS

The Base Vehicle Distribution by Class table contains base annual vehicle type

distributions that were developed for the sections falling under the requirements for traffic

projection Categories 3 and 4. For Category 3 sites, base annual vehicle type distributions are

computed from site-specific monitoring annual vehicle type distributions stored in the IMS

traffic module. For Category 4 sites, base annual vehicle distributions are computed using: (1)

normalized monitoring annual vehicle type distributions for the sites that represent similar

functional highway types and that are located in a similar geographical region, and (2) sitespecific AADT truck values from the IMS traffic module. The computational procedure for the

base annual vehicle type distributions is shown in figure 93.

154
Catel!:orv 3

/

Level 2 CTDB or /

IMS traffic module .<::....------.------'" •

Catel!:OfV 4

IMS traffic module ./

Site-specific annual vehicle type distributions by year

Selected regional base annual vehicle type distribution

Normalized regional base annual vehicle type distribution

for Category 4 sites

I Site-specific AADT values for the base year I

Category 4

Base Vehicle Volume Distribution = Site-specific AADT * Normalized regional base annual vehicle type distribution I

Category 3

Base Vehicle Distribution by Class C

Figure 93. Flowchart for computation of the base annual vehicle distribution by class.

COMPUTATION OF AXLE PER VEHICLE CLASS FACTORS

The Axle per Vehicle Class Factors table contains a representative number of axles per

vehicle for FHWA Classes 4 through 13 (trucks) for the sites in traffic projection Categories 3

and 4. This table is used in conjunction with Base Vehicle Distribution by Class and Base

Annual Axle Load Spectra by Class tables to compute annual base spectra for the sites in the

traffic projection Categories 3 and 4. The main data source for this table is class-specific axle

counts and estimated vehicle counts extracted from Level 2 LTPP CTDB data. The

computational procedure for the axles per truck factors is shown in figure 94.

155
/ CTDB Level 2 Data /

I

.. I Extract estimated annual number of vehicles by class

•

I Extract number of axles in each weight range and for

each axle type by class

Obtain total number of axles

•

for each axle type by class

I

• Categories 3 & 4

Axles per Truck = Total number of axles for each axle type by class

Estimated annual number of vehicles by class

,l,

Axle per Vehicle Class Factors (

Figure 94. Flowchart for computation of the axle-per-truck factors.

COMPUTATION OF MONTHLY DISTRIBUTION FACTORS

Depending on the availability of the monthly monitoring data, the Monthly Distribution

Factors table will be created. The proposed table contains a set of 12 representative monthly

distribution factors, one for each month of the year. The monthly distribution factors are based

on the average monthly ESAL distribution created using monthly monitoring data for all

available monitoring years, as outlined in chapter 4. The monthly distribution factors are

expressed as a percentage of the annual ESALs. These percentages serve as indicators of

monthly fluctuation of the annual load spectra for all in-service years. For example, January load

spectra may be 8 percent of the annual load spectra in terms of axle counts (in all individual load

ranges), and July load spectra may be 13 percent of the annual spectra. Thus, the monthly

distribution factors are used to compute monthly load spectra. The monthly load spectra are

computed by multiplying the projected annual axle load spectra (from the Projected Annual

Axle Load Spectra table) by the monthly distribution factors. Computed monthly load spectra

could be further summed over the selected years to obtain cumulative monthly spectra for a

desired time period.

COMPUTATION OF PROJECTED ANNUAL AXLE LOAD SPECTRA

The table Projected Annual Axle Load Spectra contains projected annual axle load

distributions (load spectra) for single, tandem, tridem, and quadruple axle groups for LTPP

156
sections computed for each in-service year. The projected annual load spectrum for each year is

computed by multiplying the base annual load spectrum (from the table Projected Annual Axle

Load Spectra) by the annual traffic projection factors (from the table Annual Axle Growth

Factors). The summation of axle counts from the Projected Annual Axle Load Spectra table

over a specified period of time yields cumulative axle loading for a given LTPP section. The

computational procedure for the projected annual axle load spectra is shown in figure 95.

Base Annual Axle Load Spectra Annual Axle Growth Factors

Projected number of axles =Base number of axles * Annual traffic projection factors

Projected Annual Axle Load Spectra

Figure 95. Flowchart for computation of projected annual axle load spectra.

SUMMARY OF TRAFFIC PROJECTION PROCESS

This section presents an overall summary of the traffic projection process, as illustrated in

figure 95. This flowchart shows the relationships between the existing IMS Traffic Module and

proposed computed parameters tables, and the way in which the tables are derived. The basic

computed parameters table is highlighted by heavy border lines. Currently, there is no provision

for the tables containing data elements shown in oval shapes (cumulative axle loads by year and

by month). However, these tables can be easily computed from the projected axle load spectra.

157
IMS Traffic Module

................................................................~!.!.s:~~~¥.~.~~~ ......\

II . Hi~torical AADT. ESALs II . i

I. ~=:J\i:l:o:ni:to:n:·n:g:A:A=D:T:. E:S:A:Ls==~

Monitoring Vehicle Distribution i I..- ....J

j Monitoring Axle Load DistributiO'D

t u~ u «•••n ••u u._ _ .i

Annnal Axle ProjectionJ.'actors

Level 2 CTDB Data

Clltegories 3 & 4 only

rr....··..···::;:·~:;··~:~~;~~ ·~~:~:..;::~:;:· ....·....··i·l

I.. Base Annua) Axle Load Spectra ..I . by Vehicle Class .

Base Vehicle Distribution by Class

Base Annual Axle Load Spectra

Projected Annual Axle Load Spectra

Monthly Distribution

Factors

Category I Only

Figure 96. Overall outline of traffic projection process.

158
CHAPTER 8. SUMMARY AND RECOMMENDATIONS

SUMMARY

Knowledge of cumulative truck axle loads is a crucial part of the pavement performance

analysis process. In order to obtain traffic loads for the entire in-service life of the LTPP

pavement sections, monitoring axle loading data available in the LTPP database for a limited

number of years have to be projected to cover the whole pavement service life. The process of

traffic loading projection is not simple and requires considerable knowledge of traffic data and

expertise. The loads should be projected in a systematic manner using a uniform approach. To

unify the process of traffic projection, a comprehensive traffic load spectra projection

methodology, applicable to all the LTPP sites, was developed and tested on case studies.

Projected load spectra and intermediate variables computed using this methodology are proposed

to be included in the IMS database.

This report describes a methodology developed for projecting axle load spectra for LTPP

sections. An axle load spectrum is defined as the distribution of single, tandem, and tridem axles

into load ranges. Typically, there are only a few years for which axle load spectra were collected

for LTPP sections using automated equipment-AVe and WIM. Data collected by automated

equipment are called monitoring data; data estimated by the SHAs before the installation of the

automated equipment are called historical data.

To obtain axle load spectra for all in-service years of LTPP sections, the traffic loads

have to be backcasted (for the years before the installation of traffic monitoring equipment) as

well as forecasted (for the years after the automated equipment stopped functioning). In this

report, this process of estimating traffic data both before and after the data collection period is

called projecting.

Because of the large differences in the quantity and quality of traffic data available for

the LTPP sites, four alternative traffic data projection procedures, called projection categories,

were developed and used:

Category 1 is intended for LTPP sections that have sufficient AVe and WIM data to

enable projection of monthly variation in traffic loads.

Category 2 is for LTPP sections with both AVe and WIM data; however, compared to

Category 1, the amount of data is insufficient for projection of monthly variation in traffic

loads.

Category 3 represents sections with adequate Ave data, but with virtually no sitespecific WIM data.

Category 4 represents LTPP sections with virtually no reliable measured AVe and WIM

monitoring data.

159
Regardless of the projection category, the basic procedure for projecting axle load spectra

for the LTPP sections includes the following steps:

1. All available annual historical and monitoring data are used to establish annual

growth factors.

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

established.

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

obtained by multiplying the base annual load spectrum by the growth factors.

4. As an option, a cumulative axle load spectrum can be obtained by summing the

annual spectra for all in-service years.

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

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

for each projection category). Case studies also included sensitivity analyses that addressed the

following topics:

• Effect of key assumptions used in the traffic load projection process.

• Reliability of historical traffic loading estimates.

• Effect of quantity of monitoring traffic data.

Traffic loads are applied to pavements by way of the truck technology that has been

evolving during the lifespan of the LTPP sections. It is important to understand the nature and

significance of these changes. The report contains a review of the evolution of motor carrier

technology in terms of economical and political changes, regulatory changes in vehicle weights

and dimensions, and engineering changes, and explains the difference between projecting traffic

loads and volumes and projecting truck technology.

