STC-PSSA: A New Model of Traffic Flow Forecasting Based on Spatiotemporal Convolution and Probabilistic Sparse Self-Attention

Abstract

Traffic flow forecasting is the foundation of the dynamic control and application of intelligent transportation systems (ITS). It is also of significant practical value in alleviating road congestion. Given the periodic and dynamic changes in traffic flow and the spatiotemporal coupling interaction of complex road networks, traffic flow forecasting is challenging and rarely yields satisfactory prediction results. To capture the dynamic spatiotemporal characteristics of traffic flow, a new model of traffic flow forecasting based on spatiotemporal convolution and probabilistic sparse self-attention (STC-PSSA) is proposed. It consists of a spatiotemporal graph convolution network (ST-GCN) module, a spatiotemporal convolution module (ST-Conv), and a probabilistic sparse attention module (PSSA). ST-GCN consists of the gated temporal convolutional network (G-TCN) and the graph convolution network (GCN), which are used to capture the temporal dependence and spatial correlation of the traffic flow, respectively. Multiple ST-GCNs are stacked to handle spatial features at various time levels. The ST-Conv captures intricate temporal dependence at the same location and dynamic spatial features at neighboring locations simultaneously. The PSSA combines dynamic spatiotemporal features and performs long-term forecasting efficiently. The experimental results demonstrate that the STC-PSSA model can accurately extract the dynamic spatiotemporal characteristics of traffic flow and outperforms the popular baseline methods in forecasting accuracy.