LANSING – Michigan researchers are using new “connected vehicle” technology to study data that could improve traffic safety by identifying locations with high potential for crashes.

Installed in recent Ford, General Motors and Toyota models, CV technology allows manufacturers to track vast amounts of driver data, from vehicle location and seatbelt use to every tap on the brakes and accelerator.

That data has become the basis of recent traffic studies from Michigan State University experts, who analyzed CV “driving events”– hard braking and swerving — to map where and when likely near-misses occurred on state roads.

About 300,000 crashes occur each year in Michigan, according to the Department of Transportation — about one every 1 minute and 40 seconds.

And about 300 near-misses occur for every crash, according to the U.S. Occupational Safety and Health Administration.

These near-misses often happen at the same times and places as crashes, according to the CV studies.

MSU engineering professor and study author Peter Savolainen said the large volume of near-miss information can fill gaps in existing traffic data, giving safety officials better tools to reduce driver and pedestrian fatalities.

Savolanien’s team used 2019 and 2020 CV data from Ford vehicles for studies that tracked the location and time of day that driving events occurred.

Ford started introducing CV technology in its cars in 2019, according to the company website. Models with CV technology, under the name Co-Pilot360, include the Edge, F-150, Mach-E, Lighting, Bronco, Escape, Mustang and Maverick.

When motorists use the Co-Pilot360 app, they consent to having their driving data shared for analytical purposes, Savolenian said. Information allowing the team to identify the car’s owner was excluded from the dataset.

Last year, one study of seven Southeast Michigan counties — Wayne, Macomb, Monroe, St. Clair, Livingston, Washtenaw and Oakland — found that areas with more traffic crash reports had a higher number of CV driving events.

That relationship was consistent on different types of roads, during different times of the day and in different levels of congestion.

Public safety officials rely on crash reports primarily to identify and address high-risk locations, Savolainen said, but it sometimes takes years for a discernible pattern of crashes to develop.

“We’re currently in a reactive mode, where we don’t recognize a problem until multiple crashes have occurred — or, in the worst case, people have been injured or killed,” he said.

Connected vehicle driving events function similarly to crash reports, Savolainen said, but the data can be collected more frequently and without an actual crash.

The researchers’ most recent study compared 2020 crash reports to CV driving events on US 23 north of Ann Arbor. The study was published in the journal Accident Analysis and Prevention.

The results showed a high volume of likely near-misses in the afternoon on a section of the northbound side, where three lanes merge into two. It also showed an increase in driving events during the COVID-19 pandemic relative to the number of people on the road.

Michele Mueller, the manager of connected vehicles at the Department of Transportation, said her team is working with industry partners to identify opportunities to implement the technology across the state.

Potentially, MDOT’s traffic alert system could be upgraded once enough connected vehicles are on the roads, Mueller said.

Connected vehicle data can be used to monitor roads for hazards like unexpected traffic backups, snow squalls and drivers traveling in the wrong direction, Mueller said.

Once a hazard has been detected, a warning could be sent to drivers through third-party navigation apps like Google Maps or Waze.

“We’re looking at these opportunities for the safety and mobility benefits, and also bringing a huge economic value to the state,” Mueller said. “If we can save one life, it’s a career win.”

Savolenian said more CV research is underway at MSU and other academic institutions, including work to identify crosswalks in Ann Arbor with the most pedestrian near-misses.