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

• Published in: Accident analysis and prevention Vol. 151; p. 105963
• Main Authors: Kadeha, Cecilia, Haule, Henrick, Ali, MD Sultan, Alluri, Priyanka, Ponnaluri, Raj
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.03.2021
• Subjects: Arterials; Crash severity; Partial proportional odds model; Wrong-way driving crashes; Wrong-way driving crashes; Arterials; Partial proportional odds model; Crash severity
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2020.105963

•Wrong-way Driving (WWD) is the movement of a vehicle in a direction opposite to the one designated for travel.•WWD crashes are common on arterials because of multiple access points.•The Bayesian partial proportional odds (PPO) model was used to model the severity of WWD crashes on arterials.•Lighting condition, presence of work zone, crash location, age and gender, airbag deployment are some of the factors that affect WWD crash severity.•Traditional countermeasures of the 4E’s can be tailored to mitigate WWD crashes. TSM&O strategies can also be used to mitigate WWD crashes in arterials. 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.