  The metro system is playing an increasingly important role in the urban
public transit network, transferring a massive human flow across space everyday
in the city. In recent years, extensive research studies have been conducted to
improve the service quality of metro systems. Among them, crowd management has
been a critical issue for both public transport agencies and train operators.
In this paper, by utilizing accumulated smart card data, we propose a
statistical model to predict in-situ passenger density, i.e., number of
on-board passengers between any two neighbouring stations, inside a closed
metro system. The proposed model performs two main tasks: i) forecasting
time-dependent Origin-Destination (OD) matrix by applying mature statistical
models; and ii) estimating the travel time cost required by different parts of
the metro network via truncated normal mixture distributions with
Expectation-Maximization (EM) algorithm. Based on the prediction results, we
are able to provide accurate prediction of in-situ passenger density for a
future time point. A case study using real smart card data in Singapore Mass
Rapid Transit (MRT) system demonstrate the efficacy and efficiency of our
proposed method.
