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Publication Number:  FHWA-HRT-16-054    Date:  October 2016
Publication Number: FHWA-HRT-16-054
Date: October 2016


Investigating the Impact of Lack of Motorcycle Annual Average Daily Traffic Data in Crash Modeling and the Estimation of Crash Modification Factors

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Advanced crash prediction methods, including the use of safety performance functions and the Empirical Bayes method, have become the standard for traffic safety decisionmakers. Safety practitioners employ these methods as a part of their safety decisionmaking process for many roadway and crash types. However, there has been very little research conducted to extend these methods specifically to the prediction of motorcycle crashes. This deficiency may be in part related to a lack of traffic count data that specifically identifies motorcycles. Motorcycle-focused average annual daily traffic (AADT) information is critical to these types of assessments in order to properly account for the exposure of drivers and motorcycle riders. However, motorcycle crashes continue to be a significant safety concern on U.S. highways; they account for over 14 percent of all fatalities.

The research team developed numerous statistical models with and without motorcycle AADT using crash and traffic records from three states (Florida, Pennsylvania, and Virginia). Ultimately, it was found that while accurate counts for motorcycle AADT are preferred, in many cases, it is appropriate to use the total AADT as a surrogate. This finding will be valuable for State and local safety analysts who want to better understand the scope of motorcycle safety risks and explore options to reduce the number of motorcycle crashes and fatalities on their roads.

Monique R. Evans
Director, Office of Safety
Research and Development


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The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this report only because they are considered essential to the objective of the document.

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Technical Report Documentation Page

1. Report No.


2. Government Accession No. 3 Recipient's Catalog No.
4. Title and Subtitle

Investigating the Impact of Lack of Motorcycle Annual Average Daily Traffic Data in Crash Modeling and the Estimation of Crash Modification Factors

5. Report Date

October 2016

6. Performing Organization Code
7. Author(s)

Craig Lyon, Bhagwant Persaud, Robert Scopatz, Scott Himes, Matt Albee, and Thanh Le

8. Performing Organization Report No.

9. Performing Organization Name and Address

8300 Boone Blvd., Ste. 700
Vienna, VA 22182-2626
M9C 3R7
Persaud Lyon, Inc.
87 Elmcrest Road
Toronto, Ontario

10. Work Unit No. (TRAIS)

11. Contract or Grant No.


12. Sponsoring Agency Name and Address

U.S. Department of Transportation
Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC 20590

13. Type of Report and Period Covered

Safety Evaluation

14. Sponsoring Agency Code

Federal Highway Administration

15. Supplementary Notes

The Federal Highway Administration (FHWA) Office of Safety Research and Development managed this study. The FHWA Office of Safety Research and Development Contract Task Order Manager was Craig Thor.

16. Abstract

The development of safety performance functions (SPFs) and crash modification factors (CMFs) requires data on traffic exposure. The analysis of motorcycle crashes can be especially challenging in this regard because few jurisdictions collect motorcycle traffic volume data systematically. To address this challenge, the project team conducted several analyses to explore (1) how much predictive power for an SPF is lost when motorcycle volumes are unknown and how this lack of information may affect the development of CMFs for motorcycle crashes, and (2) alternative methods for deriving accurate predictions of motorcycle crashes or motorcycle volumes. The results of the analyses show that when motorcycle volumes are not known, using total average annual daily traffic (AADT) on its own is sufficient for developing SPFs and CMFs. The potential bias due to missing motorcycle-specific AADT is sufficiently negligible where it exists so as not to preclude SPF and CMF development. The project team also concluded that attempting to predict motorcycle volumes is not possible using typically available roadway and county-level data. Improvement could possibly be found in trip generation type modeling at a disaggregate scale, although given the success of SPF development using total AADT, such an effort may not be worthwhile. A more significant issue in developing motorcycle crash SPFs and CMFs is working with relatively rare crash types. In the analyses undertaken, SPFs could not be developed for all motorcycle crash types or site types. More evidently, in the estimation of CMFs using simulated data, the CMF value varied significantly between simulation runs due to the low frequency of motorcycle crashes. In terms of research gaps, a database is needed that includes implemented countermeasures expected to affect motorcycle crashes along with the location, date of treatment, and treatment description. This information would aid researchers in identifying treatments that are feasible for study. The report also identifies several research gaps related to analytical methods, related gaps, and data limitations.

17. Key Words

Motorcycle crashes, safety performance functions, crash modification factors, safety evaluations, average annual daily traffic, AADT

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)


20. Security Classification
(of this page)


21. No. of Pages


22. Price


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

SI* (Modern Metric) Conversion Factors

Table of Contents

Executive Summary

Chapter 1. Introduction

Chapter 2. Current Practices for Assessing Motorcycle Safety

Chapter 3. Analysis Methods

Chapter 4. Data Collection and Summary

Chapter 5. Data Analysis and Results

Chapter 6. Conclusions and Recommendations

Chapter 7. Limitations and Future Research Needs



List of Figures

List of Tables

List of Abbreviations

AADT annual average daily traffic  
ADT average daily traffic  
BAC blood alcohol concentration  
CMF crash modification factor  
CURE cumulative residuals  
EB Empirical Bayes  
FARS Fatal Accident Reporting System  
FDOT Florida Department of Transportation  
FHWA Federal Highway Administration  
GIS geographic information system  
GLM generalized linear modeling  
HSM Highway Safety Manual  
HSIP Highway Safety Improvement Program  
IR infrared  
KABCO killed, A injury, B injury, C injury, property damage only  
LRS linear referencing system  
MAD mean absolute derivation  
MMUCC model minimum uniform crash criteria  
PAR police accident report  
PDO property damage only  
RLC red light camera  
RR relative risk  
SE standard error  
SPF safety performance function  
STD standard deviation  
TIRTL The Infra-Red Traffic Logger  
TRIS Transportation Research Information Service  
VDOT Virginia Department of Transportation  
VMT vehicle-miles traveled  



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