Inferring the structure of pedestrian flows at a transportation hub

In transportation hubs, pedestrian flows form complex network structures, leading to serious congestion at peak hours. Understanding their dynamics is crucial to managing safe and efficient transportation. Although many experimental and theoretical studies have investigated pedestrian interactions at the microscopic level, computational models that account for pedestrians’ macroscopic origin and destination (OD) demands and mesoscopic route choices in large walking facilities are rare and lack empirical validation. In other words, pedestrians’ decision-making at strategic (macroscopic) and tactical (mesoscopic) levels, other than the operational (microscopic) level, has remained largely unexplored. Here, we propose an integrated Strategic–Tactical–Operational model for transportation hub (STO-Hub model), and validate it using 0.87 million pedestrian trajectories collected over three days by means of 11 LiDAR sensors at JR Shinjuku station in Japan. Based on an abstracted graph of the main concourse with directed links between different platform entrances and gates, we employ the gravity model at the strategic layer to estimate time-varying OD demand, a logit route-choice model at the tactical layer to capture route choice behavior, and an agent-based model to reproduce interactions with the surrounding environment and pedestrians. The STO-Hub model accurately reconstructs OD demand and route-choice behavior, achieving high agreement with directed flow counts, and the simulation delineates local congested areas evident in the sensing data. By estimating OD demand and route splits and by reproducing local interactions at any selected section, the STO-Hub model captures pedestrian dynamics across all three levels, including at congested locations. We further propose a STO-Hub framework that integrates sensing, the STO-Hub model, and management plans, providing a practical 10-min-resolution basis for OD-informed pedestrian guidance and control in transportation hubs. The study fills a gap in strategic modeling and management for large transportation hubs and supports congestion prevention, improved safety, and higher operational efficiency.