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This study evaluated the quality of crowdsourced Waze data (including reports and speed) and explored promising use scenarios of Waze data to facilitate the development of intelligent transportation in Tennessee. To this end, the thoroughly assessed Waze reports quality in terms of spatiotemporal accuracy and coverage. The study found Waze users reported crash events about 2.2 minutes sooner, on average, than reports of the same events recorded in the state’s Locate/IM incident log. The reported crash locations per Waze are on average 6 feet from the Locate/IM log reported by the officials. It is found that 26% of crashes reported in Waze was matched with 67% Locate/IM crash reports, with the rest 74% reports pointing to unreported incidents. Waze speed is affected by the Wazers behaviors and tends to be slightly higher than detector speed in free-flow status. This study evaluated several novel use scenarios such as secondary crash detection, end of queue detection and tracking, level of service evaluation, work zone monitoring, wildlife hazards and crashes, and pothole detection and maintenance. Results show that Waze is a suitable data source for incident management, level of service evaluation, work zone management, roadway maintenance management, etc. when properly used and in cooperation with the agency’s other information sources.
Data about mobility provides information to improve city planning, identify traffic patterns, detect traffic jams, and route vehicles around them. Thi...
The primary objective of this study was to review the data generated by projects governed by statistically oriented system of specifications for the c...
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