Spatial-Temporal Conv-sequence Learning with Accident Encoding for Traffic Flow Prediction

In an intelligent transportation system, the key problem of traffic forecasting is how to extract the periodic temporal dependencies and complex spatial correlation. Current state-of-the-art methods for traffic flow forecasting are based on graph architectures and sequence learning models, but they do not fully exploit spatial-temporal dynamic information in the traffic system. Specifically, the temporal dependence of the short-range is diluted by recurrent neural networks, and the existing sequence model ignores local spatial information because the convolution operation uses global average pooling. Besides, there will be some traffic accidents during the transitions of objects causing congestion in the real world that trigger increased prediction deviation. To overcome these challenges, we propose the Spatial-Temporal Conv-sequence Learning (STCL), in which a focused temporal block uses unidirectional convolution to effectively capture short-term periodic temporal dependence, and a spatial-temporal fusion module is able to extract the dependencies of both interactions and decrease the feature dimensions. Moreover, the accidents features impact on local traffic congestion, and position encoding is employed to detect anomalies in complex traffic situations. We conduct a large number of experiments on real-world tasks and verify the effectiveness of our proposed method.