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

21 Scopus citations

Abstract

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
Volume23
Issue number7
DOIs
StatePublished - 1 Jul 2022

Bibliographical note

Publisher Copyright:
© 2000-2011 IEEE.

Keywords

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

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