Wrong-Way Driving: A Regional Transportation Systems Management and Operations Approach to a Regional Problem

Abstract

Wrong-way driving (WWD) has been problematic on United States highways for decades despite its rare occurrence. Since WWD crashes are rare, recent researchers have studied WWD non-crash events such as WWD 911 calls and WWD citations to understand the overall nature and trend of WWD. This paper demonstrates the regional nature of the WWD problem and proposes regional transportation systems management and operations (TSM&O) solutions to combat this problem. Specifically, it was found that 11% of all WWD multi-data events (e.g., multiple 911 calls for the same WWD event) involved travel from one county to another. Additionally, 30% of all WWD single-data and multi-data events occurred at or near interchanges between two limited access highways in counties with multiple operating agencies. This indicates that a significant proportion of WWD events could potentially travel from one limited access facility to another. Moreover, 28% of WWD events occurred on limited access facilities shared by multiple operating agencies. To emphasize the regional nature of WWD, this paper determined the vulnerable demographic groups in different regions of Florida by developing WWD crash and citation prediction models. The models’ findings indicate that certain demographic groups (e.g., elderly drivers) increase WWD risk. The models’ results can be used to improve driver education and increase law enforcement presence in high risk WWD locations. Regional TSM&O solutions, such as coordination and communication among agencies and traffic management centers (TMCs), law enforcement co-location with TMCs, and strengthening statewide TSM&O programs to manage WWD events are also proposed.

Benefit–Cost Analyses of Rectangular Flashing Beacon Wrong-Way Driving Countermeasures on Toll Road Exit Ramps in Florida

“…This research included a benefit-cost analysis of these LED WWD countermeasures resulting in a benefit-cost ratio of 13.1, however details of this analysis were not found (12). UCF researchers have built models to predict WWD crashes and non-crash events using various roadway characteristics and driver demographics, developed an optimization approach to help agencies effectively deploy WWD countermeasures, surveyed drivers and law enforcement officers about WWD behavior, analyzed the factors that affect law enforcement response time to WWD events, and evaluated LED and RFB WWD countermeasures installed on toll roads in South and Central Florida (1,(5)(6)(7)(13)(14)(15)(16)(17)(18)(19)(20)(21). However, none of this previous research involved benefit-cost analyses of ITS WWD countermeasures.…”

Modeling Wrong-Way Driving Entries at Limited Access Facility Exit Ramps in Florida

“…In addition to the non-crash WWD events, they used event durations from SunGuide reports provided by the Regional Traffic Management Center (RTMC) in Orlando to identify WWD hotspots ( 7 ). Faruk et al studied the effect of driver demographic factors on the number of WWD crashes ( 18 ). WWD 911 calls, daily vehicle miles traveled (DVMT), percentage of licensed drivers aged between 50 and 65, and percentage of Hispanic population in a county were found to be the most significant influencing factors for WWD crashes in a county ( 18 ).…”

“…Faruk et al studied the effect of driver demographic factors on the number of WWD crashes ( 18 ). WWD 911 calls, daily vehicle miles traveled (DVMT), percentage of licensed drivers aged between 50 and 65, and percentage of Hispanic population in a county were found to be the most significant influencing factors for WWD crashes in a county ( 18 ). UCF researchers also developed models for predicting different types of citations related to WWD, including median crossover citations ( 18, 19 ).…”

“…WWD 911 calls, daily vehicle miles traveled (DVMT), percentage of licensed drivers aged between 50 and 65, and percentage of Hispanic population in a county were found to be the most significant influencing factors for WWD crashes in a county ( 18 ). UCF researchers also developed models for predicting different types of citations related to WWD, including median crossover citations ( 18, 19 ). The total number of WWD citations in a county increases with the number of 911 calls, DVMT, and percentage of Hispanic population ( 18 ).…”

“…UCF researchers also developed models for predicting different types of citations related to WWD, including median crossover citations ( 18, 19 ). The total number of WWD citations in a county increases with the number of 911 calls, DVMT, and percentage of Hispanic population ( 18 ).…”