Comprehensive Analysis of Wrong-Way Driving Crashes on Alabama Interstates

Abstract

Crash data on Alabama Interstates were collected for a 5-year period from 2009 to 2013. True wrong-way driving (WWD) crashes were identified from the hard copy of crash reports and existing maps. The crash data contained 18 explanatory variables representing the driver, the temporal, vehicle, and environmental information. A Firth’s penalized likelihood logistic regression model was developed to examine the influence of the explanatory variable on the dichotomous dependent variable (type of crash, i.e., WWD versus non-WWD). This model was an appropriate tool for controlling the influence of all confounding variables on the probability of WWD crashes while considering the rareness of the event (i.e., WWD). A separate model that used the standard binary logistic regression was also developed. Two information criteria (the Akaike information criterion and the Bayesian information criterion) obtained from both developed models indicated that for this database, Firth’s model outperformed the standard binary logistic model and provided more reliable results. With Firth’s model, explanatory variables including month of the year, time of day, driver’s age, driver’s mental and physical condition, driver’s residency distance, vehicle age, vehicle damage, towing condition, airbag deployment status, and roadway condition were found to characterize WWD crashes. Using the obtained odds ratio, this paper discusses the various effects of the identified variables and recommends several countermeasures policy makers can use to address the WWD issue on Alabama Interstates.