To facilitate the management of axle load spectra, two summary statistical measures were

developed: Load Spectra Coefficients and General Equivalency Factors. To obtain benchmarks

for carrying out quality assurance and traffic data projections, a proposal to develop an LTPP

Traffic Loading Guide or a catalog is also described in the report.

CONCLUSIONS

The following are major conclusions based on the Phase 1 study.

• Traffic load projections for the majority of LTPP sites are feasible. The proposed

procedure yields axle load spectra for: (1) all in-service years of the LTPP

sections; (2) single, tandem, and tridem axles; and (3) all trucks combined

160
(FHWA vehicle Classes 4 through 13). The cumulative axle load spectra can be

obtained by summing the annual axle load spectra.

• To bestow confidence in traffic load projections, the projection procedure needs to

be transparent and well documented.

• The accuracy and reliability of the traffic projections for many of the LTPP sites

may not be as high as originally anticipated.

• The involvement of the SHAs in the predictive process is crucial. Many data

problems cannot be resolved without local involvement. Specific issues requiring

input from local agencies include:

Relationship between historical and monitoring data.

Reasons for inconsistencies in historical and monitoring data.

Vehicle weight and dimension regulations.

Access to quality assurance/quality control information, such as WIM and

AVC calibration data.

Changes in local traffic patterns (e.g., because of closing and opening of

parallel highways, changes in the location and operation of major truck

destinations or generators).

Availability of AVC and WIM data from nearby traffic monitoring

installations that are not part of the LTPP study.

• The traffic projection process is very challenging and labor-intensive. The main

reason for the labor-intensive process is the need to carry out the extensive quality

assurance activities required to resolve inconsistencies in the reported data and to

address differences between the historical and monitoring traffic data, as well as

the differences between monitoring data obtained for different years.

• Based on the experience with the case studies, the majority of the traffic

projection effort was actually spent on quality assurance issues. There is a natural

tendency to believe that the data collected by traffic monitoring equipment are

correct. To prove otherwise requires careful assessment of all available data,

engineering judgment supported by extensive knowledge of traffic load

characteristics, and the involvement of the SHAs.

• It would help if the basic quality assurance activities were done previously.

However, it is difficult to separate quality assurance from traffic projections

because of the synergy between these two activities. Also, some data problems

are revealed only during the traffic projection process.

• Base axle load spectra for individual trucks and base truck volume distributions,

required for Category 3 and 4 projections, must be selected judiciously, and this

selection must be supported by a sound, well-organized, and well-documented

161
knowledge base. In this connection, the development of an LTPP Pavement

Loading Guide or a catalog is proposed.

• The quality assurance process would greatly benefit from the development of a

knowledge base or a catalog documenting a typical range of traffic load variables.

The process would also benefit from the availability of summary measures for

traffic loads that are independent of pavement variables.

• Sensitivity analyses are useful to quantify the effect of key assumptions made

during the prediction process.

• Several data inconsistencies exist in the CTDB. For example, data in several

traffic monitoring tables are conflicting or missing; calculated ESALs reported in

the IMS appear to conflict with ESALs calculated in the course of conducting a

case study for Site 29-5483.

• The traffic prediction process triggers and requires a comprehensive quality

assurance process of underlying traffic data, particularly the comparison of annual

trends. The result of the quality assurance process is an essential and valuable

product. Without this process, the results of many studies using traffic data that

have been done or will be done are suspect and questionable.

• Traffic modeling is a highly cost-effective way to extend limited sampling data

and to compensate for the lack of data. The collection of traffic data in the field,

which requires the purchasing, installation, maintenance, and operation of AVC

and WIM equipment, and the processing of data, is expensive (with an

approximate annualized cost of $20,000 or more per site). To obtain a payback on

this investment and to preserve the integrity of the LTPP program, it is necessary

to allocate sufficient effort to fully utilize the available traffic data through the

projection process.

RECOMMENDATIONS

• Proceed with launching the Phase 2 study. The objective of the Phase 2 Traffic

Backcasting Study will be to produce and archive traffic load spectra for selected

LTPP sites using the methodology developed in Phase 1.

• Involve the representatives of the SHAs in any future traffic projection process.

• Carry out fundamental quality assurance by systematically comparing the

correspondence between the annual historical and monitoring traffic data and the

relationship between the annual monitoring traffic data obtained for different

years. It is important to note that the most efficient way to do so is within the

framework of, and simultaneously with, the traffic projection process.

162
• Seek input from those responsible for LTPP pavement performance modeling

regarding the importance of providing:

Separate projections of single-axle loads with single tires (steering axles)

versus double tires. At present, this distinction is not made in the CTDB.

However, it is possible to make it through the traffic projection process

and modeling.

Projections of total traffic volumes. Only the truck volumes were

projected in Phase 1.

163

REFERENCES

1. Long Term Pavement Performance (LTPP) Program, Long-Term Pavement Performance

Traffic Monitoring Protocol: Revised Data Collection Plan for LTPP Test Sites, February

27, 1998.

2. LTPP Program, Long-Term Pavement Performance Traffic Monitoring Protocolfor

Calibrating Traffic Data Collection Equipment, February 27, 1998.

3. Federal Highway Administration, Traffic Monitoring Guide, Third Edition, February

1995.

4. Federal Highway Administration, Researcher's Guide to the Long-Term Pavement

Performance Data, June 1997.

5. Federal Highway Administration, Development ofLTPP Traffic Backcasting Procedure,

LTPP DATS - Work Order No. 12, Task 1 - Interim Report, March 1999.

6. Hallenbeck, M., Results ofthe Empirical Analysis ofAlternative Data Collection

Sampling Plansfor Estimating Annual Vehicle Loads at LTPP Test Sites.

7. Hallenbeck, M., et aI., Vehicle Volume Distributions by Classification, Federal Highway

Administration, Washington, DC, July 1997.

8. Kim, J.R.; Titus-Glover, L.; Darter, M.I.; and Kumapley, R.K., "Axle Load Distribution

Characterization for Mechanistic Pavement Design," Transportation Research Board

Record 1629, Transportation Research Board, Washington, DC, pp. 13-23.

9. Sharma, S.c., and Leng, Y, "Seasonal Traffic Counts for a Precise Estimation of

AADT," ITE Journal, September 1994, pp. 11-28.

10. Desai, Deen, and Noble, et aI., Traffic Forecasting for Pavement Design, Final Report,

Report No. FHWA-TS-86-225, March 1988.

11. Hallenbeck, M., WSDOT Traffic Forecasting Guide, Volumes 1 and 2, Report Nos.

WA.RD #219.1 and 219.2, May 1991.

12. Cunagin, W.D., and Goff, Z.A., Improved Prediction ofEquivalent Axle Loads, Texas

A&M Research Foundation, Texas A&M University, College Station, TX, December

1997.

13. Hajek, J., Proceduresfor Estimating Traffic Loadsfor Pavement Design, Pavements and

Foundations Sections, Engineering Materials Office, Ontario Ministry of Transportation,

November 1995.

Preceding Page Blank 165
14. Roth, R.D., The Motor Carrier Industry in Transition, Transportation Technical Services,

500 Lafayette Boulevard, Fredericksburg, VA 22401, 1997.

15. Hajek, J.J.; Tighe, S.; and Hutchinson, B., "Allocation of Pavement Damage Due to

Trucks Using a Marginal Cost Method," Transportation Research Board Record 1613,

Transportation Research Board, National Records Council, Washington, DC, 1998, pp.

50-56.

16. Hallenbeck, M., Quality Assurance and Automated Error Detection for WIM and AVC

Equipment in the LTPP Project, Federal Highway Administration, Washington, DC.

17. Dahlin, Curtis, The Basisfor Using Gross Vehicle Weight Distributions of5-Axle Semis

to Determine the Validity ofWeight Data, Minnesota Department of Transportation,

October 1994.

18. American Association of State Highway and Transportation Officials, AASHTO Guide

for the Design ofPavement Structures -1993, Washington, DC, 1993.

19. Haines, B.M.; TenEck, T.; and Pietropola, K., "Pennsylvania Department of

Transportation's Weigh-In-Motion Program," paper 91-0247, Transportation Research

Board, January 1991, Washington, DC, p. 2.

20. Florida Department of Transportation, Transportation Statistics Office, Comparison of

Pavement Damage Factors Derived From Weigh-In-Motion Data Measured by Portable

vs. Permanent Systems, Final Report, December 1993, p. 33.

21. Witczak, M.W., Equivalent Wheel Load Factors, Report prepared for the Association of

American Railroads, February 1981.

