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Publication Number: FHWA-RD-99-207

Prediction of the Expected Safety Performance of Rural Two-Lane Highways

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Table of Contents

This report documents the algorithm for predicting the safety performance of rural two-lane highways that forms the basis for the Crash Prediction Module of the Interactive Highway Safety Design Model. The algorithm estimates the effect on safety performance of roadway segment parameters including lane width, shoulder width, shoulder type, horizontal curves, grades, driveway density, two-way left-turn lanes, passing lanes, and roadside design, and of intersection parameters including skew angle, traffic control, exclusive left- and right-turn lanes, sight distance, and driveways. The algorithm enables highway agencies to estimate the safety performance of existing or proposed highways and to compare the expected safety performance of geometric design alternatives.

Michael F. Trentacoste
Director, Office of Safety Research and Development

 

NOTICE

This document is disseminated under the sponsorship of the Department of Transportation in the interest of information exchange. The United States Government assumes no liability for its content 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 of this document.


Technical Report Documentation Page

1. Report No.
FHWA-RD-99-207
2. Government Accession No. 3. Recipient's Catalog No.
4. Title and Subtitle
PREDICTION OF THE EXPECTED SAFETY PERFORMANCE OF RURAL TWO-LANE HIGHWAYS
5. Report Date
6. Performing Organization
7. Author(s)
D.W. Harwood, F.M. Council, E. Hauer, W.E. Hughes, and A. Vogt
8. Performing Organization Report No.
4584-09
9. Performing Organization Name and Address
Midwest Research Institute
425 Volker Boulevard
Kansas City, Missouri 64110-2299
10. Work Unit No. (TRAIS)
11. Contract or Grant No.
DTFH61-96-C-00055
12. Sponsoring Agency Name and Address
Office of Safety Research and Development
Federal Highway Administration
6300 Georgetown Pike
McLean, Virginia 22101-2296
13. Type of Report and Period Covered
Technical Report
May 1997—September 2000
14. Sponsoring Agency Code
15. Supplementary Notes
Contracting Officer's Technical Representative (COTR): Michael S. Griffith, HRDS-06
16. Abstract
This report presents an algorithm for predicting the safety performance of a rural two-lane highway. The accident prediction algorithm consists of base models and accident modification factors for both roadway segments and at-grade intersections on rural two-lane highways. The base models provide an estimate of the safety performance of a roadway or intersection for a set of assumed nominal or base conditions. The accident modification factors adjust the base model predictions to account for the effects on safety for roadway segments of lane width, shoulder width, shoulder type, horizontal curves, grades, driveway density, two-way left-turn lanes, passing lanes, roadside design and the effects on safety for at-grade intersections of skew angle, traffic control, exclusive left- and right-turn lanes, sight distance, and driveways.

The accident prediction algorithm is intended for application by highway agencies to estimate the safety performance of an existing or proposed roadway. The algorithm can be used to compare the anticipated safety performance of two or more geometric alternatives for a proposed highway improvement.

The accident prediction algorithm includes a calibration procedure that can be used to adapt the predicted results to the safety conditions encountered by any particular highway agency on rural two-lane highways. The algorithm also includes an Empirical Bayes procedure that can be applied to utilize the safety predictions provided by the algorithm together with actual site-specific accident history data.
17. Key Words
Safety
Accident Modeling
Two-Lane Highways
Roadway Segments
Accident Prediction
Geometric Design
Empirical Bayes Estimation
At-Grade Intersections
18. Distribution Statement
No restrictions. This document is available to the public through the National Technical Information Service, Springfield, Virginia 22161.
19. Security Classif. (of this report)
Unclassified
20. Security Classif. (of this page)
Unclassified
21. No. of Pages
197
22. Price

