Estimation of Dynamic Pedestrian Route Flows in Intermodal Stations Using Re-Identification Sensor Data

Dynamic estimation of pedestrian route flows in intermodal stations is essential to understand how passengers are distributed within complex infrastructures and to support operational management, planning and sustainable mobility. However, estimating these flows is challenging because direct observations are usually partial: sensors can only be installed at selected locations, and the same detection may be compatible with several routes. To address this limitation, this paper proposes a dynamic model to estimate pedestrian route flows from re-identification sensor data. The method combines a dynamic network loading model, which propagates route demand through the network, with a flow reconstruction model that adjusts the estimates using cumulative observations and travel times recorded between sensors. To assess its performance, the model is evaluated under different sensor layouts in a representative intermodal-station network simulated in Vissim–Viswalk, where reference route and link flows are available for validation. This comparison examines how the amount and location of the available information affects the quality of the estimation. The evaluation is further extended to a larger case study based on Getafe-Central station as a real-size case study. In both experiments, the results show that the model can accurately reconstruct route flows when all routes are identifiable and can still provide acceptable estimates with fewer sensors in the analysed scenarios. The reconstructed flows therefore provide useful information for managing pedestrian movements, detecting critical areas and supporting a more efficient and sustainable use of intermodal transport facilities.