22. Hajek, 1.1., "General Axle Load Equivalency Factors," Transportation Research Board

Record 1482, Transportation Research Board, National Research Council, Washington,

DC, 1995, pp. 57-68.

166
APPENDIX A - COMPUTATIONAL PROCEDURE FOR AGGREGATION

OF MONTHLY TRAFFIC DATA

The proposed traffic projection procedure for Category 1 sites requires the monitoring of

monthly spectra. Currently, there are no monthly spectra available in the IMS database or

CTDB. To obtain monthly monitoring spectra, CTDB Level 3 daily AVC and WIM data need to

be utilized. A procedure was developed in this study to aggregate partial AVC and WIM daily

data to obtain monthly counts. This procedure is applicable to LTPP sites that satisfy the

following criteria:

• At least 210 days ofAVC.

• At least one weekday and one weekend of WIM per quarter (preferably one week

per quarter as a minimum).

• Availability ofthe above data items for at least 2 years in a 5-year period.

To better account for seasonal variations, monitored AVC data should be available for at

least one week for every calendar month for at least one year (for example, out of 5 years of parttime monitoring, there should be data for at least one week for January, February, etc.).

The following steps outline the procedure for the aggregation ofmonthly data. This

procedure needs to be executed for each site individually.

STEP 1 - OBTAIN AND PROCESS RAW LEVEL 3 AVC AND WIM DATA

In order to predict seasonality effects inherent in traffic data, traffic data collected on a

daily basis are needed. These data are stored in binary file form as Level 3 data in the CTDB.

AVC and WIM data are stored separately. To view the data, the traffic extractor program needs

to be used to create a report that can be saved in DOS text format. The report is created

separately for each LTPP section for each year. Each report needs to be manipulated further so

that relevant information can be extracted and put in a manageable table form that will contain

daily AVC or WIM data for all monitored years for a given section. The processed AVC and

WIM tables will then be imported to a relational database for further processing.

STEP 2 - USE PROCESSED LEVEL 3 DAILY WIM DATA TO CREATE A TABLE OF

ESTIMATED MONTHLY AXLE COUNTS FOR EACH MONTH, YEAR, LOAD

RANGE, AXLE TYPE, AND TRUCK CLASS (ESTIMATED_MONTHLY_WIM)

1. Export processed Level 3 daily WIM data table to Access database (WIMJaw).

2. Export calendar table to Access database (calendar table includes year, month, day

ofthe week, counts for every day ofthe week per month).

3. Create a query to separate WIM axle counts data by workdays and weekends.

167
4. For each load range, axle type, truck class, month, and year, obtain a sum of all

axle counts collected during workdays (Monitored_Workdays_Axles) and a sum

of all axle counts collected during monitored weekends

(Monitored_Weekends_Axles), then obtain counts ofmonitored workdays

(Monitored_Workdays) and monitored weekends (Monitored_Weekends).

5. For each load range, axle type, truck class, month, and year, compute estimated

monthly axle counts by obtaining average workday and weekend axle counts and

by multiplying the averages by the number of workdays (workday) and weekends

(weekend), respectively, for a given month of a given year and by summing the

products.

Estimated_Monthly_Axle_Count=(Monitored_Weekends_Axles/Monitored_Weekends) *weekend

+(Monitored_Workdays_Axles/Monitored_Workdays) *workday

STEP 3 - USE PROCESSED LEVEL 3 DAILY AVC DATA TO CREATE A TABLE OF

ESTIMATED TOTAL MONTHLY TRUCK COUNTS FOR EACH MONTH,

YEAR, AND TRUCK CLASS

1. Export processed Level 3 daily AVC data table to Access database (AVCJaw).

2. Create a query to separate AVC truck counts data by workdays and weekends.

3. For each class, month, and year, obtain a sum of all truck counts collected during

workdays (Monitored_Workdays_CLASS) and a sum of all truck counts collected

during monitored weekends (Monitored_Weekends_CLASS), then obtain counts

ofmonitored workdays (Monitored_Workdays) and monitored weekends

(Monitored_Weekends).

4. For each truck class, month, and year, compute estimated monthly truck counts by

obtaining average workday and weekend truck counts and by multiplying the

averages by the number ofworkdays (workday) and weekends (weekend),

respectively, for a given month of a given year and by summing the products.

Estimated_Monthly_CLASS=(Monitored_Weekends_CLASS/Monitored_Weekends) *weekend

+(Monitored_Workdays_CLASS/Monitored_Workdays) *workday

168
APPENDIX B - TYPICAL AXLE LOAD SPECTRA USED FOR

CATEGORY 3 AND 4 PREDICTIONS

Appendix B contains the following:

• Figures 97 through 106 - These figures compare axle load spectra for individual

truck types (vehicle classes 4 through 13) obtained for three LTPP sites (12-4000,

27-0501, and 29-4036). The axle load spectra in figures 97 through 105 are the

average spectra obtained by averaging all annual acceptable axle load spectra

available for the three sites.

• Figures 107 through 109 - These figures compare axle load spectra for vehicle

classes 6,9, and 10 obtained for two LTPP sites (12-4000 and 27-0501). The axle

load spectra are the average spectra of all acceptable annual axle load spectra

available for the two sites.

• Figures 110 through 119 - These figures compare normalized axle load spectra

for individual truck types (vehicle classes 4 through 13) obtained for three LTPP

sites (12-4000, 27-0501, and 29-4036). The normalized axle load spectra in

figures 110 through 119 are the average spectra obtained by averaging all annual

acceptable normalized axle load spectra available for the three sites.

169
1400

1200

1000

l!3

§ 800

600

400

200

0

0 8 8 8 0 0 0 N "!" ex>

Single Axle

Tandem Axle

----.---------------------1

400 _

350

300 _-

250

::J

8 200 -

.S1

x 150 «

100

50

0_

0 0 0 0 0

0 0

"!" ex>

o

o

o

N

.....

o

o

o

<0

.....

o

8o

N

8o 8

o

o 0 o 0

Axle Loads, Ib

o

o

o

<0

l{)

o

o

o

o

<0

o

o

o

ex>

<0

o

o

o

N

"

§

<0

"

o

o

o

o

ex>

--+-AnnuaL29-4036 -*-AnnuaL12-4000 -0-AnnuaL27-0501

1 lb = 0.00445 kN

Figure 97. Load spectra for Class 4 vehicles.

170
Single Axle

18000

16000

14000

12000

8

::l 10000

Q) 8000

6000

4000

2000

o

\

\

q

f\'..

/k~

j l ~'-.."x..

/- ~~

-- ~~~~

Axle Loads, Ib

Tandem Axle

40

35

30

!!3 25 c:

::l

8 20 - Q)

x 15

«

10

5

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 "'!" co N <0 0 "'!" N ...... ...... N N N t')

o 0 0 0 o 0 0 0

Axle Loads, Ib

g

o

m

o

o

o

o

<0

o

g

(b

o

o

o

co

<0

o

o

o

N

f'-.

o

o

o §

o

co

-+-AnnuaL29-4036 --*-AnnuaL12-4ooo ---<>- Annual_27-0501

1 lb = 0.00445 kN

Figure 98. Load spectra for Class 5 vehicles.

171
Single Axle

2500

2000

1500 :::J

8

(]) 1000 X

500

0

a 8 8 8 8 a a N -.r as

Axle Loads, Ib

Tandem Axle 2500 -y-- -----,

2000 +-.--\-----------------------------1

-E1 1500 -1------"I~------------ :::J

8

1000 -1------.f--r~_____J"'=----­

500 +----,I----v;.....=-~-""=._--------------------I

0

a a a a 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 8 8 8 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -.r IX) N CD 0 -.r IX) N 0 co N 0 <b IX) N CD 0 ,... ,... N N N C"> 'It 'It II') CD CD I'- I'- IX)

Axle Loads, Ib

-+-AnnuaL29-4036 ~AnnuaL12-4000 -0--AnnuaL27-0501

1 lb = 0.00445 kN

Figure 99. Load spectra for Class 6 vehicles.

172
Single Axle

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\l '<t (0 CXl 0 C\l '<t (0 CXl 0 '<t (0 CXl 0 C\l '<t (0 CXl 0 C\l '<t ... ... C\l C\l C\l C\l C') C') C') C') C') '<t '<t '<t

Axle Loads, Ib

Tandem Axle

200 w.·.·.·.·.·.·.·.·.·.·.·.·.·.·..·..·.·.·.·.·.·.·.·.·.· · ·.·.·.·.·.w.·..·.·.·.w..·.·.·.·.·.·..·.·.·.·.·.·.· m.·••·••·•·•••·••·•·•·•·•·•· ·.·.·.·.·.·.w.·.·.·.·.., .

