面向负载均衡的高铁路网列车开行方案优化方法

吴兴堂; 杨明坤; 王洪伟; 周敏; 吕金虎; 董海荣

doi:10.16383/j.aas.c210612

面向负载均衡的高铁路网列车开行方案优化方法

doi: 10.16383/j.aas.c210612

吴兴堂^{1, 2,},
杨明坤^2,,
王洪伟^3,,
周敏^2,,
吕金虎^1,,
董海荣^2,

1.
北京航空航天大学自动化科学与电气工程学院北京 100191
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
3.
北京交通大学国家轨道交通安全评估研究中心北京 100044

基金项目: 国家自然科学基金(61925302, U1834211)资助

详细信息

作者简介:
吴兴堂：北京航空航天大学博士后. 主要研究方向为轨道交通网络建模, 延误预测和延误传播. E-mail: wuxingtang@bjtu.edu.cn

杨明坤：北京交通大学硕士研究生. 主要研究方向为高铁网络建模, 延误传播建模. E-mail: 19120261@bjtu.edu.cn

王洪伟：北京交通大学副教授. 主要研究方向为基于通信的列车运行控制系统的车–地通信技术和地铁系统中的协作调度方法. E-mail: hwwang@bjtu.edu.cn

周敏：北京交通大学副教授. 主要研究方向为高铁智能调度, 行人与疏散动力学和平行系统理论. E-mail: minzhou@bjtu.edu.cn

吕金虎：北京航空航天大学教授. 主要研究方向为智能系统, 协同控制理论与技术, 复杂网络与大数据. E-mail: jhlu@iss.ac.cn

董海荣：北京交通大学教授. 主要研究方向为列车运行智能控制与优化, 调度控制一体化. 本文通信作者E-mail: hrdong@bjtu.edu.cn

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出版历程
- 收稿日期: 2021-07-03
- 录用日期: 2021-10-18
- 网络出版日期: 2021-11-14
- 刊出日期: 2022-02-18

Load-balancing Oriented Line Plan Optimization for a High-speed Railway Network

1.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191
2.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044
3.
National Research Center of Railway Safety Assessment, Beijing Jiaotong University, Beijing 100044

Funds: Supported by National Natural Science Foundation of China (61925302, U1834211)

More Information

Author Bio:
WU Xing-Tang　Postdoctor at Beihang University. His research interest covers modeling of rail transit network, delay prediction, and delay propagation

YANG Ming-Kun　Master student at Beijing Jiaotong University. His research interest covers modeling of high-speed railway network and delay propagation

WANG Hong-Wei　Associate professor at Beijing Jiaotong University. His research interest covers train-ground communication technology in communication base train-ground communication systems and cooperative scheduling approaches in subway systems

ZHOU Min　Associate professor at Beijing Jiaotong University. His research interest covers high-speed railway intelligent dispatching, pedestrian and evacuation dynamics and parallel system theory

LV Jin-Hu　Professor at Beihang University. His research interest covers intelligent systems, cooperative control theory and technology, complex networks, and big data

DONG Hai-Rong　Professor at Beijing Jiaotong University. Her research interest covers intelligent control and optimization of train operation, integration of scheduling and control. Corresponding author of this paper

摘要

摘要: 针对当前高速铁路运营过程中存在的运输需求与运力资源不匹配现象, 面向负载均衡原理研究了路网条件下运能可适配的高速铁路旅客列车开行方案优化与评估方法. 首先, 针对路网条件下列车开行方案优化, 构建以提升经济效益、社会效益和网络负载均衡为目标的非线性混合整数规划模型, 并设计基于遗传算法和粒子群算法的两阶段混合搜索求解算法. 在此基础上, 考虑开行列车在高速铁路网中的抗干扰能力, 建立了面向网络化运营场景的开行方案综合评估指标体系, 揭示了故障场景下高速铁路网络性能的演化规律. 最后, 以实际高速铁路线路数据和运营数据为场景进行仿真实验, 本文提出方法在保证运输需求和路局收益的同时能够有效地提升8.66%网络整体负载均衡性, 增强发生故障时网络的抗干扰能力.
- 高速铁路网 /
- 开行方案 /
- 负载均衡优化 /
- 网络鲁棒性
Abstract: By considering the current mismatch between traffic demand and transportation resources in high-speed railways, a load-balancing oriented line plan optimization and evaluation method of the high-speed railway is developed to adapt to the traffic demand. Specifically, a multi-objective non-linear hybrid programming model aiming to improve the economic benefit, social benefit, and network load balance is established to optimize the network line plan. Besides, a two-stage hybrid search algorithm based on genetic algorithm and particle swarm optimization algorithm is designed to solve the problem. Moreover, a comprehensive evaluation method of line plan for networked operation scale considering the operational issues and the network resilience is proposed. Finally, the structure data and the real operational date of a sub-network of China＇s high-speed railway are adopted to verify the effectiveness of the proposed method. The results show that the method proposed in this paper can effectively improve 8.66% overall load balance of the network and the network resilience while satisfying the travel demands of passengers.
- High-speed railway network /
- line plan /
- load balance optimization /
- network robustness

