Modeling Bus Passenger Flow Dynamics Using the Cell Transmission Model for Real‐Time Congestion Management

This research develops the BUS‐CTM, a novel mathematical simulation model that adapts the cell transmission model (CTM) to analyze spatiotemporal passenger flow dynamics in urban bus networks. The framework discretizes bus routes into interconnected cells bounded by adjacent stops, enabling simultaneous tracking of passenger density evolution and bus traffic interactions through a unified state‐space representation. By integrating real‐time data streams—including GPS trajectories, automatic passenger counters (APCs) records, and VISSIM‐simulated traffic dynamics—the model captures critical nonlinearities in boarding/alighting processes and network‐wide congestion propagation at shared stops. Numerical experiments on Gainesville’s RTS network demonstrate the model’s accuracy in predicting passenger distributions, achieving a 4% mean absolute percentage error (MAPE) during peak hours (6:30–9:45 a.m.) and successfully identifying bottlenecks where densities exceed 85% of capacity. The BUS‐CTM advances prior CTM adaptations through three key innovations: (1) integration of mixed‐traffic capacity reduction effects to account for bus‐induced roadway bottlenecks, (2) modular parameterization for transferability across diverse transit systems, and (3) real‐time applicability via embedded calibration protocols for door throughput ( C door = 1.2 pax/s) and fare efficiency ( γ = 0.8–1.0). These contributions provide transit agencies with a computationally efficient tool for optimizing service frequency, mitigating crowding, and improving network resilience.