180

160 +-------------~r--=---------------------------{~

$3 140 +-----------41-l"'~.....---------------------:

§ 120 +--------t:'l-------1r'~..____--'k-------------------------j

8 100 +------o:......+--~(L_-\--~._____--------------------':

80;-----=l'--+-r-----;;IIf-----b---~----------------~

60 -1-----+Y:-----"'''I-....~---+7'\_-___illl;;;E"--------------------;,

40 +----I6'H~~'O;;;.,.,..,.~---'-''--'r_--...-------------------';

2~ t~~~~!:~=:=:::::;=:~~~jS;~~~:~!j~;;o~;;~;;;;;;:;;~;=;:=j o

- --"-- _____________l

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t CXl C\l «> 0 '<t CXl C\l (0 0 '<t CXl «> 0 C\l C\l C') C') '<t '<t '<t L() L() (0 «> (0 "- CXl

8o

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o

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000

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300 +-----~-r-----------------------------__ll I

250 +- ....,"I-+- ----{l

\ ::::s 200 +------/--\----------------------------------;,

150 +------t--r-\-------------------------------1l

100 +---}-+--\-~--------------------l

50 +--~,."r--=-,...~~---""'\-\.--------------------------------l

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Axle Loads, Ib

Tridem Axle

160 .

140 +-----------------------.Ji----------------

120 +---------------------+---\-------:Il~;a_------__:

100 +---.=:..:::"""";;;::---------------------,r--F....T'-------~,.-------: ::::s

8 80 +-----...=...=-----------...........---T------......-----; OJ

60 +---------\,....---~:....-----J.3t___-::_:__Jlf_JL.------:.;3t___--------.::'=-;

40 ;---------q--lE'-----.......,~:L...'''V--------~'\--------!

20 ;------"""c'-----J'-h~-~~~---:u..:::__----------->Ir_------j

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t CXl C\l (0 0 '<t CXl (0 0 '<t CXl C\l (0 0 '<t CXl C\l (0 0 ,.... C\l C\l C\l C') '<t '<t '<t L() L() (0 (0 (0 "- "- CXl

Axle Loads, Ib

--+-Annual_29-4036 """*""" Annual_12-4000 -0-Annual_27-0501

1 lb = 0.00445 kN

Figure 100. Load spectra for Class 7 vehicles.

173
Single Axle

.~

..,

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\J """ co co 0 C\J """ co ,... ,...

o 0 g g g 0

co 0 C\J C\J C\J

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o

o

o

co

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o

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o

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o

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g

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\

........, ·.·.·.·.·.··.·· w.·.···.· j

\

7000

6000

5000

.l!l

c:

::;, 4000 8

CD 3000

2000

1000

0 -- 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

""" co C\J co 0""" co C\J co 0

""" co C\J co 0 """ co g! co ,... C\J C\l C\l C'l C'l """ 'Ot 'Ot LO LO co co co I'-

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Tridem Axle

g

o

...... - v ::!:

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""" co C\J co 0

""" co C\J co 0

""" co C\J co 0 ,... C\J C\J C\J t'J t'J """ """ """ LO LO co co co I'- I'- co

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o

o

o

C\J

,...

o

o

o

co

250

300

\"-~-"~_~---~ml

.l!l 200 \

150 ~--\\c-----------------------'

100+-----\-\-------------------------------i

50 +----... ----4\,------------------------------

o L-,--t:;::::::;~~o(Io,_CP-C=o-c!"Ftl=~r::::3:;~;::;~~~~::&:==c:t=l o

-+-Annual_29-4036 -*-AnnuaL12-4000 -0-Annual~7-0501

1 lb = 0.00445 kN

Figure 101. Load spectra for Class 8 vehicles.

174
Single Axle

o

o

o

o

o

o

50000 .........•..........................•.................·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.·.·.·.·.·.w.·,.w.·.·.·.·.·.·.·.·.·.·.·.·.·.WN.W.·.·.w.w.w.·.·.·.·.·.·.·.w.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.·.w.·.·."'.·.·.·.·.·.·.·.·.w.·.· "' -"'" "' 1

45000 +----------=-------------------------~!

40000 i-------t'-/-~)..------------------------.;

.l!l 35000 i------J'-t--~\F-------------------------~

§ 30000 +------.--Jr<---::: ,...q:=-------\\---------------------- 8 25000 +-----+¥-+-----,~--------------------___i Ql I I \ 20000 i-----+J-J1

'---\- b~\.---------------------i

15000 -f----/-¥-+-I--"\f---~'------------------­

10000 +----~-+---n- )l-----\-\-~,.--------------------i

500~ t];;;~,~J~~~~:@~;~~............ ~~~~;;;~;;;~~;;;;:~~~~~~;::;~~~, _;=;=j o 000 0 0 0 0 0 goo 000 0 0 0 0 0 0

g g g g g g g gog g g g g g g g g g g N W 00 0 N W 00 g

Axle Loads, Ib

Tandem Axle .............................................................................................................·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.w.·.·.w.·.•· ·.w.•·.·.·.·.· ·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.· ·.·.-·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.•·.·.·.w.·.·.·.·.· w.l

!

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 N W 0 00 N W 0 00 (\J W 0 00 (\J W 0 (\J N (\J C') C') LO LO W <0 W ..... ..... co

Axle Loads, Ib

Tridem Axle

35000

30000

25000

c

::J 20000

8

15000

10000

5000

- - .... - -~

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 N W 0 00 N W 0 co N W 0 N C') C') LO LO W W W ..... ..... co

Axle Loads, Ib

o

o

g

N

o

o

o

W

o

o

o

N

o

o

200 ; .- .-.............•.......························1

180

160 +-----\--\---------------------------

140 i-----\-\------------------------------'

§ 120 \

8 100+----+\--------------------------'

80 -l-----l\t-\x-------------------.-....;

60 +-----/~~~..\---------------------

40 +--ac(-----,,,,L-----..~~-------------------------i

20 +------:~................. ~-_-~_~~~..,--..;:...-..,...-----------------------------<

o +---r--.-:~)oo.oofJ""-'Qo~~3=i~I::;::8;=e:~t;::l~~==8=~:8=l~~:::::::o==&_i o

-+-Annual_29-4036 Annual_12-4000

1Ib = 0.00445 kN

Figure 102. Load spectra for Class 9 vehicles.

175
Single Axle

1200 ·.·.·.·.·..·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.·•...•...........................................................................................................•.......................................................................................................·.·.·.·.·.·.·.w.·.·.·.·.

1000 -j-----~Pk-----------------------------ll 800 -I------f-+-=-:~---------------------------j

600 -1------+-~----4>----------------------------j

400 -1-~-----f-.rE--o---....-----------------------j

200 -1-----\;.-----iP----..."*'--='lI£---,.,..~-------------------------j

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\J "'l' CD co 0 C\J "'l' CD co 0 C\J "'l' CD co 0 C\J "'l' CD co 0 C\J "'l' C\J C\J C\J C\J C\J '" '" '" '" '" "'l' "'l' "'l'

Axle Loads, Ib

Tandem Axle

200

100

0

0 0 0 0 0 0 a a 0 a a a a a a a a a a a 0 a a a a a a a a a a a a a a a a a a a a a a a a a a a 0 a a a a a a a a a a a a "'l' co C\J CD a "'l' co C\J <0 a '<t co C\J <0 a '<l' co C\J CD a C\J C\J C\J

'" cry '<l' '<l' '<l' 11') 11') CD CD CD r--. r--. co

Axle Loads, Ib

TridemAxie

800

700

600 -j-----'r'--'k--------------------------------;

500 +----....1----\-------------------------------: :::l 8 400 -1----1--+----------------------------; Q)

300 -1----+_~~r_-----------------------___;

-+-AnnuaL29-4036

2500

2000

l/)

E 1500 :::l

0

()

Q) 1000

500 ~\

0 a a 0 a a a 0 0 a 0 a 0 a a 0 0 a 0 a a a a 0 a 0 a a a a 0 a a 0 0 a a a a 0 a a co C\J CD 0 "'l' co C\J CD 0 "'l' co C\J CD a C\J

'" '" '<l' "'l' '<l' 11') 11') CD CD CD r--. r--. co

Axle Loads, Ib

"'"""*"""Annual_12-4000 --0-Annual_27-0501

lIb = 0.00445 kN

Figure 103. Load spectra for Class 10 vehicles.

