高速铁路运行控制与动态调度一体化的现状与展望

宁滨; 董海荣; 郑伟; 荀径; 高士根; 王洪伟; 孟令云; 李浥东

doi:10.16383/j.aas.2019.y000004

高速铁路运行控制与动态调度一体化的现状与展望

doi: 10.16383/j.aas.2019.y000004 cstr: 32138.14.j.aas.2019.y000004

宁滨¹,
董海荣^1,,
郑伟^2,,
荀径^1,,
高士根^1,,
王洪伟^2,,
孟令云^3,,
李浥东^4,

1.
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
2.
北京交通大学国家轨道交通安全评估研究中心北京 100044
3.
北京交通大学交通运输学院北京 100044
4.
北京交通大学计算机与信息技术学院北京 100044

基金项目: 国家自然科学基金(61790573)资助

详细信息

作者简介:
宁滨：(1959−2019) 原中国工程院院士, 北京交通大学轨道交通控制与安全国家重点实验室教授. 主要研究方向为列车运行控制系统与数字化, 网络化信号系统

董海荣：北京交通大学轨道交通控制与安全国家重点实验室教授, 轨道交通运行控制系统国家工程研究中心副主任. 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

郑伟：北京交通大学国家轨道交通安全评估研究中心教授, 副主任. 主要研究方向为高铁信号系统安全设计, 测试及评估理论方法. E-mail: wzheng1@bjtu.edu.cn

荀径：北京交通大学轨道交通控制与安全国家重点实验室副教授. 主要研究方向为先进的列车控制方法, 铁路运输优化问题和强化学习. E-mail: jxun@bjtu.edu.cn

高士根：北京交通大学轨道交通控制与安全国家重点实验室副教授. 主要研究方向为列车智能控制和多车协同优化. E-mail: sggao@bjtu.edu.cn

王洪伟：北京交通大学国家轨道交通安全评估研究中心副教授. 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

孟令云：北京交通大学交通运输学院副教授, 副院长. 主要研究方向为列车运行调度和网络容量评估. E-mail: lym@bjtu.edu.cn

李浥东：北京交通大学计算机与信息技术学院教授, 副院长. 主要研究方向为大数据分析与安全, 智能交通信息技术, 先进计算. E-mail: ydli@bjtu.edu.cn

计量
- 文章访问数: 3678
- HTML全文浏览量: 1442
- PDF下载量: 459
- 被引次数: 0
出版历程
- 收稿日期: 2019-05-10
- 录用日期: 2019-09-20
- 刊出日期: 2019-12-01

Integration of Train Control and Online Rescheduling for
High-speed Railways: Challenges and Future

1.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044
2.
National Research Center of Railway Safety Assessment, Beijing Jiaotong University, Beijing 100044
3.
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044
4.
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044

Funds: Supported by National Natural Science Foundation of China (61790573)

摘要

摘要: 高速铁路运行控制系统是高速铁路的大脑和神经系统, 对列车的安全和高效运行至关重要. 随着我国高铁里程数和客运量的快速增加, 现有的控制手段和调度方法在快速、有效解决列车运行过程中出现的突发事件(比如电力故障、突发地震、山体滑坡、异物侵限等)方面尚有一定差距. 目前列车运行控制与调度采用分层架构, 突发情况下主要依赖调度员和司机的人工经验进行应急处置, 列车晚点时间较长, 旅客满意度不高. 因此, 如何针对高速列车运行过程中可能出现的突发事件, 提升其应急处置能力, 成为保障高铁安全高效运营的一大难题. 本文围绕高铁运行控制与动态调度一体化这一前沿研究热点, 对现有运行控制和动态调度的发展现状进行梳理, 在此基础上给出一体化的基本架构, 明确其基本内涵, 最后提出了未来的主要研究方向.
- 高速铁路 /
- 运行控制 /
- 动态调度 /
- 控制调度一体化 /
- 突发事件
Abstract: As the brain and nervous system of high-speed railways, the high-speed train control system is critical to the safe and efficient operation of trains. With the expanding of the high-speed railway network in China, the existing train control and rescheduling methods are challenged by the increasing emergencies (such as power failure, sudden earthquake, landslide, foreign matter invasion, etc.) during the train operation. The existing control and rescheduling system adopting a layered structure, relies on the experience of dispatchers and drivers, and is hard to satisfy the requirements of train delay and passenger satisfaction. Therefore, how to improve the emergency handling ability is a severe problem to ensure the safe and efficient operation of high-speed railway during train operation. This paper focuses on the integration of train control and online rescheduling of high-speed railway. We conduct the state-of-the-art of existing train control and online rescheduling, and proposed the fundamental architecture, basic intension, and main research directions in this field.
- High-speed railway /
- train operation control /
- online rescheduling /
- integration of train control and rescheduling /
- emergency
注释:

