Temporal Multi-view Graph Convolutional Networks for Citywide Traffic Volume Inference

With the development of mobile position techniques, sensing the citywide traffic information has been well recognized as a crucial task for various urban computing applications, such as intelligent transportation system, location-based recommendation, and user mobility modeling. With the consideration of high cost for sensor installment and maintenance, the traffic monitoring spatial coverage is often very limited in practical urban sensing scenarios. The goal of this paper is to perform the traffic inference over road segments which lack of (with very limited) historical traffic observations. Towards this end, we propose a temporal multi-view graph convolutional network for Citywide Traffic Volume Inference (CTVI) which jointly captures the spatial-temporal dependencies across different time intervals and geographical locations. In our CTVI framework, we design our attentive multi-view graph neural architecture based on our generated spatial and feature affinity graphs, to perform the cross-layer message passing with the preservation of road segment-wise topological context. In addition, we develop a temporal self-attention module to encode the evolving traffic patterns over time, which incorporates the time-wise relation contextual signals into the main embedding space. Furthermore, we propose a joint learning objective function that consists of an unsupervised random walk enhancement and a semi-supervised spatio-temporal volume constraint to guide the learning of road segment representations for citywide traffic volume inference. Evaluation results on real-world traffic datasets demonstrate the superiority of our proposed CTVI framework as compared to state-of-the-art baselines.