Volpe developed a COVID-19 Waze Traffic Alert Dashboard in March 2020 to track relative changes in weekly traffic jam alerts for all U.S. metropolitan areas. SDC enabled the team to leverage existing crowdsourced traffic incident activity, and rapidly developed dashboard using existing code. The team has continued to provide weekly updates through 2022. The Waze dashboard provides a rapid indicator of traffic jams covering all U.S. metropolitan areas, increasing accessibility to state, metropolitan, and county-level time trends. This ongoing project aims to enhance the dashboards for broader use by U.S. DOT.

Adirondack hot spots and daily trends

Waze alerts showed how traffic jams, crashes, and hazardous parking fluctuated through the Adirondack park’s main road during hiking days. These informed options for a shuttle route, planned to start in summer 2022.

Regional crash monitoring

Waze alerts are providing crash analyses to 31 parks in the Inter-Mountain Region. Waze can supplement current NPS crash data, or in cases where NPS crash data is not available, Waze can act as the major source of crash trends.

Mount Rainier entrance congestion

Waze alerts are giving information about how popular park entrances fill up and create traffic jams. These can be related to the gateway communities and roadways used to enter the park from visitor home locations (e.g. Seattle, Portland, Spokane).

Volpe developed a COVID-19 Waze Traffic Alert Dashboard in March 2020 to track relative changes in weekly traffic jam alerts for all U.S. metropolitan areas. The SDC enabled the team to leverage existing crowdsourced traffic incident activity and rapidly developed a dashboard using existing code. The team has continued to provide weekly updates through 2022. The Waze dashboard provides a rapid indicator of traffic jams covering all U.S. metropolitan areas, increasing accessibility to state, metropolitan, and county-level time trends. This ongoing project aims to enhance the dashboards for broader use by U.S. DOT.

Crowdsourced mobile applications such as Waze can provide real-time and historical data about roadway conditions, when and where users are active. Analytical methods were used to test an application of Waze traffic alerts to a crash prediction model used to guide Tennessee law enforcement resource allocation. To target locations and times more accurately with a high crash propensity, the potential for Waze alerts to improve the temporal and spatial resolution of the model was assessed.  Using the Waze data, the spatial resolution of crash estimates was refined from 42 to 1 square miles, and the temporal resolution was refined from 4 to 1-hour time windows, while improving accuracy. The model’s incorporation of crowdsourced data has shown potential for similar types of data-driven safety approaches elsewhere. SDC enabled the integration of public and private data sources to support these analytical approaches. 

1. Flynn DFB, Gilmore MM, Dolan JP, Teicher P, Sudderth EA. Using Crowdsourced Data to Improve Models of Traffic Crash Propensity: Tennessee Highway Patrol Case Study. Transportation Research Record. March 2022. doi:10.1177/03611981221083305
https://journals.sagepub.com/doi/10.1177/03611981221083305

2. Flynn, D. F. B., M. M. Gilmore, and E. A. Sudderth. Estimating Traffic Crash Counts Using Crowdsourced Data: Pilot analysis of 2017 Waze data and Police Accident Reports in Maryland. November 2018. Report Number : DOT-VNTSC-BTS-19-01 https://rosap.ntl.bts.gov/view/dot/37256