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This document is a research report (FHWA-HRT-23-122) detailing the design and development of DREDGE (Disaggregate Realistic Artificial Data Generator), a software tool for crash safety analysis. Researchers from the University of Connecticut developed a framework to generate realistic artificial data (RAD) at both macroscopic and microscopic levels for various roadway facility types. The RAD generator can create multiple data realizations and is designed to help researchers test safety analysis models by providing datasets with predetermined safety relationships.
The research employed two primary approaches: a macroscopic method generating RAD at site levels by segment or intersection with traffic and roadway characteristics, and a microscopic approach building a high-resolution data generation process that mimics crash occurrences at the trip level. The tool accommodates a comprehensive range of crash-contributing factors, enabling the generation of crashes with details such as vehicle involvement, crash location, severity of occupant injuries, and crash type. The study was sponsored by the Federal Highway Administration's Office of Research and Development and conducted between August 2019 and March 2024, with the final report published in March 2024.
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Research, Development, and Technology Turner-Fairbank Highway Research Center 6300 Georgetown Pike McLean, VA 22101-2296
PUBLICATION NO. FHWA-HRT-23-122 MARCH 2024
DREDGE (Disaggregate Realistic Artificial Data Generator)—Design, Development, and Application for Crash Safety Analysis, Volume Ⅱ
FOREWORD
Data-driven safety analysis models help State and local agencies quantify safety data, identify high-risk roadway features, and predict the effects of proposed safety measures. However, even a model that performs well overall may not accurately represent interactions between variables for a specific location or crash because underlying relationships in the real world are unknown. One proposed solution is to generate realistic artificial data (RAD) with predetermined safety relationships built into them. Since these datasets are known, the RAD can serve as a test bed, indicating how well a model reflects underlying cause-and-effect relationships.
This report details a study performed by researchers working under the Federal Highway
Administration’s Exploratory Advanced Research Program. In the study, the researchers created a RAD generation framework on macroscopic and microscopic levels for a diverse selection of roadway facility types and developed a web-based tool. The tool’s framework provides users with the ability to generate RAD for multiple years at macroscopic segment and microscopic trip levels. This report should be of interest to academics and researchers developing crash modification factors or functions and statistical models to determine how the models best represent real-world relationships. This volume is the second in a series. Volume Ⅰ in the series is
FHWA-HRT-23-121.
Brian P. Cronin, P.E.
Director, Office of Safety and Operations
Research and Development
Notice
This document is disseminated under the sponsorship of the U.S. Department of Transportation
(USDOT) in the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained in this document.
Non-Binding Contents
Except for the statutes and regulations cited, the contents of this document do not have the force and effect of law and are not meant to bind the States or the public in any way. This document is intended only to provide information regarding existing requirements under the law or agency policies.
Quality Assurance Statement
The Federal Highway Administration (FHWA) provides high-quality information to serve
Government, industry, and the public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement.
Disclaimer for Product Names and Manufacturers
The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this document only because they are considered essential to the objective of the document. They are included for informational purposes only and are not intended to reflect a preference, approval, or endorsement of any one product or entity.
TECHNICAL REPORT DOCUMENTATION PAGE
1. Report No.
FHWA-HRT-23-122
2. Government Accession No.
3. Recipient’s Catalog No.
4. Title and Subtitle
DREDGE (Disaggregate Realistic Artificial Data Generator)—
Design, Development, and Application for Crash Safety
Analysis, Volume Ⅱ
5. Report Date
March 2024
6. Performing Organization Code:
7. Author(s):
John Ivan (ORCID: 0000-0002-8517-4354), Shanshan Zhao
(ORCID: 0000-0001-5476-6894), Kai Wang
(ORCID:0000-0003-1452-4000), Oluwaseun Olufowobi
(ORCID: 0009-0001-1339-9095), Naveen Eluru (ORCID: 0000-
0003-1221-4113), Tanmoy Bhowmik (ORCID:0000-0002-0258-
1692), Lauren Hoover (ORCID: 0009-0004-2431-0860), Md I.
Jahan (ORCID: 0000-0002-4056-7816), Sudipta Tirtha (ORCID:
0000-0002-6228-0904), and Mohamad Abdel-Aty (ORCID:
0000-0002-4838-1573)
8. Performing Organization Report No.
9. Performing Organization Name and Address
University of Connecticut
Connecticut Transportation Institute
270 Middle Turnpike, Unit 5202
Storrs, CT 06269-5202
10. Work Unit No.
11. Contract or Grant No.
693JJ31950017
12. Sponsoring Agency Name and Address
Office of Research and Development
Federal Highway Administration
6300 Georgetown Pike
McLean, VA 22101–2296
13. Type of Report and Period Covered
Final report; August 2019– March 2024
14. Sponsoring Agency Code
HRSO-2
15. Supplementary Notes
The contracting officer’s representative was Yusuf Mohamedshah (HRSO-2; ORCID: 0000-0003-0105-5559).
16. Abstract
Safety analysis primarily focuses on identifying and quantifying the influences of contributing factors to traffic collisions and the consequences of these factors. A practice of relying on observed data only allows relative comparisons between analysis methods and may not lead to determining how well the methods mimic the true underlying crash-generation process, which is often unobserved or known only partially, with varying degrees of certainty. This report describes a study, performed by researchers working under the Federal Highway Administration, to help address this data limitation.
Researchers used two approaches to generate realistic artificial data (RAD): In the macroscopic approach, researchers generated RAD at site level by segment or intersection after generating traffic and roadway characteristics by segment or intersection for various facility types. Crashes were then generated using known model structures, based on these characteristics. The microscopic approach built a high-resolution, disaggregate data-generation process that mimics crash occurrences on road facilities at the trip level and accommodates the influence of a full range of crash-contributing factors to generate crashes. The crash generation employs data describing the generated trips and involves identifying the vehicles involved in the crash, crash location, severity of occupant injuries, and crash type. These generated crashes can be aggregated at any spatial or temporal resolution to estimate and evaluate safety models. The researchers thus developed a RAD generator as a standalone, customizable software application tool that can prepare multiple realizations of RAD. The tool was evaluated using two case studies involving segment crashes and intersection crashes. This volume is the second in a series. Volume Ⅰ is FHWA-HRT-23-121.
17. Key Words
Realistic artificial data, crash analysis, RAD generator
18. Distribution Statement
No restrictions. This document is available to the public through the National
Technical Information Service, Springfield, VA 22161.
https://www.ntis.gov
19. Security Classif. (of this report)
Unclassified
20. Security Classif. (of this page)
Unclassified
21. No. of Pages
22. Price
N/A
Form DOT F 1700.7 (8-72) Reproduction of completed page authorized.
https://orcid.org/0000-0002-0258-1692 https://orcid.org/0000-0002-0258-1692 https://orcid.org/0000-0002-4056-7816 https://orcid.org/0000-0002-6228-0904 https://www.ntis.gov/ ii
SI* (MODERN METRIC) CONVERSION FACTORS
APPROXIMATE CONVERSIONS TO SI UNITS
Symbol When You Know Multiply By To Find Symbol
LENGTH
in inches 25.4 millimeters mm ft feet 0.305 meters m yd yards 0.914 meters m mi miles 1.61 kilometers km
AREA
in2 square inches 645.2 square millimeters mm2 ft2 square feet 0.093 square meters m2 yd2 square yard 0.836 square meters m2 ac acres 0.405 hectares ha mi2 square miles 2.59 square kilometers km2
VOLUME
fl oz fluid ounces 29.57 milliliters mL gal gallons 3.785 liters L ft3 cubic feet 0.028 cubic meters m3 yd3 cubic yards 0.765 cubic meters m3
NOTE: volumes greater than 1,000 L shall be shown in m3
MASS
oz ounces 28.35 grams g lb pounds 0.454 kilograms kg
T short tons (2,000 lb) 0.907 megagrams (or “metric ton”) Mg (or “t”)
TEMPERATURE (exact degrees)
°F Fahrenheit 5 (F-32)/9
Celsius °C or (F-32)/1.8
ILLUMINATION
fc foot-candles 10.76 lux lx fl foot-Lamberts 3.426 candela/m2 cd/m2
FORCE and PRESSURE or STRESS lbf poundforce 4.45 newtons N lbf/in2 poundforce per square inch 6.89 kilopascals kPa
APPROXIMATE CONVERSIONS FROM SI UNITS
Symbol When You Know Multiply By To Find Symbol
LENGTH
mm millimeters 0.039 inches in m meters 3.28 feet ft m meters 1.09 yards yd km kilometers 0.621 miles mi
AREA
mm2 square millimeters 0.0016 square inches in2 m2 square meters 10.764 square feet ft2 m2 square meters 1.195 square yards yd2 ha hectares 2.47 acres ac km2 square kilometers 0.386 square miles mi2
VOLUME
mL milliliters 0.034 fluid ounces fl oz
L liters 0.264 gallons gal m3 cubic meters 35.314 cubic feet ft3 m3 cubic meters 1.307 cubic yards yd3
MASS
g grams 0.035 ounces oz kg kilograms 2.202 pounds lb
Mg (or “t”) megagrams (or “metric ton”) 1.103 short tons (2,000 lb) T
TEMPERATURE (exact degrees) °C Celsius 1.8C+32 Fahrenheit °F
ILLUMINATION
lx lux 0.0929 foot-candles fc cd/m2 candela/m2 0.2919 foot-Lamberts fl
FORCE and PRESSURE or STRESS N newtons 2.225 poundforce lbf kPa kilopascals 0.145 poundforce per square inch lbf/in2
*SI is the symbol for International System of Units. Appropriate rounding should be made to comply with Section 4 of ASTM E380.
