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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: IEEE Transactions on Intelligent Transportation Systems

سال: 2022

ISSN: ['1558-0016', '1524-9050']

DOI: https://doi.org/10.1109/tits.2021.3068139