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面向大客流的城轨备用车投放车站选择与优化模型

叶茂 钱钟文 李俊铖 曹从咏

叶茂, 钱钟文, 李俊铖, 曹从咏. 面向大客流的城轨备用车投放车站选择与优化模型[J]. 交通运输工程学报, 2021, 21(5): 227-237. doi: 10.19818/j.cnki.1671-1637.2021.05.019
引用本文: 叶茂, 钱钟文, 李俊铖, 曹从咏. 面向大客流的城轨备用车投放车站选择与优化模型[J]. 交通运输工程学报, 2021, 21(5): 227-237. doi: 10.19818/j.cnki.1671-1637.2021.05.019
YE Mao, QIAN Zhong-wen, LI Jun-cheng, CAO Cong-yong. Selection and optimization model of standby train deployment stations on urban rail transit for large passenger flow[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 227-237. doi: 10.19818/j.cnki.1671-1637.2021.05.019
Citation: YE Mao, QIAN Zhong-wen, LI Jun-cheng, CAO Cong-yong. Selection and optimization model of standby train deployment stations on urban rail transit for large passenger flow[J]. Journal of Traffic and Transportation Engineering, 2021, 21(5): 227-237. doi: 10.19818/j.cnki.1671-1637.2021.05.019

面向大客流的城轨备用车投放车站选择与优化模型

doi: 10.19818/j.cnki.1671-1637.2021.05.019
基金项目: 

国家重点研发计划项目 2017YFB1201202

江苏省交通运输科技项目 2020Y17

详细信息
    作者简介:

    叶茂(1982-),男,安徽黄山人,南京理工大学副教授,工学博士,从事轨道交通运营与安全保障研究

  • 中图分类号: U293.5

Selection and optimization model of standby train deployment stations on urban rail transit for large passenger flow

Funds: 

National Key Research and Development Program of China 2017YFB1201202

Transportation Science and Technology Project of Jiangsu Province 2020Y17

More Information
  • 摘要: 为快速疏解城轨线路上车站的大客流,减少乘客的等待时间,研究了备用车投放问题; 在考虑列车追踪关系、列车停站时间等约束的基础上,建立了综合备用车投放时机确定、投放最佳车站选择和时刻表动态调整的多目标优化模型; 界定了城轨备用车开行条件,提出了城轨备用车投放时机的定量化判定方法; 用0-1变量表征车站是否具备备用车投放条件,并将其作为模型输入,以减小大客流车站乘客等待时间和降低运行图偏离时间(延误时间)为优化目标,构建了备用车投放的混合整数非线性规划模型,该模型通过比较不同的备用车投放方案效率得到最佳的备用车投放车站和后续开行计划; 为同时求解0-1变量与连续变量,设计了带惩罚函数的改进粒子群优化算法求解模型。研究结果表明:该方法可对所有符合备用车开行条件的车站制定投放方案,并进一步筛选出最优的备用车投放车站,最多可减少1 318 209 s的乘客等待时间,优化效率为21.9%,且改进的粒子群优化算法对混合整数非线性规划模型的适用性较好; 相比于既有城轨线路列车运行调整和时刻表优化方法,本文提出的方法在应对突发大客流的备用车投放时机上做出了更加定量化的判断,优先考虑了大客流车站的疏解能力和效率,并优化了备用车与后续列车的开行方案,可以有效解决高峰时段车站大客流问题。

     

  • 图  1  某城轨线路上下行方向最大断面满载率

    Figure  1.  Maximum section full load ratios in up and down direction of an urban rail line

    图  2  城轨线路大客流车站

    Figure  2.  Large passenger flow stations in urban rail line

    图  3  大客流车站滞留乘客数量

    Figure  3.  Stranded passenger number at station with large passenger flow

    图  4  插入备用车后的后续延误

    Figure  4.  Subsequent delay after adding a standby train

    图  5  求解算法流程

    Figure  5.  Flow of solving algorithm

    图  6  上行方向及大客流车站分布

    Figure  6.  Station distributions in up direction and large passenger flow station

    图  7  客村站客流时间分布

    Figure  7.  Time distribution of passenger flow at Kecun Station

    图  8  加开方案列车运行图

    Figure  8.  Train operation diagrams of additional plan

    图  9  方案疏解效果对比

    Figure  9.  Relieve effects comparison of two schemes

    表  1  列车各区间运行时间

    Table  1.   Running times of train at each section

    区间 1~2 2~3 3~4 4~5 5~6 6~7 7~8 8~9 9~10 10~11 11~12 12~13
    运行时间/s 119 76 106 71 101 92 114 89 64 75 86 121
    下载: 导出CSV

    表  2  列车各站停站时间

    Table  2.   Stop times of train at each station

    车站 1 2 3 4 5 6 7 8 9 10 11 12 13
    停站时间/s 98 30 30 30 30 50 34 36 37 55 40 48 40
    下载: 导出CSV

    表  3  备用车投放后到达各车站时间

    Table  3.   Arrival times at each station after deployment standby train

    车站 方案1 方案2
    1 18:33:43
    2 18:37:20
    3 18:39:06
    4 18:41:22
    5 18:43:03 18:31:57
    6 (大客流车站) 18:45:14 18:34:08
    7 18:47:36 18:36:30
    8 18:50:04 18:38:58
    9 18:52:09 18:41:03
    10 18:53:50 18:42:44
    11 18:56:00 18:44:54
    12 18:58:06 18:47:00
    13 19:00:55 18:49:49
    下载: 导出CSV

    表  4  备用车投放方案效果分析

    Table  4.   Scheme effect analysis of deployment standby trains

    方案 原拥堵时间/s 备用车投放车站编号 开行备用车后拥堵时间/s 优化效率/%
    1 5 991 932 1 4 673 723 21.9
    2 5 991 932 5 5 099 190 14.9
    下载: 导出CSV
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  • 收稿日期:  2021-06-18
  • 网络出版日期:  2021-11-13
  • 刊出日期:  2021-10-01

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