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


SI* (Modern Metric) Conversion Factors

1. INTRODUCTION
  Estimates from Historical Accident Data
  Estimates from Statistical Models
  Estimates from Before-and-After Studies
  Estimates from Expert Judgment
  A New Approach
  Organization of this Report
  Units of Measure
2. OVERVIEW OF THE ACCIDENT PREDICTION ALGORITHM
  Accident Prediction Algorithm for Roadway Segments
  Accident Prediction Algorithm for At-Grade Intersections
  Predicted Accident Frequency for an Entire Project
  Estimated Accident Severity and Accident Type Distributions
  Strengths and Weaknesses of this Approach
  Structure of the Accident Prediction Algorithm
3. BASE MODELS
  Base Model for Roadway Segments
  Base Models for At-Grade Intersections
  Calibration Procedure
4. ACCIDENT MODIFICATION FACTORS
  Development of Accident Modification Factors
  Roadway Segments
  Grades
  At-Grade Intersections
5. SENSITIVITY ANALYSIS RESULTS
  Roadway Segments
  Three-Leg STOP-Controlled Intersections
  Four-Leg Signalized Intersections
6. IMPLEMENTATION OF THE ACCIDENT PREDICTION ALGORITHM WITHIN THE IHSDM
  Accident Prediction When Site-Specific Accident History Data are not Available
  Accident Prediction When Site-Specific Accident History Data are Available
  Situations in Which the EB Procedure Should and Should Not Be Applied
  Empirical Bayes Procedure
  Example Application of the EB Procedure
  Step-by-Step Methodology for Applying the Accident Prediction Algorithm Including the EB Procedure
7. CONCLUSIONS, RECOMMENDATIONS, AND FUTURE ENHANCEMENTS
  Conclusions
  Future Enhancement of the Accident Prediction Algorithm
8. REFERENCES
9. BIBLIOGRAPHY
APPENDIX A EXPERT PANEL MEMBERSHIP
APPENDIX B DEVELOPMENT OF BASE MODELS
APPENDIX C CALIBRATION PROCEDURE TO ADAPT THE ACCIDENT PREDICTION ALGORITHMTO THE DATA OF A PARTICULAR HIGHWAY AGENCY
APPENDIX D DEFINITIONS OF ROADSIDE HAZARD RATINGS USED WITH THE ACCIDENT PREDICTION ALGORITHM

Figures

1. Flow Diagram of the Accident Prediction Algorithm for a Single Roadway Segment or Intersection.
2. Recommended Accident Modification Factor for Lane Width.
3. Accident Modification Factor for Shoulder Width.
4. Accident Modification Factor for Superelevation Deficiency.
5. Flow Diagram of the Accident Prediction Algorithm When No Site-Specific Accident History Data Are Available.
6. Flow Diagram for the Accident Prediction Algorithm When Site-Specific Accident History Data Are Available
7. Flow Diagram of Calibration Process.
8. Typical Roadway with Roadside Hazard Rating Equal to 1.
9. Typical Roadway with Roadside Hazard Rating Equal to 2.
10. Typical Roadway with Roadside Hazard Rating Equal to 3.
11. Typical Roadway with Roadside Hazard Rating Equal to 4.
12. Typical Roadway with Roadside Hazard Rating Equal to 5.
13. Typical Roadway with Roadside Hazard Rating Equal to 6.
14. Typical Roadway with Roadside Hazard Rating Equal to 7.

