Identifying Wrong-Way Driving Incidents from Regular Traffic Videos Using Unsupervised Trajectory-based Method

Currently, transportation agencies have implemented different wrong-way driving (WWD) detection systems based on loop detectors, radar detectors, or thermal cameras. Such systems are often deployed at fixed locations in urban areas or on toll roads. The majority of rural interchange terminals does not have real-time detection systems for WWD incidents. Portable traffic cameras are used to temporarily monitor WWD activities at rural interchange terminals. However, it has always been a time-consuming task to manually review those videos to identify WWD incidents. The objective of this study was to develop an unsupervised trajectory-based method to automatically detect WWD incidents from regular traffic videos (not limited by mounting height and angle). The principle of the method includes three primary steps: vehicle recognition and trajectory generation, trajectory clustering, and outlier detection. This study also developed a new subtrajectory-based metric that makes the algorithm more adaptable for vehicle trajectory classification in different road scenarios. Finally, the algorithm was tested by analyzing 357?h of traffic videos from 14 partial cloverleaf interchange terminals in seven U.S. states. The results suggested that the method could identify all the WWD incidents in the testing videos with an average precision of 80%. The method significantly reduced person-hours for reviewing the traffic videos. Furthermore, the new method could also be applied in detecting and extracting other kinds of abnormal traffic activities, such as illegal U-turns.

• Record URL:
  https://doi.org/10.1177/03611981211036378
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • Qing Chang https://orcid.org/0000-0001-8277-0794 © National Academy of Sciences: Transportation Research Board 2021.
• Authors:
  • Chang, Qing
  • 0000-0001-8277-0794
  • Ren, Jiaxiang
  • Zhou, Huaguo
  • 0000-0001-5210-036X
  • Zhou, Yang
  • 0000-0001-7839-4933
  • Song, Yukun
  • 0000-0002-2911-295X
• Publication Date: 2022-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 440-450
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2676
  • Issue Number: 2
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Algorithms; Analysis; Vehicle trajectories; Video; Wrong way driving
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01783295
• Record Type: Publication
• Files: TRIS, TRB, ATRI
• Created Date: Sep 27 2021 6:21PM