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

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, the authors 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.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2021.3067024
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Fang, Shen
  • Prinet, Véronique
  • Chang, Jianlong
  • Werman, Michael
  • Zhang, Chunxia
  • Xiang, Shiming
  • Pan, Chunhong
• Publication Date: 2022-7

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 7142-7155
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 7
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Artificial intelligence; Data fusion; Data models; Predictive models; Traffic engineering; Traffic flow
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01860119
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 30 2022 2:27PM