TRAFFIC CONGESTION MODELING WITH DEEP ATTENTION HAWKES PROCESS
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In this thesis, we focus on modeling the traffic congestion in the city of Atlanta. We are trying to predict future congestion events on the main highways in Atlanta. We present a novel framework for modeling traffic congestion events over road networks based on mutually exciting Spatio-temporal point process models. We use multi-modal data by combining traffic sensor networks data with police reports, which contain two types of triggering mechanisms for congestion events. To capture the non-homogeneous temporal dependence of the event on the past, we introduce a novel attention-based approach for the point process model. To incorporate the directional spatial dependence induced by the road network, we adapt the “tail-up” model from the spatial statistics context. We demonstrate the superior performance of our approach compared to the state-of-the-art for both synthetic and real data.