Drivers’ and cyclists’ safety perceptions in overtaking maneuvers

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.     

What constitutes traffic event severity in terms of human danger perception?

This study focuses on the severity gradation of non-collision events. Earlier theoretical work has suggested that a proper severity measure for an event should reflect the risk of personal injury, which is often split into two components, including the risk of a collision and the potential consequences had the collision taken place. While a great number of severity measures have been suggested, most of them fail to address both components, thus resulting in counter-intuitive event gradations and inconclusive outcomes in validation studies. Conversely, it has been shown that human observers often show very good agreement when given a task to rank traffic situations by their severity or level of danger. The aim of this study is to investigate in depth how human judgements of the severity of traffic situations can be expressed by means of objective safety indicators. In this study, a set of video-recorded traffic situations, in which a cyclist passes straight through an intersection while a left or right-turning motor vehicle crosses the cyclist’s path, were analysed. Binary logistic regression was used to develop models assessing the most important traffic severity indicators associated with human feelings of danger. The results showed that the initial conditions of a traffic event, defined as a start of an evasive action, contain the most important information for explaining its severity. Moreover, variables related to both proximity and collision consequences are important and should be integrated into severity measures.