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This paper proposes the Spatio-Temporal Crowdedness Inference Model (STCIM),a framework to infer the passenger distribution inside the whole urban railtransit (URT) system in real-time. Our model is practical since the model isdesigned in a probabilistic manner and only based on the entry and exittimestamps information collected by the automatic fare collection (AFC) system.Firstly, the entire URT system is decomposed into several components ofstations and segments. By decomposing a passenger's travel actions intoentering, traveling, transferring, and exiting, we build a statistical model toestimate the passengers' lingering time within each component and thepassengers' destination based on historical AFC data. Then, the passengers'spatial distribution is predicted in real-time based on each passenger'selapsed travel time and their entry station. The effectiveness of the scheme isvalidated with a real dataset from a real URT system.

A Unified Probabilistic Framework for Spatiotemporal Passenger Crowdedness Inference within Urban Rail Transit Network