176
Single A>de

o o o

o

o

o

o

o

o

o

<Xl

(')

o

o

o

CD

(')

o

o

(')

o

o

o

C\J

(')

o

o

o

g

o

o

o

<Xl

C\J

o

o

o

CD

C\J

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\J V CD <Xl 0 C\J V CD <Xl 0 C\J V C\J C\J C\J

Axle Loads, Ib

Tandem Axle

5000 +-------,f-T--\..---4;::-------------- ----':

<3 4000 +- ---JiJL..- "'"---::l--= -...;

Ql "- 3000 -t---...... k........ --~~---------.."'-=---------------------i:

2000 +---w... !L----------'''l....... .---------------------'

1000 +-------_~---------"I£~------------------<

o .f.....;Dr-~~~~~~~~Q!C~a-thQ_Ia.ti~~!!O_I:IlQo[MJ_O,O:i:IlO_a.cHlrD_ChI-::r_...---4

o

i

!

J

0 0 0 0 0 0 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\l CD 0 V co CD 0 l{) l{) CD CD CD r--- coo

8

C\l

.....

o

8co

o

o

o

v

1000 .·"·"·.""",·.w.·.·",""'''"'"",,,,''w,,·.w.w.·.·.·.w.·.,,,,''"'''"'w.w.·.w.·.·.·.·.·.·.·.·.·.W.WN"w,w,·.·,,·.·.·.·.·.·.·.·.·.·.·.·.·.·.,·.w,,·.·,,·.w.·.·.·.·.·.·.·.·.·ow.·.·.·.·.·.·.·.,· ""',..w " "w..""w"··l

900 -I------------......-.a::::~-----------------------~

800 f'~

700 r \ !

000

500 L \. !

400 -1---F...... ~..,.::~~------,+----------------!

300 +--_-Ji,--jIL------------""\..---------------;

200 -I---=="IO----------------.~--------------___i

100 -1 "l.. .....~------------____4

a -!-.a,.--chO-orltJo'-an~1:H:~oQlo!lJ-tiTOoOri::H:lfTO_Ori:l_a.l~~!IOoQoC:H::IiTO_OrI:H::IiTO_OrI::H:IiTO_OrI:H:IfrQl o

g

o

o

<Xl

o

o

o

Ie

g

o

- - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 v <Xl C\J CD 0 V <Xl C\J CD 0 V <Xl C\J C\J (') (') v v v Ltl Ltl CD CD CD

Axle Loads, Ib

o

g

o

C\J

o

g o

o

o

C\J

500 ·.·.·",·"w.·.·.·.·.·.·.·.·.·.·.·.w.,·.·.·.·.·.·.·.·.·.·.·.·",·.·"""'.·.·"·.·.·"·.·,,,·.·.·.·.·.·.·.·.·.w.·.·,,,,,·",·.·."w.·.·.,·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.· ".•,.•..................."..'w..•.•.•.•"'•.....•......w.,·.·.·.'·.'''l

450 -1--....,..-------------------------------<:

400 +---\\-----------------------------;

350 -/-----\----------------------------------cl

300 +------\-\---------------------------------1

8 250 +----\-\---------------------------1

200 +----\-\----------------------------;:

150 -I-----\-\----------------------------i

100+-----\-\--------------------------i: .... 50 +---------'...~..----~------------------------------<:

o+---,--a-,..-""'I"'4~~a=8=T:O__a__a__a_,.a._a__a,.._cr_n::i_"'"t:l_i:l__l::l_rla_D_a__a_r_a__a_-a-....l..

o 0 g § i5

--+- AnnuaL29-4036 -M-Annual_12-4000

1 lb = 0.00445 kN

Figure 104. Load spectra for Class 11 vehicles.

177
Single Axle

o

o

o

.·,.·.·.·.·,w.,·.·.·.·,.·.w.·.·.·.·.·.·m.·.·.·.·,w__m.w.,·.·.·.·.·.·'.".·.·.·,w."m.w,w_,w .·m 'w.,,_.·.·.w.·.·.•.•.•.•...".".•.•.•..,wmmmnm...•'."..m.•'.".·.w.•.w." ""."." ".w,_.''I.

10000

8000 -t-\+------------------------'!~ 6000 ;-+\\-----------------------!

4000 +----\\-------------------------------1

2000 +---'\-lr..--~~~-'1.-----------------------1

o -l-I],-OO~~:HllOo(l!!QoC:IQoQlQ~M:lPaorQoChD4:J,O_o.tl!-Q,C:HlTa-o!rO-l:J,GIa.a-a.cf-Q,l::l-r--l

o 8

o

C\I

12000

Tandem Axle

...............................................................................................................................................................................u .

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 C\l <D 0 00 <D 0 00 C\l <D 0 "'" 00 C\l <D 0 ..... ..... C\I C\l C\I (') "'" "'" "'" III III <D <D <D "- "- 00

Axle Loads, Ib

Tridem Axle

3500

3000

.l!l 2500

c

=' 2000 0

0

1500

1000

500

§

o

00

o

o

o

<D

"-

o

o

o

C\J

"-

o

o o

o

o

;?;

o

o

o

o

<D

o

o

o

(l)

III

o

o

o

C\J

III

o

o

o

00

o

o oo

o

o

o

g

<:'J

o

o

o

C\I

<:'J

o

o

o

<Xl

C\I

o

o

o

C\I

o

o

o

o

C\I

50 m.·.·.·.,w,w.·.·.w.·.·.·.·.·.·.·.·.·,w.·.·.·.·.·,w.w.·.·.·.·.·,.·.·.·.·.·.·,w.·.·.·.·.".·,.·.·.·.·.·.·.·.w_.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.w.·,.·.·.".·.·,.·.·.·.·.·.w.·.·.·.w.·.·.·.·.·.·.w.·.·.·.·.·.·.·.·.·.·.·.·.wn.·.·.·.·.·.·.·.·.·.w.·.·.·.·.·.·.·.·.·.·.·.,,"'.·...'w..." ..w •••••w.•....\

40 -{---------------+-\--------------------:

35 +---------------+-~.____--------------------<:

.l!l

c30+-----------------I--~~:)_---------------------i =' 8 25 -j----------=-c::;;;Cr------:f-------'''''='-------------------';

20 +-----------J'-------\:---....,.------\-----------------:

15 +--_.-------Ie,----\---::F-------\--------------------j

10 t---~;:_-_;t_----of'=------:;$;;;;:;;;;;:::::.--'-'o~-~---------_____j

5t--------'....G~....::::!lol""'-;;O;::"::zi'~------.=~~-----___:_---_=_____i

o-l--.--l:l-!"-=;;::a...-*--M-*"'"*-*-'K-i~~~~h_M,.::w~:::::c:~::::a::::a::::oo;:::e:::a::::::o_l o 0 0 0 0

o 0 0 0 o 0 0 0

<Xl C\I (l)

Axle Loads, Ib

-+-AnnuaL29-4036 AnnuaL12-4000 -0-AnnuaL27-0501

1 lb = 0.00445 kN

Figure 105. Load spectra for Class 12 vehicles.

178
Single Axle

......... ! ...... -~ - .~ ! 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\J V <0 00 0 C\J V <0 00 0 C\J V <0 00 0 C\J V <0 00 0 C\J V C\J C\J C\J C\J C\J C') C') C') C') C') v v v

Axle Loads, Ib

Tandem Axle

1200

1000 \

800 ;--+-\----------------------------1 ! 600+--\+------------------------l

400 i--\-t-------------------------~ 200+---t----:::=-~"--"'''*=_=_---------------------__!

o-l-c~~I3Q~~!IQoQ~;O:tI;:e.o()QpQoQrGQ,,a.a,,a.a,a_a.a_a.a.g,.a.g,.~a_,.~ o

250

200

.....•.•.•.•.•.•.,.•.•.•.•..-.•..-.•.•.•.•.•.•.•.•.•.•.•••.•.•.•.•.."'.."0'"..•.•.•.•.•••.•.•.•.•.•.•••.•.•.•.•.•.•.•.•.•.•••.•.•••..-.•.•.•...•"0'" "0'".•.-...•........-...........•...............•.............................................•........"0'"..•.•.•.•.•.•..-.•.,."""0'"•.•"0'" ·..,..·.·.·.·.·.·.·.·."0'"."0'"•.,..·.·.·.·."0'"."0'".·.·.1

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t <Xl C\l <0 0 '<t <Xl (0 0 '<t <Xl C\l (0 0 '<t <Xl <0 0 .... .... C\l C\l C\l C') '<t '<t v It) It) (0 <0 (0 I"- <Xl

Axle Loads, Ib

Tridem Axle

100 i------..~~G~~.....l---------\=----------------__j

150 -1----\-------------;..-------------------------;

8

50 -1--------------------..:-------------------;

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 v 00 C\J <0 0 v 00 C\J <0 0 v 00 C\J <0 0 v 00 C\J <0 0 C\J C\J C\J C') C') v v v l!) l!) <0 <0 <0 "" "" 00

Axle Loads, Ib

30 " w·······..·····················..·······.. ······ i

25 +-_~'" -{i!

