交通运输系统工程与信息 ›› 2023, Vol. 23 ›› Issue (2): 208-216.DOI: 10.16097/j.cnki.1009-6744.2023.02.022

• 系统工程理论与方法 • 上一篇    下一篇

考虑列车运行净能耗与载客量差异的时刻表优化方法

张惠茹1,2,豆飞*1,2,魏运1,2,刘洁1,2,宁尧1,2   

  1. 1. 北京市地铁运营有限公司,北京 100044;2. 地铁运营安全保障技术北京市重点实验室,北京 100044
  • 收稿日期:2023-01-13 修回日期:2023-03-01 接受日期:2023-03-02 出版日期:2023-04-25 发布日期:2023-04-19
  • 作者简介:张惠茹(1994- ),女,内蒙古呼和浩特人,工程师,博士
  • 基金资助:
    北京市科技新星计划项目(Z211100002121098);国家重点研发计划项目(2020YFB160070X)

A Timetable Optimization Method Considering Train Operation Net Energy Consumption and Passenger Load Difference

ZHANG Hui-ru1,2, DOU Fei*1,2, WEI Yun1,2, LIU Jie1,2 , NING Yao1, 2   

  1. 1. Beijing Mass Transit Railway Operation Corp. LTD., Beijing 100044, China; 2. Beijing Key Laboratory of Subway Operation Safety Technology, Beijing 100044, China
  • Received:2023-01-13 Revised:2023-03-01 Accepted:2023-03-02 Online:2023-04-25 Published:2023-04-19
  • Supported by:
    Beijing Nova Program (Z211100002121098);National Key Research and Development Program of China (2020YFB160070X)

摘要: 城市轨道交通对缓解城市化进程所带来的巨大客流压力具有重要意义,但其日常运营需要消耗大量能源,因此在保持现有基础设施条件不变的前提下进行列车运输组织优化是一种有效且可行的节能手段。本文在充分考虑客流空间分布特征的条件下,建立一种考虑列车节能操纵的时刻表优化模型,以使列车净能耗和乘客在车时间达到双目标最优;设计一种基于仿真的非支配排序遗传算法,在最大加速度加速-匀速-惰行-最大减速度制动的最优驾驶策略基础上,通过优化列车区间运行时间和停站时间,找到一组能耗和时间平衡的帕累托最优解,以此实现城市轨道交通列车节能时刻表优化。以北京地铁昌平线实际运营数据为例进行一系列实验,结果表明:本文提出的优化模型可以得到不同载客量条件下分布均匀且完全收敛的帕累托最优解;与原始时刻表相比,固定列车载客量和考虑列车载客量空间差异的净能耗优化率分别达到 5.5%和18.79%。

关键词: 城市交通, 节能时刻表, 多目标优化, 列车操纵, 客流

Abstract: Urban rail transit is of great significance to alleviate the huge passenger flow pressure brought about by the urbanization process. However, the daily operation always consumes a considerable amount of energy. An effective and feasible energy-saving method is to optimize the train operation strategy while keeping the existing infrastructure unchanged. In this paper, considering the spatial distribution of passenger flow, a timetable optimization model for the energy-saving operation of trains is established to make the net energy consumption of trains and passengers' traveling time achieve bi-objective optimization. A simulation-based non-dominated sorting genetic algorithm is designed to solve the model. With the optimal driving strategy of maximum acceleratio-cruising-coasting-maximum deceleration braking, a set of Pareto optimal solutions of time vs. energy consumption are obtained by optimizing the running time in the interstation sections and the dwell time at stations. A series of experiments are carried out using the actual data of the Changping Line in Beijing Metro. The results show that Pareto optimal solutions with uniform distribution and complete convergence under different passenger load conditions can be obtained by the proposed optimization model. Compared with the original timetable, the net energy consumption optimization rate of fixed train passenger load and the one considering the spatial difference of train passenger load can reach 5.5% and 18.79% respectively.

Key words: urban traffic, energy-efficient timetable, multi-objective optimization, train operation, passenger flow

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