MS-Net: Multi-Source Spatio-Temporal Network for Traffic Flow Prediction

Shen Fang, Veronique Prinet, Jianlong Chang, Michael Werman, Chunxia Zhang, Shiming Xiang*, Chunhong Pan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Predicting urban traffic flow is a challenging task, due to the complicated spatio-temporal dependencies on traffic networks. Urban traffic flow usually has both short-term neighboring and long-term periodic temporal dependencies. It is also noticed that the spatial correlations over different traffic nodes are both local and non-local. What's more, the traffic flow is affected by various external factors. To capture the non-local spatial correlations, we propose a Dilated Attentional Graph Convolution (DAGC). The DAGC utilizes a dilated graph convolution kernel to expand the nodes' receptive field and exploit multi-order neighborhood. Technically, the lower-order neighborhood corresponds to local spatial dependencies, while the higher-order neighborhood corresponds to non-local spatial dependencies between nodes. Based on DAGC, a Multi-Source Spatio-Temporal Network (MS-Net) is designed, which suffices to integrate long-range historical traffic data as well as multi-modal external information. MS-Net consists of four components: a spatial feature extraction module, a temporal feature fusion module, an external factors embedding module, and a multi-source data fusion module. Extensive experiments on three real traffic datasets demonstrates that the proposed model performs well on both the public transportation networks, road networks, and can handle large-scale traffic networks in particular the Beijing bus network which has more than 4,000 traffic nodes.

Original languageAmerican English
Pages (from-to)7142-7155
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number7
StatePublished - 2022

Bibliographical note

Funding Information:
This work was supported in part by the Major Project for New Generation of AI under Grant 2018AAA0100400 and in part by the National Natural Science Foundation of China under Grant 91646207, Grant 62072039, Grant 62076242, and Grant 61976208.

Publisher Copyright:
© 2000-2011 IEEE.


  • Graph convolution
  • artificial intelligence
  • deep attention mechanism
  • deep learning
  • traffic flow prediction
  • traffic network


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