Data-driven analysis and forecasting of highway traffic dynamics

The unpredictable elements involved in a vehicular traffic system, like human interaction and weather, lead to a very complicated, high-dimensional, nonlinear dynamical system. Therefore, it is difficult to develop a mathematical or artificial intelligence model that describes the time evolution of traffic systems. All the while, the ever-increasing demands on transportation systems has left traffic agencies in dire need of a robust method for analyzing and forecasting traffic. Here the authors demonstrate how the Koopman mode decomposition can offer a model-free, data-driven approach for analyzing and forecasting traffic dynamics. By obtaining a decomposition of data sets collected by the Federal Highway Administration and the California Department of Transportation, the authors are able to reconstruct observed data, distinguish any growing or decaying patterns, and obtain a hierarchy of previously identified and never before identified spatiotemporal patterns. Furthermore, it is demonstrated how this methodology can be utilized to forecast highway network conditions.

• Record URL:
  http://dx.doi.org/10.1038/s41467-020-15582-5
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20411723
• Supplemental Notes:
  • © 2021 A.M. Avila and I. Mezić.
• Authors:
  • Avila, A M
  • Mezic, I
• Publication Date: 2020-4-29

Media Info

• Media Type: Digital/other
• Pagination: e2090
• Serial:
  • Nature Communications
  • Volume: 11
  • Issue Number: 1
  • Publisher: Nature Publishing Group
  • ISSN: 2041-1723
  • Serial URL: http://www.nature.com/ncomms/index.html

Subject/Index Terms

• TRT Terms: Data analysis; Data files; Traffic characteristics; Traffic forecasting
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01765580
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 22 2021 10:21AM