1

5+----------+--------------'~---------"~----l

0~ !

l

15 +---------\-----~~~~>._--------------l

10 +------------:lr--~~---------"'Q__-------"..........:__----------§

-+-AnnuaL29-4036 """*""" AnnuaL12-4000 -0-AnnuaL27-0501

1 lb = 0.00445 kN

Figure 106. Load spectra for Class 13 vehicles.

179
2500

2000

1500 :::J

8

.!!1 1000

500

a

a 8 8 8

a a a N ..r co

Single Axle

Axle Loads, Ib

Tandem Axle

1600

1400

1200

.!9 1000 c:

:::J

8 800

.!!1

x 600

«

400

200

0

a a a a a a a a a 0 0 0 0 § 0 0 0 0 0 0 0 0 0 0 § 8 8 0 a 0 0 0 0 0 53 0 0 ..r CXl N co 0 'OS" N CXl N co .... .... N N N (t) 'OS" 'OS" LO LO

Axle Loads, Ib

o

o

o

o

co

8

o 8o

CXl

co

o

o

o

N

r--

o

a

a

co

r--

o

o

o

o

CXl

-0-AnnuaL27-0501

1 lb = 0.00445 kN

Figure 107. Comparison of axle load spectra for Class 6 vehicles.

180
Single Axle

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\I '<l" (0 co 0 C\I '<l" (0 co 0 C\I '<l" (0 co 0 C\I '<l" (0 co 0 C\I '<l" ,.- ,.- C\I C\I C\I C\I C\I (') (') (') (') (') '<l" '<l" '<l"

Axle Loads, Ib

Tandem Axle

25000

30000 .Wo·.·.·.·.·.·.,·.w.·.·.·.·.·.·.·.·.·.·~·.,·.·.·.·.·.·~.. ""'''w "w w.~w ·.·.·.~'".w.·.·.w.·.·.·.·.·.·.·,,·.·.·.·.·.·.·.·.·.·.,·.·~·.·.·.·.·.·.·.·.w.·.···.·.·.·.·.·.·.·.·.·."'.·.·.·.·.w.·.·.·,,·.·.·.·.·.·.·.·.·.· •.·.·.·.·.·.·.·.·.·.·.·,,·.w.--•.•...•.•"'1)

I \

20000 +-------,#--+\----------------------------;

15000 -t------t----.:O\-\------------------------i:

10000+-----.,{-/-----'\-----------------------'

5000 +-----l-f--~-UI£-:-:-C"'\::r--------------------------;: r

o +.~-~==l~.... ~~~~·-Kc::...,.....:.~:;2ic::s,c:loAr-~~a_t:Jrl:]O'-O'-a---CTl-a-l-a·.a-'-0-'rO- -a,.-Qoi--I.1!l'-a-a-,a. --0, -a--CTl-a-lI.1!l:l-..,...-~ o

..............................................................................................................................................................................................................................................................................................................................................................................................'

5000

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 "<t co C\I <0 0 "<t co C\I <0 0 "<t co f)l <0 0 "<t co C\I <0 0 ,.- ,.- C\I C\I C\I (') (') "<t "<t "<t l!) <0 <0 <0 I'- I'- co

Axle Loads, Ib

25000

20000

.l!!

c

::J 15000

8

CD 10000

Tridem Axle

50 +------J'-----\---------------------------------~~

.l!! 1

§ 40 +-----.f---+-------------------------------------i; 8

OJ 30 +----1'-----1-,--------------------------------;;

I

20 ;--~n----~.__------------------------_I~

10 ;------",..----~.__-----------------------_§~

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<l" co C\I <0 0 '<l" co C\I <0 0 '<l" co C\I <0 0 '<l" co C\I <0 0 ,.- ,.- C\I C\I C\I (') (') '<l" '<l" '<l" l!) l!) (0 <0 <0 I'- I'- co

Axle Loads, Ib

"""'*-AnnuaL12-4000

1 lb = 0.00445 kN

Figure 108. Comparison of axle load spectra for Class 9 vehicles.

181
Single Axle

1200

1000

·.·.",·.·.·,.·.",·.·.w.·.·.w.·.w.·.·.w.·.·,.w.·.-·.,,"v.·.vuu.w.·.·.·.wu.w.·.·.·.w.·.·owu.·.·.·.w.-·.·.·.·.-·.·.·.·.·.·.·.·.·.w.·.·.w.·.w.w.w.·.·.·.·.w.·.·.w.·.·.·.·.w.-'wu.wu_",·.,·.w.·.,·._v.·."'w.·.·.·.·.·.-w.·.,,,,_w.w.·."'''1

oS 800 +---------jr---+--------------------------~

600 +------t-----'\-------------------------------:;

! 400 +------+----(1-------------------------------:;

200

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\l CD 00 0 C\l CD IX) 0 CD 00 0 CD 00 0 .... .... .... C\l C\l C\l C\l C') C') C') C')

Axle Loads, Ib

Tandem Axle

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\l CD 0 00 C\l CD 0 00 C\l CD 0 00 C\l CD 0 .... .... C\l C\l C\l C') C') l!) l!) CD CD CD I'- I'- 00

Axle Loads, Ib

Tridem Axle

o

o

o

00

o

o

o

800 " mm.•.•.•.•.••"Q " "' ····.." " " 1

~\ I

600 -1---.....,./~-\-\-------------------l1

S! 500 4 +------,1---\--------------------------------<: I !

I ! 300 +----+----h-----------------------------l. P I 200 +----=:F-------3c------------------------------.,~

10~ ~~ ~~~ J o

- - - .... - - - - - - - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 IX) 10 0 00 &l 10 0 IX) 10 0 C\l C') l!) 10 10 10 I'- IX)

Axle Loads, Ib

-o-AnnuaL27-0501

o 0

g o

o

g o

o

o

Annual_12-4000

2500 "'·.···.·.. · ".·.·.···.·.··.·· · m .•.•.•.............................................................................................................................................." •." ".".•.•.•,.

2000 +------r-r---------------------------------------:;I

\ I §1500 \

1000 -1-------\-\--~--------------------_____'

500 +-----~~r--------------------------------i:

o-l-...-*-.,..-""""..;:~I:;;A=~~~~~~_a__a__«:l_rO__a__D__I::M_a_a__a_l o

1 lb = 0.00445 kN

Figure 109. Comparison of axle load spectra for Class 10 vehicles.

182
Single Axle

25 --.

0_20 +------11-------------------------------'

::l

815 +-------+--I*------'lir-------------------------J

J!1

-g~10 +------t4f--J'~---1r_\_------------------------l

.!:ll

~5+------.,(---l----------'"'"..,..,-r---'¢-----------------l

Axle Loads, Ib

Tandem Axle 30 .- ----,

5-

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 § 8 8 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

'<t !Xl N CD 0 '<t !Xl N N l8 0 (1; !Xl N CD 0 ...... ...... N N N M '<t It) CD CD r- r- !Xl

Axle Loads, Ib

"<fe. 25 -1-----------14.------------------------1

.l!l

§ 20 +-----------(~Ik____--+-----------------

8

15 +---\------+-----/t~--1._\_-----.-~-----------------.----------1 «

.!:ll -g 10 +-----'\__-----j~--f----'-'''W-\__-----

~29-4036 --*-12-4000 -0-27-0501

1 lb = 0.00445 kN

Figure 110. Normalized load spectra for Class 4 vehicles.

183
Single Axle

35

0 30

25 :::J

8 20 Jl1

15

al

.t::l 10

... 5

0

0 8 8 § 0 0 8 § § § § § § § § § § § § N 'V 2 o N 'V CD 0 ..... ..... ..... .... N C') ;

Axle Loads, Ib

Tandem Axle

120 -r----------------------------------,

'*'100 +-*-------------------------------------1

80+--+-----------------------------------1 '\

\ x 60 -j----'I---------------------------------j

..., 40 +--+------------------------------------1 \

\P1

2: :~:j)~~:Lh:::~;~;~~~~jj,~;;;~~~~~~~:::--<IIro..~::::~::~::~: ~:~~~~~:~:~~:~::::~:~::::~:~: o o

o oo

o

CD

o

o

o

N

.....

g

o

<0

.....

o

o

o

o

8 o8

CD

N

o

8

Axle Loads, Ib

o

8 o

o

o

o

o

o

ffl

o

o

o

o

<0

o

g

<2i

o

o

o

co

<0

o

o

o

N

I'-

g

o

<0

I'-

o

o

o

o

co

--+- 29-4036 -0-27-0501

1 lb = 0.00445 kN

Figure 111. Normalized load spectra for Class 5 vehicles.

