Crowding on platforms is increasing. But when is full, too full? And if overcrowding is imminent, is there a way to warn train staff in advance? To be able to operate a station effectively in critical situations, it must be possible to monitor the current situation precisely and predict future scenarios reliably. With ASE's Pedestrian Analytics System (PAS), it is possible to accurately measure passenger flows and densities to gain valuable information on station usage and predict potentially critical situations.
Jessica Weibel (ETH Zurich) wrote her master's thesis on this topic at ASE. Her thesis deals with machine learning algorithms that can be used to optimize the predictions regarding the utilization of platforms at Amsterdam Zuid station.
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