01626300

Component

This study focuses on differentiating the confounding factors that contribute to Wrong-Way Driving (WWD) crashes from other type of crashes on Alabama divided highways by performing statistical analysis. Crash data from 2009 to 2013 including 112 verified WWD crashes is compared with 49,599 other kind of crashes on the same class of roads during the same period. A simple descriptive data analysis was conducted to identify different explanatory variables contributing to WWD crashes. The results illustrated characteristics of a WWD crash, including crash severity, temporal distribution, driver characteristics, vehicle characteristics, and environmental conditions. In addition, the Firth’s penalized-likelihood logistic regression model was used to identify the statistically significant contributing factors. Odds ratios (OR) of different variables were calculated to measure how each factor affect WWD crashes when compared with other types of crashes. The results show that wrong-way (WW) drivers are more likely to be older and driving under influence (DUI). Furthermore, WWD crashes were found to be more prevalent in urban areas and dark road conditions. Dark roadway with no lighting conditions was found to have the largest OR. To complement the analysis, the contributing factors of those fatal WWD crashes also were investigated. Finally, several countermeasures for reducing WWD crashes on the studied facilities are discussed based on the data analysis results.

This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.

01618707

17-05146

English

16p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References (23) ; Tables

Crash causes; Crash characteristics; Crash severity; Wrong way driving

Alabama

Highways; Safety and Human Factors

Transportation Research Board Annual Meeting 2017 Paper #17-05146

PRP, TRIS, TRB, ATRI

Dec 8 2016 12:00PM