A new study published in Analysis and prevention of accidents shows how biometric data can be used to find potentially difficult and dangerous areas of urban infrastructure before an accident occurs. Lead author Megan Ryerson led a team of researchers from the Stuart Weitzman School of Design and the School of Engineering and Applied Science to collect and analyze eye-tracking data from cyclists on the streets of Philadelphia. The team found that individual measures can provide a more proactive approach to designing safer roads for cyclists and pedestrians.
Current federal rules for installing safe transportation interventions at a hazardous grade crossing, such as a crosswalk with a traffic light, require either a minimum of 90 to 100 pedestrians crossing that location every hour, or a minimum of five pedestrians struck by a driver at this location. location in one year. Ryerson says the practice of planning security responses reactively with a “literal human cost” has motivated her and her team to find more proactive security measures that don’t require waiting for results. tragic.
Part of the challenge, says Ryerson, is that transportation systems are designed and refined using metrics such as crash or fatality data instead of human behavior data to help understand what makes a hazardous area or what specific interventions would be most effective. This reactive approach also fails to capture where people might want to cross but don’t because they consider it too dangerous and, if it were safe, more people would use it.
“Today we have the technology, the data science, and the ability to study safety in ways that we did not have when the field of transportation safety was born,” said Ryerson. “We don’t need to be reactive in planning safe transportation systems; instead, we can develop innovative and proactive ways to assess the security of our infrastructure. “
The team developed an approach to assess cognitive workload, a measure of a person’s ability to perceive and process information, in cyclists. Cognitive workload studies are frequently used in other areas of transportation, such as air traffic control and driving simulations, to determine what designs or conditions allow people to process the information around them. But studies examining cognitive workload among cyclists and pedestrians are not as common due to a number of factors, including the difficulty of developing realistic cycling simulations.
Researchers at Ryerson’s lab examined how different infrastructure designs cause changes in cognitive workload and stress in urban cyclists. In 2018, the team took 39 cyclists on a U-shaped route from JFK Boulevard and Market Street, down 15th Street to 20th Street, then back to 15th Street and the Market. The riders wore Tobii eye-tracking goggles equipped with an inward and outward facing camera and a gyroscope capable of collecting data on eye and head movements 100 times per second.
As well as being one of Philadelphia’s newest protected cycle routes at the time, and therefore a new experience for all study participants, it also features a dramatic change in infrastructure along the route from 8-10 minutes, including a mix of cycle paths, car-bike mixing areas and totally unprotected areas. “We felt that, in a small segment of space, our subjects could experience a range of transportation infrastructure designs that can elicit different responses to stress and cognitive workload,” said Ryerson.
One of the main findings of the study is the ability to correlate locations that have a disproportionately high number of crashes with a consistent biometric response that indicates increased cognitive workload. If a person’s cognitive workload is high, says Ryerson, it doesn’t necessarily mean they’re going to crash, but it does mean that a person is less able to process new information, such as a pedestrian or a pedestrian. driver entering the cycle lane, and react appropriately. A high cognitive workload means that the threat of an accident is increased.
Additionally, the researchers found that the stress areas were consistent between expert cyclists and those with less experience or confidence. This has implications for current safety management approaches, which typically focus on pedestrian and cyclist education interventions. Education is always important, says Ryerson, but these results show that infrastructure design is just as important in terms of safe spaces.
“Even though you’re a more competent rider than I am, we still have very similar stress and workload profiles as we go through town,” says Ryerson. “Our conclusion, that safety and stress are a function of infrastructure design and not the individual, is a shift in perspective for the transportation safety community. We can and must build safety into our systems. transport.”
The Ryerson Lab is currently analyzing a separate eye-tracking dataset of cyclists traveling on Spruce and Pine Streets before and after the installation of protected bike lanes in 2019-2020, an experiment that will allow a closer study of the impacts of ‘a design intervention.
Overall, according to Ryerson, research shows that it is possible to be more proactive when it comes to safety and that city planners could use individual data to identify areas where traffic intervention might be useful – before someone is hit by a car. “The COVID-19 pandemic has encouraged so many of us to walk and cycle for travel and recreation. Unfortunately, it has also led to an increase in accidents. We need to proactively design safer streets and not wait for more accidents and fatalities. We can use the way people feel when they move around the city as a way to design safer transportation systems, ”she says.
The full list of authors are Megan Ryerson, Carrie Long, Michael Fichman, Joshua Davidson, Kristen Scudder, George Poon and Matthew Harris of Penn; Michelle Kim of Swarthmore; and Radhika Katti of Carnegie Mellon University.
Warning: AAAS and EurekAlert! are not responsible for the accuracy of any press releases posted on EurekAlert! by contributing institutions or for the use of any information via the EurekAlert system.