1) 　收稿日期 2019-05-10 录用日期 2019-09-20　Manuscript received May 10, 2019; accepted September 20, 2019　国家自然科学基金 (61790573) 资助　Supported by National Natural Science Foundation of China (61790573)　本文责任编委吕宜生　Recommended by Associate Editor LV Yi-Sheng　1. 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 2. 北京交通大学国家轨道交通安全评估研究中心北京 100044 3. 北京交通大学交通运输学院北京 100044 4. 北京交通大学计算机与信息技术学院北京 100044　1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044 2. National Research Center of Railway Safety Assessment, Beijing Jiaotong

2) University, Beijing 100044 3. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044 4. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044

HTML全文

图 1 突发事件对乘车出行造成巨大影响

Fig. 1 Strong influences on train travel caused by emergencies

下载: 全尺寸图片幻灯片

图 2 高铁运行控制与动态调度

Fig. 2 Train control and online rescheduling for high-speed railways

下载: 全尺寸图片幻灯片

图 3 运行控制与动态调度一体化示意图

Fig. 3 Integration of train control and online rescheduling

下载: 全尺寸图片幻灯片

参考文献(66)

[1]	宁滨, 唐涛, 李开成, 董海荣. 高速列车运行控制系统. 北京: 科学出版社, 2012. Ning Bin, Tang Tao, Li Kai-Cheng, Dong Hai-Rong. High-speed train operation control system. Beijing: Science Press, 2012.
[2]	何华武. 中国高速铁路创新与发展. 中国铁路, 2010, 12: 5−8 doi: 10.3969/j.issn.1001-683X.2010.12.002 2 He Hua-Wu. Innovation and development of high-speed railway in China. Chinese Railways, 2010, 12: 5−8 doi: 10.3969/j.issn.1001-683X.2010.12.002
[3]	汪希时. 智能铁路运输系统ITS-R. 北京: 中国铁道出版社, 2004. Wang Xi-Shi. Intelligent Railway Transportation System ITS-R. Beijing: Science Press, 2004.
[4]	王同军. 智能铁路总体架构与发展展望. 铁路计算机应用, 2018, 27(7): 1−8 doi: 10.3969/j.issn.1005-8451.2018.07.003 4 Wang Tong-Jun. Overall framework and development prospect of intelligent railway. Railway computer application, 2018, 27(7): 1−8 doi: 10.3969/j.issn.1005-8451.2018.07.003
[5]	刘朝英, 莫志松. 京津城际高速铁路信号系统集成. 北京: 中国铁道出版社, 2010. Liu Chao-Ying, Mo Zhi-Song. Beijing-tianjin Intercity High-speed Railway Signal System Integration. Beijing: Science Press, 2010.
[6]	铁路部电务段. CTCS-3级列控系统总体技术方案. 北京: 中国铁道出版社, 2008. Railway Electrical Section. Overall Technical Plan of CTCS-3 train Control System. Beijing: China Railway Press, 2008.
[7]	肖代宁, 刘红燕. 国外高速铁路列车运行控制系统. 铁道标准设计, 2008, 4: 128−130 doi: 10.3969/j.issn.1004-2954.2008.12.041 7 Xiao Dai-Ning, Liu Hong-Yan. Foreign high-speed railway train operation control system. Railway Standard Design, 2008, 4: 128−130 doi: 10.3969/j.issn.1004-2954.2008.12.041
[8]	柴天佑. 自动化科学与技术发展方向, 自动化学报, 2018, 44(11): 1923−1930 Chai Tian-You. Development directions of automation science and technology. Acta Automatica Sinica, 2018, 44(11): 1923−1930
[9]	9 Ning B, Tang T, Gao Z Y, Yan F, Wang F Y, Zeng D D. Intelligent railway systems in China. IEEE Intelligent Systems, 2006, 21(5): 80−83 doi: 10.1109/MIS.2006.99
[10]	周东华, 纪洪泉, 何潇. 高速列车信息控制系统的故障诊断技术. 自动化学报, 2018, 44(7): 1153−1164 10 Zhou Dong-Hua, Ji Hong-Quan, He Xiao. Fault diagnosis techniques for the information control system of high-speed trains. Acta Automatica Sinica, 2018, 44(7): 1153−1164
[11]	11 Yang C H, Yang C, Peng T, Yang X Y. A fault-injection strategy for traction drive control systems. IEEE Transactions on Industrial Electronics, 2017, 64(7): 5719−5727 doi: 10.1109/TIE.2017.2674610
[12]	唐涛, 黄良骥. 列车自动驾驶系统控制算法综述. 铁道学报, 2003, 25(2): 98−102 doi: 10.3321/j.issn:1001-8360.2003.02.021 12 Tang Tao, Huang Liang-Ji. A survey of control algorithm for automatic train opetation. Journal of the China Railway Society, 2003, 25(2): 98−102 doi: 10.3321/j.issn:1001-8360.2003.02.021
[13]	辛斌, 陈杰, 彭志红. 智能优化控制: 概述与展望. 自动化学报, 2013, 39(11): 1831−1848 13 Xin Bin, Chen Jie, Peng Zhi-Hong. Intelligent optimized control: overview and prospect. Acta Automatica Sinica, 2013, 39(11): 1831−1848
