Tracking the source of congestion based on a probabilistic Sensor Flow Assignment Model

Tracking the source of congestion, namely where the congested traffic flow comes from and goes to, is a key prerequisite to understanding the causes of traffic congestion and facilitates more efficient strategies. In this paper, the authors track the congestion source by estimating the path flow passing through the congested link. A probabilistic sensor flow assignment model is first developed to infer the whereabouts of each vehicle converging into the congestion. Unlike classical path flow estimation methods, the authors view path flow as the assigned results of sensor flows rather than OD flows. With this new perspective, an assigned rule, which incorporates route choice preference of drivers and spatial–temporal constraint of vehicular trajectory, is constructed to output more realistic assignments. Moreover, as this model finds most possible destination-path combinations rather than partial paths as assigned results, the complete trip of tracking vehicles, including both driving paths and ODs, can be reconstructed. With the reconstructed trips, disaggregated and hybrid path flow estimation methods are developed to track the source of traffic congestion on the bottleneck link.The open-source pNEUMA dataset is employed to test the proposed and benchmark methods. It demonstrates that the authors' methods can produce a more realistic traffic pattern for congestion tracking. Significant improvements in estimation accuracy have been achieved with the use of sensor flow assignment model. The proposed disaggregated method has also been tested with a city-scale road network. Experiment results demonstrate that the authors' method is more robust to the uncertainty caused by possible destinations than benchmark.

• Record URL:
  • https://doi.org/10.1016/j.trc.2024.104736
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0968090X24002572
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2024 Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Cao, Qi
  • 0000-0003-3242-8071
  • Yuan, Jian
  • 0000-0002-7202-0946
  • Ren, Gang
  • 0000-0002-3412-0831
  • Qi, Yao
  • 0009-0006-4768-7964
  • Li, Dawei
  • 0000-0002-5936-2116
  • Deng, Yue
  • Ma, Wanjing
• Publication Date: 2024-8

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 104736
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 165
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Bottlenecks; Origin and destination; Sensors; Traffic congestion; Traffic flow; Trajectory control
• Geographic Terms: Shangyu (China)
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01925749
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 29 2024 3:12PM