Modeling Wrong-way Driving (WWD) crash severity on arterials in Florida.

Wrong-way Driving (WWD) is the movement of a vehicle in a direction opposite to the one designated for travel. WWD studies and mitigation strategies have exclusively been focused on limited-access facilities. However, it has been established that WWD crashes on arterial corridors are also severe and relatively more common. As such, this study focused on determining factors influencing the severity of WWD crashes on arterials. The analysis was based on five years of WWD crashes (2012-2016) that occurred on state-maintained arterial corridors in Florida. Police reports of 2,879 crashes flagged as "wrong-way" were downloaded and individually reviewed. The manual review of the police reports revealed that of the 2,879 flagged WWD crashes, only 1,890 (i.e., 65.6 %) occurred as a result of a vehicle traveling the wrong way. The Bayesian partial proportional odds (PPO) model was used to establish the relationship between the severity of these WWD crashes and different driver attributes, temporal factors, and roadway characteristics. The following variables were significant at the 90 % Bayesian Credible Interval (BCI): day of the week, lighting condition, presence of work zone, crash location, age and gender of the wrong-way driver, airbag deployment, alcohol use, posted speed limit, speed ratio (i.e., driver's speed over the posted speed limit), and the manner of collision. Based on the model results, specific countermeasures on Education, Engineering, Enforcement, and Emergency response are discussed. Potential Transportation Systems Management and Operations (TSM&O) strategies for WWD detection systems on arterials to minimize WWD frequency and severity are also proposed.

Severity analysis of crashes on express lane facilities using support vector machine model trained by firefly algorithm.

OBJECTIVE: Express lanes (ELs) provide an alternative way for improving the capacity of the existing freeway network without considerably expanding the roadway footprint. Although much research has been done to explore factors contributing to crashes on these facilities, there is not much discussion on factors influencing their injury severity. This study explored factors influencing the injury severity of crashes on EL facilities. METHOD: A Support Vector Machine (SVM) model trained by the Firefly Algorithm was used to identify factors influencing the injury severity of crashes on EL facilities. The analysis was based on three years of crash data (2012-2014) from four EL facilities in California, totaling 61 miles. RESULTS: The results indicated that the following factors increased the probability of an injury or a fatality: concrete barriers, high average annual daily traffic, rolling or mountainous terrain, weekend, adverse road surface condition, and nighttime condition. Moreover, wide right and left shoulder widths decreased the probability of having an injury or a fatality. CONCLUSIONS: The results provide insights into the influence of different geometric characteristics and crash-related factors on the severity of crashes on EL facilities. The study findings may assist agencies to better understand the impacts of factors contributing to injury and fatal crashes on EL facilities and implement strategies to reduce the severity of these crashes.