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