Investigating Pedestrian Crossing Patterns at Crossing Locations Based on Trajectory Data Collected by UAV

Abstract

Previous studies on pedestrian crossing have mostly focused on pedestrian crossing decisions; while as an important behavioral aspect, the pedestrian crossing process, i.e., their motions during the entire crossing process, has been narrowly studied. Understanding how pedestrian moves across the street during their entire crossing process helps identify risky movements and reasons for such movements, which can further help in the implementation of effective countermeasures. Therefore, this paper proposed a new and easily applied approach for investigating and understanding the pattern of the pedestrian crossing process at crosswalks based on vision-based trajectory tracking technology and UAV (unmanned aerial vehicle) data. This study uses UAV for collecting video data which is timesaving and has a sufficient coverage area, compared to other methods. For trajectory extraction, the vision-based Deep-SORT-Yolov5 architecture is applied. An improved DBSCAN (density-based spatial clustering of applications with noise) algorithm is introduced for clustering and identification of patterns of pedestrian crossing processes based on their trajectories. This approach is tested via a case study involving six marked crosswalks in Shanghai, China. By using the proposed method, different crossing patterns are extracted and compared. The results show reasonable outputs of trajectory patterns, which reasonably explain the potential instincts of the pedestrians and affecting factors on the behavior of the pedestrian crossing process. Suggestions are made based on the results. This paper contributes to a more comprehensive safety analysis of pedestrian crossings by considering the pedestrian crossing process. The model, along with the UAV-based trajectory observation method, provides an easily-applied and low-cost way of traffic data collection for the purpose of pedestrian safety evaluation.