Understanding the interaction between cyclists and motorized vehicles at unsignalized intersections: Results from a cycling simulator study.

Introduction: With cycling gaining more popularity in urban areas, it is vital to obtain accurate knowledge of cyclists' behavior to develop behavioral models that can predict the cyclist's intent. Most conflicts between cyclists and vehicles happen at crossings where the road users share the path, especially at unsignalized intersections. However, few studies have investigated and modeled the interaction between cyclists and vehicles at unsignalized intersections.

Method: A bike simulator experiment was conducted to scrutinize cyclists' response process as they interacted with a passenger car at an unsignalized intersection. An existing unsignalized intersection in Gothenburg was simulated for test participants. Two independent variables were varied across trials: the difference in time to arrival at the intersection (DTA) and intersection visibility (IV). Subjective and quantitative data were analyzed to model the cyclists' behavior.

Results: When approaching the intersection, cyclists showed a clear sequence of actions (pedaling, braking, and head turning). The distance from the intersection at which cyclists started braking was significantly affected by the two independent variables. It was also found that DTA, looking duration, and pedaling behavior significantly affected cyclists' decisions to yield. Finally, the questionnaire outputs show that participants missed eye contact or communication with the motorized vehicle.

Conclusions: The kinematic interaction between cyclists and vehicles, along with the cyclist's response process (visual and kinematic), can be utilized to predict cyclists' yielding decision at intersections. From the infrastructural perspective, enhancing visibility at intersections has the potential to reduce the severity of interactions between cyclists and vehicles. The analysis of the questionnaire emphasizes the significance of visual communication between cyclists and drivers to support the cyclist's decision-making process when yielding.

Practical Applications: The models can be used in threat assessment algorithms so that active safety systems and automated vehicles can react safely to the presence of cyclists in conflict scenarios.