The capability to accurately and reliably estimate the road traffic conditions during snow events is of critical importance in improving the efficiency and effectiveness of the MnDOT?s 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.
In this project, a computerized system will be developed to estimate the normal-condition-regain times of metro snow routes by using the traffic flow data. The proposed system will be based on the results from the previous MnDOT-sponsored research, which has developed a robust process to determine the normal-speed-regain time at each detector station. In this process, the time-variant speed-density variation at each detector station is compared with a normal-speed-recovery pattern during a snow event, and a time meeting the user-specified conditions is identified. A normal-speed-recovery function is developed for this process for each detector station using the traffic data collected from the normal dry days. By using the traffic data with an automated process, the proposed system can provide the consistent estimates of the normal-condition-regain times across the metro freeway network and save substantial amount of the staff time.