PREDICTIVE SAFETY

Using Data to Prevent Crashes

2-MIN READ

How the Volpe Center and Tennessee Highway Patrol use real-time crowdsourced waze data and machine learning to predict crashes and deploy safety resources proactively.

Traditional datasets sourced solely from police crash reports weren’t providing the Tennessee Highway Patrol with enough real-time information to accurately predict where accidents were likely to occur or where patrols were needed most.

To solve this, the Volpe Center’s Safety Data Initiative integrated crowdsourced waze crash and hazard data into a machine learning algorithm. This dynamic model enables state safety officials to achieve key objectives to:

Predict the specific time and location of high-risk accident windows on interstates.
Move from reactive incident management to proactive collision prevention.
Optimally deploy highway patrol units to target dangerous driving corridors.
Improve overall emergency response times when collisions do occur.

By updating crowdsourced data every two minutes, the Volpe Center data science team builds detailed grid models that give state authorities a clearer, context-rich picture of highway conditions than ever before.

"Waze data provide important contextual information to inform state and local safety applications."

— Dan Flynn, Volpe National Transportation Systems Center

Impact:

By combining real-time waze data with machine learning, the Volpe Center and Tennessee Highway Patrol transformed highway safety management and resource deployment.

Key Results Checklist:

Predictive Machine Learning: Built an advanced algorithm fed by real-time crowdsourced accident and hazard data to forecast interstate collision risks.
Two-Minute Data Refresh Rate: Utilized near-instantaneous data updates to maintain active, dynamic grid models for state trooper dispatching.
Targeted Enforcement: Directed highway patrol resources to high-risk zones before accidents happened, actively preventing crashes.
Faster Emergency Response: Reduced response times for first responders by positioning units closer to predicted incident hotspots.

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