Traffic Animation of Pedestrian and Vehicle Interactions in Non-Signalized Mid-Block Regions

Traffic animation enables the visualization of potential conflicts among road users, supporting the development of safety measures and serving applications in education and entertainment. This paper presents a factor-based approach to generate animations of pedestrians interacting with vehicles in mid-block crossing scenarios. A mid-block refers to a road segment between intersections without traffic signals. In these settings, vehicles and pedestrians should coordinate dynamically to avoid accidents as there is a lack of designated crossing control. Furthermore, this dynamic environment provides an opportunity to produce traffic animation that depicts the diverse and intricate interactions between vehicles and pedestrians. We propose a dynamic graph that tracks leader-follower relationships among vehicles, as well as the evolving gap regions between them. In addition, pedestrians evaluate these gap regions using five intuitive factors, which are derived from gap characteristics. These factors are adjusted region distance, degree of passage, region growth rate, region flow ratio, and vacancy ratio. By tuning the influences of these factors, animators can simulate a wide range of pedestrians behaviors, from cautious waiting to assertive movement. To evaluate the performance of the proposed approach, we conducted experiments assessing effects of these factors on route decisions and pedestrian interaction styles. Additionally, we analyzed the effects of varying pedestrian and vehicle volumes on average travel time and distance. The results demonstrated the system’s ability to produce plausible animations of pedestrian-vehicle interactions. Our system serves as a customizable tool for animators to generate diverse pedestrian crossing behaviors adapted to varying vehicle gap scenarios. Our approach could be applied to various domains, such as education, training, and immersive entertainment. In these areas, tailored pedestrian crossing behaviors enhance the richness and engagement of traffic scenarios.