Predicting the traffic conditions in urban networks is a priority for traffic management centres. This becomes very challenging, especially when the network is affected by traffic incidents that vary in both time and space. Although data-driven modelling can be considered an ideal tool for short-term traffic predictions, its performance is severely degraded if little historical traffic information is available under incident conditions. This paper addresses this challenge by integrating data-driven and traffic simulation modelling approaches. Instead of directly predicting the traffic states using limited historical data, we employ a traffic simulation reinforced by data-driven models. The traffic simulation uses newly reported incident information and the estimated origin-destination (OD) demand flows to capture the complex interaction between drivers and road network, and predicts traffic states under extreme conditions. We showcase the capability of the proposed data-driven enforced traffic simulation platform for incident impact analysis in a real-life sub-network in Sydney, Australia.


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    Titel :

    Integrating data-driven and simulation models to predict traffic state affected by road incidents


    Beteiligte:

    Erschienen in:

    Transportation Letters ; 14 , 6 ; 629-639


    Erscheinungsdatum :

    2022-07-03


    Format / Umfang :

    11 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt





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