184
Single Axle

30

0

25

::J 20 8

.m 15

10 .~

5 . ....

0

0 80 80 § § § § § § § § § N to o N ............

Axle Loads, Ib

Tandem Axle

18

16.

'eft 14 $

c

::J 12 .

8 10 x

« 8. "C

Q)

.~ 6

4 . ....

2

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ex> N to 0 .,. ; to 0 ex> N to 0 ...... ...... N N In In to to t- t- ex>

Axle Loads, Ib

-+-29-4036 ~12·~ ~27-0501

1 lb = 0.00445 kN

Figure 112. Normalized load spectra for Class 6 vehicles.

185
Nonnalized Axle Counts,

% Normalized Axle Counts,

% Normalized Axle Counts,

%

I\:l I\:l ...A. ...I. f\) f\) c.u c.u I\:l I\:l

0 c.n0 c.n

0 c.no c.n0 c.n0 c.n0 c.n0 c.n

0 c.n0 c.n

0 c.n

oI I I I

IJ

0tmmn1

0

4000

I

4000! 2000

+

8000 8000 *01.1

1II1I1 4000

"Tj

~.

12000I I I I I

, (JQ I\:l 6000 12000 e; c.o

(l)

0w -1-1

1 I~II 8000

- 0> 16000 16000 - 20000 rJ11

IIIIII

I

w 10000

20000

Z 12000 0 24000 24000 3 1'( I TI I 1 I 1 1 I E 14000

e:. 28000 28000 ~. ofto'\J

I 1 1 1 1I1 ': 16000

32000 32000

0- .,

> 18000

-

+

>

~~mH: ;--1

[20000rl

0 36000 ~~ 5:

; III en

- CD i~ :i" 00 r- :: CD , CD <Q.

0\ tn

0 40000 Ii

g 40000 1; CD

'ij I\:l fi a. !!' 22000

> (l) .m .m , x 5' CD 0 5' 44000 5' 44000 1

Cil

0

i

0 24000

48000 48000;1/. 1-.1 1 1 """T

I 1 1 26000 ....

(J 52000 52000

1

1

I 1 1 1 1 28000

- 56000

§ I\jl

II

I I I I I I I

I tn 56000I

I 30000

.......:J

<: 60000 60000 32000 (l)

::r'

~.

I ("l 64000 !r' I I I I I I I I :~ 34000 ?

64000

-(l)

tn

--

cr' 36000 68000 68000

II I\:l

-..J

............................................1

i

0

6 72000

~OOll!

c.n 72000

40000

I

00

76000 76000

VI 42000

; 80000 80000 ..........-••-.-.-.~ ........... ......-•••••-.-.-.-.-...............•••v.,..-.r.r••-.r.......:.- 44000 V~· ~~~~N~WN••~mV ~J
Single Axle

25

':!!- 0

,Ii 20 c

:>

0

'-' 15 Q)

u

Q) 10

.!::!

lii

E 5 0

z

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\l """ <0 IX) 0 C\l """ <0 IX) 0 gj """ <0 IX) 0 """ <0 IX) 0 C\l ... ... ... C\l C\l C\l C\l C'l C'l C'l C'l """ """

Axle Loads, Ib

Tandem Axle

.·.·.·.w.·.,,,,,,,·.·.w.·.·.·.·.·.w.w.w.w.·.w.·.,·.,"'''',,,"'''w.w.·."""""",·.·.·.·.w.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.,·.w.·.·.·",·,,·.·.·.·.·.·.·.v.·.·."","""w.·.·.·.·.w.·.·.·.·.·.·.·.·.·.·.·.·············.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·."w.·.·.·.·.·.·.v.,,"w,.·.·.·"l

i

1

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

""" IX) C\l <0 0

""" IX) C\l <0 0""" IX) C\l <0 0

""" IX) <0 0 ... .... C\l C\l C\l C') C') """ """ """ 10 10 <0 <0 <0 "- IX)

Axle Loads, Ib

Tridem Axle

35

':!!- 0

30 ,Ii

c

::l 25 0

'-' Ql 20

u 15 Ql

.!::!

lii 10

E0 5

z

4

2

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

""" IX) C\l <0 0 """ IX) C\l <0 0 """ IX) C\l <0 0

""" IX) C\l <0 0 .... C\l C\l C\l C'l C'l """ """ """ 10 10 <0 <0 <0 "- "- IX)

Axle Loads, Ib

8+-------\--------JC\-----.....---------------------'

6+------\-------+--+----~t__,~---~---------------;

18

16

'if

,Ii 14-1----\--------------------------------------i

c

:>12-1-----l~-------------------------------------! 8

Ql10+-----\------------------------------------!

U

Q)

.!::!

lii

E

oz

-+-29-4036 ~12-4000 -0-27·0501

1 lb = 0.00445 kN

Figure 114. Normalized load spectra for Class 8 vehicles.

187
5

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\l '<t <0 co 0 C\l '<t <0 co 0 C\l '<t <0 co 0 C\l '<t <0 co 0 '<t C\l C\l C\l C\l C\l C') C') C') C') C') '<t '<t

Axle Loads, Ib

Tandem Axle

Single Axle

if! 35 " " " ·..·.·..·..· ·.·.·..·.·.·.·.. m ••••·.••·•·.·.·.·•·.·•·•·•·•·•·•·•·•·.·•·•·.·.·.·.·.•·•·•·••·•·••·••••wmw.·.·..· ·.w.·..·.·.·.·.·.·.· 'w.·.·.·.·.·.·.·.·.·.·"")

30 -{--------l.)---------------------------~

::l 25 +--------+-\F~----------------------------....;

<3

Ql 20 +--------+----'F'-\--\----------------------------.....;

15

"'iii10+---------t.'Ff.:.---\-~----------------------------!

E

C5

z

14 m .

§

o

co

o

o oo

o

co

<0

o 0 o 0

g 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\l <0 0 co C\l <0 0 '<t co C\l <0 C\l N C') C') '<t '<t '<t ll) ll)

Axle Loads, Ib

Tridem Axle

o

g o

o o

8

2+----I-----------~~.....=---------------------;

6-t---::::III~,......---~~--:-::::iI......."."=--b------------------

4-r--r;----=o:~oor~~i\-----------~

if! 12

ui

'E

::l 10

<3

(J)

"C

.§

"'iii

E

oz

.z: ....... - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 <0 0 '<t co C\l <0 0 '<t co C\l <0 0 C') '<t '<t '<t ll) ll) <0 <0 <0 r-- r-- co

Axle Loads, Ib

o

o

o

o

o

o

co

C\l

o

o

C\l

o

o

o

<0

o

o

o

C\l

o

o

90 w.........•.•Q ·.·.·.wm.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.w.·.·.·.wn.w.·.w.·.·.·.w.w.·.·.·.·.·.·.·.w.·.·.w.w.w.·m.·.·.·.·.·.·.·.·.·.·.·.·.·.·.W.·m.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·,.·.·.·.·.·.·.·.w.·.·.·.·.·.w.·.•w.·.·.•·.·,.w ·.w.w.·.·.....•.w.•.......

if! 80 \

70 +------1\k--------------------------------------:

5 60 +----+------------------------------------;

50 -1-----+-\-----------------------------: \ "5l 40 -1----""\,-------------------------------:

j 30 -l---'~,,--\\-----------------------: 20 -1----~~n---------------------------:

10 +------b:::.......... ,--...-----------------------------;

O-J.1---r-*"""T'"--r*"=l~~:5;;l~;::tlF~~=....... 8::::t~~~~~~~;:e!==~~l=O=;::t~'O""'1~=e!"l"'a_-tl-o..l. o

-+-29-4036 ~12-4000 --0-27·0501

1 lb = 0.00445 leN

Figure 115. Normalized load spectra for Class 9 vehicles.

