Citation

Rahman MA, Sun X, Das S, Khanal S. Int. J. Transp. Sci. Technol. 2021; 10(2): 167-183.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.ijtst.2020.12.003

PMID

unavailable

Abstract

Roadway departure (RwD) crashes are a major contributor of rural two-lane (R2L) highway crashes and fatalities. For targeted reduction of crashes and fatalities due to roadway departure, a thorough understanding of factors associated with RwD crashes is necessary. This study quantitatively assessed the available pre-crash factors that might influence the RwD crashes by developing a logit model comparing roadway, crash environment, and the vehicle and driver-related characteristics of 122,978 crashes that occurred in Louisiana over thirteen years. With a high prediction accuracy (81.9% area under the receiver operating characteristics curve), the model presented significant individual associations across crash characteristics with RwD crashes on R2L highways, for example - animals on roadways, snow/sleet/hail, 50-55 mph speed limit, AADT of 1,001 to 5,000 vehicles per day, drug intoxication, motorcycles, driving during 12 am to 6 am, curve radius of 501-1,000 ft., absence of streetlight, alcohol intoxication. Investigation on these top factors using association rules mining reveals findings such as - a higher likelihood of RwD crashes can be strongly associated animal presence coupled with the absence of streetlights, male drivers during the early morning (12 am to 6 am), male drivers driving with no passengers, drivers being intoxicated by both drugs and alcohol, etc.

FINDINGS from this study are expected to help highway safety specialists not only in identifying and predicting RwD crashes but also in an improved understanding of associated contributing factors leading to the application of proper countermeasures for the strategic reduction of RwD crashes.

Language: en

Keywords

Association rule mining; Crash contributing factor; Logit model; Roadway departure crashes; Rural two-lane highways