Silva Ricardo, Kang Soong Moon, Airoldi Edoardo M
Department of Statistical Science and Centre for Computational Statistics and Machine Learning, University College London, London WC1E 6BT, United Kingdom;
Department of Management Science and Innovation, University College London, London WC1E 6BT, United Kingdom; and.
Proc Natl Acad Sci U S A. 2015 May 5;112(18):5643-8. doi: 10.1073/pnas.1412908112. Epub 2015 Apr 20.
Public transportation systems are an essential component of major cities. The widespread use of smart cards for automated fare collection in these systems offers a unique opportunity to understand passenger behavior at a massive scale. In this study, we use network-wide data obtained from smart cards in the London transport system to predict future traffic volumes, and to estimate the effects of disruptions due to unplanned closures of stations or lines. Disruptions, or shocks, force passengers to make different decisions concerning which stations to enter or exit. We describe how these changes in passenger behavior lead to possible overcrowding and model how stations will be affected by given disruptions. This information can then be used to mitigate the effects of these shocks because transport authorities may prepare in advance alternative solutions such as additional buses near the most affected stations. We describe statistical methods that leverage the large amount of smart-card data collected under the natural state of the system, where no shocks take place, as variables that are indicative of behavior under disruptions. We find that features extracted from the natural regime data can be successfully exploited to describe different disruption regimes, and that our framework can be used as a general tool for any similar complex transportation system.
公共交通系统是大城市的重要组成部分。在这些系统中广泛使用智能卡进行自动收费，为大规模了解乘客行为提供了独特的机会。在本研究中，我们使用从伦敦交通系统的智能卡获得的全网络数据来预测未来的交通流量，并估计由于车站或线路的意外关闭而导致的中断影响。中断或冲击会迫使乘客就进出哪些车站做出不同的决定。我们描述了这些乘客行为的变化如何导致可能的过度拥挤，并对给定中断情况下车站将如何受到影响进行建模。然后，这些信息可用于减轻这些冲击的影响，因为交通当局可以提前准备替代解决方案，例如在受影响最严重的车站附近增加公交车。我们描述了统计方法，这些方法利用在系统自然状态下（即没有冲击发生）收集的大量智能卡数据作为表示中断情况下行为的变量。我们发现，从自然状态数据中提取的特征可以成功地用于描述不同的中断状态，并且我们的框架可以用作任何类似复杂交通系统的通用工具。
