GT-LSTM: A spatio-temporal ensemble network for traffic flow prediction

Abstract

Traffic flow prediction plays an instrumental role in modern intelligent transportation systems. Numerous existing studies utilize inter-embedded fusion routes to extract the intrinsic patterns of traffic flow with a single temporal learning approach, which relies heavily on constructing graphs and has low training efficiency. Different from existing studies, this paper proposes a spatio-temporal ensemble network that aims to leverage the strengths of different sequential capturing approaches to obtain the intrinsic dependencies of traffic flow. Specifically, we propose a novel model named graph temporal convolutional long short-term memory network (GT-LSTM), which mainly consists of features splicing and patterns capturing. In features splicing, the spatial dependencies of traffic flow are captured by employing self-adaptive graph convolutional network (GCN), and a non-inter-embedded approach is designed to integrate the spatial and temporal states. Further, the aggregated spatio-temporal states are fed into patterns capturing, which can effectively exploit the advantages of temporal convolutional network (TCN) and bidirectional long short-term memory network (Bi-LSTM) to extract the intrinsic patterns of traffic flow. Extensive experiments conducted on four real-world datasets demonstrate that the proposed network obtains excellent performance in both forecasting accuracy and training efficiency.