We present a novel framework for modeling traffic congestion events over road networks. Using multi-modal data by combining count data from traffic sensors with police reports that report traffic incidents, we aim to capture two types of triggering effect for congestion events. Current traffic congestion at one location may cause future congestion over the road network, and traffic incidents may cause spread traffic congestion. To model the non-homogeneous temporal dependence of the event on the past, we use a novel attention-based mechanism based on neural networks embedding for point processes. To incorporate the directional spatial dependence induced by the road network, we adapt the “tail-up” model from the context of spatial statistics to the traffic network setting. We demonstrate our approach’s superior performance compared to the state-of-the-art methods for both synthetic and real data.


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    Title :

    Spatio-Temporal Point Processes With Attention for Traffic Congestion Event Modeling


    Contributors:


    Publication date :

    2022-07-01


    Size :

    3086684 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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