Identifying Wrong-Way Driving Hotspots by Modeling Crash Risk and Assessing Duration of Wrong-Way Driving Events

Abstract

Because wrong-way driving (WWD) crashes are often severe, it is important for transportation agencies to identify WWD hotspot segments appropriate for potential implementation of advanced WWD countermeasures. Two approaches to identify these hotspot segments were developed and applied to a case study of limited-access highways in Central Florida. The first approach used a Poisson regression model that predicted the number of WWD crashes in a roadway segment based on WWD citations, 911 calls, traffic volumes, and interchange designs. Combining this predicted crash value with the actual number of WWD crashes in the segment gave the WWD crash risk of the segment. Ranking roadway segments by WWD crash risk provided agencies with an understanding of which segments had high WWD crash frequencies and high potential for future WWD crashes. This approach was previously applied to South Florida; the research presented here extended this approach to Central Florida. The second approach was based on operational data collected in traffic management centers and could be used if accurate WWD 911 and citation data with GPS location were not available or as a supplement to the first approach. The approach identified and ranked WWD hotspots on the basis of the reported duration of WWD events. In Central Florida, the results of the two approaches agreed with each other and were used by agencies to decide where to implement advanced WWD countermeasures. Together, these approaches can help transportation agencies determine regional WWD hotspots and cooperate to implement advanced WWD countermeasures at these locations.