Reliable traffic flow prediction is of great value in the field of transportation, which, for example, contributes to traffic control and public safety. The key of achieving better performance is to well capture the non-linear spatial-temporal dependency. The state-of-the-art works consider both aspects, but they ignore the effect of the global trend on local dynamics and fail to capture long-term dynamic dependencies. In this article, we propose a novel Global-Local Temporal Convolutional Network (GL-TCN) to break through these limitations. Specifically, a novel local temporal convolutional mechanism is proposed to capture the long-term local dynamics effectively. Meanwhile, the global and local flow patterns are integrated to handle the effect of the global flow trend on local dynamics. To the best of our knowledge, this is the first work to utilize the temporal convolutional network for traffic flow prediction. Experiments on two real-world datasets demonstrate the superior performance of our method over several state-of-the-art baselines.
Global-Local Temporal Convolutional Network for Traffic Flow Prediction
IEEE Transactions on Intelligent Transportation Systems ; 23 , 2 ; 1578-1584
01.02.2022
1352065 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Global spatio‐temporal dynamic capturing network‐based traffic flow prediction
DOAJ | 2023
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