Abstract
Traffic prediction is a fundamental operation in real-time traffic analysis. A precise prediction of traffic condition can benefit both road users and traffic management agencies. However, since road traffic is decided by multiple static and dynamic factors, traffic prediction is still a challenging task. As the core indicator of traffic condition, many works focus on traffic speed prediction using time-series forecasting approaches. Although current methods take into account the static road topology while modelling, they fail to consider (1) the semantic closeness between road components and (2) congestion caused by upstream/downstream traffic propagation. In this paper, we introduce a Spatial-Temporal Dynamic Graph Network using JS-Graph, which considers both static road features and dynamic traffic flows when forecasting. Specifically, we first propose a data-driven ‘JS-Graph’ method that describes the semantic similarity between road nodes. It models the complex spatial correlations that cannot be captured by the traditional spatial adjacency graph. Secondly, we design a dynamic graph attention network that considers the traffic dynamics that happened in previous time slices when predicting the current one to capture the congestion propagation phenomena. Extensive experiments conducted on real-world datasets show that our proposed method is significantly better than baselines.
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Acknowledgment
This work was supported by National Natural Science Foundation of China (No. 61802273, No. 62102277), Postdoctoral Science Foundation of China (No. 2020M681529), Science and Technology Plan Project of Suzhou (No. SYG202139), Natural Science Foundation of Jiangsu Province (No. BK20210703).
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Li, P., Fang, J., Chao, P., Zhao, P., Liu, A., Zhao, L. (2022). JS-STDGN: A Spatial-Temporal Dynamic Graph Network Using JS-Graph for Traffic Prediction. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham. https://doi.org/10.1007/978-3-031-00123-9_15
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DOI: https://doi.org/10.1007/978-3-031-00123-9_15
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