UPenn study uses biometric data to identify unsafe urban infrastructure
A new study published in Accident Analysis & Prevention shows how biometric data can be used to find potentially challenging and dangerous areas of urban infrastructure before a crash occurs. Lead author Megan Ryerson led a team of researchers in the Stuart Weitzman School of Design and the School of Engineering and Applied Science in collecting and analyzing eye-tracking data from cyclists navigating Philadelphia’s streets.As explained in a piece by Penn Today, current federal rules for making safe transportation interventions require the notation of crashes. This reactive approach relies on previous human cost before new considerations are made. Seeking to minimize harmful events altogether, Ryerson and her team sought to capture data on human behavior to better understand what factors make an area unsafe rather than previous data. The team developed a quantitative methodology to evaluate cognitive workload, a measure of mental effort put forth by someone in response to certain tasks, in cyclists when faced with various infrastructure designs. One of the main findings was a correlation between locations with disproportionately high numbers of crashes and consistent biometric responses that indicated increased cognitive workload. While high cognitive workload doesn’t mean a person will definitely crash, it can be inferred that one is less effective in processing new information, which could lead to d...
A new study published in Accident Analysis & Prevention shows how biometric data can be used to find potentially challenging and dangerous areas of urban infrastructure before a crash occurs. Lead author Megan Ryerson led a team of researchers in the Stuart Weitzman School of Design and the School of Engineering and Applied Science in collecting and analyzing eye-tracking data from cyclists navigating Philadelphia’s streets.
As explained in a piece by Penn Today, current federal rules for making safe transportation interventions require the notation of crashes. This reactive approach relies on previous human cost before new considerations are made. Seeking to minimize harmful events altogether, Ryerson and her team sought to capture data on human behavior to better understand what factors make an area unsafe rather than previous data.
The team developed a quantitative methodology to evaluate cognitive workload, a measure of mental effort put forth by someone in response to certain tasks, in cyclists when faced with various infrastructure designs.
One of the main findings was a correlation between locations with disproportionately high numbers of crashes and consistent biometric responses that indicated increased cognitive workload. While high cognitive workload doesn’t mean a person will definitely crash, it can be inferred that one is less effective in processing new information, which could lead to d...
