考虑碳排放效果的城轨列车开行方案编制方法

林立; 孟学雷; 程晓卿; 韩正; 付艳欣

doi:10.3963/j.jssn.1674-4861.2023.05.018

考虑碳排放效果的城轨列车开行方案编制方法

doi: 10.3963/j.jssn.1674-4861.2023.05.018

林立^{1, 2,},
孟学雷^1, ,,
程晓卿²,
韩正¹,
付艳欣¹

1.
兰州交通大学交通运输学院兰州 730070
2.
北京交通大学轨道交通控制与安全国家重点实验室北京 100044

基金项目:

国家自然科学基金项目 71861022

甘肃省自然科学基金项目 22JR5RA355

甘肃省优秀研究生“创新之星”项目 2023CXZX-522

详细信息

作者简介:
林立（1995—），博士研究生. 研究方向：轨道交通运行管理、决策优化. E-mail：linli1217@foxmail.com

通讯作者:
孟学雷(1979—)，博士，教授. 研究方向：轨道交通运行管理、决策优化等研究. E-mail：mxl@mail.lzjtu.cn

中图分类号: U231+.92
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出版历程
- 收稿日期: 2023-09-02
- 网络出版日期: 2024-01-18

A Method for Developing Service Plan of Urban Rail Train Considering Carbon Emissions Impacts

LIN Li^{1, 2
,},
MENG Xuelei^{1
, ,},
CHENG Xiaoqing²,
HAN Zheng¹,
FU Yanxin¹

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

摘要

摘要: 为解决开行方案编制不合理导致的乘客出行舒适度差、企业运营成本高、碳排放量大等问题，研究了1种考虑碳排放效果的城轨列车开行方案编制方法。在目标函数中增加了对乘客舒适度及碳排放效果的考虑，同时限定了能力、服务频率及交路起讫站设置等约束，进而建立了多交路、多编组的城轨列车开行方案模型。考虑到模型变量维度高、求解复杂等特点，对经典人工蜂群算法中蜜源更新策略进行改进并应用于模型求解。利用大量数据实验对参数进行标定，计算分析目标函数权重设定对求解结果的影响，与单一交路、单一编组的模式进行结果对比分析，并与传统人工蜂群算法展开求解质量及收敛速度的比较分析。结果表明：①目标函数值与其权重系数呈现负相关，由于解空间的限制，目标函数值变化范围有限；②较之单一交路运营模式，大小交路多编组模式下企业运营成本降低了18.22%，碳排放量减少了18.17%，二者降幅都比较显著；③较之单一编组运营模式，大小交路多编组模式下乘客出行成本降低了3.37%，企业运营成本下降了3.12%，碳排放量减少了3.32%，所有目标函数值均得到了改善；④较之传统人工蜂群算法，改进后算法求得的总目标值下降了2.49%，收敛速度提高了12.84%。结果验证了所提方法对于降低企业成本、减少碳排放量的有效性。
- 交通规划 /
- 城轨列车 /
- 开行方案 /
- 人工蜂群算法 /
- 碳排放
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.
- traffic planning /
- urban rail trains /
- train service plan /
- artificial bee colony algorithm /
- carbon emission

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图 1 运行线路示意图

Figure 1. Schematic diagram of train running line

下载: 全尺寸图片幻灯片

图 2 上行客流分类示意图

Figure 2. Passenger flow classification in up direction

下载: 全尺寸图片幻灯片

图 3 目标函数随权重变化曲线

Figure 3. Curve of objective functions changing with different weights

下载: 全尺寸图片幻灯片

图 4 算法迭代过程图

Figure 4. Iterative process diagram of two algorithms

下载: 全尺寸图片幻灯片

表 1 模型相关参数取值

Table 1. Values of the related parameters

参数	含义	取值	单位
c	非工作时间价值系数	25	元/h
t_trans	单个乘客1次换乘所需的时间	4.8	min
v	列车平均运行速度	35	km/h
t_上	单个乘客平均上下车时间	1.51	s
n	每节车厢的车门对数	5	对
m′	短编组列车的编组数量	4	辆
m″	长编组列车的编组数量	6	辆
A^m′	短编组列车的定员	121 0	人/列
A^m″	长编组列车的定员	186 0	人/列
ε	车辆公里费用	60	元/（辆·km）
η	火力发电比例	0.8
P_μ^m′	短编组列车的黏着质量	146.2	t
P_μ^m″	长编组列车的黏着质量	222.2	t
ξ_min	最小满载率	50	%
ξ_max	最大满载率	120	%
Z_min	小交路车站数量的最小值	8	个
Z_max	小交路车站数量的最大值	23	个
Ο	可运用的车辆数	210	辆
f_min	最小服务频率	6	列
f_线	线路通过能力	30	列

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表 2 不同权重对应的目标函数值

Table 2. The objective functions value under different weights

权重	S_a	S_b	f₁^m′/列	f₁^m″/列	f₂^m′/列	f₂^m″/列	W₁/元	W₂/元	W₃/kg	U
(1, 3, 6)	10	17	4	13	0	4	805 752.63	289 728.12	17 612.24	0.345
(2, 3, 5)	10	18	4	14	0	6	722 767.16	317 298.72	19 291.77	0.402
(3, 3, 4)	10	18	5	16	0	6	608 919.84	364 172.64	22 137.89	0.510
(4, 3, 3)	7	18	5	16	2	6	548 566.71	377 858.40	23 479.60	0.534
(5, 3, 2)	7	20	6	16	2	6	515 722.99	403 631.04	24 524.76	0.560
(6, 3, 1)	7	22	6	16	2	6	501 898.38	409 655.52	24 890.88	0.571

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表 3 不同模型对比分析

Table 3. Comparative analysis of different models

模型	S_a	S_b	f₁^m′/列	f₁^m″/列	f₂^m′/列	f₂^m″/列	W₁/元	W₂/元	W₃/kg	U
本文	10	18	5	16	0	6	608 919.84	364 172.64	22 137.89	0.510
单交路			8	20	0	0	584 132.43	445 302.24	27 052.15	0.611
单编组	10	18	0	20	0	6	630 135.51	375 891.12	22 898.34	0.535

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表 4 不同算法计算结果对比分析

Table 4. Comparative analysis of different algorithms

算法	迭代次数	S_a	S_b	f₁^m′/列	f₁^m″/列	f₂^m′/列	f₂^m″/列	W₁/元	W₂/元	W₃/kg	U
IABC	190	10	18	5	16	0	6	608 919.84	364 172.64	22 137.89	0.510
ABC	218	10	20	4	17	0	6	605 254.32	374 617.56	22 783.49	0.523

下载: 导出CSV

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