Crowdsourced mobile applications are an emerging and rich source of real-time data about roadway conditions. U.S. DOT Volpe Center researchers are using these new data sources from the private sector to estimate where and when motor vehicle crashes are likely to occur.
How crowdsourced data can help estimate crash risk
The crowdsourced Waze app passively collects information about traffic conditions when users have it open on their smartphones and tablets. It also allows users to actively report information about accidents, weather conditions, traffic jams, road closures, and road hazards.
In a 2017 pilot study, Volpe Center researchers integrated user-reported crash information from the Waze app with crash data from the state of Maryland. The team used a machine learning approach to estimate where and when police-reportable accidents were likely to occur over a six-month period. The model provides accurate estimates of crash risk within one-mile-area grid cells across the state of Maryland.
Most Waze accidents in the Maryland pilot occurred late in the work week (Thursday – Friday), and during commute hours (7 A.M. – 9 A.M. and 3 P.M. – 6 P.M.) (Data visualization by Michelle Gilmore, U.S. DOT Volpe Center).
The Volpe Center team has since extended the Waze crash estimation models to a full year, and to three additional states: Virginia, Connecticut, and Utah. The analytical approach can support nationwide studies or state and local applications of Waze data.
Most Waze accidents in the pilot occurred in the Capital Beltway region. (Data visualization by Michelle Gilmore, U.S. DOT Volpe Center.)
Understanding crash risk to reduce traffic fatalities
The Waze pilot is part of the U.S. DOT’s Safety Data Initiative (SDI), which aims to better understand and mitigate crash risk by integrating public and private data sources.
In 2017, more than 37,000 people died in motor vehicle crashes in the United States, and in 2016 more than 3 million people were injured. Models that estimate the timing and location of crashes can help emergency responders, traffic management centers, and law enforcement proactively allocate resources to locations with the highest accident potential.
How the Volpe Center is putting crowdsourced data to work at the state and local level
The first phase of the Waze pilot project provided the foundational research to develop Waze applications in collaboration with local and state agencies.
The city of Bellevue, WA is collaborating with the Volpe Center to test if Waze data can offer novel insights for Bellevue Vison Zero, an effort to reduce traffic deaths and serious injury collisions to zero by 2030. The Volpe Center team integrated several sources of traffic data to create interactive dashboards that city transportation officials can use to identify traffic crash patterns. The team is also developing crash estimation models to identify conditions, times, and locations with high crash risk to guide Bellevue’s transportation safety investment decisions.
The Volpe team is also working with the Tennessee Highway Patrol to integrate the Waze data into the state’s existing crash prediction model. Tennessee’s current model estimates crash risk across 42 square mile grids and in four hour time blocks. Volpe researchers are using the high-resolution Waze data to develop a model that can predict crash risk every hour for one-square-mile grids. The improved model will help the Tennessee Highway Patrol better target high-risk times and areas when they prioritize patrol locations.
Read the pilot study report or get in touch with Volpe Center researchers Erika Sudderth, Daniel Flynn, or Michelle Gilmore for more information on how the U.S. DOT is using data to reduce traffic fatalities and inform safety policy decisions.
The U.S. DOT Volpe Center performed the pilot study on behalf of the U.S. DOT Bureau of Transportation Statistics, Office of the Director and is implementing the state and local case studies on behalf of the U.S. DOT Office of the Assistant Secretary of Transportation Policy.
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