考虑动车组周转和到发线运用的高铁列车运行多计划协同调整

周敏; 顾灏璇; 董海荣; 刘仁伟; 刘瑄

doi:10.16383/j.aas.c230379

考虑动车组周转和到发线运用的高铁列车运行多计划协同调整

doi: 10.16383/j.aas.c230379

1.
北京交通大学自动化与智能学院北京 100044
2.
中国铁路北京局集团有限公司北京 100860

基金项目: 国家自然科学基金 (61925302, U2368204, 62103033)资助

详细信息

作者简介:
周敏：北京交通大学自动化与智能学院副教授. 2019年获得北京交通大学交通信息工程及控制专业博士学位. 主要研究方向为高铁智能优化与调度, 人群应急管控. E-mail: zhmin@bjtu.edu.cn

顾灏璇：北京交通大学自动化与智能学院硕士研究生. 主要研究方向为高铁智能优化与调度

董海荣：北京交通大学自动化与智能学院教授. 主要研究方向为智能交通系统建模优化, 自主感知与协同控制, 人工智能. 本文通信作者. E-mail: hrdong@bjtu.edu.cn

刘仁伟：中国铁路北京局集团有限公司高级工程师. 主要研究方向为高铁行车调度与运输管理

刘瑄：北京交通大学自动化与智能学院博士研究生. 主要研究方向为列车速度曲线优化, 智能调度

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出版历程
- 收稿日期: 2023-06-18
- 录用日期: 2023-11-19
- 网络出版日期: 2024-07-22
- 刊出日期: 2024-08-20

Multi-plan Collaborative Rescheduling of High-speed Train Operation Considering the Utilization of Rolling Stock and Arrival and Departure Tracks

1.
School of Automation and Intelligence, Beijing Jiaotong University, Beijing 100044
2.
China Railway Beijing Group Co., Ltd., Beijing 100860

Funds: Supported by National Natural Science Foundation of China (61925302, U2368204, 62103033)

More Information

Author Bio:
ZHOU Min　Associate professor at the School of Automation and Intelligence, Beijing Jiaotong University. He received his Ph.D. degree in traffic information engineering and control from Beijing Jiaotong University in 2019. His research interest covers intelligent optimization and scheduling for high-speed railways, and crowd emergency management and control

GU Hao-Xuan　Master student at the School of Automation and Intelligence, Beijing Jiaotong University. His research interest covers intelligent optimization and scheduling for high-speed railways

DONG Hai-Rong　Professor at the School of Automation and Intelligence, Beijing Jiaotong University. Her research interest covers modeling and optimization of intelligent transportation systems, autonomous perception and cooperative control, and artificial intelligence. Corresponding author of this paper

LIU Ren-Wei　Senior engineer at China Railway Beijing Group Co., Ltd.. His research interest covers high-speed rail operation rescheduling and transportation management

LIU Xuan　Ph.D. candidate at the School of Automation and Intelligence, Beijing Jiaotong University. His research interest covers train speed profile optimization and intelligent rescheduling

摘要

摘要: 随着我国高速铁路快速发展, “八纵八横”高铁路网加密成型, 呈现出运行环境复杂、行车密度高及长交路跨线运营等典型特征. 一旦遭受大风、红光带、接触网挂异物和设备故障等突发事件, 则将导致列车偏离运行计划, 进而影响到发线运用和动车组(Eletric multiple units, EMU)周转计划. 如何在调整运行图的同时保证动车组和到发线运用的可行性是提高列车运行调整效率的关键. 针对区间双向中断场景下到发线运用冲突和动车组接续计划失效问题, 采用取消列车、变更列车到发时刻、更换到发线、备用动车组接续等策略对运行图、动车组和到发线运用计划进行调整. 基于事件−活动网络建立考虑动车组接续和到发线运用的列车运行协同调整模型, 设计两阶段求解方法对模型求解. 运用京津城际实际数据对模型和方法进行仿真验证, 结果表明相比于先到先服务(First come, first served, FCFS)策略, 多计划协同调整策略能有效降低列车晚点时间. 与整体求解方法相比, 两阶段求解方法能够保证模型求得解的质量且有效提高模型求解效率.
- 多计划协同调整 /
- 动车组周转 /
- 到发线运用 /
- 事件−活动网络 /
- 两阶段求解方法
Abstract: With the rapid development of high-speed railways in China, the “eight vertical and eight horizontal” high-speed railway network has become denser, presenting typical characteristics such as complex operating environments, high train densities, and long-distance cross-line operations. Once facing sudden events as strong winds, red light bands, foreign objects on overhand lines and equipment failures, trains will deviate from the operating plan, which in turn affects the departure line operation and electric multiple units (EMUs) turnover plan. Ensuring the feasibility of EMUs and departure line operations while adjusting timetable is crucial to improving the efficiency of train operation adjustments. To address departure line operations conflicts and EMU connection plans failure under the scenario of bidirectional interruptions between sections, strategies as canceling trains, changing train departure times, switching departure lines, backup EMU connections are employed to adjust timetable, EMUs, and departure line operation plans. A train operation coordination adjustment model considering EMU connections and departure line operations is established based on event-activity network, a two-stage solving method is designed to solve the model. The model and method are validated using actual data from Beijing-Tianjin intercity railway, the results show that compared to “first come, first served (FCFS)” strategy, multi-plan coordination adjustment method can effectively reduce train delay time. Compared with overall solving method, the two-stage solving method can ensure the model solution quality and effectively improve the model solving efficiency.
- Multi-plan collaborative rescheduling /
- utilization of rolling stock /
- arrival and departure track utilization /
- event-activity network /
- two-stage solution

