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Artificial Neural Networks for Forecasting Passenger Flows on Metro Lines

机译：人工神经网络预测地铁客流

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摘要

Forecasting user flows on transportation networks is a fundamental task for Intelligent Transport Systems (ITSs). Indeed, most control and management strategies on transportation systems are based on the knowledge of user flows. For implementing ITS strategies, the forecast of user flows on some network links obtained as a function of user flows on other links (for instance, where data are available in real time with sensors) may provide a significant contribution. In this paper, we propose the use of Artificial Neural Networks (ANNs) for forecasting metro onboard passenger flows as a function of passenger counts at station turnstiles. We assume that metro station turnstiles record the number of passengers entering by means of an automatic counting system and that these data are available every few minutes (temporal aggregation); the objective is to estimate onboard passengers on each track section of the line (i.e., between two successive stations) as a function of turnstile data collected in the previous periods. The choice of the period length may depend on service schedules. Artificial Neural Networks are trained by using simulation data obtained with a dynamic loading procedure of the rail line. The proposed approach is tested on a real-scale case: Line 1 of the Naples metro system (Italy). Numerical results show that the proposed approach is able to forecast the flows on metro sections with satisfactory precision.
机译：预测运输网络上的用户流量是智能运输系统（ITS）的一项基本任务。实际上，大多数运输系统的控制和管理策略都是基于用户流的知识。为了实施ITS策略，对某些网络链路上的用户流量的预测（作为其他链路上用户流量的函数）（例如，传感器实时提供数据的情况）可能会起到重要作用。在本文中，我们建议使用人工神经网络（ANN）来预测地铁车载乘客流量，作为车站旋转闸口乘客数量的函数。我们假设地铁站的旋转门通过自动计数系统记录了进入的乘客数量，并且这些数据每隔几分钟（时间汇总）可用。目的是根据先前时期收集的旋转闸门数据，估算线路的每个轨道部分（即​​，两个连续的站点之间）的机上乘客。周期长度的选择可能取决于服务时间表。人工神经网络通过使用通过铁路线动态加载程序获得的模拟数据进行训练。该提议的方法已在实际情况下进行了测试：那不勒斯地铁系统（意大利）的1号线。数值结果表明，该方法能够较好地预测地铁路段的流量。

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