Comprehensive Analysis of Wrong-Way Driving Crashes on Alabama Interstates

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 lo...