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. The team found that individual-based metrics can provide a more proactive approach for designing safer roadways for bicyclists and pedestrians.

Current federal rules for installing safe transportation interventions at an unsafe crossing—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 says that the practice of planning safety interventions reactively with a “literal human cost,” has motivated her and her team to find more proactive safety metrics that don’t require waiting for tragic results.
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