Real-Time Estimation of Pedestrian Inflow Rates from Saturated Sensor Counting Data in a Complex Metro Station

Many ITS applications in public transport stations benefit from knowing the pedestrian flows inside and outside the building for the purpose of predicting waiting times or applying safety measures. Counting people inside the station is often easier than outside the building because the building is a closed system with well-defined cross-sections like doors or stairs instead of the open area outside. The question is, given a set of people counters inside an infrastructure, how to determine the pedestrian inflow into the building. The inflow may be higher than the people counts inside the station because bottlenecks limit the maximum rate of people entering the infrastructure. We present a real-time estimation model which uses people counts inside a metro station and extrapolates the expected inflow once the counting sensors saturate. The model is validated with a case study at a large subway station next to Vienna´s largest football stadium.