Determination of Normal Condition Regain Time During Snow Events Using Traffic Flow Data

This paper presents an automatic process to determine the normal condition regain time (NCRT) using the traffic flow data a given snow event. To reflect the different traffic flow behavior during the day and night time periods, two types of the normal conditions are defined for each detector station. The normal condition for the day time is defined with the average speed-density patterns, while the time-dependent average speed patterns are used for representing the night time periods. In particular, the speed-density functions for the speed recovery and reduction periods were calibrated separately for a given location to address the well-known traffic hysteresis phenomenon. The resulting NCRT estimation process determines the NCRT as the time when the snow day speed recovers to the target level of the normal recovery speed at the corresponding density level for the day time periods. The application results with the snow routes in Twin Cities, Minnesota, show the promising possibilities of the estimated NCRT values as the reliable operational measures that can address the subjectivity and inconsistency issues associated with the current bare-lane regain times through the visual inspection.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AH010 Standing Committee on Surface Transportation Weather.
  • Corporate Authors:

    Transportation Research Board

    500 Fifth Street, NW
    Washington, DC  United States  20001
  • Authors:
    • Kwon, Eil
    • Park, Chongmyung
    • Lund, Steve
    • Peters, Thomas
  • Conference:
  • Date: 2016

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 13p
  • Monograph Title: TRB 95th Annual Meeting Compendium of Papers

Subject/Index Terms

Filing Info

  • Accession Number: 01591959
  • Record Type: Publication
  • Report/Paper Numbers: 16-6043
  • Files: TRIS, TRB, ATRI
  • Created Date: Feb 29 2016 4:54PM