Dynamic traffic correlations based spatio-temporal graph convolutional network for urban traffic prediction

Accurate urban traffic prediction is a critical issue in Intelligent Transportation Systems (ITS). It is challenging since urban traffic usually indicates high dynamic spatio-temporal correlations, leading to uncertainty and complexity of traffic status. Since the transporta-tion network is a graph structure practically, existing works have applied Graph Convolutional Network (GCN) on urban traffic prediction with a pre-defined adjacency matrix based on node distance or connectivity. However, in many urban traffic scenarios, spatio-temporal dependencies among traffic data usually change over time, so using a fixed adjacency matrix cannot describe the dynamic dependencies. To track the dynamic spatio-temporal dependencies among traffic data, we propose a novel deep learning framework, Dynamic Traffic Correlation-based Spatio-Temporal Graph Convolutional network (DTC-STGCN), to forecast traffic flow and speed accurately. DTC-STGCN extracts a dynamic adjacency matrix from different traffic characters to describe dynamic spatio-temporal cor-relations. Moreover, an attention and dynamic adjacency matrix-based GCNs framework is proposed to capture urban traffic dynamic spatial features, while a long-short-term mem-ory network (LSTM) is used to capture urban traffic temporal features, respectively. Finally, we feed the spatio-temporal features generated by GCN and LSTM, with real road segments into a hybrid graph convolution framework to simultaneously model the dynamic spatial and temporal dependencies for traffic predictions. The experiments on two real-world datasets demonstrate that the proposed DTC-STGCN model consistently outperforms the state-of-the-art traffic prediction baselines on MAE and RMSE over 10%, and achieve a stable performance for two specific tasks (long-term traffic prediction and peak time pre-diction). And ablation study validates the effectiveness of dynamic adjacency matrix, atten-tion mechanism, respectively. (c) 2022 Elsevier Inc. All rights reserved.