Leveraging Snow Plow Dashboards Cams and Connected Vehicle Speed Data to Improve Winter Operations Performance Measures
Recent technology has enabled the use of mobile phone applications to poll traffic and roadway condition data via crowdsourcing. Researchers from Iowa State University and Purdue University noted the possible application of this data to provide a framework for agencies to dynamically monitor changing road and weather conditions. The researchers compared qualitative assessments of snowplow dashboard images with recorded interstate vehicle speeds.
The image data, which consisted of approximately 1,120 geotagged images taken by snowplow cameras, was classified by users into seven different categories of road condition—e.g., "wet," "dry," "icy," and others. Then, connected vehicle speed data was used to analyze speeds at the tagged locations in a two-hour window around the images’ recorded times. This allowed for an analysis of the impact made by the snowplows.
The authors noted that the methodology could be extrapolated to explore future pairings of crowdsourced and agency-sourced data, as well as be used to schedule optimal snowplow deployment frequencies based on time-of-impact assessments.
- The "patchy snow" condition was found to have a significant improvement in driving speeds after a snowplow passed through, with the fastest 10 percent of speeds increasing by 6 mi/h for up to 30 minutes after the plow's passing.
- "Icy" conditions did not experience as significant an impact on fast-moving travelers, but the minimum speed improved by 9 mi/h after the plow passed through its route.
- "Slushy" conditions were found to have the greatest improvement in speed distributions, with minimum speeds raising almost 19 mi/h faster after the snowplow. In combination with the results for icy conditions, this suggests that snowplows are particularly effective at reducing potentially hazardous driving conditions—as reflected by the low minimum speed that drivers felt was necessary—while not necessarily contributing to a higher top speed along a route with wintery conditions.
- A Kolmogorov-Smirnov (KS) test was performed to compare every before-plow/after-plow pairing of recorded vehicle speeds, in order to identify when the greatest slow-down of traffic before the plow correlated to the highest increased speed after the plow. The test found that speed data taken 17-12 minutes before the plow had the greatest improvement on the average speeds 25-35 minutes after the plow went through.
- The authors noted that the methodology could be extrapolated to explore future pairings of crowdsourced and agency-sourced data, as well as be used to schedule optimal snowplow deployment frequencies based on time-of-impact assessments.