Preliminary Study on Space Utilization and Emergent Behaviors of Group vs. Single Pedestrians in Real-World Trajectories
This work addresses the problem of analyzing pedestrian dynamics for crowd simulation and space design, but it is incremental as it focuses on setting up the classification and metrics without full analysis.
This study developed a framework to classify group and single pedestrians from real-world trajectory data, using a Transformer-based model and a metric framework for spatial and behavioral analysis, but did not provide quantitative results as it is a preliminary version.
This study presents an initial framework for distinguishing group and single pedestrians based on real-world trajectory data, with the aim of analyzing their differences in space utilization and emergent behavioral patterns. By segmenting pedestrian trajectories into fixed time bins and applying a Transformer-based pair classification model, we identify cohesive groups and isolate single pedestrians over a structured sequence-based filtering process. To prepare for deeper analysis, we establish a comprehensive metric framework incorporating both spatial and behavioral dimensions. Spatial utilization metrics include convex hull area, smallest enclosing circle radius, and heatmap-based spatial densities to characterize how different pedestrian types occupy and interact with space. Behavioral metrics such as velocity change, motion angle deviation, clearance radius, and trajectory straightness are designed to capture local adaptations and responses during interactions. Furthermore, we introduce a typology of encounter types-single-to-single, single-to-group, and group-to-group to categorize and later quantify different interaction scenarios. Although this version focuses primarily on the classification pipeline and dataset structuring, it establishes the groundwork for scalable analysis across different sequence lengths 60, 100, and 200 frames. Future versions will incorporate complete quantitative analysis of the proposed metrics and their implications for pedestrian simulation and space design validation in crowd dynamics research.