A new study published in Accident Analysis & Prevention demonstrates how biometric data can be used to identify potentially challenging and dangerous areas of urban infrastructure prior to a crash. Megan Ryerson, the study’s lead author, led a team of researchers from the Stuart Weitzman School of Design and the School of Engineering and Applied Science in collecting and analyzing eye-tracking data from cyclists riding through Philadelphia’s streets. Individual-based metrics, according to the team, can provide a more proactive approach to designing safer roadways for bicyclists and pedestrians.
Current federal rules for installing safe transportation interventions, such as a crosswalk with a traffic signal, require either a minimum of 90-100 pedestrians crossing this location every hour or a minimum of five pedestrians struck by a driver at that location in one year. Ryerson claims that the practice of reactively planning safety interventions with a “literal human cost” has motivated her and her team to seek more proactive safety metrics that do not necessitate waiting for tragic outcomes.
According to Ryerson, one of the challenges is that transportation systems are designed and refined using metrics such as crash or fatality data rather than data on human behavior to help understand what makes an area unsafe or what specific interventions would be the most effective. This reactive approach also misses places where people might want to cross but don’t because it’s too dangerous, and where, if it were safe, more people would use.
By analyzing eye-tracking data from cyclists navigating Philadelphia’s streets, researchers found that these individual-based metrics can provide a more proactive approach for designing safer roadways for bicyclists and pedestrians.
“Today, we have the technology, data science, and the ability to study safety in ways that we did not have when the field of transportation safety was founded,” Ryerson says. “We don’t have to be reactive when it comes to planning safe transportation systems; instead, we can develop innovative, proactive methods to assess the safety of our infrastructure.”
The team devised a method for assessing cognitive workload, which is a measure of a person’s ability to perceive and process information, in cyclists. Cognitive workload studies are commonly used in other fields of transportation, such as air traffic control and driving simulations, to determine what designs or conditions allow people to process information around them. However, studies on cognitive workload in bicyclists and pedestrians are uncommon due to a variety of factors, including the difficulty of developing realistic cycling simulations.
Ryerson’s lab investigated how different infrastructure designs affect cognitive workload and stress in urban cyclists. In 2018, the team had 39 cyclists travel along a U-shaped route from JFK Boulevard and Market Street, down 15th Street to 20th Street, and back to JFK Boulevard and Market Street. Tobii eye-tracking glasses with an inward and outward-facing camera and a gyroscope capable of collecting eye and head-movement data 100 times per second were worn by the riders.
Along with being one of Philadelphia’s newest protected bike lanes at the time, and thus a new experience for all study participants, the route also features a dramatic change in infrastructure along the 8-10-minute route, including a mix of protected bike lanes, car-bike mixing zones, and completely unprotected areas. “We felt that in a short period of time, our subjects could experience a variety of transportation-infrastructure designs that could elicit different stress and cognitive workload responses,” Ryerson says.
The ability to correlate locations with a disproportionately high number of crashes with a consistent biometric response that indicates increased cognitive workload is one of the study’s main findings. According to Ryerson, having a high cognitive workload does not necessarily mean that a person will crash, but it does mean that a person is less able to process new information, such as a pedestrian or a driver entering the bike lane, and react appropriately. A high cognitive workload increases the risk of a crash.
Furthermore, the researchers discovered that stressful areas were consistent among expert cyclists and those who were less experienced or confident. This has implications for current approaches to managing safety, which typically focus on education interventions for pedestrians and cyclists. According to Ryerson, education is still important, but these findings show that infrastructure design is just as important in terms of making spaces safe.
“Even if you’re a better cyclist than I am, we have very similar stress and workload profiles as we travel through the city,” Ryerson says. “Our discovery that safety and stress are a function of infrastructure design rather than individual behavior represents a paradigm shift for the transportation-safety community. Our transportation systems can and must be made safer.”
The Ryerson lab is now analyzing a separate eye-tracking dataset collected from cyclists traveling Spruce and Pine streets before and after the installation of protected bike lanes in 2019-20, an experiment that will allow for a more in-depth examination of the effects of a design intervention.
Overall, Ryerson says, the study demonstrates that it is possible to be more proactive about safety, and that city planners could use individual-level data to identify areas where a traffic intervention might be useful – before anyone is hit by a car. “Because of the COVID-19 pandemic, many of us chose to walk and bike for commuting and recreation. Unfortunately, it also increased the number of crashes. We must design safer streets ahead of time, rather than waiting for more accidents and deaths to occur. We can design safer transportation systems by observing how people feel as they move through the city “she claims.
- How Retail Zipline’s Series a Pitch Deck Ticked Every Box for Emergence Capital
- Satellite Crop Monitoring
- Backed by Former Facebook and Twitter Execs, Tagg Launches Social Branding App for Gen Z
- China’s AI giant SenseTime readies Hong Kong IPO
- Real-Life Green Goblin Caught On Camera Soaring Through City On Actual Hoverboard
- Elon Musk Just Became Twitter’s Largest Shareholder