LTPHM: Long-term Traffic Prediction based on Hybrid Model

Traffic prediction is a classical spaial-temporal prediction problem with many real-world applications. In general, existing traffic prediction methods capture the complex spatial-temporal features by iterative mechanism or non-iterative mechanism. However, the iterative mechanism often causes the prediction error accumulation and the non-iterative mechanism is hard to capture the dynamic propagation information. The shortcomings of both mechanisms lead to their poor performance in long-term prediction tasks. Target at the shortcomings of existing methods, in this paper, we propose a novel deep learning framework called Long-term Traffic Prediction based on Hybrid Model (LTPHM), which is designed to simulate the dynamic transmission process of traffic information on the road network by connecting the prediction values of the current step with the next step. Each spatial-temporal module uses graph convolution (GCN) with an adaptive matrix to capture spatial dependence. Besides, we use Gated Dilated Convolution Networks (GDCN) and Gated Linear Unit convolution networks (GLU) to capture temporal dependence. Since LTPHM integrates the advantages of both iterative and non-iterative prediction, it can efficiently capture the complex and dynamic spatial-temporal features, especially the long-range temporal sequences. Experiments with three real-world traffic datasets demonstrate the effectiveness of our proposed model.