[14]	14 Howlett. A new look at the rate of change of energy consumption with respect to journey time on an optimal train journey. Transportation Research Part B: Methodological, 2016, 94: 387−408 doi: 10.1016/j.trb.2016.10.004
[15]	15 Khmelnitsky E. On an optimal control problem of train operation. IEEE Transactions on Automatic Control, 200, 45(7): 1257−1266
[16]	16 Zhuan X, Xia X H. Optimal scheduling and control of heavy haul trains equipped with electronically controlled pneumatic braking systems. IEEE Transactions on Control Systems Technology, 2007, 15(6): 1159−1166 doi: 10.1109/TCST.2007.899721
[17]	17 Dong H R, Gao S G, Ning B. Cooperative control synthesis and stability analysis of multiple trains under moving signaling systems. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(10): 2730−2738 doi: 10.1109/TITS.2016.2518649
[18]	18 Gao S G, Dong H R, Ning B. Cooperative prescribed performance tracking control for multiple high-speed trains in moving block signaling system. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(7): 2740−2749 doi: 10.1109/TITS.2018.2877171
[19]	19 Shangguan W, Yan X H, Cai B G, Wang J. Multiobjective optimization for train speed trajectory in CTCS high-speed railway with hybrid evolutionary algorithm. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4): 2215−2225 doi: 10.1109/TITS.2015.2402160
[20]	20 Tuyttens D, Fei H Y, Mezmaz M, Jalwan J. Simulation-based genetic algorithm towards an energy-efficient railway traffic control. Mathematical Problems in Engineering, 2013, 2(27): 343−347
[21]	21 Chou M, Xia X, Kayser C. Modelling and model validation of heavy-haul trains equipped with electronically controlled pneumatic brake systems. Control Engineering Practice, 2007, 15(4): 501−509 doi: 10.1016/j.conengprac.2006.09.006
[22]	22 Song Q, Song Y D, Tang T, Ning B. Computationally inexpensive tracking control of high-speed trains with traction/braking saturation. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4): 1116−1125 doi: 10.1109/TITS.2011.2143409
[23]	23 Faieghi M, Jalali A. Robust adaptive cruise control of high speed trains. ISA Transactions, 2014, 53(2): 533−541 doi: 10.1016/j.isatra.2013.12.007
[24]	Bai W, Lin Z, Dong H, et al. Distributed cooperative cruise control of multiple high-speed trains under a state-dependent information transmission topology. IEEE Transactions on Intelligent Transportation Systems, 2019. DOI: 10.1109/TITS.2019.2893583
[25]	25 Mao Z H, Tao G, Jiang B, Yan X G. Adaptive actuator compensation of position tracking for high-speed trains with disturbances. IEEE Transactions on Vehicular Technology, 2018, 67(7): 5706−5717 doi: 10.1109/TVT.2018.2808360
[26]	26 Dong H R, Ning B, Cai B G, Hou Z S. Automatic train control system development and simulation for high-speed railways. IEEE Circuits and Systems Magazine, 2010, 10(2): 6−18 doi: 10.1109/MCAS.2010.936782
[27]	27 Dong H R, Zhu H N, Li Y D, Lv Y S, Gao S G, Zhang Q, Ning B. Parallel intelligent systems for integrated high-speed railway operation control and dynamic scheduling. IEEE Transactions on Cybernetics, 2018, 48(12): 3381−3389 doi: 10.1109/TCYB.2018.2852772
[28]	28 Gu Q, Tang T, Cao F, Karimi H R, Song Y D. Peak power demand and energy consumption reduction strategies for trains under moving block signalling system. Mathematical Problems in Engineering, 2013.
[29]	29 Yan X H, Cai B G, Ning B, Shanggan W. Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements. Transportation Research Part C: Emerging Technologies, 2016, 69: 60−78 doi: 10.1016/j.trc.2016.05.019
[30]	30 Zhou L, Tong L, Chen J, Tang J J. Joint optimization of high-speed train timetables and speed profiles: a unified modeling approach using space-time-speed grid networks. Transportation Research Part B: Methodological, 2017, 97(6): 157−181
