By Eva Shipp and Shawn Turner
A growing number of higher-tech cars and trucks on the road are talking to us. And if we pay attention to what they’re saying, we could prevent a lot of crashes and save a lot of lives.
Nearly all new cars today are equipped with some degree of self-driving capability and internet connectivity. Every time one of those cars corrects a lane-departing drift, brakes automatically, or senses a sharp speed boost, it sends a message. These signals amount to millions of messages and four terabytes of data every day.
And every one of those messages, we believe, potentially holds a tiny seed of insight to help inform decisions about how and where to apply road safety improvements in the most cost-efficient way.
Safety improvements, whether they involve a turning lane, traffic signal or sign, or speed limit change, are based on studies of crash statistics. Transportation agencies examine how, when, and where collisions have happened over a given timeframe, typically at least three years. Engineers and safety researchers often need lots of data to ensure limited funds are spent on upgrades that represent the highest public benefit.
But crash reports, though thorough, can only provide general information about the circumstances leading to a crash at a single point in time. Vehicle performance data can fill in the gaps in our knowledge of driver behavior and provide a continuous and comprehensive record of pre-crash and crash conditions.
This long-established method has served us well, preventing likely collisions based on a history of crashes that happened at a given location, or are associated with a particular roadway characteristic. Our new approach may help prevent collisions based on relating vehicle performance messages to crashes and understanding more about the circumstances that lead to crashes. With new, large sources of connected car data, we can measure human behavior in ways we didn’t think were possible 20 years ago.
By using such data to track the factors that contribute to crashes, we can identify potential trouble spots on roadways and explore possible connections to driver behavior (like distracted or impaired driving). And we can more confidently predict where and when crashes are more probable.
We’re losing roughly 3,600 Texans a year in car crashes, so we believe it’s time to try a new approach, one that’s integrated with current practice. Can we prevent crashes by studying near misses, by studying incidents of hard braking, or speed variation or steering variation? We don’t yet know. But that’s why we do research. Just imagine the payoff. The number of lives we could save would be well worth the research investment.
The research underway now gives us growing evidence that we can use vehicle message data to fast-track improvements for intersection safety, pedestrian safety and enhanced safety in the state’s oil and gas production regions. And, we believe this new approach can help Texas reach its goal of cutting the number of fatal crashes in half by 2035.
Traffic deaths in Texas have risen about 20% since the recession a decade ago, climbing to 3,610 last year. We haven’t had a fatality-free day on Texas roads for nearly 20 years.
If we can learn from the messages we’re getting from the expanding fleet of connected vehicles, we can develop actions to improve safety. And that will benefit all of us, whether we own one of those advanced vehicles or not.
Eva Shipp is a research scientist and epidemiologist at the Texas A&M Transportation Institute.
Shawn Turner is a senior research engineer at TTI.
This article was originally published in The Dallas Morning News, October 27, 2020.