Being able to accurately and reliably estimate traffic conditions during snow events is critical to transportation agencies.
Typically, state DOTs use measurements such as “time to bare pavement”—based on the visual inspection of plow drivers—to gauge the progress of snow operations. These estimates are limited, however, by the subjectivity and inconsistency of human-based measurements.
Now, new research sponsored by the Minnesota Department of Transportation and led by U of M Duluth civil engineering professor Eil Kwon aims to take the guesswork out of assessing traffic conditions during winter weather events.
In the first phase of this project, researchers developed a prototype process that uses data on traffic speed, flow, and density collected by loop detectors in the Twin Cities metro area to estimate the point at which traffic patterns return to normal—an indicator that the roadway surface has “recovered.” In the newly published second phase, researchers further analyzed the traffic flow patterns during snow events under normal and snow conditions and refined the earlier prototype into a traffic-data-based measurement process for snow operations.
“We found that by comparing the variation patterns in traffic flow during a snow event with those during normal weather conditions, we could successfully identify the recovery status of the traffic flow at a given location,” Kwon says.
Based on their findings, the researchers developed a new process to identify the Normal Condition Regain Time (NCRT)—as an alternative to the traditional “time to bare pavement” measurement used to gauge the progress of maintenance operations during a winter weather event.
Future research plans include the development of an operational version of the NCRT estimation system that can be used on a daily basis to analyze and improve snow operations, and the creation of an online version that can be used for coordinating snow operations in real time.