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Measuring fine-grained metro interchange time via smartphones

机译：通过智能手机测量细粒度的地铁换乘时间

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摘要

High variability interchange times often significantly affect the reliability of metro travels. Fine-grained measurements of interchange times during metro transfers can provide valuable insights on the crowdedness of stations, usage of station facilities and efficiency of metro lines. Measuring interchange times in metro systems is challenging since agent operated systems like automatic fare collection systems only provide coarse-grained trip information and popular localization services like GPS are often inaccessible underground. In this paper, we propose a smartphone-based interchange time measuring method from the passengers' perspective. It leverages low-power sensors embedded in modem smart phones to record ambient contextual features, and utilizes a two-tier classifier to infer interchange states during a metro trip, and further distinguishes 10 fine-grained cases during interchanges. Experimental results within 6 months across over 14 subway lines in 3 major cities demonstrate that our approach yields an overall interchange state inference F1-measurement of 91.0% and an average time error of less than 2 min at an inference interval of 20 s, and an average accuracy of 89.3% to distinguish the 10 fine-grained interchange cases. We also conducted a series of case studies using measurements collected from crowdsourced users during 3 months, which reveals findings previously unattainable without fine-grained interchange time measurements, such as portions of waiting time during interchange, interchange directions, usage of facilities (stairs/escalators/lifts), and the root causes of long interchange times. (C) 2017 Published by Elsevier Ltd.
机译：高可变性互换时间通常会严重影响地铁出行的可靠性。地铁换乘期间对换乘时间的细粒度测量可以提供有关车站拥挤，车站设施使用和地铁线路效率的宝贵见解。测量地铁系统中的换乘时间是具有挑战性的，因为诸如自动票价收集系统之类的代理操作系统仅提供粗粒度的旅行信息，而地下GPS等常见的本地化服务通常是无法访问的。在本文中，我们从乘客的角度提出了一种基于智能手机的互换时间测量方法。它利用嵌入在现代智能手机中的低功率传感器来记录周围环境的特征，并利用两层分类器来推断出地铁旅行期间的交换状态，并在交换过程中进一步区分出10种细粒度的情况。在3个主要城市的14条地铁线上进行的6个月内的实验结果表明，我们的方法得出的总体互换状态推断F1测度为91.0％，平均时间误差在20 s的推断间隔内不到2分钟，并且10个细粒度互换案例的平均准确度为89.3％。我们还进行了一系列案例研究，使用了3个月内从众包用户那里收集的数据，这些数据揭示了以前没有细粒度的交换时间测量数据无法获得的结果，例如交换期间的等待时间，交换方向，设施的使用（楼梯/自动扶梯） / lifts），以及互换时间长的根本原因。 （C）2017由Elsevier Ltd.发布

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  • 来源
    《Transportation research》 |2017年第8期|153-171|共19页
  • 作者

    Gu Weixi; Zhang Kai; Zhou Zimu; Jin Ming; Zhou Yuxun; Liu Xi; Spanos Costas J.; Shen Zuo-Jun (Max); Lin Wei-Hua; Zhang Lin;

  • 作者单位

    Tsinghua Univ, Tsinghua Berkeley Shenzhen Inst, Beijing, Peoples R China;

    Tsinghua Univ, Grad Sch Shenzhen, Beijing, Peoples R China;

    Swiss Fed Inst Technol, Comp Engn & Networks Lab, Zurich, Switzerland;

    Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA;

    Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA;

    Tsinghua Univ, Tsinghua Berkeley Shenzhen Inst, Beijing, Peoples R China;

    Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA;

    Univ Calif Berkeley, Dept Ind Engn & Operat Res, Berkeley, CA 94720 USA;

    Univ Arizona, Dept Syst & Ind Engn, Tucson, AZ 85721 USA;

    Tsinghua Univ, Tsinghua Berkeley Shenzhen Inst, Beijing, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Underground public transport; Location-based service; Smartphone; Crowdsourcing;

    机译：地下公共交通;基于位置的服务;智能手机;众包;

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