, Professor, UMD-Civil Engineering
The ability to accurately and reliably estimate the performance of winter maintenance activities is critical for improving the efficiency and effectiveness of Minnesota Department of Transportation operations. Phase 1 of this research produced a prototype process to estimate the speed variation points during recovery periods for given routes using the traffic flow data collected from field detectors. In this Phase 2 project, the prototype process was enhanced with the expanded data set and traffic-data-based alternative measures were developed for snow maintenance operations. The enhanced process were applied to selected snow events in the metro freeway network to estimate those measures for the selected routes.
An automatic process was developed to determine the normal condition regain time (NCRT) using the traffic flow
data for a given snow event. To reflect the different traffic flow behavior during day and night time periods, two
types of the normal conditions were defined for each detector station. The normal condition for day time was defined
with the average speed-density patterns under dry weather conditions, while the time-dependent average speed
patterns were used for representing 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 determined the NCRT as the time when the speed
level on a given snow day recovers to the target level of the normal recovery speed at the corresponding density for
the day time periods. The sample application results with the snow routes in the Twin Cities, Minnesota, show the
promising possibilities for the estimated NCRT values to be used as the reliable operational measures, which could
address the subjectivity and inconsistency issues associated with the current bare-lane regain times determined
through visual inspections.