CBTC系统列车运行仿真与优化策略
|
摘要
基于通信的列车控制系统(Communication Based Train Control, CBTC)是城市轨道交通移动闭塞系统的关键组成部分之一。本文针对CBTC系统性能分析和优化策略展开研究,并开发出一套针对于城市轨道交通的列车运行仿真系统,对于辅助城市轨道交通列车运行控制系统设计,提高列车运行效率和保障列车运行安全具有理论研究意义和实际应用价值。本文的主要研究成果包括：
列车速度控制数学模型是列车运行仿真的基础,本文通过对IEEE1474.1推荐安全制动模型的研究,分析了基于动力学模型和基于能量平衡模型的两种列车超速防护算法,并进行了比较,根据两种算法的关系在仿真系统中进行综合运用,以提高列车超速防护算法的效率。以列车超速防护算法为基础,在仿真系统的列车速度控制模型中分别建立紧急制动触发曲线算法、全常用制动触发曲线算法、命令速度算法和列车实际速度控制算法。该算法经过仿真测试可以应用到实际的列车超速防护系统中。
论文针对城市轨道交通系统中在某些情形下,列车在车站区域的追踪间隔过大而导致系统整体性能降低的问题,对已有的CBTC系统列车追踪间隔算法进行了分析,提出了一种基于站台限速值和限速区域参数调整的正线列车追踪间隔的优化方法。该方法通过建立系统模型和仿真得到站台限速设置、列车追踪间隔和列车旅行速度的关系,从而获得优化的设置方案。通过仿真测试,表明该方法能够优化系统关键区域的列车追踪间隔,提高系统性能。
CBTC列车和非CBTC列车混合运行模式下的列车追踪间隔计算是实际工程系统设计中一个比较困难的问题。为解决不同控制模式的转换区域或在同一区域列车处于不同控制模式下的追踪间隔计算问题,本文提出了一种基于线路特定位置与其闭塞区域的追踪间隔计算方法,位置-闭塞区域方法(PBAM, Position-BlockArea Method)。应用该方法在仿真系统中可计算出线路上任意一点的追踪间隔,并可得到线路上连续的追踪间隔曲线。车辆段到正线间的转换区域是典型的混合驾驶模式的线路区段,实际工程应用中,在系统设计阶段往往比较难以准确地求得该区段的列车追踪间隔,若应用论文中提出PBAM方法,通过列车运行仿真,可以计算出列车在该区段的各个位置的追踪间隔,从而可以验证系统设计是否满足要求。
准确的能耗计算是其他能耗优化算法实现的基础。本文从CBTC列车控制系统速度调整算法的角度对列车运行的能耗计算进行了研究,由于列车控制系统最终实现列车运行控制,并决定了列车能耗的大小,因此该计算方式能具有较高的准确性和可控性。在能耗分析中,单独分析了列车在制动过程中的再生制动能量,为下一步多专业协调研究提高系统对再生制动能量的吸收率打下基础。
在满足系统列车调整需要的情况下实现节能和环保,是目前研究的一项重要课题,本文提出了一种基于遗传算法的列车运行能耗优化算法。该算法以降低列车牵引能耗为目标,对列车在区间的运行速度进行组合优化,得到列车对应于不同运行等级的能耗优化速度曲线的满意解。通过仿真测试,该方法除了在需要以最高能力运营的高峰时间之外,其他运行等级对应的能耗优化速度曲线均有很好的节能效果。能耗优化之后的列车速度曲线,能充分利用节能坡的土建特点进行能耗优化,同时也可尽量避免因区间限速导致的过大的制动和牵引。
论文最后给出了CBTC仿真系统的设计与实现,从系统设计的层面提出了智能列车调度系统(ITDS)的实现模型,在保持现有列车自动监控系统结构稳定性的同时加强系统对先进算法的应用能力,并提供一个开放的算法测试平台,为后续的进一步研究提供条件。
Communication Based Train Control system, CBTC, is one of the most important components of the moving block system in the urban rail transit system. The CBTC system performance analysis and the optimization are studied in this thesis, and meanwhile an urban rail transit train simulation system is developed for the test of the study, hereby it is called CBTC Simulation System. The CBTC Simulation System cannot only be used for the theory study but also as the design assistant in the real project of the train control system to improve the system performance and safety. The main contributions of this thesis are:
Train speed control mathematical model is the basis of the train movement simulation. On the study of the IEEE1474.1recommended safety braking model, the kinematic based algorithm and the energy balance based algorithm are analyzed and compared with each other for the calculation of the over-speed protection function. The calculation speed is improved by synthesizing the two algorithms, i.e. choosing whichever algorithm in the different braking stage. Derived from the emergency trigger speed, full service braking speed profile, command speed profile and the train actual speed profile is established in the CBTC Simulation System. The speed regulation algorithm simulated and tested can be adopted in the train over speed protection system.
