Find in Library

The pedestrian and cyclist-related accident fatality rate is higher than that of other traffic accidents. One of the pedestrian behaviours that leads to traffic accidents is the act of moving rapidly onto the road from a blind spot without warning. Expert drivers practice hazard-anticipatory driving and will naturally seek to reduce uncertainty by attempting to fit their current driving context into a pre-existing category. Risk management is the process of identifying hazards and assessing and controlling risks to attain safety. The purpose of this study was to evaluate the influence that driving context-altering road environmental elements exert on the road-crossing behaviour of pedestrians and cyclists. Thus, this study attempted to identify covert hazards (obscured pedestrians and cyclists). A logistic regression analysis was employed along with data from the near-miss incident database, in which approximately 140,000 near-crash-relevant events were registered in 2017. By using the logistic regression analysis along with the annotations recorded in the database, we constructed a predictive model to identify covert hazards. The study demonstrated the feasibility of using a set of environmental elements that shape the driving context to construct a predictive model that identifies covert hazards.