(Revised March 2003) iii
TABLE OF CONTENTS
APPENDIX A. MICROSCOPIC GENERATION COMPARISONS
APPENDIX B. TWO-LANE, TWO-WAY UNDIVIDED SEGMENT POISSON
REGRESSION
APPENDIX C. NEGATIVE BINOMIAL REGRESSION
APPENDIX D. POISSON REGRESSION—RANDOM PARAMETERS
APPENDIX E. NEGATIVE BINOMIAL—RANDOM PARAMETERS
APPENDIX F. UNIVARIATE POISSON LOGNORMAL
APPENDIX G. MULTIVARIATE POISSON LOGNORMAL
APPENDIX H. FOUR-LEG URBAN SIGNALIZED INTERSECTION POISSON
REGRESSION
APPENDIX I. NEGATIVE BINOMIAL REGRESSION
APPENDIX J. POISSON REGRESSION—RANDOM PARAMETERS
APPENDIX K. NEGATIVE BINOMIAL—RANDOM PARAMETERS
APPENDIX L. UNIVARIATE POISSON LOGNORMAL
APPENDIX M. MULTIVARIATE POISSON LOGNORMAL
REFERENCES
iv
LIST OF FIGURES
Figure 1. Graph. Comparison of crash type results
Figure 2. Graph. Comparison of driver demographic results
Figure 3. Graph. Comparison of vehicle characteristic results
Figure 4. Graph. Comparison of crash severity results v
LIST OF TABLES
Table 1. Poisson regression of 150 mi (dataset 1)
Table 2. Poisson regression of 300 mi (dataset 2)
Table 3. Poisson regression of 500 mi (dataset 3)
Table 4. Poisson regression of 750 mi (dataset 4)
Table 5. Poisson regression of 1,000 mi (dataset 5)
Table 6. Poisson regression of 150 mi (dataset 6)
Table 7. Poisson regression of 300 mi (dataset 7)
Table 8. Poisson regression of 500 mi (dataset 8)
Table 9. Poisson regression of 750 mi (dataset 9)
Table 10. Negative binomial regression of 150 mi (dataset 1)
Table 11. Negative binomial regression of 300 mi (dataset 2)
Table 12. Negative binomial regression of 500 mi (dataset 3)
Table 13. Negative binomial regression of 750 mi (dataset 4)
Table 14. Negative binomial regression of 1,000 mi (dataset 5)
Table 15. Negative binomial regression of 150 mi (dataset 6)
Table 16. Negative binomial regression of 300 mi (dataset 7)
Table 17. Negative binomial regression of 500 mi (dataset 8)
Table 18. Negative binomial regression of 750 mi (dataset 9)
Table 19. Poisson regression—random parameters of 150 mi (dataset 1)
Table 20. Poisson regression—random parameters of 300 mi (dataset 2)
Table 21. Poisson regression—random parameters of 500 mi (dataset 3)
Table 22. Poisson regression—random parameters of 750 mi (dataset 4)
Table 23. Poisson regression—random parameters of 1,000 mi (dataset 5)
Table 24. Poisson regression—random parameters of 150 mi (dataset 6)
Table 25. Poisson regression—random parameters of 300 mi (dataset 7)
Table 26. Poisson regression—random parameters of 500 mi (dataset 8)
Table 27. Poisson regression—random parameters of 750 mi (dataset 9)
Table 28. Negative binomial—random parameters of 150 mi (dataset 1)
Table 29. Negative binomial—random parameters of 300 mi (dataset 2)
Table 30. Negative binomial—random parameters of 500 mi (dataset 3)
Table 31. Negative binomial—random parameters of 750 mi (dataset 4)
Table 32. Negative binomial—random parameters of 1,000 mi (dataset 5)
Table 33. Negative binomial—random parameters of 150 mi (dataset 6)
Table 34. Negative binomial—random parameters of 300 mi (dataset 7)
Table 35. Negative binomial—random parameters of 500 mi (dataset 8)
Table 36. Negative binomial—random parameters of 750 mi (dataset 9)
Table 37. Negative binomial—random parameters of 1,000 mi (dataset 10)
Table 38. Univariate Poisson lognormal of 150 mi (dataset 1)
Table 39. Univariate Poisson lognormal of 300 mi (dataset 2)
Table 40. Univariate Poisson lognormal of 500 mi (dataset 3)
Table 41. Univariate Poisson lognormal of 750 mi (dataset 4)
Table 42. Univariate Poisson lognormal of 1,000 mi (dataset 5)
Table 43. Univariate Poisson lognormal of 150 mi (dataset 6)
Table 44. Univariate Poisson lognormal of 300 mi (dataset 7) vi
Table 45. Univariate Poisson lognormal of 500 mi (dataset 8)
Table 46. Univariate Poisson lognormal of 750 mi (dataset 9)
Table 47. Univariate Poisson lognormal of 1,000 mi (dataset 10)
Table 48. Multivariate Poisson lognormal of 150 mi (dataset 1)
Table 49. Multivariate Poisson lognormal of 300 mi (dataset 2)
Table 50. Multivariate Poisson lognormal of 500 mi (dataset 3)
Table 51. Multivariate Poisson lognormal of 750 mi (dataset 4)
Table 52. Multivariate Poisson lognormal of 1,000 mi (dataset 5)
Table 53. Multivariate Poisson lognormal of 150 mi (dataset 6)
Table 54. Multivariate Poisson lognormal of 300 mi (dataset 7)
Table 55. Multivariate Poisson lognormal of 500 mi (dataset 8)
Table 56. Multivariate Poisson lognormal of 750 mi (dataset 9)
Table 57. Poisson regression of 1,000 intersections (dataset 1)
Table 58. Poisson regression of 2,000 intersections (dataset 2)
Table 59. Poisson regression of 3,000 intersections (dataset 3)
Table 60. Poisson regression of 4,000 intersections (dataset 4)
Table 61. Poisson regression of 5,000 intersections (dataset 5)
Table 62. Poisson regression of 1,000 intersections (dataset 6)
Table 63. Poisson regression of 2,000 intersections (dataset 7)
Table 64. Poisson regression of 3,000 intersections (dataset 8)
Table 65. Poisson regression of 4,000 intersections (dataset 9)
Table 66. Poisson regression of 5,000 intersections (dataset 10)
Table 67. Negative binomial regression of 1,000 intersections (dataset 1)
Table 68. Negative binomial regression of 2,000 intersections (dataset 2)
Table 69. Negative binomial regression of 3,000 intersections (dataset 3)
Table 70. Negative binomial regression of 4,000 intersections (dataset 4)
Table 71. Negative binomial regression of 5,000 intersections (dataset 5)
Table 72. Negative binomial regression of 1,000 intersections (dataset 6)
Table 73. Negative binomial regression of 2,000 intersections (dataset 7)
Table 74. Negative binomial regression of 3,000 intersections (dataset 8)
Table 75. Negative binomial regression of 4,000 intersections (dataset 9)
Table 76. Negative binomial regression of 5,000 intersections (dataset 10)
Table 77. Poisson regression—random parameters of 1,000 intersections (dataset 1)
Table 78. Poisson regression—random parameters of 2,000 intersections (dataset 2)
Table 79. Poisson regression—random parameters of 3,000 intersections (dataset 3)
Table 80. Poisson regression—random parameters of 4,000 intersections (dataset 4)
Table 81. Poisson regression—random parameters of 5,000 intersections (dataset 5)
Table 82. Poisson regression—random parameters of 1,000 intersections (dataset 6)
Table 83. Poisson regression—random parameters of 2,000 intersections (dataset 7)
