Over the past few decades, numerous adaptive traffic signal control (ATSC) algorithms have been proposed to alleviate traffic congestion and optimize traffic mobility using real-time traffic data, such as data from connected vehicles (CVs). However, most of the existing ATSC algorithms do not consider optimizing traffic safety, likely because of the lack of tools to evaluate safety in real time. In this paper, we propose a novel ATSC algorithm for real-time safety optimization. The algorithm utilizes a traditional Reinforcement Learning approach (i.e., Q-learning) as well as recently developed extreme value theory (EVT) real-time crash prediction models. The algorithm was validated using real-world traffic video data collected from two signalized intersections in British Columbia. The results indicated that, compared with an existing fully actuated signal controller, the developed algorithm can significantly reduce the real-time crash risk by 43% to 45% at the intersection’s approaches even at low CVs market penetration rates.


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    Title :

    Real-Time Crash-Risk Optimization at Signalized Intersections


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:
    Reyad, Passant (author) / Sayed, Tarek (author) / Essa, Mohamed (author) / Zheng, Lai (author)


    Publication date :

    2021-12-12




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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