Evaluating cyclist biometrics to develop urban transportation safety metrics

•A proactive safety metric is developed using field data from cyclists’ biometrics.•Biometric stress indicators are associated with roadway features.•Cluster analysis is used to categorize like stress readings across cyclists.•Higher biometric stress readings correlate with infrastructure designs that are less safe. The transportation safety paradigm for urban transportation – particularly safety for those walking and cycling – relies on counting crashes to parameterize safety. These objective measures of safety are spatially static and reflective of past events: they can be enriched by including the human response to risk at diverse infrastructure designs. This perceived risk has been well captured qualitatively in the transportation safety literature; in the following study, we seek to develop a quantitative methodology that captures perceived risk as a continuous measure of human biometrics. Building on diverse safety–critical fields, we hypothesize that the perception of safety can be measured proactively with traveler biometrics, including eye and head movements, such that high readings of biometric indicators correlate with less safe areas. We collect biometric data from cyclists traversing an urban corridor with a protected, yet not continuously, cycle lane. By isolating and correlating peaks in cyclist biometric measures with infrastructure design, we develop a set of continuous variables – lateral head movements, gaze velocity, and off-mean gaze distance, both independently and as a vector – that allow for the evaluation of urban infrastructure based on perceived risk. The results reflect that higher biometric readings correspond to less safe (i.e., unprotected) areas, indicating that perceived risk can be measured proactively with biometric data.