Wrong-way driving (WWD) crashes can have significant impacts on freeway safety and operations. Deploying intelligent transportation systems (ITS) WWD countermeasures at freeway exit ramps can effectively reduce WWD crash risk (WWCR), but these countermeasures are expensive. In this paper, a WWCR segment model and WWD countermeasures optimization algorithm are developed for all Florida limited access facilities (56 roadways with 1,375 exits) to identify the optimal locations for ITS countermeasure deployment. These were previously developed for specific toll-road networks within Florida, but never for a statewide network. Multiple WWCR models were investigated, with the final Poisson model using four-exit segments and 5 years of WWD event data. This model showed that more WWD events and higher crossing road traffic volumes increased WWCR, while certain interchange designs increased or decreased WWCR. Sixty-three segments containing 169 exit ramps without ITS WWD countermeasures were identified as WWD hotspots; these ramps were compared with the 169 ramps with the highest WWCR selected by the optimization algorithm. The algorithm selected 96 ramps not in the hotspots (improved resource utilization of 56.8%) and provided a 38.6% increase in WWCR reduction. Comparing WWD detection and turnaround data from 31 sites with rectangular flashing beacon ITS countermeasures to the optimization indicated a significant positive association between WWCR and turnaround percentage (higher WWCR at sites with lower turnaround percentage), verifying the accuracy of the optimization. By showing the transferability, scalability, accuracy, and benefits of this approach, this paper can help agencies reduce WWD and improve freeway safety and operations.


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    Titel :

    Statewide Application of Wrong-Way Driving Crash Risk Modeling and Countermeasures Optimization Algorithm to Identify Optimal Locations for Countermeasure Deployment on Florida Limited Access Facilities


    Weitere Titelangaben:

    Transportation Research Record


    Beteiligte:
    Sandt, Adrian (Autor:in) / Al-Deek, Haitham (Autor:in) / Blue, Patrick (Autor:in) / McCombs, John (Autor:in)


    Erscheinungsdatum :

    2021-01-01




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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