We thought we’d be piloting flying cars to work by now. Instead, we’re living in a less glamorous future where the roads are clogged with an increasing amount of cars from rideshare services like Uber and Lyft, and autonomous (self-driving) vehicles are slowly but surely becoming a confusing reality.
A study from the University of Illinois found that in this environment, city commuters may be less likely to confront auto accidents if they are willing to increase their trip time. The study, published in the journal Transportation Research Part C, introduces a tool that helps compute the association between traffic accidents and city road networks.
“Zipcars, rideshares – and eventually autonomous vehicles – have led to a huge disruption in transportation,” Richard Sowers, a professor of mathematics and industrial and enterprise systems engineering and lead author of the study, said in a release. “We identified a need for a tool that could help city planners, insurers and researchers communicate best practices for traffic-routing problems in different cities – from a safety perspective.”
In this example, the researchers defined “safety” as the number of accidents per mile.
The tool combined traffic speed, accident count, trip origin and destination data culled from New York City taxi services and police reports, which were used to create an algorithm.
The team approached the safety issue as a routing problem, Sowers said. As it turned out, the shortest route between two points, by distance, often have the most car accidents.
“Our algorithm works like a tuning parameter between the number of accidents and trip time to produce a mathematical function, or curve, which visually captures this tradeoff,” Sowers said.
That’s where the compromise comes in: the safest route may not be the shortest.
Take the long way home
The study showed that a Manhattan commuter willing to increase their travel time by 15% during the morning or evening rush hour could reduce the number of accidents they encountered on their route by up to 18%.
But don’t get your hopes up – the researchers aren’t planning on rolling this out as an app for users everywhere, but instead plan on making it available to professionals and data experts who study traffic patterns, like city planners.
In the future, the researchers hope to branch out into new cities by obtaining new data from professional drivers there, just as they obtained driver data from NYC cab drivers.
“One possible source of data could be the rideshare, taxi and autonomous vehicle services,” Sowers said. “As cities continue to face the challenges of extra congestion caused by these services, they may choose to introduce new rules requiring these companies to share data in exchange for their use of their roads.”
Although Sowers imagines other applications for the algorithm, “For now, at this early stage of our research, we feel that observing the number of accidents addresses the most immediate and socially important concern.”
Richard Sowers led the study, along with contributions by graduate student Daniel Carmody.
This research was supported by the National Science Foundation.