Traffic Prediction Based on Adaptive Spatio-Temporal Graph Network
Accurately extracting spatiotemporal dependencies in complex traffic situations and improving the accu-racy of traffic prediction is an important step in building intelligent transportation systems.In this paper,a traffic pre-diction model based on adaptive spatiotemporal graph network(Ada-STGN)is proposed.The model deals with the time dependence by using a multi-layer stacking gating residual time convolution(GRes-TCN)structure Adaptive multi-head spatial attention module(Ada-SAN)is used to model different subspace dependence patterns and obtain spatial dependence relations.The prediction experiments were carried out on two real public transport datasets,METR-LA and PEMS-BAY,respectively.Compared with the baseline model,Ada-STGN reduces the prediction error and has better prediction performance.
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