Data-Driven Metro Passenger Trajectory Inference Under Physical Constraints and Congestion Dynamics

Understanding the fine-grained trajectories of metro passengers, especially at the train and route levels, is essential for analyzing system-level dynamics and individual behavior. However, existing approaches often rely on strong behavioral priors or simplified boarding assumptions, limiting their generality and realism. This study proposes a fully data-driven framework for passenger trajectory inference that explicitly incorporates physical capacity constraints and crowding effects. Entry, tra