An optimization approach for deployment of intelligent transportation systems wrong-way driving countermeasures

Abstract

The nature of wrong-way driving (WWD) crashes on limited access facilities makes it difficult for agencies to combat them effectively. WWD countermeasures equipped with Intelligent Transportation Systems (ITS) technologies, such as warning lights and detection devices, have been proven to reduce WWD. However, agencies often cannot deploy these ITS countermeasures at all exit ramps due to their expense. This paper discusses an innovative WWD countermeasure optimization approach to help agencies identify the optimal deployment locations based on available resources. The approach consists of a WWD crash risk (WWCR) model and a WWD countermeasures optimization algorithm. The WWCR model uses non-crash WWD events, interchange designs, and traffic volumes to predict the number of WWD crashes on multi-exit segments of limited access facilities. Then, the optimization algorithm uses the model results to identify the best exits for ITS countermeasure deployment based on WWCR reduction, available resources, and other applicable constraints. This approach was applied to the Florida’s Turnpike Enterprise (FTE) toll road network. Twenty-four segments were identified as WWD hotspots due to high WWCR. Three different optimization scenarios were tested to show how different constraints affect the results. These scenarios resulted in 38% to 41% of the maximum possible WWCR reduction by equipping only 39 of the 196 FTE ramps (20%) and could help FTE better utilize its investment by 36% compared to only equipping ramps in the identified 24 WWD hotspots. Other agencies could personalize this approach based on their available resources, and preferred WWD countermeasures to achieve similar benefits.