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.
CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow tracking
Exploring the impact of weather on Metro demand forecasting using machine learning method
Qiushi Honors College, Tianjin University（天津大学求是学部）；School of Traffic and Transportation, Beijing Jiaotong University（北京交通大学交通运输学院）；Maynooth International Engineering College, Fuzhou University（福州大学梅努斯国际工程学院）；Harbin Institute of Technology（哈尔滨工业大学）；Beijing Forestry University（北京林业大学）
Public Transportation Demand Analysis: A Case Study of Metropolitan Lagos
Carnegie Mellon University, Africa（卡内基梅隆大学（非洲校区））；African Center of Excellence in Data Science, University of Rwanda（卢旺达大学非洲数据科学卓越中心）；Oxford Man Institute of Quantitative Finance, Oxford University（牛津大学牛津曼量化金融研究所）
Smart Metro: Deep Learning Approaches to Forecasting the MRT Line 3 Ridership
Technological University of the Philippines（菲律宾技术大学）；National University（国立大学）；Philippine Coding Camp（菲律宾编程营）；University of the Philippines Open University（菲律宾大学开放大学）
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping
The Hong Kong University of Science and Technology（香港科技大学）；The Shanghai AI Laboratory（上海人工智能实验室）；University of Cologne（科隆大学）；Qing Yuan Research Institute of Shanghai Jiao Tong University（上海交通大学清源研究院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