Table 84. Poisson regression—random parameters of 3,000 intersections (dataset 8)
Table 85. Poisson regression—random parameters of 4,000 intersections (dataset 9)
Table 86. Poisson regression—random parameters of 5,000 intersections (dataset 10)
Table 87. Negative binomial—random parameters of 1,000 intersections (dataset 1)
Table 88. Negative binomial—random parameters of 2,000 intersections (dataset 2)
Table 89. Negative binomial—random parameters of 3,000 intersections (dataset 3)
Table 90. Negative binomial—random parameters of 4,000 intersections (dataset 4) vii
Table 91. Negative binomial regression—random parameters of 5,000 intersections
(dataset 5)
Table 92. Negative binomial—random parameters of 1,000 intersections (dataset 6)
Table 93. Negative binomial—random parameters of 2,000 intersections (dataset 7)
Table 94. Negative binomial—random parameters of 3,000 intersections (dataset 8)
Table 95. Negative binomial—random parameters of 4,000 intersections (dataset 9)
Table 96. Negative binomial—random parameters of 5,000 intersections (dataset 10)
Table 97. Univariate Poisson lognormal of 1,000 intersections (dataset 1)
Table 98. Univariate Poisson lognormal of 2,000 intersections (dataset 2)
Table 99. Univariate Poisson lognormal of 3,000 intersections (dataset 3)
Table 100. Univariate Poisson lognormal of 4,000 intersections (dataset 4)
Table 101. Univariate Poisson lognormal of 5,000 intersections (dataset 5)
Table 102. Univariate Poisson lognormal of 1,000 intersections (dataset 6)
Table 103. Univariate Poisson lognormal of 2,000 intersections (dataset 7)
Table 104. Univariate Poisson lognormal of 3,000 intersections (dataset 8)
Table 105. Univariate Poisson lognormal of 4,000 intersections (dataset 9)
Table 106. Univariate Poisson lognormal of 5,000 intersections (dataset 10)
Table 107. Multivariate Poisson lognormal of 1,000 intersections (dataset 1)
Table 108. Multivariate Poisson lognormal of 2,000 intersections (dataset 2)
Table 109. Multivariate Poisson lognormal of 3,000 intersections (dataset 3)
Table 110. Multivariate Poisson lognormal of 4,000 intersections (dataset 4)
Table 111. Multivariate Poisson lognormal of 5,000 intersections (dataset 5)
Table 112. Multivariate Poisson lognormal of 1,000 intersections (dataset 6)
Table 113. Multivariate Poisson lognormal of 2,000 intersections (dataset 7)
Table 114. Multivariate Poisson lognormal of 3,000 intersections (dataset 8)
Table 115. Multivariate Poisson lognormal of 4,000 intersections (dataset 9)
Table 116. Multivariate Poisson lognormal of 5,000 intersections (dataset 10)
APPENDIX A. MICROSCOPIC GENERATION COMPARISONS
This appendix documents a comparison of the generated microscopic realistic artificial data
(RAD) that researchers generated for this report with the source data employed for generation
(NHTSA n.d.). The comparison results are presented for crash type, driver demographics, vehicle characteristics, and crash severity variables. Overall, the results illustrate that the RAD represent the source data reasonably well. Figure 1 through figure 4 illustrate these comparison results.
Source: FHWA.
Figure 1. Graph. Comparison of crash type results.
Figure 2. Graph. Comparison of driver demographic results.
Figure 3. Graph. Comparison of vehicle characteristic results.
Figure 4. Graph. Comparison of crash severity results.
APPENDIX B. TWO-LANE, TWO-WAY UNDIVIDED SEGMENT POISSON REGRESSION
Table 1 through table 9 contain the model estimation results for Poisson regression on two-lane, two-way undivided segments for datasets 1 through 9, which were generated by the project (as described in volume 1).
Table 1. Poisson regression of 150 mi (dataset 1).
Variables
K† A† B† C† PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Intercept −66.698* 13221 −3.560 1.783 −6.848 3.897 −4.042 1.974 −0.697 0.835
Ln (Length) 2.844 0.961 0.984 0.078 1.200 0.168 0.906 0.082 0.952 0.036
Ln (AADT) 2.751 1.385 0.490 0.144 0.898 0.326 0.354 0.151 0.342 0.065
Shoulder Width
8 ft Base Level
<2 ft −13.512* 3804.5 0.790 0.560 −19.015* 10109.5 0.864 0.642 −0.028 0.353
≥2 ft < 4 ft −0.774 1.325 0.498 0.187 −0.581 0.441 0.441 0.197 0.412 0.088
≥4 ft < 6 ft 2.916 1.490 0.318 0.172 −0.189 0.304 −0.028 0.188 0.220 0.081
≥6 ft < 8 ft −23.189* 7067.421 0.097 0.215 −0.733 0.442 −0.155 0.220 0.305 0.092
Lane Width
>12 ft Base Level
9 ft or less — — — — — — — — — —
9.5 ft–10.5 ft −15.982* 2163 −0.012 0.440 0.076 0.926 0.028 0.578 0.219 0.200
11 ft–11.5 ft 0.821 1.912 0.165 0.200 0.033 0.400 0.014 0.231 0.035 0.097
Speed Limit
45 mph Base Level
25 mph 0.623* 18777.62 0.922 0.381 −0.051 0.657 0.996 0.357 0.376 0.147
30 mph 2.756* 29538.03 1.202 0.465 −0.317 0.916 0.780 0.487 −0.143 0.238
35 mph 28.253* 13227.19 0.453 0.668 −18.355* 6226.64 1.050 0.457 −0.784 0.336
40 mph 2.341* 23545.65 −0.288 0.567 −1.628 1.141 −1.452 0.781 −0.844 0.254
50 mph 4.566* 25063.45 −0.216 0.677 1.365 0.790 0.508 0.532 0.097 0.219
55 mph 19.033* 13227.19 0.354 0.335 −0.131 0.488 −0.182 0.327 −0.173 0.121
Roadside Hazard Rating (Zegeer et al. 1988)
3 Base Level
4 0.632* 1.634 0.303 0.188 −0.387 0.358 0.035 0.204 0.152 0.084
5 −0.833 1.766 0.398 0.245 0.346 0.462 0.378 0.264 0.137 0.117
6 −18.741* 9773.750 0.048 0.339 −0.404 0.680 0.065 0.338 0.080 0.150
Variables
K† A† B† C† PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
7 4.464* 3.107 −0.527 0.566 −17.781* 4508.09 0.064 0.501 0.137 0.230
Pavement Condition 0.512 0.492 −0.013 0.029 0.012 0.058 0.029 0.034 −0.002 0.014
Pavement Roughness 0.019 0.025 −0.001 0.004 −0.007 0.009 0.001 0.004 0.000 0.002
Presence of Lighting −21.728* 13587.73 0.543 0.352 −17.602* 3789.50 0.543 0.345 −0.709 0.218
Presence of Horizontal
Curve −17.430* 14901.54 −15.615 666.718 −18.327* 4904.58 −1.152 0.722 −0.218 0.231
Vertical Curve
Approaching Slope 0.469 10318.99 −0.113 0.442 1.068 2911.52 −0.085 0.410 −0.062 0.125
Vertical Curve Leaving
Slope −2.825* 10166.13 −0.286 0.412 −0.512* 2972.77 −0.045 0.419 0.120 0.140
Grade −8.067* 12357.37 0.091 0.511 −13.360* 3555.84 −0.124 0.501 −0.043 0.166
Curvature Degree 0.022 0.025 0.002 0.009 0.011 0.019 0.008 0.007 −0.009 0.006
Fit Statistics
AIC 102.29 N/A 1204.9 N/A 464.34 N/A 1148.4 N/A 3027.5 N/A
BIC 242.83 N/A 1345.44 N/A 604.89 N/A 1288.96 N/A 3168.00 N/A
—No data.
*Variables are insignificant at a 90-percent confidence level.
