Because wrong-way driving (WWD) crashes are often severe, it is important for transportation agencies to identify WWD hotspot segments appropriate for potential implementation of advanced WWD countermeasures. Two approaches to identify these hotspot segments were developed and applied to a case study of limited-access highways in Central Florida. The first approach used a Poisson regression model that predicted the number of WWD crashes in a roadway segment based on WWD citations, 911 calls, traffic volumes, and interchange designs. Combining this predicted crash value with the actual number of WWD crashes in the segment gave the WWD crash risk of the segment. Ranking roadway segments by WWD crash risk provided agencies with an understanding of which segments had high WWD crash frequencies and high potential for future WWD crashes. This approach was previously applied to South Florida; the research presented here extended this approach to Central Florida. The second approach was based on operational data collected in traffic management centers and could be used if accurate WWD 911 and citation data with GPS location were not available or as a supplement to the first approach. The approach identified and ranked WWD hotspots on the basis of the reported duration of WWD events. In Central Florida, the results of the two approaches agreed with each other and were used by agencies to decide where to implement advanced WWD countermeasures. Together, these approaches can help transportation agencies determine regional WWD hotspots and cooperate to implement advanced WWD countermeasures at these locations.
Wrong-way driving (WWD) often leads to severe collisions that cause serious injuries and deaths. Conventional “Wrong Way” signs can reduce WWD events, but can be insufficient in some cases. In areas with many WWD events, transportation agencies can be proactive by considering the use of countermeasures with advanced technologies to actively warn motorists of WWD violations. This paper analyzes recent performance data collected from two types of advanced technology WWD countermeasures implemented in Florida: light-emitting diode (LED) signs in South Florida and rectangular flashing beacon (RFB) signs in Central Florida. The 17 LED sites experienced a 38% reduction in WWD citations and 911 calls after the signs were installed. Images taken by the on-site cameras were examined to see how many vehicles turned around for both the RFB and LED treatment sites. Over 77% of the 170-detected wrong-way vehicles self-corrected their wrong-way movement at the RFB sites (each with two sets of signs and multiple cameras) and 14% self-corrected at the LED sites (each with one set of signs and one camera). Surveys were also conducted regarding these two WWD countermeasures. More than 73% of the 2,052 respondents preferred RFBs over LEDs, mainly due to the double set of RFB signs and their flashing pattern. The performance and survey results show that both the LEDs and RFBs have effectively reduced WWD movements. However, modifications could be made to both countermeasures to improve their detection ability and make wrong-way drivers more likely to turn around.
Wrong-way driving (WWD) is hazardous on high-speed limited access facilities. Traditional signage and pavement markings will not always prevent intoxicated or confused drivers from entering these facilities the wrong way. To better alert wrong-way drivers, agencies can consider WWD countermeasures equipped with advanced technologies (including warning lights and detection devices) on exit ramps. However, these countermeasures can be expensive for agencies to install on entire roadways or corridors. This paper develops an innovative WWD crash risk (WWCR) reduction approach consisting of a WWCR segment model and an optimization algorithm that can be used to help agencies decide where to install WWD countermeasures. The approach examines segments of limited access facilities to determine the interchanges where advanced technology WWD countermeasures will provide the greatest reduction in WWCR based on an agency’s available resources. A hypothetical example application of this approach is shown for the Central Florida Expressway Authority toll road network. This example shows how the WWCR reduction approach can help agencies identify the optimal investment level and high-risk locations. It also shows how the optimization algorithm can provide significant cost savings compared with equipping entire roadway segments (57% savings) or corridors (83% savings). Agencies can customize the algorithm by adding constraints to represent various scenarios and make the algorithm applicable for networks ranging from single roadways to statewide systems. This WWCR reduction approach could be utilized by agencies nationwide to help them save resources and prioritize investment.
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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