Spatiotemporal traffic forecasting: review and proposed directions

This paper systematically reviews studies that forecast short-term traffic conditions using spatial dependence between links. The authors extract and synthesise 130 research papers, considering two perspectives: (1) methodological framework and (2) methods for capturing spatial information. Spatial information boosts the accuracy of prediction, particularly in congested traffic regimes and for longer horizons. Machine learning methods, which have attracted more attention in recent years, outperform the naïve statistical methods such as historical average and exponential smoothing. However, there is no guarantee of superiority when machine learning methods are compared with advanced statistical methods such as spatiotemporal autoregressive integrated moving average. As for the spatial dependency detection, a large gulf exists between the realistic spatial dependence of traffic links on a real network and the studied networks as follows: (1) studies capture spatial dependency of either adjacent or distant upstream and downstream links with the study link, (2) the spatially relevant links are selected either by prejudgment or by correlation-coefficient analysis, and (3) studies develop forecasting methods in a corridor test sample, where all links are connected sequentially together, assume a similarity between the behaviour of both parallel and adjacent links, and overlook the competitive nature of traffic links.

• Record URL:
  • https://doi.org/10.1080/01441647.2018.1442887
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/7802200
• Supplemental Notes:
  • © 2018 Informa UK Limited, trading as Taylor & Francis Group. Abstract republished with permission of Taylor & Francis.
• Authors:
  • Ermagun, Alireza
  • Levinson, David
• Publication Date: 2018-11

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 786-814
• Serial:
  • Transport Reviews
  • Volume: 38
  • Issue Number: 6
  • Publisher: Routledge
  • ISSN: 0144-1647
  • EISSN: 1464-5327
  • Serial URL: http://www.tandfonline.com/loi/ttrv20

Subject/Index Terms

• TRT Terms: Literature reviews; Machine learning; Network links; Spatial analysis; Statistical analysis; Traffic flow; Traffic forecasting
• Subject Areas: Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01682597
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 2 2018 5:19PM