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首页> 外文期刊>Journal of Advanced Transportation >A Real-Time Timetable Rescheduling Method for Metro System Energy Optimization under Dwell-Time Disturbances
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A Real-Time Timetable Rescheduling Method for Metro System Energy Optimization under Dwell-Time Disturbances

机译：停留时间干扰下的地铁系统能量优化的实时时间表重新安排方法

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摘要

Automatic Train Systems (ATSs) have attracted much attention in recent years. A reliable ATS can reschedule timetables adaptively and rapidly whenever a possible disturbance breaks the original timetable. Most research focuses the timetable rescheduling problem on minimizing the overall delay for trains or passengers. Few have been focusing on how to minimize the energy consumption when disturbances happen. In this paper, a real-time timetable rescheduling method (RTTRM) for energy optimization of metro systems has been proposed. The proposed method takes little time to recalculate a new schedule and gives proper solutions for all trains in the network immediately after a random disturbance happens, which avoids possible chain reactions that would attenuate the reuse of regenerative energy. The real-time feature and self-adaptability of the method are attributed to the combinational use of Genetic Algorithm (GA) and Deep Neural Network (DNN). The decision system for proposing solutions, which contains multiple DNN cells with same structures, is trained by GA results. RTTRM is upon the foundation of three models for metro networks: a control model, a timetable model and an energy model. Several numerical examples tested on Shanghai Metro Line 1 (SML1) validate the energy saving effects and real-time features of the proposed method.
机译：近年来，自动列车系统（ATSS）引起了很多关注。每当可能的干扰打破原始时间表时，可靠的ATS可以自适应地且快速地重新安排时间表。大多数研究侧重于最小化火车或乘客的整体延迟的时间表重新安排问题。很少有专注于如何在干扰发生时最小化能量消耗。本文提出了一种用于地铁系统能量优化的实时时间表重新安排方法（RTTRM）。所提出的方法几乎没有时间重新计算新的时间表，并在随机干扰发生后立即给网络中所有列车给出适当的解决方案，这避免了可能会衰减再生能量的再利用的链反应。该方法的实时特征和自适应归因于遗传算法（GA）和深神经网络（DNN）的组合使用。用于提出含有具有相同结构的多个DNN单元的解决方案的决策系统被GA结果训练。 RTTRM在地铁网络的三种型号的基础上：控制模型，时间表模型和能量模型。在上海地铁1号线（SML1）上测试了几个数值示例验证了所提出的方法的节能效果和实时特征。

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  • 来源
    《Journal of Advanced Transportation》 |2019年第4期|5174961.1-5174961.11|共11页
  • 作者

    Yang Guang; Wang Junjie; Zhang Feng; Zhang Shiwen; Gong Cheng;

  • 作者单位

    Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China;

    Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China;

    Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China;

    Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China;

    Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA;

  • 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
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