Wisconsin State Patrol using predictive analytics to focus resources at traffic crash blackspots


From the start of this year, the Wisconsin State Patrol (WSP) has been providing its statewide regional posts with a new predictive analytics tool to help determine where crash risks are highest, helping law enforcement determine where best to deploy their resources.

Using accurate and consistent data from the State Patrol and local law enforcement agency crash reports statewide, the predictive analytics system integrates existing technologies to highlight areas where the risk of traffic crashes is most prevalent. Focusing law enforcement in areas experiencing unusually high crash rates helps to discourage speeding and other reckless driving behaviors that may lead to crashes, while also reducing incident response times when crashes do occur.

Predictive analytics uses a crash database connected to established mapping tools used by State Patrol. Every time a law enforcement officer transmits a crash report, it is fed into the state’s crash database, which then shares the information with two existing resources: the Wisconsin Department of Transportation’s (WisDOT) Community Maps, and WSP’s Mobile Architecture for Communications Handling (MACH) applications.

The State Patrol works with the University of Wisconsin-Madison’s Traffic Operations and Safety (TOPS) Laboratory to develop and enhance the predictive analytics tools and Community Maps, which is Google Maps with an overlay marking locations where traffic incidents involving fatalities, injuries and crashes have occurred.

Once on patrol, troopers remotely access predictive analytics through MACH’s mapping feature, allowing them to analyze data and decide where and when their presence would be most effective. If there is an area within their patrol sector with a high crash rate during a specific time, they can adjust plans to patrol that area during that timeframe.

Beyond its mapping functionality, MACH allows law enforcement from different agencies to see and communicate with one another. If State Patrol and local police are doing impaired driving enforcement in the same area, they are able to see the locations of all officers and coordinate their efforts.

“The goal is to reduce highway crashes,” explained WSP Colonel Charles Teasdale. “Rather than solely focusing on areas where there is a higher rate of speeding or other traffic violations, predictive analytics gives our posts the information they need to pinpoint locations where and when crashes are happening, so we can create a presence in these areas.”

WisDOT’s Bureau of Transportation Safety Program and Policy chief, Randy Romanski, said, “Having current data at traffic safety commission meetings gives us information we can use to find the right solutions to problems. For example, we might look at a stretch of highway where there is a high crash rate and consider a road realignment as a potential solution.

“But after injecting predictive analytics into the discussion, we may see that many of the crashes involved alcohol or other driver behavior-related factors. This may lead us to rethink the issue and work with law enforcement to use high-visibility deployments to change behavior and reduce crashes, which can be more timely and cost effective than a roadway realignment.”

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About Author


Tom has edited Traffic Technology International (TTi) magazine and its Traffic Technology Today website since May 2014. During his time at the title, he has interviewed some of the top transportation chiefs at public agencies around the world as well as CEOs of leading multinationals and ground-breaking start-ups. Tom's earlier career saw him working on some the UK's leading consumer magazine titles. He has a law degree from the London School of Economics (LSE).