Traffic incidents are a leading contributor to non-recurring congestion and secondary crashes. Each year congestion and crashes together cost the United States over 1 trillion dollars. Once traffic queues are formed, it is difficult to dissipate them and return traffic to normal operations. Therefore, real-time and accurate incident detection plays a critical role in Traffic Incident Management (TIM). This research focuses on highway traffic incident detection. It divides a highway network into short segments and correlates temporal and spatial data from adjacent segments for detecting incidents. Due to incidents being relatively rare compared to normal traffic patterns, we propose a method that combines oversampling with the attention mechanism and use an ablation study to prove its effectiveness in improving supervised incident detection.


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    Titel :

    Enhancing Traffic Incident Detection Through ADASYN-Attention Fusion: A Comparative Study with RITIS Data


    Beteiligte:
    Liu, Ruifeng (Autor:in) / Xie, Yuanchang (Autor:in) / Stamatiadis, Polichronis (Autor:in) / Gartner, Nathan (Autor:in) / Ge, Tingjian (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    782935 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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