基于灵活编组的城轨车底运用计划及鲁棒客流控制策略研究
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作者单位:

北京交通大学

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中图分类号:

U231+92

基金项目:

中央高校基本科研业务费研究生创新项目 (2020YJS217), 国家自然科学基金青年基金 (72001019), 国家杰出青年科学基金 (71825004)


Joint optimization for rolling stock circulation plan based on flexible train composition and robust passenger flow control strategy on urban rail transit lines
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Affiliation:

Beijing Jiaotong University

Fund Project:

the Fundamental Research Funds for the Central Universities (2020YJS217), National Natural Science Foundation of China Youth Fund (72001019),The National Science Fund for Distinguished Young Scholars (71825004)

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    摘要:

    针对通勤客流需求的动态性、不均衡性和随机性等复杂特征, 提出了基于灵活编组的城轨车底运用计划及鲁棒客流控制策略两阶段随机规划模型. 第一阶段为编组类型指派与车底运用计划优化模型, 以极小化系统运营成本为目标; 第二阶段为车站协同限流鲁棒优化模型, 以极小化乘客等待时间为目标. 通过线性化方法将原模型重构为可被CPLEX等优化软件直接求解的混合整数线性规划模型. 算例结果表明, 灵活编组模式在仅增加0.5%乘客等待时间的基础上,可降低约30.2%的系统运营费用, 说明灵活编组方案在满足客流需求的同时可合理的降低运营费用. 此外, 本文所得鲁棒客流控制策略能够避免传统鲁棒优化方法过于保守的问题, 对实际运营过程中随机客流需求具有较好的适应性.

    Abstract:

    A two stage stochastic programming model of rolling stock circulation plan based on flexible train composition mode and robust passenger flow control strategy is proposed to satisfy the dynamic, non-equilibrium and stochastic passenger demands. The first stage is the optimization model of the train composition assignment and rolling stock circulation plan, and the objective is to minimize the operating costs of urban metro system. The second stage is the optimization model of the robust passenger flow control strategy on all the stations of the urban rail transit line, which aims at minimizing the total passenger waiting time. The original model is reformulated as a mixed-integer linear programming model through some linearization methods, which can be directly solved by some optimization softwares (e.g., CPLEX/GUROBI). The numeral experiments demonstrate that the flexible train composition mode can reduce the operating costs by 30.2% with only 0.5% increase of the total passenger waiting time, which shows that the flexible train composition mode can reasonably reduce the operating costs while meeting the passenger demands. In addition, the robust passenger flow control strategy can avoid the conservative optimization results, which can be adapted to the stochastic passenger demands in the actual operation environment.

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  • 收稿日期:2021-11-24
  • 最后修改日期:2022-04-16
  • 录用日期:2022-04-27
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