A Quick and Reliable Traffic Incident Detection Methodology Using Connected Vehicle Data

Highway incidents are responsible for 50% - 70% of total highway delay time nationwide. The fundamental method to reduce the delay time is to detect and verify incidents early, and remove the incident quickly. Many methodologies have been studied and implemented in past 3 decades, however, due to the large spacing between the detection stations – normally 1/3 – 1/4 miles apart, all the methods have had limited capability to detect and verify incidents quickly. The emergence of Connected Vehicle (CV) technology provides a new data source, which would significantly improve the incident detecting and verifying methods. This paper documents the findings of a research project which created a digital simulation testing environment to test the usage of CV data for quick incident detection and verification. A great effort has been made to generate a large amount of incident scenarios data, and the data were used for quick incident detection. A set of new MOEs and algorithms were developed for the study. A comparison was made to conventional incident detection algorithm such as California Algorithm #7. The research outcome indicates that using CV data could significantly reduce the detection time, from minutes to just seconds. Some future studies are suggested in the paper. In the near future, with the increase of CV market penetration, this methodology would be very promising in quick incident detection with low false alarm rate.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ABJ35 Standing Committee on Highway Traffic Monitoring.
  • Authors:
    • Wolfgram, Joshua
    • Huang, Peter X
    • Zhao, Yi
    • Christofa, Eleni
    • Xiao, Lin
  • Conference:
  • Date: 2018


  • English

Media Info

  • Media Type: Digital/other
  • Features: References; Tables;
  • Pagination: 5

Subject/Index Terms

Filing Info

  • Accession Number: 01660326
  • Record Type: Publication
  • Report/Paper Numbers: 18-05089
  • Files: TRIS, TRB, ATRI
  • Created Date: Feb 20 2018 9:27AM