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, transfer, and egress walking durations are modeled using non-parametric Kernel Density Estimation (KDE) at the platform level. Based on these distributions, we construct a confidence-based model to estimate the probability of each feasible itinerary. A congestion-aware penalty function is introduced to reduce the confidence of infeasible itineraries involving overloaded in-vehicle links. To balance inference accuracy and computational efficiency, we develop a dynamic batch-size adjustment algorithm that iteratively updates train loads and refines probabilities. The framework is validated using large-scale AFC and timetable data from Chengdu Metro. Results demonstrate that the proposed method effectively suppresses violations of physical capacity constraints, improves behavioral plausibility, and provides reliable inputs for downstream applications such as resilience analysis and passenger behavior modeling.