†Ratings are based on the KABCO Injury Classification Scale: K = Fatal Injury; A = Incapacitating/Serious Injury; B = Nonincapacitating/Visible Injury;
C = Possible Injury/Complaint; PDO = Property Damage Only (NHTSA 2017).
AADT = average annual daily traffic; AIC= Akaike information criterion; BIC = Bayesian information criterion; N/A = not applicable; PDO = property damage only; std. error = standard error.
Table 2. Poisson regression of 300 mi (dataset 2).
Variables
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Intercept −62.409* 10429.82 −5.449 1.211 −9.001 2.389 −1.836 1.011 −2.982 0.476
Ln (Length) 0.957 0.406 1.146 0.068 1.200 0.127 1.011 0.060 1.006 0.026
Ln (AADT) 0.490 0.546 0.636 0.085 0.414 0.150 0.519 0.076 0.612 0.034
Shoulder Width
8 ft Base Level
<2 ft 18.736* 7023.832 −0.260 0.264 −0.399 0.431 −0.333 0.237 0.027 0.117
≥2 ft < 4 ft 18.126* 7023.832 −0.368 0.274 −0.542 0.460 −0.525 0.249 −0.060 0.121
≥4 ft < 6 ft 17.973* 7023.832 −0.313 0.273 −0.827 0.475 −0.407 0.247 −0.005 0.120
≥6 ft < 8 ft 16.850* 7023.832 −0.658 0.266 −0.794 0.445 −0.782 0.242 −0.465 0.119
Lane Width
>>12 ft Base Level
9 ft or less 17.530* 4100.589 −0.245 0.237 0.063 0.514 −0.330 0.200 −0.134 0.095
9.5 ft–10.5 ft 16.620* 4100.589 −0.560 0.269 −0.518 0.554 −0.336 0.224 −0.305 0.107
11 ft–11.5 ft 19.461* 35559.45 −10.003 427.783 −12.462 3114.82 −11.436 404.751 0.527 1.020
Speed Limit
45 mph Base Level
25 mph 18.651* 6529.2 0.331 0.431 0.241 1.170 −0.075 0.396 0.103 0.196
30 mph −1.464 12762 −1.407 0.761 1.888 0.756 −0.750 0.476 −0.870 0.259
35 mph −0.337 8413.9 −0.794 0.383 0.038 0.784 −1.140 0.372 −1.039 0.180
40 mph −1.311 84015 −0.874 0.342 −0.347 0.747 −0.463 0.281 −0.194 0.133
50 mph −0.279 8926.8 −0.055 0.347 0.058 0.863 −0.460 0.326 −0.141 0.151
55 mph 17.338* 65292 −0.687 0.272 −0.055 0.634 −0.775 0.239 −0.530 0.117
Roadside Hazard Rating (Zegeer et al. 1988)
3 Base Level
4 −1.383 0.585 0.093 0.070 0.089 0.129 −0.012 0.066 0.017 0.028
5 0.171 0.164 0.052 0.026 0.142 0.052 −0.009 0.021 0.020 0.010
6 0.021 0.020 0.000 0.003 0.002 0.005 0.002 0.002 0.003 0.001
7 −16.084* 4045.797 −0.658 0.323 −0.405 0.519 −0.451 0.285 −0.480 0.121
Pavement Condition −15.759* 6302.943 −0.265 0.586 −13.484 549.429 −1.692 1.004 0.286 0.171
Pavement Roughness −0.730 3787.960 −0.360 0.236 0.062 0.251 −0.114 0.188 −0.013 0.077
Presence of Lighting −13.977 2704.390 −0.418 0.207 0.448 0.322 −0.030 0.185 0.045 0.072
Presence of Horizontal
Curve
0.798 3787.961 0.601 0.267 −0.192 0.386 0.132 0.238 0.040 0.098
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Vertical Curve
Approaching Slope −0.094 0.129 0.004 0.006 −0.034 0.025 0.001 0.006 0.002 0.003
Vertical Curve Leaving
Slope −1.383 0.585 0.093 0.070 0.089 0.129 −0.012 0.066 0.017 0.028
Grade 0.171 0.164 0.052 0.026 0.142 0.052 −0.009 0.021 0.020 0.010
Curvature Degree 0.021 0.020 0.000 0.003 0.002 0.005 0.002 0.002 0.003 0.001
Fit Statistics
AIC 151.36 N/A 1958.8 N/A 2249.4 N/A 2249.4 N/A 5847.4 N/A
BIC 299.17 N/A 2106.65 N/A 2397.18 N/A 2397.18 N/A 5995.256 N/A
—No data.
*Variables are insignificant at a 90-percent confidence level.
Table 3. Poisson regression of 500 mi (dataset 3).
Variables
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Intercept −10.582* 4.997 −7.817 0.905 −2.538 1.308 −3.431 0.790 −2.020 0.334
Ln (Length) 1.157 0.264 0.963 0.046 1.162 0.085 0.999 0.044 1.012 0.019
Ln (AADT) 0.766 0.389 0.869 0.068 0.268 0.102 0.447 0.058 0.544 0.026
Shoulder Width
8 ft Base Level
<2 ft −17.782* 3672.071 0.342 0.152 0.288 0.320 0.794 0.137 0.244 0.069
≥2 ft < 4 ft 0.585 0.550 0.449 0.110 0.456 0.189 0.466 0.107 0.274 0.046
≥4 ft < 6 ft 0.253 0.547 0.103 0.111 0.438 0.185 0.328 0.104 0.292 0.043
≥6 ft < 8 ft −0.741 0.781 −0.004 0.130 −0.015 0.232 0.187 0.122 0.151 0.051
Lane Width
>>12 ft Base Level
9 ft or less 1.380 0.953 0.395 0.204 −0.217 0.352 0.338 0.180 0.075 0.080
9.5 ft–10.5 ft 0.336 0.600 0.253 0.113 0.335 0.183 0.128 0.110 0.201 0.045
11 ft–11.5 ft −17.171* 10097.34 1.172 0.383 −0.784 1.061 0.264 0.390 0.179 0.172
Speed Limit
45 mph Base Level
25 mph 0.051 0.982 0.541 0.220 0.830 0.361 0.419 0.225 0.446 0.093
30 mph −18.766* 68973 −0.194 0.297 0.183 0.518 0.510 0.247 0.131 0.114
35 mph −19.028* 82727 −0.567 0.439 0.587 0.460 0.330 0.300 −0.416 0.163
40 mph −18.258* 3766.9 −0.337 0.244 −1.765 0.622 −0.526 0.243 −0.928 0.114
50 mph −18.088* 5012.2 0.109 0.259 −0.530 0.554 0.006 0.260 −0.247 0.116
55 mph −0.701 0.623 −0.226 0.161 −0.609 0.266 −0.172 0.154 −0.212 0.063
Roadside Hazard Rating (Zegeer et al. 1988)
3 Base Level
4 −0.237 0.527 0.097 0.110 0.341 0.201 −0.116 0.104 0.036 0.044
5 −0.705 0.749 0.217 0.138 0.582 0.244 −0.017 0.128 0.120 0.056
6 −1.549 1.230 0.326 0.173 0.101 0.322 −0.054 0.165 0.212 0.072
7 −18.645* 3594.820 0.367 0.238 0.633 0.405 −0.495 0.266 0.220 0.103
Pavement Condition 0.044 0.089 0.027 0.017 −0.010 0.025 −0.003 0.015 −0.007 0.006
Pavement Roughness 0.014 0.012 0.000 0.002 0.003 0.003 0.003 0.002 0.001 0.001
Presence of Lighting −16.589* 2903.590 −0.483 0.219 0.418 0.323 −0.167 0.201 −0.592 0.095
Presence of Horizontal
−16.846* 4205.718 −15.196 330.239 −14.118 339.152 −1.577 0.580 0.219 0.105
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Vertical Curve
Approaching Slope −0.250 3194.766 0.077 0.211 0.042 0.310 0.216 0.184 −0.175 0.084
Vertical Curve
Leaving Slope −10.742* 2019.942 −0.072 0.165 0.345 0.346 0.067 0.139 −0.137 0.078
Grade −2.117 3194.767 −0.070 0.238 −0.367 0.425 −0.210 0.212 0.173 0.098
Curvature Degree −0.074 0.075 −0.008 0.006 −0.010 0.012 0.002 0.004 0.003 0.002
Fit Statistics
AIC 301.21 N/A 3446.6 N/A 1593.9 N/A 3713.9 N/A 10221 N/A
BIC 481.89 N/A 3627.29 N/A 1774.57 N/A 3894.63 N/A 10401.48 N/A
Table 4. Poisson regression of 750 mi (dataset 4).
