Drivers' and cyclists' safety perceptions in overtaking maneuvers. (January 2022) - Rasch, Alexander; Moll, Sara; López, Griselda; García, Alfredo; Dozza, Marco
Highlights: We study drivers' and cyclists' perceived safety in cyclist-overtaking maneuvers. Bayesian ordinal logistic regression can model and predict perceived safety. Drivers' perceived safety depends on the presence and timing of oncoming traffic. Cyclists' perceived safety depends on lateral clearance and overtaking speed. Perceived safety depends on the highest collision risk specific to each road user. Abstract: Drivers overtaking cyclists on rural roads are a safety concern, as drivers need to handle the interaction with the cyclist and possibly an oncoming vehicle. Improving the maneuver's outcome requires an understanding of not only the objective, measurable safety metrics, but also the subjective, perceived safety of each road user. Previous research has shown that the perceived safety of the cyclist is most at risk at the passing moment, when driver and cyclist are closest to each other. However, to develop safety measures, it is necessary to know how both road users perceive safety, by understanding the factors that influence their perceptions during the overtaking maneuver. This study measured the perceived safety of drivers in a test-track experiment in Sweden and the perceived safety of cyclists in a field test in Spain. For both drivers and cyclists, we developed Bayesian ordinal logistic regression models of perceived safety scores that take as input objective safety metrics representing the different crash risks at the passing moment. Our results show that while drivers' perceived safety decreases when there is an oncoming vehicle with a low time-to-collision, cyclists' perceived safety is reduced by a small lateral clearance and a high overtaking speed. Although our datasets are heterogeneous and limited, our results are in line with previous research. In addition, the Bayesian models presented in this paper are novel and may be improved in future studies once more naturalistic data become available. We discuss how our models may support infrastructure development and regulation, policymaking, driver coaching, the development of active safety systems, and automated driving by providing a possible method for predicting perceived safety.