Quantified Cycling Safety: Towards a Mobile Sensing Platform to Understand Perceived Safety of Cyclists

Abstract

Today’s level of cyclists’ road safety is primarily estimated using accident reports and self-reported measures. However, the former is focused on post-accident situations and the latter relies on subjective input. In our work, we aim to extend the landscape of cyclists’ safety assessment methods via a two-dimensional taxonomy, which covers data source (internal/external) and type of measurement (objective/subjective). Based on this taxonomy, we classify existing methods and present a mobile sensing concept for quantified cycling safety that fills the identified methodological gap by collecting data about body movements and physiological data. Finally, we outline a list of use cases and future research directions within the scope of the proposed taxonomy and sensing concept.