Volume 41 Issue 5
Oct.  2023
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Article Navigation >  Journal of Transport Information and Safety  > 2023  >  41(5): 176-184
LIN Li, MENG Xuelei, CHENG Xiaoqing, HAN Zheng, FU Yanxin. A Method for Developing Service Plan of Urban Rail Train Considering Carbon Emissions Impacts[J]. Journal of Transport Information and Safety, 2023, 41(5): 176-184. doi: 10.3963/j.jssn.1674-4861.2023.05.018
Citation: LIN Li, MENG Xuelei, CHENG Xiaoqing, HAN Zheng, FU Yanxin. A Method for Developing Service Plan of Urban Rail Train Considering Carbon Emissions Impacts[J]. Journal of Transport Information and Safety, 2023, 41(5): 176-184. doi: 10.3963/j.jssn.1674-4861.2023.05.018
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A Method for Developing Service Plan of Urban Rail Train Considering Carbon Emissions Impacts

doi: 10.3963/j.jssn.1674-4861.2023.05.018
  • 1.

    School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

  • 2.

    State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

  • Received Date: 2023-09-02
    Available Online: 2024-01-18
    Abstract
  • An unreasonable train service plan may result in poor comfort level, high operating costs, and high carbon emissions. In order to deal with these issues, a method for train service plan considering carbon emission is established in the context of multi-routing and multi-marshalling modes. Specifically, passenger comfort and carbon emissions are introduced into the objective function and constraints regarding capacity, service frequency, and terminal station arrangements are also considered. Considering the complexity and high dimensionality of the problem, improvements are made in the honey source updating strategy of the classic artificial bee colony (ABC) algorithm, which is adopted to solve the optimization problem. Experiments are conducted to calibrate the parameters. A computational analysis is conducted to evaluate the impact of weights of objective functions on the solutions. Furthermore, the solutions of this model are compared to those from the models in the context of single-route and single-marshalling mode. Additionally, solution quality and convergence speed of the proposed algorithm are compared to those using the traditional ABC algorithm. The results indicate that: ① The objective function value is negatively correlated with its weight coefficients, and the change range of objective function value is limited due to the limited solution space. ② The operating costs is decreased by 18.22% and carbon emissions by 18.17% compared to those under the single routing mode, both of which show significant reductions. ③ In contrast to those under the single marshalling mode, the travel cost, operating cost and carbon emissions are decreased by 3.37%, 3.12% and 3.32%, respectively. All objective function values are improved. ④ Comparing to the traditional ABC algorithm, the proposed algorithm achieves a 2.49% decrease in the total objective value, and the convergence speed is improved by 12.84%. The results verify the effectiveness of the proposed method in reducing operation costs and carbon emissions.

     

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