Crowding Prediction of In-Situ Metro Passengers Using Smart Card Data
This work addresses crowd management for public transport agencies and train operators, but it is incremental as it builds on existing statistical models and data.
The paper tackles the problem of predicting passenger crowding in metro systems by developing a statistical model that forecasts time-dependent origin-destination matrices and estimates travel times using smart card data, with a case study on Singapore's MRT system demonstrating its efficacy and efficiency.
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.