Spatio-temporal prediction of freeway congestion patterns using discrete choice methods

Predicting freeway traffic states is, so far, based on predicting speeds or traffic volumes with various methodological approaches ranging from statistical modeling to deep learning. Traffic on freeways, however, follows specific patterns in space–time, such as stop-and-go waves or mega jams. These patterns are informative because they propagate in space–time in different ways, e.g., stop and go waves exhibit a typical propagation that can range far ahead in time. If these patterns and their propagation become predictable, this information can improve and enrich traffic state prediction. In this paper, the authors use a rich data set of congestion patterns on the A9 freeway in Germany near Munich to develop a mixed logit model to predict the probability and then spatio-temporally map the congestion patterns by analyzing the results. As explanatory variables, the authors use variables characterizing the layout of the freeway and variables describing the presence of previous congestion patterns. The authors find that a mixed logit model significantly improves the prediction of congestion patterns compared to the prediction of congestion with the average presence of the patterns at a given location or time.

• Record URL:
  • https://doi.org/10.1016/j.ejtl.2024.100144
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2192437624000190
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/21924376
• Supplemental Notes:
  • © 2024 The Authors. Published by Elsevier B.V. on behalf of Association of European Operational Research Societies (EURO). Abstract reprinted with permission of Elsevier.
• Authors:
  • Metzger, Barbara
  • 0000-0003-3466-5425
  • Loder, Allister
  • Kessler, Lisa
  • 0000-0002-4100-4812
  • Bogenberger, Klaus
  • 0000-0003-3868-9571
• Publication Date: 2024

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 100144
• Serial:
  • EURO Journal on Transportation and Logistics
  • Volume: 13
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2192-4376
  • Serial URL: https://www.journals.elsevier.com/euro-journal-on-transportation-and-logistics/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Choice models; Freeways; Machine learning; Mathematical prediction; Traffic congestion; Travel patterns
• Geographic Terms: Bavaria (Germany)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01934071
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 17 2024 9:15AM