Tennessee Highway Patrol: Using predictive analytics to help prevent road accidents and save lives
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- IBM® Cognos® Business Intelligence
- IBM SPSS® Modeler
Tech Stack
- Predictive Analytics
- Data Modeling
- Geotagging
Implementation Scale
- Enterprise-wide Deployment
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Security & Public Safety
Applicable Functions
- Logistics & Transportation
Use Cases
- Predictive Maintenance
- Traffic Monitoring
Services
- Data Science Services
About The Customer
The Tennessee Highway Patrol (THP) is a law enforcement agency established in 1929 and headquartered in the state capital, Nashville. The THP is responsible for enforcing federal and state traffic laws. A division of the Tennessee Department of Safety, the organization provides assistance to motorists, investigates traffic accidents and plays a role in criminal interdiction. As budgets for law enforcement become ever tighter, the THP was looking for ways to work smarter with their limited resources to achieve better results.
The Challenge
The Tennessee Highway Patrol (THP) was faced with the challenge of improving highway safety and reducing accident rates without increasing staff levels. The THP aimed to anticipate and prevent accidents by identifying road accident hotspots, prosecute drunk drivers, enforce the use of seatbelts more effectively, and respond to incidents with the most appropriate resources. The THP sought to enable a new data-driven approach to traffic safety that would look for meaningful patterns in the past and apply them to current conditions to predict future events.
The Solution
IBM helped the THP build a predictive model for traffic accidents using IBM predictive analytics for crime prevention and prediction. The model was fed with geotagged historical crash and DUI data from the previous three years, historical weather data, and data about special events. The model seeks out correlations between incidents and external factors: location, time of day, day of the week, time of the year, public holidays, weather conditions and proximity to public events. Given new data on all of these external factors, the model can then extrapolate forward to predict future incidents. During a six-month pilot phase, the THP focused on geographic areas with the highest propensity for severe accidents, dividing the state up into six-by-six mile squares and predicting traffic risks for each in four-hour increments.
Operational Impact
Quantitative Benefit
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