6 ways analytics can help manage traffic and reduce accidents
According to the National Highway Traffic Safety Administration, US traffic fatalities are on the rise. As a result, all of us doing our part to prevent unnecessary accidents as much as possible is imperative. One organization that is leading the fight against traffic casualties is the Tennessee Highway Patrol (THP). Part of this fight includes deploying IBM predictive analytics, and even though the organization hasn’t leveraged the full power of predictive analytics throughout the agency, its recent successes should be emulated. Here are six key takeaways from the THP initiative.
1. Tight budgets can correlate with high outcomes. Despite having to do more with less, THP was still able to improve public safety by reducing accidents and drunk-driving incidents, thanks to IBM SPSS technology.
2. The past can sometimes predict the future. Using the IBM solution, THP leveraged key patterns from historical data—accidents, driving under the influence (DUI) arrests, weather, sporting events, parades and so on—to forecast future events.
3. Room is always available for trailblazers. THP was not deterred from achieving its goal of averting traffic accidents even though a predictive analytics model had not been previously used to improve traffic safety. THP goes down as being the first state police department in the US to do so.
4. Being proactive versus reactive is a good thing. Rather than responding to accidents after they occur and relying solely on intuition, IBM predictive analytics empowered THP to maximize effectiveness and help determine how to deploy officers and provide the best patrol routes for particular shifts.