In order to solve the problem that the system performance is impacted by increased train headway in the station area in an urban rail transit system, the calculation algorithm of train headway was analyzed and a headway optimization method was provided based on the platform speed restriction and the speed restriction area parameters adjustment for the CBTC system. In this method, an optimized train headway can be gotten from the relationship of platform speed restriction setting, train headway and travel speed. The relationship is obtained by a simulation. From the simulated and tested on the CBTC Simulation System, it is proved that this method can optimize the headway in the critical area of the system so that to improve the whole system performance.
It is always a difficult task to calculate the train tracking headway in the mixed operation of CBTC mode train and Non-CBTC mode train. In order to solve this problem to calculate the headway in either the adjacent area of two different system control modes or a area with different control mode trains, a particular position and block area method, PBAM (Position-BlockArea Method), is introduced in this thesis. With this method, the CBTC Simulation System can calculation the headway for every particular position to produce the position-headway profile. The transfer track between depot/yard and the mainline is the typical area of the mixed mode train operation that is difficult to calculate the headway in the real project. By using the PBAM method in the CBTC Simulation System, the headway of the transfer track in every position can be precisely calculated and the position-headway profile can be provided to prove the current system design is acceptable or not.
The precision of the energy consumption during the train movement is the basis of the algorithms for energy consumption optimization. This thesis studied the train energy consumption algorithm from the aspect of the CBTC train speed regulation. Since CBTC system calculates the train tracking/braking status in the train speed regulation algorithm and sends the current tracking/braking command and the effort to the train tracking/braking system, which determines the train energy consumption in the operation, this algorithm can have more precision and controllability. The regenerative braking energy is specifically described in the algorithm for the further study of the regenerative braking energy absorption.
Satisfied the train speed regulation, energy saving and pollution reduction is another important study in the urban rail transit train control system. In this thesis, a genetic algorithm based train traction energy saving method is studied. The object is to reduce the train traction energy by the combinatorial optimization of the train speed profile in the inter-station section. The energy saving optimized speed profiles will be generated for each performance level. By the test of simulation, energy optimized speed profile can make the best use of the energy saving civil grade and avoid the frequent braking and tracking due to the inter-section speed restriction. The effective energy saving can be achieved for each performance level except in the rush hour that imposed the maximum train speed by the timetable. The energy optimized speed profile can make the best use of the energy saving civil grade and avoid the frequent braking and tracking due to the inter-section speed restriction.
The CBTC simulation system design and the implementation are described in the last of the thesis. In order to avoid the dramatically modifying the original system structure and provide an open testing platform for the future advanced algorithm study, an ITDS model, Intelligent Train Dispatching System is designed in the system design level.
