In 1799, French army engineers discovered the Rosetta Stone, a slab of rock that created a revolution in archeology by helping Egyptologists crack the code of hieroglyphics. Today’s modern fleet owners, insurers, and government officials would love to come across a similar tell all to help them crack the code of vehicle crash costs. Instead, they are faced with a dizzying array of agencies, measurement standards, and definitions that make it nearly impossible to answer a critical, but complex, question: How much, on average, does it cost when the operator of a fleet vehicle gets into a crash with another person or thing?
They could start by looking at data from the National Highway Traffic Safety Administration (NHTSA) and try to make sense of its National Automotive Sampling System (NASS), which contains data from the Crash Report Sampling System (CRSS) and the Crash Investigation Sampling System (CISS). They could get data from NHTSA’s Fatality Analysis Reporting System (FARS), or refer to crash data guidelines established by NHTSA’s Model Minimum Uniform Crash Criteria (MMUCC). Or they could look at crash data from the U.S Department of Transportation (USDOT), the American Association of State Highway and Transportation Officials (AASHTO), or the National Safety Council (NSC). Try as they might, they will not discover one single commonly agreed-upon list of the cost of various kinds of vehicle crashes.
Although there is no single set of costs, it is not for lack of trying. All of these agencies battle against the odds to unearth their own Rosetta Stone. If they did, they might be able to understand, not some ancient civilization, but the value of proposed measures to increase highway and road safety.
Every action society takes to improve traffic safety comes with a cost. For example, if a city is experiencing a high number of crashes at intersections, they may want to install green arrow turn signals for right hand turns at every major intersection. Although, that could cost millions and often, city budgets are stretched thin. But, if the city could calculate the cost of all crashes that happened when a vehicle made a “free” right turn and struck another vehicle or person, and compare the cost of installing the new signals, the city would see that the final costs of the new signals would make sense.
Fleet owners often find themselves in the same bind. Would installing a rear- or side-view radar systems pay for itself by reducing the number of crashes their fleet vehicles are involved in? Do camera and monitor systems pay for themselves? Does operator training make financial sense? It’s hard to answer any of these questions without some good, solid data.
The increasingly deep penetration into all facets of the industry by artificial intelligence, the Internet of Things, and machine learning may lead to the day when it will be possible to follow all the costs associated with a crash. After all, produce growers are able to follow the path of a single peach as it goes from a tree in an orchard in Georgia to the produce section of a store in France. How much longer until we can do the same with all the details of a car crash?
Many traffic safety related agencies reach out to fleet owners to gather data and information that can help them get closer to making realistic estimates of vehicular crashes. As transportation companies continue to take their own steps to reduce the incidence and cost of crashes involving their fleets, helping these government agencies improve their data collection might make financial sense for all parties involved.