Tables

1. Default Distribution for Accident Severity Level on Rural Two-Lane Highways.
2. Default Distribution for Accident Type and Manner of Collision on Rural Two-Lane Highways.
3. Accident Modification Factors for Shoulder Types on Two-Lane Highways.
4. Accident Modification Factors for Grade of Roadway Sections.
5. Accident Modification Factors for Installation of Left-turn Lanes on the Major-Road Approaches to Intersection on Two-Lane Rural Highways.
6. Sensitivity of Safety to ADT for Nominal Conditions for Roadway Segments.
7. Sensitivity of Safety to Lane Width on Roadway Segments.
8. Sensitivity of Safety to Shoulder Type and Width on Roadway Segments.
9. Sensitivity of Safety to Horizontal Curve Length and Radius on Roadway Segments.
10. Sensitivity of Safety to Horizontal Curve Superelevation Deficiency on Roadway Segments.
11. Sensitivity of Safety to Percent Grade on Roadway Segments.
12. Sensitivity of Safety to Driveway Density on Roadway Segments.
13. Sensitivity of Safety to Presence of Passing Lanes and Short Four-Lane Sections on Roadway Segments.
14. Sensitivity of Safety to Roadside Hazard Rating on Roadway Segments.
15. Sensitivity of Safety to Extreme Combinations of Geometric Design and Traffic Control Features.
16. Sensitivity of Safety to Major-Road Turn Lanes at Three-Leg STOP-Controlled Intersections.
17. Sensitivity of Safety to Skew Angles at Three-Leg STOP-Controlled Intersections.
18. Sensitivity of Safety to Limited Intersection Sight Distance at Three-Leg STOP-Controlled Intersections.
19. Sensitivity of Safety to Major-Road Turn Lanes at Four-Leg STOP-Controlled Intersections.
20. Sensitivity of Safety to Skew Angle at Four-Leg STOP-Controlled Intersections.
21. Sensitivity of Safety to Limited Intersection Sight Distance Deficiencies at Four-Leg STOP-Controlled Intersections.
22. Sensitivity of Safety to Major-Road Turn Lanes at Four-Leg Signalized Intersections.
23. Overdispersion Parameters for Base Models and Minimum Accident Frequencies for EB Procedure.
24. Application of Empirical Bayes Procedure to Roadway Segment 1.
25. Application of Empirical Bayes Procedure to Roadway Segment 2.
26. Application of Empirical Bayes Procedure to Roadway Segments 1 and 2 Combined.
27. Application of Empirical Bayes Procedure to Intersection 1(Four-Leg STOP-Controlled Intersection).
28. Application of Empirical Bayes Procedure to Roadway Segments 1 and 2 and Intersection 1 Combined.
29. Model Parameters and Goodness of Fit for Equation (42).
30. Descriptive Statistics for Roadway Segments Used in Modeling.
31. Descriptive Statistics for 382 Three-Leg STOP Controlled Intersections in Minnesota Used in Modeling.
32. Model Parameters and Goodness of Fit for Equation (52).
33. Model Parameters and Goodness of Fit for Equation (53).
34. Model Parameters and Goodness of Fit for Equation (54).
35. Model Parameters and Goodness of Fit for Equation (55).
36. Model Parameters and Goodness of Fit for Equation (56).
37. Model Parameters and Goodness of Fit for Equation (57).
38. Descriptive Statistics for 324 Four-Leg STOP-Controlled Intersections in Minnesota.
39. Model Parameters and Goodness of Fit for Equation (58).
40. Model Parameters and Goodness of Fit for Equation (59).
41. Model Parameters and Goodness of Fit for Equation (60).
42. Model Parameters and Goodness of Fit for Equation (61).
43. Model Parameters and Goodness of Fit for Equation (62).
44. Descriptive Statistics for 49 Four-Leg Signalized Intersections in California and Michigan Used in Modeling.
45. Model Parameters and Goodness of Fit for Equation (63).
46. Model Parameters and Goodness of Fit for Equation (64).
47. Model Parameters and Goodness of Fit for Equation (65).
48. Model Parameters and Goodness of Fit for Equation (66).
49. Model Parameters and Goodness of Fit for Equation (67).
50. Model Parameters and Goodness of Fit for Equation (68).
51. Minimum Requirements for Calibration Levels 1 and 2
52. Data Needs for Calibration Levels 1 and 2.
53. Estimate Mileage by ADT Level and Horizontal Alignment.
54. Estimate Mileage by ADT Level and Vertical Alignment.
55. Estimate Mileage by ADT Interval.
56. Estimate Proportion of Mileage by Terrain.
57. Calculate the Predicted Annual Number of Non-Intersection Accidents as a Function of ADT.
58. Develop Estimates Required for Alignment Component of the Procedure.
59. Estimate Mileage by ADT Level and Vertical Alignment.
60. Illustration of How Average Percent Grade Can Be Applied Across Lane and Shoulder Width Combinations.
61. Estimate Proportion of Mileage by Terrain.
62. Estimate Mileage by ADT Interval, Lane and Shoulder Widths.
63. Predicting Total Non-Intersection Accidents as a Function of ADT, Lane Width, and Shoulder Width.
64. Minimum Sample Sizes by Type of Intersection.
65. Example of Desired Sample Stratification of Three- and Four-Leg STOP-Controlled Intersections Based on Major Road ADT.
66. Example of Desired Sample Stratification of Four-Leg Signalized Intersections Based on Major Road ADT.
67. Example of Desired Sample Stratification of Three-Leg STOP-Controlled Intersections Based on Major-Road ADT and Highway District.

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