Evaluating cyclist biometrics to develop urban transportation safety metrics.

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.

Safety at the edge: a safety framework to identify edge conditions in the future transportation system with highly automated vehicles.

Automated driving systems (ADS) have the potential for improving safety but also pose the risk of extending the transportation system beyond its edge conditions, beyond the operating conditions (operational design domain (ODD)) under which a given ADS or feature thereof is specifically designed to function. The ODD itself is a function of the known bounds and the unknown bounds of operation. The known bounds are those defined by vehicle designers; the unknown bounds arise based on a person operating the system outside the assumptions on which the vehicle was built. The process of identifying and mitigating risk of possible failures at the edge conditions is a cornerstone of systems safety engineering (SSE); however, SSE practitioners may not always account for the assumptions on which their risk mitigation resolutions are based. This is a particularly critical issue with the algorithms developed for highly automated vehicles (HAVs). The injury prevention community, engineers and designers must recognise that automation has introduced a fundamental shift in transportation safety and requires a new paradigm for transportation epidemiology and safety science that incorporates what edge conditions exist and how they may incite failure. Towards providing a foundational organising framework for the injury prevention community to engage with HAV development, we propose a blending of two classic safety models: the Swiss Cheese Model, which is focused on safety layers and redundancy, and the Haddon Matrix, which identifies actors and their responsibilities before, during and after an event.