CLSTAN: ConvLSTM-Based Spatiotemporal Attention Network for Traffic Flow Forecasting

Abstract

Traffic flow forecasting is the essential part of intelligent transportation sSystem (ITS), which can fully protect traffic safety and improve traffic system management capability. Nevertheless, it is still a challenging problem, which is influenced by many complex factors, including regional distribution and external factors (e.g., holidays and weather). To combine various factors to forecast traffic flow, we presented a novel neural network structure called ConvLSTM-based Spatiotemporal Attention Network (CLSTAN). Specifically, our proposed model is composed of four modules: a preliminary feature extraction module, a spatial attention module, a temporal attention module, and an information fusion module. The spatiotemporal attention module can efficiently learn the complex spatiotemporal patterns of traffic flow through the attention mechanism. The spatial attention module uses a series of initial traffic flow maps as input and obtains the weights of the various regions through a ConvLSTM. The temporal attention module uses the spatially weighted traffic flow map as input and acquires the complex spatiotemporal patterns of traffic flow by a ConvLSTM that introduces an attention mechanism. Finally, the information fusion module integrates spatiotemporal information from multiple time dimensions to forecast future traffic flow. Moreover, to confirm the validity of our method, our experiments were conducted extensively on the TaxiBJ and BikeNYC datasets, and ultimately, CLSTAN performed better than other baseline experiments.