计算机应用 ›› 2019, Vol. 39 ›› Issue (9): 2757-2764.DOI: 10.11772/j.issn.1001-9081.2019020350

• 应用前沿、交叉与综合 • 上一篇    下一篇

基于换乘导向的大型客运枢纽高铁列车接续优化

乔俊1, 孟学雷1, 王东先1, 汤霖2   

  1. 1. 兰州交通大学 交通运输学院, 兰州 730070;
    2. 中国铁路兰州局集团有限公司 兰州车站, 兰州 730070
  • 收稿日期:2019-03-05 修回日期:2019-05-12 出版日期:2019-09-10 发布日期:2019-05-28
  • 通讯作者: 孟学雷
  • 作者简介:乔俊(1993-),女,陕西西安人,硕士研究生,主要研究方向:轨道交通运行管理与决策优化;孟学雷(1979-),男,山东泰安人,教授,博士,主要研究方向:运输组织优化、轨道交通运行管理与决策优化;王东先(1992-),男,甘肃武威人,硕士研究生,主要研究方向:轨道交通运行管理与决策优化;汤霖(1989-),男,甘肃武威人,硕士,主要研究方向:运输组织优化。
  • 基金资助:

    国家自然科学基金资助项目(71861022,61563028)。

High-speed train connection optimization for large passenger transport hub based on transfer orientation

QIAO Jun1, MENG Xuelei1, WANG Dongxian1, TANG Lin2   

  1. 1. School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou Gansu 730070, China;
    2. Lanzhou Railway Station, China Railway Lanzhou Bureau Group Company Limited, Lanzhou Gansu 730070, China
  • Received:2019-03-05 Revised:2019-05-12 Online:2019-09-10 Published:2019-05-28
  • Supported by:

    This work is partially supported by the National Natural Science Foundation of China (71861022, 61563028).

摘要:

针对高速铁路成网条件下的客运枢纽高铁列车接续优化问题,分析了枢纽内的旅客换乘过程,提出了中长途客流的换乘满意度概念;以平均换乘满意度和枢纽车站列车到发均衡性为优化目标,以大站合理发车时间、合理终到时间、车站作业间隔时间、旅客换乘时间、车站到发线能力等为约束条件,建立了基于换乘协同的大型客运枢纽高速列车接续优化模型。设计了改进染色体编码方式和选择策略的遗传算法对算例进行了求解。改进后的遗传算法同基本遗传算法、基本模拟退火算法相比,目标函数中所求的平均换乘满意度分别增加了5.10%、2.93%,枢纽车站列车到发均衡性分别提高了0.27%、2.31%,算例结果验证了改进遗传算法的有效性和稳定性,表明所提方法可以有效地提高大型枢纽高铁列车的接续质量。

关键词: 换乘满意度, 同站换乘, 异站换乘, 列车接续, 改进遗传算法

Abstract:

In view of the optimization of high-speed train connection in passenger transport hub under the condition of high-speed railway network, the concept of transfer satisfaction of medium and long distance passenger flow was proposed by analyzing the passenger transfer process in hub, and a high-speed train connection optimization model for large passenger transport hub based on transfer orientation was proposed with the average transfer satisfaction and the arrival and departure equilibrium of trains at hub stations as the optimization objective and with the constraint conditions of reasonable originating time of large stations, reasonable terminating time, station operation interval time, passenger transfer time and station arrival and departure line capacity. A genetic algorithm with improved chromosome coding mode and selection strategy was designed to solve the example. Compared with the basic genetic algorithm and the basic simulated annealing algorithm, the improved genetic algorithm increases the average transfer satisfaction in the objective function by 5.10% and 2.93% respectively, and raises the equilibrium of arrival and departure of trains at hub stations by 0.27% and 2.31% respectively. The results of the example verify the effectiveness and stability of the improved genetic algorithm, which indicates that the proposed method can effectively optimize the quality of the high-speed train connection in large passenger transport hub.

Key words: transfer satisfaction, transfer inside one station, transfer between different stations, train connection, improved genetic algorithm

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