基于时空网络的城际公交化列车开行方案优化

朱昌锋; 安醇; 唐兆鑫; 成琳娜; 王傑; 章超; 符云琪

doi:10.19818/j.cnki.1671-1637.2025.06.014

基于时空网络的城际公交化列车开行方案优化

doi: 10.19818/j.cnki.1671-1637.2025.06.014

朱昌锋^1, ,,
安醇¹,
唐兆鑫¹,
成琳娜^{1, 2},
王傑¹,
章超¹,
符云琪¹

1.
兰州交通大学交通运输学院，甘肃兰州 730070
2.
五邑大学轨道交通学院，广东江门 529000

基金项目:

国家自然科学基金项目 72161024

甘肃省教育厅“双一流”重大研究项目 GSSYLXM-04

详细信息

通讯作者:
朱昌锋(1972-)，男，甘肃天水人，兰州交通大学教授，博士研究生导师，工学博士，从事交通运输规划与管理研究

中图分类号: U292.31
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- 文章访问数: 167
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- 被引次数: 0
出版历程
- 收稿日期: 2024-09-10
- 录用日期: 2025-08-25
- 修回日期: 2025-06-19
- 刊出日期: 2025-12-28

Optimization of intercity public transportation train operation plan based on spatio-temporal network

1.
School of Transportation, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China
2.
School of Rail Transit, Wuyi University, Jiangmen 529000, Guangdong, China

Funds:

National Natural Science Foundation of China 72161024

Gansu Provincial Department of Education's "Double First Class" Research Key Project GSSYLXM-04

More Information

Corresponding author: ZHU Chang-feng (1972-), male, professor, PhD, cfzhu003@163.com

Article Text (Baidu Translation)

摘要

摘要: 针对城际旅客出行中的有限理性行为，构建了考虑旅客出行时间偏差的决策价值函数，刻画了有限理性条件下城际铁路客流分配过程；将列车运行时刻信息离散化，构建了城际铁路公交化开行方案时空网络，并以企业运营成本和旅客出行成本最小化为优化目标，综合考虑了车流及客流时空守衡、列车停站时间、列车安全间隔、列车开行数量等约束条件，进而构建了城际公交化列车开行方案的多目标优化模型；考虑到模型求解的复杂性，通过构建列车开行备选集，设计了基于备选列车集和NSGA-Ⅱ的开行方案优化模型求解算法；以长株城际铁路公交化线路为实例，验证了模型及算法的有效性。研究结果表明：停站模式和开行数量直接影响旅客出行成本和运营成本，提高非站站停列车开行比例仅会减少部分旅客的出行成本，但造成整体旅客出行成本增大，而在均开行站站停列车的条件下，随开行数量增大，旅客出行成本降低，但企业运营成本攀升；随着开行数量增加，城际公交化列车平均断面满载率不断下降且不同列车断面满载率的差异性分布特征显著。该研究构建的优化模型及求解算法可为制定科学的开行方案提供理论决策支撑。
- 城际铁路 /
- 列车开行方案 /
- 多目标优化 /
- 时空网络 /
- 公交化运营 /
- 有限理性
Abstract: A decision value function considering travel time deviation was constructed to address the bounded rationality behavior of intercity passengers and describe the process of intercity train passenger flow allocation under bounded rationality conditions. The train operation time information was discretized. A spatio-temporal network of operation plan was established for the intercity public transport trains, with the optimization objective being set to minimize enterprise operating cost and passenger travel cost. By considering constraints such as spatio-temporal balance between traffic flow and passenger flow, train stopping time, train safety interval, and train operation quantity, a multi-objective optimization model was constructed for the operation plan of intercity public transport trains. In light of the complexity of model solving, an optimization model solving algorithm based on the candidate train set and NSGA-Ⅱ was designed by constructing a train operation backup set. With the public transport line of the Changsha-Zhuzhou intercity train as an example, the effectiveness of the model and algorithm was verified. According to the optimization results, the passenger travel cost and operating cost are directly affected by the stopping mode and the number of trains. The higher proportion of non-stop trains will only reduce travel cost for some passengers, but increase the travel cost for all passengers. Under the condition of running stopping trains at all stations, as the number of trains goes up, the travel cost of passengers goes down, but the operating cost of enterprises rises. With the larger number of trains, the average section full load rate of the intercity public transport train of continues to decrease, and the differential distribution characteristics of the full load rates of different train sections are significant. The constructed optimization model and solving algorithm can provide a theoretical decision-making basis for formulating scientific operation plans.
- intercity railway /
- train operation plan /
- multi-objective optimization /
- spatio-temporal network /
- public transportation operation /
- bounded rationality

HTML全文

图 1 城际旅客出行过程

Figure 1. Intercity passenger travel process

下载: 全尺寸图片幻灯片

图 2 城际铁路公交化列车开行方案时空网络

Figure 2. Sapito-temporal network of intercity public transportation train operation plan

下载: 全尺寸图片幻灯片

图 3 调整策略说明

Figure 3. Explanation of adjustment strategy

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图 4 基于备选列车集的NSGA-Ⅱ算法框架

Figure 4. NSGA-Ⅱ algorithm framework based on candidate train set

下载: 全尺寸图片幻灯片

图 5 长株城际公交化列车开行方案时空网络

Figure 5. Sapito-temporal network of Changsha-Zhuzhou intercity transit train operation plan

下载: 全尺寸图片幻灯片

图 6 Hv算法迭代进化曲线

Figure 6. Hv algorithm iterative evolution curve

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图 7 Pareto前沿解

Figure 7. Pareto frontier solution

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图 8 部分Pareto解下公交化列车开行方案

Figure 8. Public transit train operation plan under partial Pareto solution

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图 9 部分Pareto解下公交化列车断面满载率

Figure 9. Section load factor of public transit trains under partial Pareto solution

下载: 全尺寸图片幻灯片

表 1 模型参数取值

Table 1. Model parameters values

参数	取值	参数	取值
$\overline{c_{\mathrm{M}}}$, /(元·列^-1)^[33]	4 000	ε/min^[25]	5
$\overline{c_{\mathrm{L}}}$/(min·元^-1)^[33]	150	N(Ω)/列^[30]	6
C_k/(人·列^-1)^[30]	600	δ/min	1
ρ₁/(min·元^-1)^[25]	0.62	σ^[35]	0.88
ρ₂/(min·元^-1)^[25]	0.3	κ^[30]	0.88
α₁^[36]	1	ξ^[35]	0.69
α₂^[36]	1	λ^[35]	2.25
$t_{i, i}^{\min }$/min	1	ζ^[30]	0.61

下载: 导出CSV

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