Evaluating the Reliability, Coverage, and Added Value of Crowdsourced Traffic Incident Reports from Waze
This is a manuscript of a proceeding published as Amin-Naseri, Mostafa, Pranamesh Chakraborty, Anuj Sharma, Stephen B. Gilbert, Mingyi Hong. "Evaluating the reliability, coverage, and added value of crowdsourced traffic incident reports from Waze." No. 18-03519. 2018. Transportation Research Board 97th Annual Meeting, Washington, DC, January 7-11, 2018. Posted with permission.
Traffic managers strive to have the most accurate information on road conditions, normally by using sensors and cameras, to act effectively in response to incidents. The prevalence of crowdsourced traffic information that has become available to traffic managers brings hope and yet raises important questions about the proper strategy for allocating resources to monitoring methods. Although many researches have indicated the potential value in crowdsourced data, it is crucial to quantitatively explore its validity and coverage as a new source of data. This research studied crowdsourced data from a smartphone navigation application called Waze to identify the characteristics of this social sensor and provide a comparison with some of the common sources of data in traffic management. Moreover, this work quantifies the potential additional coverage that Waze can provide to existing sources of the Advanced Traffic Management System (ATMS). One year of Waze data was compared with the recorded incidents in the Iowa’s ATMS in the same timeframe. Overall, the findings indicated that the crowdsourced data stream from Waze is an invaluable source of information for traffic monitoring with broad coverage (covering 43.2% of ATMS crash and congestion reports), timely reporting (on average 9.8 minutes earlier than probe-based alternative), and reasonable geographic accuracy. Waze reports currently make significant contributions to incident detection and were found to have potential for further complementing the ATMS coverage of traffic conditions. In addition to these findings, the crowdsourced data evaluation procedure in this work provides researchers with a flexible framework for data evaluation.