Variables
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Intercept −15.751* 5.097 −6.558 0.786 −4.271 1.451 −3.059 0.670 −2.020 0.320
Ln (Length) 0.939 0.253 0.989 0.037 1.087 0.073 1.065 0.033 1.046 0.016
Ln (AADT) 1.033 0.370 0.846 0.052 0.200 0.085 0.453 0.043 0.460 0.021
Shoulder Width
8 ft Base Level
<2 ft −0.289 1.147 0.163 0.173 −0.403 0.528 0.937 0.135 0.209 0.078
≥2 ft < 4 ft −1.343 1.132 0.504 0.092 0.875 0.194 0.523 0.090 0.303 0.040
≥4 ft < 6 ft 0.583 0.594 0.121 0.101 0.367 0.213 0.575 0.091 0.258 0.042
≥6 ft < 8 ft 0.201 0.669 0.072 0.117 0.592 0.224 0.373 0.108 0.259 0.048
Lane Width
>12 ft Base Level
9 ft or less −16.465* 2043.232 0.225 0.173 0.259 0.350 0.046 0.156 0.228 0.075
9.5 ft–10.5 ft 0.476 0.593 0.017 0.099 0.021 0.193 0.124 0.086 0.064 0.042
11 ft–11.5 ft −16.412 3375.189 0.015 0.234 0.044 0.529 0.223 0.203 0.473 0.096
Speed Limit
45 mph Base Level
25 mph 1.968* 1.515 0.372 0.198 −0.429 0.505 0.766 0.183 0.136 0.087
30 mph 3.859* 1.699 0.309 0.209 0.487 0.386 0.865 0.186 −0.060 0.103
35 mph 4.421* 1.214 −0.072 0.277 1.385 0.319 0.805 0.200 −0.300 0.117
40 mph −15.287* 2356.470 −0.949 0.234 −0.674 0.388 −0.550 0.207 −1.064 0.100
50 mph −15.549* 3298.388 −0.403 0.274 −0.444 0.501 0.220 0.231 0.021 0.095
55 mph 0.181 1.121 −0.311 0.129 −0.580 0.259 −0.066 0.135 −0.295 0.055
Roadside Hazard Rating (Zegeer et al. 1988)
3 Base Level
4 3.154* 0.912 0.082 0.095 0.110 0.184 −0.067 0.087 0.036 0.039
5 2.140* 1.102 0.058 0.120 0.245 0.236 0.176 0.107 0.054 0.051
6 2.655* 1.225 −0.057 0.153 0.681 0.284 0.266 0.133 0.230 0.063
7 3.538* 1.264 −0.130 0.207 0.147 0.422 0.338 0.174 0.154 0.087
Pavement Condition 0.026 0.092 0.002 0.015 0.041 0.030 −0.012 0.013 0.012 0.006
Pavement Roughness 0.002 0.009 0.003 0.001 0.000 0.003 0.001 0.001 0.000 0.001
Presence of Lighting 0.281 1.065 −0.234* 0.197 −0.299 0.371 −0.686 0.191 −0.507 0.088
Presence of Horizontal
−17.481* 1993.639 −2.822* 1.002 −1.566 1.007 −1.416 0.451 0.182 0.102
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Vertical Curve
Approaching Slope
0.128 1.044 0.527 0.240 −0.006 0.504 0.115 0.153 −0.013 0.071
Vertical Curve Leaving
Slope
1.569 1.614 0.129 0.145 −0.211 0.394 0.078 0.131 −0.060 0.060
Grade −1.661 1.842 −0.586* 0.268 −0.035 0.558 −0.131 0.184 0.066 0.084
Curvature Degree 0.043 0.013 −0.010 0.011 −0.019 0.021 0.003 0.008 0.000 0.004
Fit Statistics
AIC 286.79 N/A 3815.9 N/A 1636.9 N/A 4357.2 N/A 9471.1 N/A
BIC 463.989 N/A 3993.09 N/A 1814.11 N/A 4534.37 N/A 9648.25 N/A
Table 5. Poisson regression of 1,000 mi (dataset 5).
Variables
K A B C PDO
Parameter
Estimate
Std.
Error
Paramete r
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Paramete r
Estimate
Std.
Error
Intercept −31.452* 1760.461 −6.809 0.630 −5.467 1.031 −2.938 0.545 −1.978 0.249
Ln (Length) 0.468 0.186 1.004 0.033 1.086 0.057 0.986 0.030 1.044 0.014
Ln (AADT) 1.330 0.342 0.821 0.046 0.542 0.076 0.385 0.039 0.481 0.018
Shoulder Width
8 ft Base Level
<2 ft −16.852* 1732.218 0.518 0.113 0.649 0.208 0.476 0.122 0.356 0.056
≥2 ft < 4 ft −0.132 0.494 0.156 0.079 0.516 0.134 0.460 0.073 0.380 0.033
≥4 ft < 6 ft −0.047 0.469 0.166 0.077 0.088 0.144 0.359 0.073 0.265 0.033
≥6 ft < 8 ft −0.515 0.612 0.011 0.093 0.316 0.156 0.007 0.092 0.234 0.038
Lane Width
>12 ft Base Level
9 ft or less 0.616 0.840 0.007 0.159 0.004 0.282 0.441 0.133 0.260 0.062
9.5 ft–10.5 ft −0.013 0.645 0.021 0.086 0.270 0.136 0.056 0.076 0.133 0.034
11 ft–11.5 ft −16.119* 2424.845 0.480 0.203 0.338 0.377 0.286 0.195 0.404 0.085
Speed Limit
45 mph Base Level
25 mph 17.524* 1760.455 0.725 0.172 0.675 0.293 0.494 0.156 0.670 0.074
30 mph −0.253 3591.022 0.597 0.173 0.927 0.277 0.423 0.160 0.370 0.081
35 mph 17.349* 1760.455 0.211 0.189 0.306 0.308 0.018 0.168 0.010 0.085
40 mph 16.745* 1760.455 −0.503 0.186 −0.922 0.350 −0.653 0.164 −0.658 0.081
50 mph 18.193* 1760.455 −0.163 0.239 −0.252 0.428 −0.151 0.208 −0.070 0.099
55 mph 16.423* 1760.455 −0.247 0.124 −0.160 0.211 −0.255 0.108 −0.006 0.053
Roadside Hazard Rating (Zegeer et al. 1988)
3 Base Level
4 0.175 0.501 0.227 0.077 0.065 0.134 0.240 0.071 0.130 0.032
5 0.376 0.682 0.267 0.094 0.122 0.166 0.232 0.089 0.207 0.040
6 1.849 0.720 0.275 0.122 0.609 0.197 0.151 0.117 0.309 0.050
7 1.050 1.230 0.245 0.169 0.405 0.282 0.638 0.142 0.377 0.067
Pavement Condition 0.031 0.079 0.012 0.011 0.010 0.019 0.008 0.010 0.002 0.005
Pavement Roughness −0.012 0.010 0.001 0.001 −0.001 0.002 −0.002 0.001 −0.001 0.001
Presence of Lighting −16.573* 1550.651 −0.163 0.153 −0.971 0.312 0.054 0.138 −0.441 0.069
K A B C PDO
Parameter
Estimate
Std.
Error
Paramete r
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Paramete r
Estimate
Std.
Error
Presence of Horizontal
Curve −16.460* 1920.232 −1.343 0.380 −0.724 0.506 −1.053 0.304 0.282 0.075
Vertical Curve
Approaching Slope −3.731* 3.897 −0.113 0.144 −0.010 0.182 −0.017 0.124 −0.044 0.053
Vertical Curve Leaving
Slope −2.798* 3.873 −0.215 0.122 0.128 0.180 −0.014 0.108 0.031 0.049
Grade 3.217* 3.986 0.206 0.163 0.028 0.230 −0.007 0.146 0.025 0.064
Curvature Degree −0.016 0.026 0.003 0.002 −0.006 0.006 0.003 0.002 0.001 0.001
Fit Statistics
AIC 431.73 N/A 3220.9 N/A 6632.4 N/A 7271.9 N/A 17936 N/A
BIC 627.60 N/A 3416.78 N/A 6828.22 N/A 7467.77 N/A 18131.8 N/A
Table 6. Poisson regression of 150 mi (dataset 6).