HTML全文

图 1 两阶段混合搜索算法流程图

Fig. 1 Flow chart of the two-stage hybrid algorithm

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图 2 列车服务网络建模示意图

Fig. 2 Schematic of train service network modelling

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图 3 北京−天津−保定高速铁路线路示意图

Fig. 3 High-speed railway line of BTBN

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图 4 北京−天津−保定高速铁路路网结构示意图

Fig. 4 Network model of the BTBN high-speed railway

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图 5 初始方案

Fig. 5 Original line plan

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图 6 算法收敛过程

Fig. 6 Algorithm convergence process

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图 7 优化方案

Fig. 7 Optimized plan

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图 8 基于随机移除策略的网络可达率变化图

Fig. 8 NRR changes under random attack

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图 9 基于随机移除策略的服务损失率变化图

Fig. 9 SLR changes under random attack

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图 10 基于负载压力移除策略的网络可达率变化图

Fig. 10 NRR changes under LP-based attack

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图 11 基于负载压力移除策略的服务损失率变化图

Fig. 11 SLR changes under LP-based attack

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表 1 GA参数列表

Table 1 Parameter list of GA

参数	定义
$ genLength $	基因长度
$ popSize $	种群数量
$ gaMaxGen $	最大迭代次数
$ p $	变异概率

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表 2 PSO参数列表

Table 2 Parameter list of PSO

参数	定义
$ parSize $	粒子群数量
$ psoMaxGen $	最大迭代次数
$ \alpha $, $ \beta $	学习因子

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表 3 北京−天津−保定高速铁路车站信息

Table 3 Station information of BTBN

车站名称	车站编号	车站股道数
北京	0	22
涿州东	1	4
高碑店东	2	4
保定	3	8
廊坊	4	4
天津	5	16
胜芳	6	4
霸州西	7	4
白沟	8	6
白洋淀	9	4
武清	10	2

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表 4 参数取值

Table 4 Parameters value

参数	取值
$ popSize $	50
$ gaMaxGen $	300
$ p $	0.005
$ psoMaxGen $	500
$ \alpha $	2
$ \beta $	2
$ parSize $	50

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表 5 初始方案和优化方案计算结果

Table 5 Results of original plan and oprimized plan

指标	初始方案	优化方案
路局收益 (元)	4 345 102.8400	5 943 671.7200
旅客总乘车时间 (小时)	49 886.0800	64 888.9533
网络负载均衡度	0.6801	0.7390

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表 6 开行方案运输特性评价方法对比结果

Table 6 Comparison results of transportation evaluation method of line plan

指标属性	具体指标	初始方案	优化方案
能力指标	旅客周转量 (人·公里)	12156035	16474979
	客座周转量 (人·公里)	20033200	23391500
	空座位走行距离 (人·公里)	7877165	6916521
	客座利用率 (%)	60.68	70.43
技术指标	列车需求数量 (对)	104	125
	列车平均走行距离 (公里)	175.115	170.248
	列车总开行时间 (小时)	60.71	70.94
旅客满意度指标	列车总停站次数	300	287
	停站方式数量	13	21
	直达列车数量	0	2

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表 7 节点负载压力排序结果

Table 7 Sort results of node load-pressure

序号	节点介数	节点负载压力
序号	节点介数	初始方案	优化方案
1	北京	涿州东	武清
2	霸州西	武清	涿州东
3	涿州东	高碑店东	霸州西
4	白沟	霸州西	高碑店东
5	高碑店东	白洋淀	白洋淀
6	白洋淀	白沟	白沟
7	胜芳	胜芳	保定
8	保定东	保定	胜芳
9	天津	北京	廊坊
10	廊坊	廊坊	天津
11	武清	天津	北京

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