Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction

Abstract

Traffic flow prediction plays an important role in ITS (Intelligent Transportation System). This task is challenging due to the complex spatial and temporal correlations (e.g., the constraints of road network and the law of dynamic change with time). Existing work tried to solve this problem by exploiting a variety of spatiotemporal models. However, we observe that more semantic pair-wise correlations among possibly distant roads are also critical for traffic flow prediction. To jointly model the spatial, temporal, semantic correlations with various global features in the road network, this paper proposes T-MGCN (Temporal Multi-Graph Convolutional Network), a deep learning framework for traffic flow prediction. First, we identify several kinds of semantic correlations, and encode the non-Euclidean spatial correlations and heterogeneous semantic correlations among roads into multiple graphs. These correlations are then modeled by a multi-graph convolutional network. Second, a recurrent neural network is utilized to learn dynamic patterns of traffic flow to capture the temporal correlations. Third, a fully connected neural network is utilized to fuse the spatiotemporal correlations with global features. We evaluate T-MGCN on two real-world traffic datasets and observe improvement by approximately 3% to 6% as compared to the state-of-the-art baseline.