Variables
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Paramete r
Estimate
Std.
Error
Intercept −19.149* 10998.67 −4.694 2.139 −5.136 4.328 −1.940 1.747 −1.536 0.852
Ln (Length) 0.545 0.422 0.865 0.083 1.555 0.208 1.015 0.078 0.968 0.037
Ln (AADT) 0.533 0.996 0.695 0.170 0.428 0.329 0.395 0.138 0.413 0.067
Shoulder Width
8 ft Base Level
<2 ft −20.420* 24227.87 0.864 0.531 −15.263* 3531.042 0.687 0.560 0.714 0.262
≥2 ft < 4 ft −12.502* 18084.20 0.525 0.691 −16.908* 2784.792 1.117 0.569 0.804 0.267
≥4 ft < 6 ft 0.115* 0.865 0.275 0.172 0.735* 0.364 0.250 0.143 0.366 0.068
≥6 ft < 8 ft −17.600* 7235.989 0.518 0.196 1.162* 0.402 0.042 0.193 0.154 0.094
Lane Width
>>12 ft Base Level
9 ft or less — — — — — — — — — —
9.5 ft–10.5 ft 1.544 1.707 0.395 0.461 −0.512 1.214 −0.119 0.425 0.028 0.205
11 ft–11.5 ft −18.574* 6138.690 −0.239 0.250 −0.350 0.525 −0.299 0.216 0.013 0.101
Speed Limit
45 mph Base Level
25 mph 18.006* 10998 0.105 0.361 1.196 0.759 0.316 0.325 0.408 0.149
30 mph 1.546* 23648 1.318 0.413 1.049 1.049 0.664 0.441 0.075 0.239
35 mph −0.149* 21251 −0.228 0.763 1.068 1.223 −0.118 0.630 0.326 0.244
40 mph 0.466* 17020 −1.046 0.657 0.928* 0.992 −1.671 0.761 −0.745 0.246
50 mph −0.417* 21180 −1.302 1.051 −16.001* 2273.451 −0.491 0.645 −0.129 0.257
55 mph 17.814* 10998 −0.116 0.289 0.114 0.662 0.022 0.264 −0.107 0.123
Roadside Hazard Rating (Zegeer et al. 1988)
3 Base Level
4 0.941* 1.160 0.284 0.195 0.110 0.414 0.293 0.173 0.195 0.080
5 −15.919* 5770 0.441 0.288 −0.037 0.564 0.395 0.244 0.009 0.120
6 2.540 1.986 0.567 0.355 −0.161 0.749 0.072 0.318 0.122 0.146
7 −13.932* 13063 0.989 0.657 0.648 0.999 −0.108 0.578 0.005 0.265
Pavement Condition −0.109 0.178 0.000 0.035 −0.007 0.073 −0.011 0.029 0.005 0.014
Pavement Roughness −0.034 0.030 −0.010 0.005 0.012 0.010 −0.004 0.004 −0.001 0.002
Presence of Lighting −17.037* 10913 −0.571 0.559 −17.676* 1495.695 0.122 0.399 −0.337 0.208
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Paramete r
Estimate
Std.
Error
Presence of Horizontal
Curve −18.902* 14864 −1.721 1.009 −15.769* 1921.014 −0.347 0.516 0.146 0.189
Vertical Curve
Approaching Slope −0.729 79728 −0.125 0.501 −0.641 0.923 0.305 0.297 0.211 0.158
Vertical Curve Leaving
Slope −2.137* 8271.0 −0.312 0.459 −0.782 0.883 0.268 0.280 0.070 0.140
Grade −12.347* 9774.5 0.137 0.574 1.152 0.983 −0.431 0.381 −0.257 0.191
Curvature Degree 0.015 0.043 0.001 0.010 −0.009 0.028 0.008 0.008 0.008 0.003
Fit Statistics
AIC 112.98 N/A 1111.6 N/A 377.43 N/A 1272.8 N/A 3216.9 N/A
BIC 253.709 N/A 1252.28 N/A 518.16 N/A 1413.54 N/A 3357.64 N/A
Table 7. Poisson regression of 300 mi (dataset 7).
Variables
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Intercept −70.276* 6853.18 −5.939 1.394 −2.073 2.287 −3.565 1.169 −3.483 0.551
Ln (Length) 1.978 0.476 0.996 0.062 1.039 0.106 1.098 0.055 1.061 0.025
Ln (AADT) 2.604 0.737 0.720 0.093 0.269 0.141 0.468 0.078 0.540 0.037
Shoulder Width
8 ft Base Level
<2 ft −0.420 1.405 0.869 0.242 1.300 0.369 0.399 0.290 0.335 0.123
≥2 ft < 4 ft −19.610* 2836.41 0.409 0.154 0.232 0.260 0.582 0.130 0.474 0.062
≥4 ft < 6 ft −3.631 1.544 0.345 0.168 0.232 0.284 0.300 0.147 0.452 0.066
≥6 ft < 8 ft −1.319 1.011 0.179 0.181 −0.023 0.332 0.406 0.149 0.324 0.070
Lane Width
>>12 ft Base Level
9 ft or less −10.809* 4970.7 0.340 0.296 0.430 0.440 −0.184 0.271 0.279 0.117
9.5 ft–10.5 ft 3.161* 1.407 0.303 0.163 −0.093 0.286 −0.018 0.145 0.402 0.066
11 ft–11.5 ft 20.987* 5937.2 −10.377 438.029 −19.810 0.377 −10.505 424.481 0.620 1.031
Speed Limit
45 mph Base Level
25 mph 23.630* 6853.17 0.553 0.344 −1.287 1.088 0.212 0.276 0.435 0.142
30 mph −0.087* 12195.5 0.614 0.502 0.729 0.824 0.663 0.314 0.467 0.199
35 mph 7.959* 14121.6 0.689 0.418 0.938 0.704 −0.854 0.530 −0.013 0.206
40 mph −0.832 9903.19 −0.889 0.458 −0.826 0.801 −0.748 0.309 −1.000 0.192
50 mph 2.575* 10769.3 −0.564 0.398 −0.522 0.658 −0.705 0.331 −0.021 0.135
55 mph 18.908* 6853.17 −0.239 0.228 0.010 0.396 −0.413 0.175 −0.025 0.090
Roadside Hazard Rating (Zegeer et al. 1988)
3 Base Level
4 1.983* 1.494 0.407 0.163 0.653 0.271 −0.172 0.128 −0.101 0.061
5 3.699* 1.748 0.652 0.200 −0.071 0.372 −0.032 0.164 0.039 0.077
6 4.784* 2.314 0.565 0.279 −0.188 0.514 −0.004 0.232 −0.048 0.107
7 −22.811* 8772.52 0.380 0.395 0.431 0.700 0.232 0.330 0.134 0.145
Pavement Condition 0.637 0.269 0.006 0.027 −0.032 0.045 0.018 0.022 0.024 0.010
Pavement Roughness −0.007 0.018 −0.002 0.003 0.001 0.005 0.000 0.002 0.001 0.001
Presence of Lighting 2.598* 1.564 −0.180 0.315 −0.645 0.682 −0.813 0.337 −0.283 0.126
Presence of Horizontal
−17.738* 8177.78 −1.404 1.005 −18.823* 6979.17 −1.538 1.004 0.431 0.172
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Vertical Curve
Approaching Slope −0.179 0.638 0.109 0.274 78.738* 4871.27 −0.088 0.255 −0.103 0.115
Vertical Curve Leaving
Slope 10.850* 5.899 0.069 0.245 0.367 0.314 −0.194 0.226 −0.132 0.104
Grade −9.878* 5.974 −0.222 0.335 −78.778* 4871.27 0.181 0.299 0.098 0.136
Curvature Degree 0.101 0.030 0.000 0.009 −0.010 0.021 −0.002 0.009 −0.002 0.004
Fit Statistics
AIC 149.84 N/A 1714.8 N/A 827.88 N/A 2015.2 N/A 4742 N/A
BIC 309.704 N/A 1874.67 N/A 987.73 N/A 2175.06 N/A 4901.83 N/A
Table 8. Poisson regression of 500 mi (dataset 8).