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图 1 具有中断区间的双线线路

Fig. 1 The double-line railway with interruption section

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图 2 区间中断条件下的车站示意图

Fig. 2 Diagram of station with interruption section

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图 3 事件−活动网络

Fig. 3 Event-activity network

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图 4 到达列车与出发列车

Fig. 4 Arriving and departing trains

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图 5 动车组接续示意图

Fig. 5 EMU connection diagram

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图 6 调整后的列车时刻表

Fig. 6 Rescheduled train timetable

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图 7 不同中断场景下不同策略的结果

Fig. 7 The results of different strategies under different interruption scenarios

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图 8 求解时间

Fig. 8 Solution time

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表 1 决策变量定义

Table 1 Definition of decision variables

序号	决策变量	含义	类型
1	$ x_{e} $	事件$e $发生的实际时刻	整数
2	$ y_{t} $	列车$t $是否被取消运行	0-1
3	$ \varphi_{a} $	列车停站活动a是否发生	0-1
4	$ \lambda_{a} $	两列车之间的顺序活动	0-1
5	$ \theta_{t,\;s}^{p} $	列车$t $在$s $站是否占用p股道	0-1
6	$ \delta_{t_{e},\;t_{f}}^{p} $	两列车是否占用相同到发线$p $	0-1

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表 2 参数定义

Table 2 Definition of parameters

序号	参数	定义
1	$T $	列车集合, $t \in T$
2	$S $	车站集合, $s \in S$
3	$P $	车站股道集合, $ p\in P$
4	$E $	事件集合, $ e \in E $
5	${{E}} _{{\mathrm{origin}}}^{{\mathrm{dep}}} $	始发站列车的出发事件
6	$A $	活动集合, $a \in A$
7	$ {{A}}_{{\mathrm{rol}}} $	动车组接续活动集合
8	$ {{t}}_{{{e}}} $	与事件$e $相关联的列车
9	$ {{p}}_{{{e}}} $	事件$e $计划发生时刻
10	$M $	足够大的正整数
11	$ \omega _{1} $, $ \omega _{2} $, $ \omega _{3} $	目标函数惩罚系数
12	${{L}}_{{a}} $	活动$a $最小间隔时间

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表 3 相关参数取值

Table 3 Values of relative parameters

序号	参数	取值
1	列车最小停站时间${L}_{a} $	2 min
2	同向列车最小到发间隔${L}_{a} $	3 min
3	占用相同股道列车最小安全间隔${L}_{a} $	2 min
4	动车组最小接续时间${L}_{a} $	15 min
5	北京南站备用动车组	9 列
6	滨海站备用动车组	3 列
7	$M $	1 440 min
8	$ \omega_{1} $	1 000
9	$ \omega_{2} $	1
10	$ \omega_{3} $	1

