Long Short Term Spatio-temporal Neural Network for Traffic Speed Forecasting

Traffic prediction is an important component of Intelligent Transportation System (ITS), and accurately predicting dynamic traffic conditions is a challenge to be solved. Capturing the locality and globality of spatiotemporal correlations plays a significant role in prediction, and existing methods cannot completely capture them. This paper proposes a Long Short Term Spatio-temporal Neural Network (LS2TNN) model for traffic speed prediction. The spatial gated graph convolutional attention module and time convolutional attention module in the model are used to capture long-term and short-term spatiotemporal correlations. Static graphs cannot effectively describe dynamic traffic flow. This paper uses predefined edge dynamic edge weights graphs and dynamic edge dynamic edge weights graphs for Graph Convolution Network (GCN), in order to better describe the dynamic traffic situation in real scenes. Experiments conducted on two real datasets showed that LS2TNN achieved advanced performance compared to the baselines.