[31]	31 Yang L X, Li K P, Gao Z Y, Li X. Optimizing trains movement on a railway network. Omega, 2012, 40(5): 619−633 doi: 10.1016/j.omega.2011.12.001
[32]	32 Carvajal-Carreo W, Cucala A, Fernández-Cardador A. Fuzzy train tracking algorithm for the energy efficient operation of CBTC equipped metro lines. Engineering Applications of Artificial Intelligence, 2016, 53: 19−31 doi: 10.1016/j.engappai.2016.03.011
[33]	33 Li S K, Yang L X, Gao Z Y. Adaptive coordinated control of multiple high-speed trains with input saturation. Nonlinear Dynamics, 2016, 83(4): 2157−2169 doi: 10.1007/s11071-015-2472-8
[34]	34 Li S K, Yang L X, Gao Z Y. Coordinated cruise control for high-speed train movements based on a multi-agent model. Transportation Research Part C: Emerging Technologies, 2015, 56: 281−292 doi: 10.1016/j.trc.2015.04.016
[35]	Baek J, Lee C. The simulation of train separation control algorithm by movement authority using beacon. In: Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, 2007.
[36]	36 Pan D, Zheng Y P. Dynamic control of high-speed train following operation. Promet-traffic and Transportation, 2014, 26(4): 291−297 doi: 10.7307/ptt.v26i4.1256
[37]	37 Takagi R. Synchronisation control of trains on the railway track controlled by the moving block signalling system. IET Electrical Systems in Transportation, 2012, 2(3): 130−138 doi: 10.1049/iet-est.2011.0053
[38]	Ding Y, Bai Y, Liu F, et al. Simulation algorithm for energy-efficient train control under moving block system. In: Proceedings of the 2009 World Congress on Computer Science and Information Engineering, 2009.
[39]	39 Zhao Y B, Ioannou P. Positive train control with dynamic headway based on an active communication system. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(6): 3095−3103 doi: 10.1109/TITS.2015.2435515
[40]	40 Xun J, Yin Y, Liu R H, Liu F. Cooperative control of high-speed trains for headway regulation: a self-triggered model predictive control based approach. Transportation Research Part C, 2019, 102: 106−120 doi: 10.1016/j.trc.2019.02.023
[41]	41 Szpigel B. Optimal train scheduling on a single track railway operational research. Operations Research, 1972, 72: 343−352
[42]	赵鹏. 高速铁路运营组织. 北京: 中国铁道出版社, 2009. Zhao Peng. High Speed Railway Operation Organization. Beijing: Science Press, 2009.
[43]	43 Cordeau J, Toth P, Vigo D. A survey of optimization models for train. Transportation Science, 1998, 32(4): 380−404 doi: 10.1287/trsc.32.4.380
[44]	44 Hansen I. Railway network timetabling and dynamic traffic management. International Journal of Civil Engineering, 2010, 8(1): 19−32
[45]	45 Cacchiani V, Huisman D, Kidd M, Kroon L, Toth P, Veelenturf L, Wagenaar J. An overview of recovery models and algorithms for real-time railway rescheduling. Transportation Research Part B: Methodological, 2014, 63: 15−37 doi: 10.1016/j.trb.2014.01.009
[46]	46 Törnquist J, Persson J. N-tracked railway traffic re-scheduling during disturbances. Transportation Research Part B, 2007, 41(3): 342−362 doi: 10.1016/j.trb.2006.06.001
[47]	47 Törnquist J. Design of an effective algorithm for fast response to the rescheduling of railway traffic during disturbances. Transportation Research Part C, 2012, 20: 62−78 doi: 10.1016/j.trc.2010.12.004
[48]	48 Schöbel A. Integer programming approaches for solving the delay management problem. Algorithmic Methods for Railway Optimization, 4359, 4359: 145−170
[49]	49 Dollevoet T, Huisman D, Schmidt M, Schöbel A. Delay management with rerouting of passengers. Transportation Science, 2012, 46: 74−89 doi: 10.1287/trsc.1110.0375
[50]	50 Hou Z P, Dong H R, Gao S G, Nicholson G, Chen L, Roberts C. Energy-saving metro train timetable rescheduling model considering ATO profiles and dynamic passenger flow. IEEE Transactions on Intelligent Transportation Systems, 2019