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表 4 调整后列车运行计划

Table 4 The rescheduled train plans

列车编号	始发时刻	终到时刻	停站方案	后续列车
D1	06:21	07:17	51111511113	U3
D2	06:26	07:29	51111711133	U5
D3	07:01	08:04	51111711133	U6
D4	07:07	07:45 (07:37)	311117	U4
D5	07:28	09:02 (08:37)	311313(7)11133	U8
D6	07:53	08:31 (08:23)	311113	U7
D7	08:31	10:03 (09:41)	311113(7)13133	U9
D8	09:03	10:33 (10:06)	311113(7)11133	U11
D9	09:08	09:46 (09:38)	311113	U10
D10	11:09 (09:25)	12:42 (10:28)	511113(5)11135	U16(−)
D11	11:33 (09:46)	12:11 (10:16)	311113	—
D12	11:00 (09:57)	12:32 (11:00)	7(5)11113(5)11135	−(U12)
D13	11:03 (10:16)	12:37 (11:23)	3(5)1111511135	U13
D14	11:09 (10:52)	12:47 (12:02)	7(5)11313(7)11(3)133	U14
D15	11:41 (11:30)	12:19 (12:00)	5(3)11113	—
U1	06:51	07:49 (07:33)	424226	D10 (D6)
U2	06:58	07:46 (07:28)	422226	D7
U3	07:41	09:13 (08:44)	44222422226	D13 (D8)
U4	08:28 (08:25)	09:16 (08:55)	422226	D14
U5	08:30	11:18 (09:33)	6422242224(2)6	D11
U6	08:47 (08:45)	11:26 (09:55)	442224244(2)4(2)6	D15
U7	09:32 (09:30)	11:38 (10:00)	424(2)24(2)4	−(D13)
U8	09:43	11:47 (10:53)	4422244(2)424(2)4	—
U9	10:44	12:14 (11:47)	42242422226	—
U10	11:00 (10:53)	11:50 (11:23)	422226	—
U11	11:29	13:03 (12:39)	64222424226	—
U12	12:26	13:56 (13:29)	62242422226	—
U13	12:57	14:31 (13:52)	4222242(4)2226	—
U14	13:14	14:42 (14:09)	62222422226	—
U15	13:32	14:28 (14:14)	424226	—
U16	14:19	15:49 (15:22)	62222424226	—

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表 5 不同中断场景下考虑不同策略的列车运行调整

Table 5 Train reschedule considering different strategies under different interruption scenarios

中断场景	策略	目标值	$ P^{{\mathrm{o}}} $(%)	求解时间(s)	取消列车数量(列)	列车总延误时间(s)	$ P^{{\mathrm{d}}} $ (%)
(9:20, 1, 100)	组合策略	13 080	2.35	159.0	0	12 905	2.37
(9:20, 1, 100)	FCFS	13 388	2.35	47.3	0	13 211	2.37
(9:00, 2, 90)	组合策略	13 303	2.55	361.0	0	13 173	2.31
(9:00, 2, 90)	FCFS	13 642	2.55	73.8	0	13 478	2.31
(10:00, 4, 60)	组合策略	7 821	0.06	17.0	0	7 671	0.33
(10:00, 4, 60)	FCFS	7 826	0.06	6.9	0	7 696	0.33
(11:00, 3, 30)	组合策略	5 994	0.21	3.0	0	5 864	0.21
(11:00, 3, 30)	FCFS	6 007	0.21	2.7	0	5 876	0.21

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表 6 各中断场景下整体求解结果

Table 6 The overall solution results under each interruption scenario

中断场景	求解方法	目标值	求解时间(s)	取消列车数量(列)	总延误时间(s)
(9:20, 1, 100)	整体求解	12 382	4 180	0	12 362
(9:00, 2, 90)	整体求解	12 453	5 304	0	12 269
(10:00, 4, 60)	整体求解	7 510	53	0	7 395
(11:00, 3, 30)	整体求解	5 896	5	0	5 786

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表 7 不同权重系数下模型求解结果对比

Table 7 Comparison of solution results under different weight coefficients

$ \omega_{1} $	$ \omega_{2} $	$ \omega_{3} $	目标值	取消列车数(列)	总延误时间(s)	求解时间(s)
1	1	1	12 819	0	12 723	330.0
1	10	1	127 865	0	12 767	425.0
1	100	1	1 278 215	0	12 767	359.5
1	1 000	1	12 796 015	0	12 795	314.0
10	1	1	12 819	0	12 723	326.0
100	1	1	12 819	0	12 723	319.0
1 000	1	1	12 819	0	12 726	307.0
1	1	10	17 214	3	17 064	251.8
1	1	100	18 130	3	17 223	227.0
1	1	1 000	25 463	3	19 585	605.7

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