188
15

10

5

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\J '<t (0 <Xl 0 C\J '<t (0 <Xl 0 C\J '<t (0 <Xl 0 C\J '<t (0 <Xl 0 C\J '<t C\J C\J C\J C\J C\J

'" '" '" '" '" '<t '<t '<t

Axle Loads, Ib

Single Axle

.,g :: m.W m m •• •••.............................................................................................................•.•.•.•.•.•m .•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.•.••.••••.• ...........................j:

::::l 25 +------~f-+--------------------------------;:

8

OJ 20 +--------1--1,---------------------------------..,.;

"i

.~

5

z

Tandem Axle

5

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t <Xl C\l (0 0 '<t <Xl C\l (0 0 '<t <Xl C\l (0 0 '<t <Xl (0 0 C\l C\l C\l C'l C'l '<t '<t '<t LO LO (0 (0 (0 I"- <Xl

Axle Loads, Ib

Tridem Axle

.,g 25 .·.·.·.·.·.·",·.·.·.·.·.·.·.·"·.·.·.·.·.w.·.·.·,,,·.·,,""'·.·."""""'w.·.'w.w.,,,"w.w.·.·,,""'''w.w.·.·.·.·.·.w,,''w.·.·.w.·.·.·.·.·.·.·.·.·.w.·.·.·.·.w.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.-.·.·.·.·.·.·.·.·.·.-.-.·"w.",,,,,,,,,,,,,,,,,,,,,,,,,,,,,,"'''''''''''']:

.i20--t-------o------------------------------------:

c

::::l

8

<D 15 +----f--t-----i~--------------------___J

16 10 +-----1:.---4---+----..:=\----------------------------; .~

"iii

E

5

z

60

0 50

.i

c

::::l

0 40

()

OJ

30

"i

.~ 20

5

z 10

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

'<t <Xl C\J (0 0 '<t <Xl C\J (0 0 '<t <Xl C\J (0 0 '<t <Xl C\J (0 0 C\J C\J C\J

'" '" '<t '<t '<t LO LO (0 (0 (0 l"- I"- <Xl

Axle Loads, Ib

...........29-4036 """*-12-4000 -0-27-0501

1 lb = 0.00445 kN

Figure 116. Normalized load spectra for Class 10 vehicles.

189
Single Axle

o

o oo o

o o

o

o

o

o

o

o

C')

o

o oo

o

o

o

o

c:s

o

o

o

o

o

o

g o

o

15

o

o oo o

20 " ~".." ~.~.O ·.·.·.·.·.~.~ · ·.~.~.w.·.~.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.··· · · w ·.·.·.· ~.~ ~.~j

<f. 18

16 -t------

J

-1.:p4/TI.=:'t:.,-------------------------------<

is 14 -t-----.J-+:~f-+.\-,-------------------------:

(.) 12 +------f"'-JF--++-----------------------------i Ql / I , 10 +-------,'oJ-7t-p--tM~,...~-----------------------;

"C 8 -t-----++-~It=~~-------------------------:

I1l 6 +----t/11I'~---=~.J§~~~:__-----------------_j '-o~ 4 +-.............;;;r;;~/·!~~----'"X~rl~~l:lIll;:~-------------------: .-., '\A.:~

z +---~----..f-------...x:~K.~~~-~:"C~~k: ~1i.-:~-~-~-~-~-or.-1-1-0-",-).-c.-hi-0-0(-~-c.-~-0-..r.-).-c.-!-rl-0-0(-~-c.-~-0-~-I-~-~-;=:==j

o 0 000 0 o 0 0 0 0 0 o 0 000 0

;!

Axle Loads, Ib

Tandem Axle

........................, , , ,.,......•........................................................

0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t eo C\l co 0 '<t eo co 0 '<t eo C\l co 0 '<t eo C\l co 0 C\l C\l C\l C') '<t '<t '<t LO LO co co co I'- I'- eo

Axle Loads, Ib

TridemAxle

25

0

.ri 20 c

8

Ql 15

"C 10 Ql

N

'a

E 5

(;

z

,... - .A... - 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t eo C\J co 0 '<t eo C\J <0 0 '<t eo C\J <0 0 C\J C\J C') C') -.t -.t -.t LO LO <0 <0 <0 I'- I'- eo

Axle Loads, Ib

o

o

o

o 0 o 0 o 0 C\J coo

o

al

o 0

80 : ·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.·.··.····.·.·.·.·n.··.·.w.··.· ····.·····.·.· ··.··.·.· ····.·.·.·· ························-...· ···················1

<f. 70 \ !

vi 60 1-------\:----------------------------------~

'E \ j <3 50 +-----\--------------------------------------i~

Ql \ n 1

40 \ / '\. Ql30+--------\----r~-1_.._-----------------------------~ 120 y \ z 10 / \. \

o+-~/...,..._~~__H__=l_=l::&-i=!~~_a_Ma_c~_a_r_l~~~a_c!_O__Q_r_!a_cf__Cl_i

-+-29-4036 -4E-12-4000 -0-27-0501

1 lb = 0.00445 kN

Figure 117. Nonnalized load spectra for Class 11 vehicles.

190
(f)

5'

ec

(0

(l)

.........................l _._._•••••••• _~

1

Normalized Axle Counts,%

16000

....... ...... r\) rv UJ c,.:) .,J::Ir.

t1I0 t1I0 t1I0 t1I0

2000

24000

6000

8000

10000 r~~+-+-+--LL

12000

14000

20000

22000

38000

42000

40000

36000

34000

44000

26000i

28000

30000

32000

18000

r

-~6'

-i

(l)

a>o

Normalized Axle Counts,%

I\)W .j>. t1I

o 0 0 0 0

o

oII I I II

II,

8000

4000

12000

56000

72000

76000

80000 ~~..."Jm'Nl~J~~.~t<o ..

J.....m.j

64000

52000

16000 [lI=t=1:::+=-+

28000 E~=t=1:::+=-+

20000

68000

24000

48000

60000

32000

36000

r

§. 40000

'"6' 44000

-i

5: 3»

(l)

I\)

t1I

Normalized Axle Counts,%

I\)

t1I0 t1I0

................................................1.•.•.•.•.•.............-:J

o

oI I I I I!

4000

8000

16000

20000

28000

24000

32000

12000

80000

36000 (l) r

_~ 40000

6'

tooo+tw

a>

a:

II o

(0o

.J:>,

.J:>,

Vl

......

...... N

g.

.....

()-('D

00zo

~.

0- S

('D

'"1

(J

i"

00

......

......

00

"Tj

~.

......

\0

......
Single Axle

35

0

30 <Ii

C

::::l 25 0

0

lJl 20

"'C 15

t

10

5 z 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 C\I '<t <0 co 0 C\I '<t <0 co 0 C\I '<t <0 co 0 C\I '<t <0 co 0 C\I '<t ,.. ,.. ,.. ,.. ,.. C\I C\I C\I C\I C\I C') C') C') C') C') '<t '<t '<t

Axle Loads, Ib

Tandem Axle

o

o

o

o

co

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 '<t co C\I <0 0 '<t co C\I <0 0 co C\I <0 0 '<t co C\I ,.. C\I C\I C\I C') C') '<t '<t l{) l{) <0 <0 <0 .....

Axle Loads, Ib

TridemAxle

20 "'.""..·.·.·.·.·.·.·.·.· ·..·.·.·.·.·.·.·..·.·.·.·.·.·..w,,· " " ·.·.·.·.·.·.·.·.·.·.·.w.·.·..·.·.·.·.·.-."''''''''''<

cf!. 18 +---------~lf-----------------------------jl

16 ~--------_f_'1\\--------------------------::1 ::I "JIl \

8 14 /J. Jt II

~~ :=3~~======~r~/~'""~~1~~:~\======~~===================== '\. JJ \ \. It ========~

o

8o

co

o

o

o

ie

o

o

.....

o

o

o

co

<0

o

o

o

;g

8o

o

<0

o

o

o

<0

l{)

8

o

C\I

l{)

o

o oo oo

o

o

o

o

o

o o

o

o

o

o

o

<0

,..

18 "" "" ,on .

16 +------------------------""Ir--------~

cf!.

14

12 +----------'It---------/:II''':--/--\------j'--''r-:f----'\-----------:

o

lJl 10 +--__------1+-------I-1f-t--o---\.............-+--.........----''t------------'

8t---~--_T-t-~~'O"""~Ht_"'.:---"1RJ,~r_---t------~

6 +------""=---1--\----;f-~'r--+--t\,.._-\::-+-\-ft:r-----\__---------: § 4~-~-----"~~~F-I--+-~......"..~;;::K...+----j'---''____'[f--k--~;::::"7"+------­

z 2+------=~~~+-=_=~~'----_t--\---J~----=-....--\'~....~-___::----;

O+--r-~=--~:---Jj~,--...,..}E-+E---+Hh_~(_.,..--r----,.--r----r-~~~Cf-lo'R__a-~

o 0 0 0

000

a5

Axle Loads, Ib

--+-29-4036 ~12-4000 -0-27-0501

1 lb = 0.00445 kN

Figure 119. Normalized load spectra for Class 13 vehicles.

192