论文标题
使用智能卡数据对原位地铁乘客的拥挤预测
Crowding Prediction of In-Situ Metro Passengers Using Smart Card Data
论文作者
论文摘要
地铁系统在城市公共交通网络中发挥了越来越重要的作用，使城市每天在太空中转移了大量的人类流量。近年来，已经进行了广泛的研究以提高地铁系统的服务质量。其中，人群管理一直是公共交通机构和火车运营商的关键问题。在本文中，通过利用累积的智能卡数据，我们提出了一个统计模型来预测封闭的地铁系统内部任何两个相邻站点之间任何两个相邻站点之间的板载乘客数量。提出的模型执行了两个主要任务：i）通过应用成熟的统计模型来预测时间依赖性的起源 - 原始矩阵（OD）矩阵； ii）估计地铁网络不同部分所需的旅行时间成本，并通过截短的正常混合分布具有预期最大化（EM）算法。根据预测结果，我们能够为未来时间点提供准确的原位乘客密度预测。使用新加坡质量快速运输（MRT）系统中的真实智能卡数据的案例研究证明了我们提出的方法的功效和效率。
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.