[51]	51 Schlechte T, Borndorfer R, Erol B. Micro-macro transformation of railway networks. Journal of Rail Transport planning and Management, 2011, 1: 38−48 doi: 10.1016/j.jrtpm.2011.09.001
[52]	52 Corman F, D’Ariano A, Pranzo M. Effectiveness of dynamic reordering and rerouting of trains in a complicated and densely occupied station area. Transportation Planning and Technology, 2011, 34(4): 341−362 doi: 10.1080/03081060.2011.577152
[53]	53 D'Ariano A, Pacciarelli D, Pranzo M. A branch and bound algorithm for scheduling trains in a railway network. European Journal of Operational Research, 2007, 183(2): 643−657 doi: 10.1016/j.ejor.2006.10.034
[54]	54 Corman F, D'Ariano A, Pacciarelli D, Pranzo M. A tabu search algorithm for rerouting trains during rail operations. Transportation Research Part B: Methodological, 2010, 44(1): 175−192 doi: 10.1016/j.trb.2009.05.004
[55]	55 Meng L Y, Zhou X S. Simultaneous train rerouting and rescheduling on an N-track network: a model reformulation with network-based cumulative flow variables. Transportation Research Part B, 2014, 67: 208−234 doi: 10.1016/j.trb.2014.05.005
[56]	56 Mazzarello M, Ottaviani E. A traffic management system for real-time traffic optimisation in railways. Transportation Research Part B, 2007, 41: 246−274 doi: 10.1016/j.trb.2006.02.005
[57]	57 Luan X J, Wang Y H, De Schutter B, Meng L Y, Lodewijks G, Corman F. Integration of real-time traffic management and train control for rail networks − Part 1: Optimization problems and solution approaches. Transportation Research Part B: Methodological, 2018, 115: 41−71 doi: 10.1016/j.trb.2018.06.006
[58]	58 Luan X J, Wang Y H, De Schutter B, Meng L Y, Lodewijks G, Corman F. Integration of real-time traffic management and train control for rail networks − Part 2: Extensions towards energy-efficient train operations. Transportation Research Part B: Methodological, 2018, 115: 72−94 doi: 10.1016/j.trb.2018.06.011
[59]	59 Ghaemi N, Zilko A A, Yan F, Cats O, Kurowick D, Goverde R M P. Impact of railway disruption predictions and rescheduling on passenger deeays. Journal of Rail Transport Planning and Management, 2018, 8(2): 103−122
[60]	60 Jespersen-Groth J, Potthoff D, Clausen J, Huisman D, Kroon L, Maróti G, Nielsen M N. Disruption management in passenger railway transportation. Robust and Qnline Large-scale Optimization, 2009: 399−421 doi: 10.1016/j.trc.2010.12.004
[61]	Ghaemi N, Goverde R M P. Review of railway disruption management practice and Literature. In: Proceedings of the 6th International Conference on Railway Operations Modelling and Analysis, Narashimo, Japan: Rail Tokyo, 2015. 037-1−037-16
[62]	62 Louwerse I, Huisman D. Adjusting a railway timetable in case of partial or complete blockades. European Journal of Operational Research, 2014, 235(3): 583−593 doi: 10.1016/j.ejor.2013.12.020
[63]	63 Acuna-Agost R, Michelon P, Feillet D, Gueye S. SAPI: Statistical analysis of propagation of incidents. A new approach for rescheduling trains after disruptions. European Journal of Operational Research, 2011, 215(1): 227−243 doi: 10.1016/j.ejor.2011.05.047
[64]	64 Zhan S G, Kroon L G, Veelenturf L P, Wagenaar J C. Real-time high-speed train rescheduling in case of a complete blockage. Transportation Research Part B: Methodological, 2015, 78: 182−201 doi: 10.1016/j.trb.2015.04.001
[65]	65 Zhu Y Q, Rob M. Goverde R M P. Railway timetable rescheduling with flexible stopping and flexible short-turning during disruptions. Transportation Research Part B: Methodological, 2019, 123(1): 149−181
[66]	66 Xu P J, Corman F, Peng Q Y, Luan X J. A train rescheduling model integrating speed management during disruptions of high-speed traffic under a quasi-moving block system. Transportation Research Part B: Methodological, 2017, 104: 638−666 doi: 10.1016/j.trb.2017.05.008