Traffic incidents are a common occurrence in urban traffic networks, but predicting their impacts is challenging because of network complexity and the dynamic spatial and temporal dependencies inherent in traffic data. Nevertheless, the prediction of traffic incident impacts is crucial for global positioning systems to provide drivers with real-time route recommendations for bypassing congested roads. To this end, we formulated nonrecurrent congestion measures to quantify these impacts and developed a new method to identify the influential features that locally affect individual incidents. Because traffic incident impacts are determined by a complex entanglement of local features, a meaningful feature that can explain their impacts globally may not exist. Consequently, to identify all influential local features, we applied the local interpretable model-agnostic explanations (LIME) technique to the proposed nonrecurrent congestion measures. The proposed method was validated using real user trajectory data and incident data provided by the NAVER Corporation and the Korean National Police Agency, respectively.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Quantifying incident impacts and identifying influential features in urban traffic networks


    Beteiligte:
    Lee, JuYeong (Autor:in) / Kwak, JiIn (Autor:in) / Oh, YongKyung (Autor:in) / Kim, Sungil (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2023-12-31


    Format / Umfang :

    22 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    A System for Incident Detection in Urban Traffic Networks

    Hawas, Yaser E. / Mohammad, Mohammad Sherif | IEEE | 2015


    A binary logit-based incident detection model for urban traffic networks

    Hawas, Yaser E. / Ahmed, Faisal | Taylor & Francis Verlag | 2017


    A Deep Learning Approach for Traffic Incident Detection in Urban Networks

    Zhu, Lin / Guo, Fangce / Krishnan, Rajesh et al. | IEEE | 2018



    Effective variables for urban traffic incident detection

    Siripanpornchana, Chaiyaphum / Panichpapiboon, Sooksan / Chaovalit, Pimwadee | IEEE | 2015