Drivers overtaking cyclists and pedestrians: Modeling road-user behavior for traffic safety

Doktorsavhandling, 2023

In a world aiming to shift to more sustainable modes of transportation, vulnerable road users (VRUs) like cyclists and pedestrians are still confronted with significant barriers to safety, particularly on rural roads where overtaking maneuvers represent a frequent and dangerous interaction with motorized traffic. If drivers misjudge their kinematics, even near-crashes without physical contact can harm the perceived safety of the VRU, which may decrease the willingness to continue cycling or walking on these roads. Crash risks when overtaking VRUs exist in different overtaking phases: when approaching the VRU, steering out, passing, and eventually returning. To make overtaking VRUs safer, improvements to policymaking, infrastructure, and vehicles are needed. However, these improvements need models that can describe or predict road-user behavior in overtaking, which was the objective of this thesis. Based on data sets obtained from a test-track experiment, field-test studies, and naturalistic studies, this thesis developed behavioral models for both objective and perceived safety of drivers and VRUs in different overtaking phases. The results indicate that drivers’ and VRUs’ behavior is mainly influenced by their highest crash or injury risk. The descriptive models showed that a close oncoming vehicle could reduce a driver’s safety margins to the VRU in all phases. Furthermore, the VRU behavior may affect the driver’s behavior; for instance, through lane positioning and, for pedestrians, walking direction. Infrastructure design and policymaking should focus on preventing overtaking in areas where oncoming vehicles are hard to estimate and enforcing sufficient clearances to the cyclist, stratified by speed. The predictive models can help vehicle safety systems adapt to drivers to become more acceptable, for instance, when assisting drivers in the decision to overtake or not. They may further help optimize road networks’ objective and perceived safety.