Variables
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Intercept
−39.721*
9579.61
4 −5.897 0.867 −4.339 1.421 −2.245 0.722 −1.817 0.338
Ln (Length) 1.547 0.565 1.181 0.046 1.180 0.079 1.099 0.042 1.059 0.019
Ln (AADT) 1.474 0.874 0.740 0.064 0.617 0.108 0.409 0.056 0.461 0.025
Shoulder Width
8 ft Base Level
<2 ft
−19.103*
8881.43
5 0.547 0.137 0.691 0.247 0.676 0.141 0.294 0.069
≥2 ft < 4 ft −0.025* 1.219 0.120 0.110 −0.035 0.204 0.595 0.100 0.276 0.046
≥4 ft < 6 ft 0.559* 1.040 −0.074 0.110 0.289 0.181 0.315 0.102 0.244 0.044
≥6 ft < 8 ft −18.713* 5988.73 −0.171 0.127 0.157 0.214 0.127 0.120 0.192 0.051
Lane Width
>>12 ft Base Level
9 ft or less −16.355* 7761.93 0.417 0.193 −0.576 0.359 0.273 0.165 0.148 0.082
9.5 ft–10.5 ft 1.419* 1.179 0.192 0.111 0.019 0.193 0.110 0.103 0.080 0.047
11 ft–11.5 ft −16.400* 26043.6 0.852 0.371 0.652 0.639 −0.032 0.406 0.448 0.170
Speed Limit
45 mph Base Level
25 mph 0.027* 17044.9 0.535 0.220 1.186 0.328 0.955 0.200 0.517 0.093
30 mph −0.899* 21564.7 0.500 0.237 −0.882 0.646 0.129 0.296 −0.089 0.128
35 mph −0.009* 23983.0 0.126 0.356 −0.040 0.633 0.107 0.339 −0.614 0.197
40 mph 19.604* 9579.6 −0.544 0.266 −1.358 0.550 −0.337 0.231 −0.967 0.118
50 mph −0.361* 15763.5 −0.156 0.298 −0.245 0.473 0.066 0.263 0.023 0.106
55 mph 17.779* 9579.6 −0.123 0.160 −0.252 0.264 −0.116 0.157 −0.254 0.064
Roadside Hazard Rating (Zegeer et al. 1988)
3 Base Level
4 0.432* 1.572 −0.080 0.107 0.015 0.183 −0.009 0.101 0.089 0.044
5 1.240* 1.558 −0.052 0.134 0.400 0.236 0.217 0.124 0.129 0.057
6 −0.146* 2.414 −0.251 0.178 0.395 0.312 −0.005 0.162 0.218 0.073
7 −19.523* 10584. 0.146 0.228 0.740 0.436 0.199 0.222 0.103 0.107
Pavement Condition 0.130 0.202 0.015 0.017 −0.012 0.028 −0.019 0.014 0.005 0.007
Pavement Roughness 0.005 0.016 0.002 0.002 −0.006 0.003 0.002 0.002 0.002 0.001
Presence of Lighting 2.760* 1.848 0.071 0.204 −0.022 0.352 −0.139 0.191 −0.642 0.098
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Presence of Horizontal
Curve −16.626* 111651 −1.794* 0.710 −13.960* 354.738 −1.578 0.581 0.589 0.093
Vertical Curve
Approaching Slope −4.946* 7532.0 −0.224 0.220 0.147 0.257 0.360 0.176 −0.084 0.085
Vertical Curve Leaving
Slope −16.818* 5489.0 −0.230* 0.194 0.007 0.195 0.120 0.121 −0.117 0.073
Grade 5.606* 7532. 0.258 0.249 0.059 0.297 −0.294 0.201 0.110 0.098
Curvature Degree −0.131* 0.205 −0.001 0.005 0.004 0.008 −0.015 0.007 0.003 0.002
Fit Statistics
AIC 114.54 N/A 3299.6 N/A 1602.2 N/A 3681.8 N/A 9321.9 N/A
BIC 292.598 N/A 3477.65 N/A 1780.27 N/A 3859.90 N/A 9499.99 N/A
Table 9. Poisson regression of 750 mi (dataset 9).
Variables
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Intercept −14.361 4.822 −5.176 0.767 −4.568 1.334 −3.863 0.666 −2.368 0.306
Ln (Length) 0.600 0.212 1.071 0.039 1.080 0.072 1.032 0.035 1.033 0.016
Ln (AADT) 0.956 0.303 0.814 0.051 0.432 0.085 0.561 0.042 0.527 0.020
Shoulder Width
8 ft Base Level
<2 ft 0.791* 1.116 0.417 0.162 −0.511 0.434 0.723 0.139 0.193 0.075
≥2 ft < 4 ft −0.187 0.642 0.386 0.094 0.156 0.179 0.561 0.086 0.324 0.040
≥4 ft < 6 ft 1.619 0.503 0.337 0.097 0.299 0.176 0.420 0.090 0.313 0.040
≥6 ft < 8 ft 1.066 0.636 0.088 0.119 0.381 0.197 0.155 0.110 0.160 0.048
Lane Width
>>12 ft Base Level
9 ft or less 0.892 1.323 0.093 0.178 0.341 0.299 −0.204 0.162 0.242 0.071
9.5 ft–10.5 ft 0.684 0.582 0.182 0.095 0.237 0.175 0.094 0.086 0.141 0.040
11 ft–11.5 ft 0.259 1.662 0.279 0.220 0.186 0.440 −0.128 0.213 0.343 0.095
Speed Limit
45 mph Base Level
25 mph 2.458 0.716 0.348 0.180 0.255 0.463 0.420 0.187 0.231 0.089
30 mph −23.593* 62341.13 −0.173 0.232 1.097 0.394 0.688 0.176 0.201 0.097
35 mph 0.119 0.959 0.250 0.216 1.127 0.382 0.236 0.216 −0.237 0.117
40 mph −1.065 1.186 −0.835 0.204 −0.778 0.483 −0.368 0.174 −1.002 0.102
50 mph −21.986* 39201.42 −0.144 0.238 −1.631 1.033 −0.126 0.226 −0.085 0.103
55 mph −0.701 0.819 −0.433 0.124 0.132 0.281 −0.300 0.120 −0.150 0.055
Roadside Hazard Rating (Zegeer et al. 1988)
3 Base Level
4 −22.244* 11618.73 −0.050 0.095 0.083 0.180 −0.020 0.085 0.131 0.039
5 0.792 0.732 0.217 0.121 0.318 0.214 −0.194 0.110 0.148 0.049
6 −1.735 1.522 0.168 0.155 0.207 0.282 0.152 0.130 0.200 0.063
7 0.331 1.635 0.612 0.191 0.228 0.389 −0.063 0.181 0.460 0.081
Pavement Condition 0.119 0.098 −0.018 0.015 −0.004 0.027 −0.010 0.013 0.007 0.006
Pavement Roughness −0.022 0.012 −0.001 0.002 0.000 0.003 0.004 0.001 −0.001 0.001
Presence of Lighting 2.372 1.134 −0.553 0.217 −0.402 0.363 −0.303 0.168 −0.308 0.082
Presence of Horizontal
Curve −21.347* 25841.54 −1.179 0.452 −14.166*
325.46
−2.317 0.708 0.133 0.102
Vertical Curve
Approaching Slope 354.650* 29838.59 0.085 0.160 −0.189 0.226 −0.151 0.124 −0.069 0.069
Vertical Curve Leaving
Slope
0.002 0.644 −0.033 0.130 −0.189 0.207 0.045 0.125 −0.077 0.060
Grade −354.529* 29838.59 0.026 0.188 0.437 0.266 0.113 0.158 0.099 0.082
Curvature Degree 0.024 0.069 0.003 0.012 −0.007 0.022 0.000 0.010 −0.007 0.005
Fit Statistics
AIC 309.14 N/A 3640.7 N/A 3640.7 N/A 4090.2 N/A 9241.9 N/A
BIC 483.52 N/A 3815.08 N/A 3815.08 N/A 4264.59 N/A 9416.28 N/A
APPENDIX C. NEGATIVE BINOMIAL REGRESSION
Table 10 through table 18 contain the model estimation results for negative binomial regression on two-lane, two-way undivided segments for datasets 1 through 9.
Table 10. Negative binomial regression of 150 mi (dataset 1).
