Development of a Road Condition Recovery Time Estimation System for Winter Snow Events

Principal Investigator(s):

Eil Kwon, Professor, UMD-Civil Engineering

Project summary:

The ability to accurately and reliably estimate the road traffic conditions during snow events is of critical importance in improving the efficiency and effectiveness of the Minnesota Department of Transportation's (MnDOT) winter maintenance operations. Currently, lane-regain times after snow events are manually reported by the plow-truck drivers through their visual inspections of the road conditions. This research developed a Normal Condition Regain Time (NCRT) estimation system, which automatically determines the NCRT at detector stations on the metro-freeway network for given snow events. The NCRT process is based on the findings that the speed level during the recovery process reaches a stable free-flow-speed (FFS), whose value is generally lower than the pre-snow FFS at a same location. Further, the speed-density (U-K) relationship of the traffic flow after snow is cleared exhibits a similar but shifted-down pattern of the normal-day U-K relationship at a given location. In this study, the after-snow traffic condition with a stable but shifted-sown pattern of the normal-day U-K relationship is defined as the "wet-normal" condition, and the NCRT is defined as the time when the U-K data during a snow event starts to follow the wet-normal U-K pattern at a given station. The NCRT estimation system first collects the traffic and weather data for the metro-freeway network and determines the normal-day U-K relationships for the detector stations whose traffic data include both uncongested and congested regions. The normal-day U-K relationships are then applied to calibrate the wet-normal U-K patterns at given locations using the traffic data collected during snow events. Finally, the NCRTs are determined for each station by comparing the U-K data trajectory during a given event with the wet-normal U-K pattern at given locations. The NCRT estimation system has been applied to a set of the sample snow events.

Project details:

  • Project number: 2016033
  • Start date: 10/2015
  • Project status: Completed
  • Research area: Transportation Safety and Traffic Flow
  • Topics: Safety, Snow and ice control