It can be expensive for agencies to deploy wrong-way driving (WWD) countermeasure technologies on limited access facilities. This paper discusses a WWD crash risk (WWCR) reduction approach to help agencies determine the most cost-effective deployment locations. First, a directional WWCR model identifies roadway segments with high WWCR (WWD hotspots), then two optimization algorithms identify individual exits and mainline sections with high WWCR for priority deployment of WWD countermeasure technologies. This new approach was applied to the Central Florida Expressway Authority (CFX) toll road network to determine priority deployment locations for “Wrong Way” signs with Rectangular Flashing Beacons (RFBs). After modeling each direction of the CFX roadways separately, fifteen WWD hotspot segments were identified. WWCR reduction values were calculated for each exit by determining how far wrong-way vehicles travel based on WWD 911 call data. The exit ramp optimization algorithm was then tested for four investment levels using actual RFB deployment costs and real-world constraints. These optimization results could help CFX better utilize its investment by between 9% and 28% compared with only deploying RFBs at exits in the WWD hotspot segments. The mainline optimization algorithm, which considered the WWCR reduction caused by RFBs already deployed at CFX exit ramps, showed that State Road (SR) 408, SR 417, and SR 528 have mainline sections with high WWCR. These results show how the WWCR reduction approach can help agencies identify WWD hotspot segments and high-WWCR exits not in these segments (lone wolf exits), better utilize their investment, and determine mainline sections with high WWCR.


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    Title :

    Responding to the Wrong-Way Driving Problem with Limited Resources by Optimizing the Placement of Wrong-Way Driving Countermeasure Technologies on Limited Access Facilities


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    2019-05-05




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English