2025/04/05 by Ali Mohammadi, Amir Hossein Kalantari, Gustav Markkula +1 · 1 voice
Engineering · Social Sciences · #Transportation Planning and Optimization #Transportation and Mobility Innovations #Urban Transport and Accessibility
paper · doi:10.1016/j.trf.2025.03.026
openalex publication_date 2025/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/02
• The study compares how cyclists interact with pro (truck/taxi) vs. non-pro (car) drivers at unsignalized intersections. • Game theory models predict cyclist-driver interactions better than traditional logit models. • Professional drivers are less likely to yield to cyclists, showing a need for targeted road safety measures. • The study uses naturalistic data to assess how driver type affects cyclist-vehicle interactions. According to crash data reports, most collisions between cyclists and motorized vehicles occur at unsignalized intersections (where no traffic lights regulate vehicle priority). In the era of automated driving, it is imperative for automated vehicles to ensure the safety of cyclists, especially at these intersections. In other words, to safely interact with cyclists, automated vehicles need models that can describe how cyclists cross and yield at intersections. So far, only a few studies have modeled the interaction between cyclists and motorized vehicles at intersections, and none of them have explored the variations in interaction outcomes based on the type of drivers involved. In this study, we compare non-professional drivers (represented by passenger car drivers) and professional drivers (truck and taxi drivers). We also introduce a novel application of game theory by comparing logit and game theoretic models’ analyses of the interactions between cyclists and motorized vehicles, leveraging naturalistic data. Interaction events were extracted from a trajectory dataset, and cyclists’ non-kinematic cues were extracted from videos and incorporated into the interaction events’ data. The modeling outputs showed that professional drivers are less likely to yield to cyclists than non-professional drivers. Furthermore, the behavioral game theoretic models outperformed the logit models in predicting cyclists’ crossing decisions.