Affiliations: College of Computer Science, Chongqing University, Chongqing, China
Correspondence:
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Corresponding author. Liang Ge, College of Computer Science, Chongqing University, Chongqing 400044, China. E-mail: [email protected].
Abstract: Urban traffic flow prediction is a critical problem in the intelligent transportation system, and it’s very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the complex road network and dynamic traffic conditions. However, existing methods either rely too much on prior knowledge or the data itself when modeling spatial-temporal dependency and few researchers consider them in combination. In this paper, a new spatial-temporal network for traffic flow prediction, which can comprehensively capture the complex spatial and temporal dependency based on prior knowledge and data-driven, is proposed. In particular, in the perspective of local and global spatial dependency in road networks, we construct a dynamic weighted graph by finding the spatial and semantic neighborhoods of road nodes based on road networks and the similarities between traffic data on different roads. Besides, the temporal trend module and implicit temporal dependency module are combined to capture the temporal transitivity of traffic flow and implicit dependencies between time point pairs. The experiment results of our proposed model outperform the state-of-the-art baselines.