Variables
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Paramete r
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Intercept −106.430* 736 −3.374 1.926 −6.907 3.976 −4.612 2.196 −0.116 1.084
Ln (Length) 3.253 1.128 0.983 0.085 1.196 0.171 0.911 0.090 0.963 0.046
Ln (AADT) 3.766 2.058 0.487 0.154 0.898 0.332 0.397 0.167 0.334 0.082
Shoulder Width
8 ft Base Level
<2 ft −37.250* 1820 0.822 0.608 −31.793* 9653 0.856 0.694 −0.191 0.462
≥2 ft < 4 ft −2.548 1.921 0.439 0.206 −0.564 0.446 0.379 0.222 0.425 0.118
≥4 ft < 6 ft 1.471 1.458 0.290 0.186 −0.192 0.312 −0.101 0.209 0.193 0.106
≥6 ft < 8 ft −42.294* 48921 0.147 0.228 −0.725 0.450 −0.217 0.247 0.313 0.122
Lane Width
>12 ft Base Level
9 ft or less —
9.5 ft–10.5 ft −32.966* 1043 −0.018 0.471 0.105 0.938 0.141 0.616 0.292 0.257
11 ft–11.5 ft 2.608 2.532 0.091 0.220 0.021 0.410 0.035 0.258 −0.014 0.129
Speed Limit
45 mph Base Level
25 mph −1.004 98319 0.971 0.411 −0.066 0.674 1.011 0.400 0.309 0.205
30 mph −1.288 16325 1.225 0.516 −0.362 0.962 0.706 0.566 −0.128 0.317
35 mph 47.609* 736 0.427 0.702 −29.571* 2759 1.128 0.515 −0.840 0.395
40 mph −5.967* 1180 −0.253 0.599 −1.603 1.150 −1.567 0.827 −0.957 0.308
50 mph 6.154* 13855 −0.103 0.693 1.390 0.807 0.450 0.587 −0.040 0.292
55 mph 37.303* 73637 0.382 0.358 −0.134 0.500 −0.243 0.359 −0.280 0.164
Roadside Hazard Rating (Zegeer et al. 1988)
3 Base Level
4 −0.523 2.044 0.295 0.204 −0.395 0.366 0.103 0.228 0.228 0.113
5 −1.659 2.048 0.392 0.265 0.348 0.472 0.423 0.294 0.206 0.154
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Paramete r
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
6 −39.451* 54412 0.043 0.360 −0.401 0.689 0.093 0.373 0.125 0.197
7 3.745* 3.626 −0.459 0.597 −28.674* 16883 0.162 0.560 0.136 0.315
Pavement Condition 0.834 0.691 −0.017 0.031 0.013 0.059 0.037 0.037 −0.011 0.018
Pavement Roughness 0.035 0.035 −0.001 0.004 −0.007 0.009 0.001 0.004 −0.001 0.002
Presence of Lighting −39.846* 80863 0.498 0.375 −28.333* 13142 0.560 0.382 −0.624 0.254
Presence of Horizontal
Curve −33.535* 9359.0 −32.630* 3275306 −30.841* 4138 −1.242 0.762 −0.207 0.266
Vertical Curve
Approaching Slope 2.524* 48274 −0.103 0.444 1.025* 9056 −0.068 0.425 −0.060 0.143
Vertical Curve Leaving
Slope −2.270 50046 −0.271 0.413 −0.304* 98257 −0.044 0.431 0.119 0.159
Grade −21.311* 61599 0.078 0.514 −24.060* 11523 −0.142 0.519 −0.045 0.190
Curvature Degree 0.029 0.039 0.001 0.010 0.011 0.019 0.009 0.008 −0.010 0.007
Fit Statistics
AIC 98.654 N/A 1197.6 N/A 466.05 N/A 1135.5 N/A 2909.5 N/A
BIC 244.411 N/A 1343.33 N/A 611.80 N/A 1281.22 N/A 3055.24 N/A
Overdispersion 0.356 — 2.43 — 4.30 — 1.397 — 1.978 —
Table 11. Negative binomial regression of 300 mi (dataset 2).
Variables
K A B C PDO
Parameter
Estimate Std. Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Intercept −72.159* 4642 −4.967 1.415 −8.814 2.656 −2.299 1.126 −2.867 0.625
Ln (Length) 0.932 0.397 1.179 0.080 1.175 0.141 1.007 0.065 1.054 0.034
Ln (AADT) 0.410 0.533 0.648 0.102 0.379 0.169 0.520 0.084 0.627 0.045
Shoulder Width
8 ft Base Level
<2 ft 21.665* 30679.285 −0.266 0.317 −0.399 0.509 −0.315 0.264 −0.113 0.153
≥2 ft < 4 ft 21.129* 30679.285 −0.437 0.329 −0.468 0.531 −0.477 0.276 −0.164 0.158
≥4 ft < 6 ft 21.000* 30679.285 −0.321 0.328 −0.795 0.548 −0.388 0.274 −0.131 0.158
≥6 ft < 8 ft 19.801* 30679.285 −0.763 0.322 −0.823 0.519 −0.764 0.269 −0.611 0.156
Lane Width
>>12 ft Base Level
9 ft or less 20.254* 1850.0 −0.366 0.273 0.017 0.560 −0.321 0.223 −0.136 0.128
9.5 ft–10.5 ft 19.899* 1850.0 −0.634 0.310 −0.499 0.612 −0.374 0.250 −0.253 0.143
11 ft–11.5 ft 18.822* 1611.0 −24.272* 4701 −31.757* 38745 −23.525* 164053 0.680 1.060
Speed Limit
45 mph Base Level
25 mph 21.899* 2952 0.253 0.515 0.180 1.197 −0.203 0.440 0.288 0.261
30 mph −1.164 5766 −1.609 0.856 1.527 0.909 −0.839 0.515 −0.842 0.327
35 mph −0.112 3798 −0.883 0.441 −0.103 0.840 −1.224 0.394 −0.964 0.222
40 mph −0.920 3819 −0.889 0.404 −0.350 0.800 −0.555 0.314 −0.141 0.188
50 mph −0.041 4061 −0.074 0.408 0.089 0.899 −0.471 0.355 −0.046 0.204
55 mph 20.358* 2955 −0.740 0.323 −0.037 0.671 −0.813 0.263 −0.473 0.160
Roadside Hazard Rating (Zegeer et al. 1988)
3 Base Level
4 −0.413 0.830 0.072 0.171 −0.064 0.307 −0.225 0.142 0.010 0.078
5 −21.990* 11334.468 0.076 0.218 0.042 0.376 −0.036 0.179 −0.055 0.098
6 −22.723* 17061.511 0.196 0.293 −0.388 0.535 −0.239 0.253 0.138 0.127
7 −21.947* 30091.028 0.606 0.397 0.888 0.665 0.038 0.353 0.105 0.193
Pavement
Condition 0.083 0.171 0.051 0.030 0.155 0.058 0.004 0.024 0.018 0.013
Pavement
Roughness 0.023 0.019 0.000 0.003 0.001 0.005 0.002 0.003 0.003 0.001
K A B C PDO
Parameter
Estimate Std. Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Presence of
Lighting −0.407 23536 −0.714 0.374 −0.232 0.606 −0.430 0.309 −0.603 0.152
Presence of
Horizontal Curve −18.759* 2673 −0.295 0.632 −31.899* 65480 −1.719 1.020 0.295 0.206
Vertical Curve
Approaching Slope −0.819 1605 −0.400 0.254 0.060 0.280 −0.117 0.193 −0.043 0.092
Vertical Curve
Leaving Slope −17.139* 1156 −0.444 0.229 0.411 0.327 −0.008 0.193 −0.002 0.085
Grade 0.992* 16045 0.654 0.291 −0.138 0.406 0.122 0.245 0.112 0.114
Curvature Degree −0.099 0.129 0.004 0.007 −0.047 0.030 0.001 0.007 0.002 0.003
Fit Statistics
AIC 155.31 N/A 1916.5 N/A 836.23 N/A 2238.1 N/A 5603.7 N/A
BIC 326.773 N/A 2087.9 N/A 1007.6 N/A 2409.55 N/A 5775.14 N/A
Overdispersion 102 — 0.910 — 0.457 — 2.183 — 2.158 —
Table 12. Negative binomial regression of 500 mi (dataset 3).
Variables
K A B C PDO
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Parameter
Estimate
Std.
Error
Intercept −12.122* 6.385 −7.907 1.048 −2.428 1.570 −3.551 0.860 −1.760 0.449
Ln (Length) 1.071 0.323 1.007 0.055 1.134 0.101 1.008 0.049 1.022 0.026
Ln (AADT) 0.762 0.470…
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