Freeway’s Traffic Flow Breakdown Prediction Utilizing Disturbance Metrics Based on Trajectory Data

There have been limited efforts to investigate the potential of using detailed trajectory data obtained from connected vehicles and/or other sensors in deriving measures for use in real-time traffic state estimation. This study utilizes a hybrid machine learning approach that classifies the traffic states as a function of traffic disturbance and safety surrogate metrics estimated based on detailed trajectories combined with macroscopic traffic metrics. The investigated disturbance metrics are the number of oscillations, and a measure of disturbance duration based on the time exposed time to collisions. The study, first, used unsupervised clustering techniques to classify traffic states into “breakdown” and “non-breakdown” in terms of both mobility and safety. Then, the categorized traffic state was used as a binary response to the macroscopic and microscopic metrics, as features, to train supervised machine learning techniques for predicting traffic flow breakdown in the following 5-min interval in real-time operations. The study found that the utilizing disturbance and safety surrogate metrics in the real-time classification of traffic flow state increases the accuracy of prediction.

• Record URL:
  https://doi.org/10.1061/9780784483534.033
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784483534
• Supplemental Notes:
  • © 2021 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Azizi, Leila
  • Hadi, Mohammed
  • Aghili, Maryamossadat
• Publication Date: 2021

Language

• English

Media Info

• Pagination: pp 378 - 390
• Monograph Title: International Conference on Transportation and Development 2021: Transportation Operations, Technologies, and Safety

Subject/Index Terms

• TRT Terms: Cluster analysis; Connected vehicles; Crashes; Machine learning; Macroscopic traffic flow; Mathematical oscillations; Metrics (Quantitative assessment); Microscopic traffic flow; Real time information; Sensors; Vehicle trajectories
• Identifier Terms: Surrogate Safety Assessment Model
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01777560
• Record Type: Publication
• ISBN: 9780784483534
• Files: TRIS, ASCE
• Created Date: Jul 23 2021 3:26PM