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The findings of this study call for more attention to WWD crashes from different aspects such as driver age group, time of day, day of week, and DUI drivers. Based on the analysis results of WWD distance, the study explained why a 5-mile radius of WWD crash location should be studied for WWD fatal crashes with unknown entry points.

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Despite many other methods that identify only the contributing factors, this method can identify possible associations between various contributing factors. This is an inherent advantage of the MCA method, which can provide a major opportunity for state departments of transportation (DOTs) to select safety countermeasures that are associated with multiple safety benefits.

Crash data on Alabama Interstates were collected across a five-year time period from 2009 to 2013, and true wrong-way driving (WWD) crashes were identified using the hardcopy of crash reports and existing maps. The crash data contained 18 explanatory variables representing the driver, 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 vs. non-WWD). This model is an appropriate tool to control 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 using the standard binary logistic regression was also developed. Two information criteria (AIC and BIC) obtained from both developed models indicate that for our database, Firth's model outperforms the standard binary logistic model and provides more reliable results. Using Firth's model, explanatory variables including month of the year, time of the day, driver age, driver 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. Based on the obtained odds ratio (OR), this paper discusses the various effect of the identified variables and recommends several countermeasures for policy makers in order to reduce the WWD issue on Alabama Interstates.

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The proposed method can diminish one of the challenges in front of transportation engineers, which is to identify high WWD crash locations due to insufficient information in crash reports. The results are helpful for transportation professionals to take proactive steps to identify locations for implementing safety countermeasures at high risk signalized parclo interchanges.