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Mining missing train logs from Smart Card data

机译:从智能卡数据中挖掘丢失的火车日志

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摘要

This paper shows how to recover the arrival times of trains from the gate times of metro passengers from Smart Card data. Such technique is essential when a log, the set of records indicating the actual arrival and departure time of each bus or train at each station and also a critical component in reliability analysis of a transportation system, is missing partially or entirely. The procedure reconstructs each train as a sequence of the earliest exit times, called S-epochs, among its alighting passengers at each stations. The procedure first constructs a set of passengers, also known as reference passengers, whose routing choices are easily identifiable. The procedure then computes, from the exit times of the reference passengers, a set of tentative S-epochs based on a detection measure whose validity relies on an extreme-value characteristic of the platform-to-gatemovement of alighting passengers. The tentative S-epochs are then finalized to be a true one, or rejected, based on their consistencies with bounds and/or interpolation from prescribed 5-epochs of adjacent trains and stations. Tested on 12 daily sets of trains, with varying degrees of missing logs, from three entire metro lines, the method restored the arrival times of 95% of trains within the error of 24 s even when 100% of logs was missing. The mining procedure can also be applied to trains operating under special strategies such as short-turning and skip-stop. The recovered log seems precise enough for the current reliability analysis performed by the city of Seoul. (C) 2015 Elsevier Ltd. All rights reserved.
机译:本文展示了如何根据智能卡数据从地铁乘客的登机口时间中恢复火车的到达时间。当部分或全部缺少日志,表示每个公共汽车或火车在每个车站的实际到达和离开时间的记录集以及交通系统可靠性分析中的重要组成部分时,这种技术至关重要。该程序将每个列车按照其在每个车站的下车乘客的最早出站时间序列(称为S时期)进行重构。该过程首先构造一组乘客,也称为参考乘客,其路线选择很容易识别。然后,该程序根据参考乘客的出站时间,根据检测度量来计算一组临时S历元,其有效性取决于下车乘客的站台至登机口运动的极值特性。然后,根据暂定S历元与相邻列车和车站的指定5历元的界限和/或内插值的一致性,最终将其确定为真实S历元或将其拒绝。在三条整个地铁线路上的12组每日火车上测试了不同程度的失木,即使缺少100%的原木,该方法也能在24 s的误差内将95%的火车到达时间恢复。采矿程序也可以应用于在特殊策略下运行的列车,例如短弯和跳停。对于首尔市当前进行的可靠性分析,恢复的日志似乎足够精确。 (C)2015 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Transportation research》 |2016年第2期|170-181|共12页
  • 作者单位

    Seoul Natl Univ, Dept Ind Engn, San 56-1 Shilim Dong, Seoul 151742, South Korea|Samsung Adv Inst Technol, Software Solut Lab, Seoul, South Korea;

    Seoul Natl Univ, Dept Ind Engn, San 56-1 Shilim Dong, Seoul 151742, South Korea;

    Seoul Natl Univ, Dept Ind Engn, San 56-1 Shilim Dong, Seoul 151742, South Korea|Korea Railrd Res Inst, Policy Technol Convergence Res Div, Seoul, South Korea;

    Seoul Natl Univ, Dept Ind Engn, San 56-1 Shilim Dong, Seoul 151742, South Korea;

  • 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

    Data mining; Smart Card data; Train log; Passenger behavior;

    机译:数据挖掘;智能卡数据;培训日志;乘客行为;

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