列车速度控制数学模型是列车运行仿真的基础,本文通过对IEEE1474.1推荐安全制动模型的研究,分析了基于动力学模型和基于能量平衡模型的两种列车超速防护算法,并进行了比较,根据两种算法的关系在仿真系统中进行综合运用,以提高列车超速防护算法的效率。以列车超速防护算法为基础,在仿真系统的列车速度控制模型中分别建立紧急制动触发曲线算法、全常用制动触发曲线算法、命令速度算法和列车实际速度控制算法。该算法经过仿真测试可以应用到实际的列车超速防护系统中。
论文针对城市轨道交通系统中在某些情形下,列车在车站区域的追踪间隔过大而导致系统整体性能降低的问题,对已有的CBTC系统列车追踪间隔算法进行了分析,提出了一种基于站台限速值和限速区域参数调整的正线列车追踪间隔的优化方法。该方法通过建立系统模型和仿真得到站台限速设置、列车追踪间隔和列车旅行速度的关系,从而获得优化的设置方案。通过仿真测试,表明该方法能够优化系统关键区域的列车追踪间隔,提高系统性能。
CBTC列车和非CBTC列车混合运行模式下的列车追踪间隔计算是实际工程系统设计中一个比较困难的问题。为解决不同控制模式的转换区域或在同一区域列车处于不同控制模式下的追踪间隔计算问题,本文提出了一种基于线路特定位置与其闭塞区域的追踪间隔计算方法,位置-闭塞区域方法(PBAM, Position-BlockArea Method)。应用该方法在仿真系统中可计算出线路上任意一点的追踪间隔,并可得到线路上连续的追踪间隔曲线。车辆段到正线间的转换区域是典型的混合驾驶模式的线路区段,实际工程应用中,在系统设计阶段往往比较难以准确地求得该区段的列车追踪间隔,若应用论文中提出PBAM方法,通过列车运行仿真,可以计算出列车在该区段的各个位置的追踪间隔,从而可以验证系统设计是否满足要求。
准确的能耗计算是其他能耗优化算法实现的基础。本文从CBTC列车控制系统速度调整算法的角度对列车运行的能耗计算进行了研究,由于列车控制系统最终实现列车运行控制,并决定了列车能耗的大小,因此该计算方式能具有较高的准确性和可控性。在能耗分析中,单独分析了列车在制动过程中的再生制动能量,为下一步多专业协调研究提高系统对再生制动能量的吸收率打下基础。
在满足系统列车调整需要的情况下实现节能和环保,是目前研究的一项重要课题,本文提出了一种基于遗传算法的列车运行能耗优化算法。该算法以降低列车牵引能耗为目标,对列车在区间的运行速度进行组合优化,得到列车对应于不同运行等级的能耗优化速度曲线的满意解。通过仿真测试,该方法除了在需要以最高能力运营的高峰时间之外,其他运行等级对应的能耗优化速度曲线均有很好的节能效果。能耗优化之后的列车速度曲线,能充分利用节能坡的土建特点进行能耗优化,同时也可尽量避免因区间限速导致的过大的制动和牵引。
论文最后给出了CBTC仿真系统的设计与实现,从系统设计的层面提出了智能列车调度系统(ITDS)的实现模型,在保持现有列车自动监控系统结构稳定性的同时加强系统对先进算法的应用能力,并提供一个开放的算法测试平台,为后续的进一步研究提供条件。
Communication Based Train Control system, CBTC, is one of the most important components of the moving block system in the urban rail transit system. The CBTC system performance analysis and the optimization are studied in this thesis, and meanwhile an urban rail transit train simulation system is developed for the test of the study, hereby it is called CBTC Simulation System. The CBTC Simulation System cannot only be used for the theory study but also as the design assistant in the real project of the train control system to improve the system performance and safety. The main contributions of this thesis are:
Train speed control mathematical model is the basis of the train movement simulation. On the study of the IEEE1474.1recommended safety braking model, the kinematic based algorithm and the energy balance based algorithm are analyzed and compared with each other for the calculation of the over-speed protection function. The calculation speed is improved by synthesizing the two algorithms, i.e. choosing whichever algorithm in the different braking stage. Derived from the emergency trigger speed, full service braking speed profile, command speed profile and the train actual speed profile is established in the CBTC Simulation System. The speed regulation algorithm simulated and tested can be adopted in the train over speed protection system.
In order to solve the problem that the system performance is impacted by increased train headway in the station area in an urban rail transit system, the calculation algorithm of train headway was analyzed and a headway optimization method was provided based on the platform speed restriction and the speed restriction area parameters adjustment for the CBTC system. In this method, an optimized train headway can be gotten from the relationship of platform speed restriction setting, train headway and travel speed. The relationship is obtained by a simulation. From the simulated and tested on the CBTC Simulation System, it is proved that this method can optimize the headway in the critical area of the system so that to improve the whole system performance.
It is always a difficult task to calculate the train tracking headway in the mixed operation of CBTC mode train and Non-CBTC mode train. In order to solve this problem to calculate the headway in either the adjacent area of two different system control modes or a area with different control mode trains, a particular position and block area method, PBAM (Position-BlockArea Method), is introduced in this thesis. With this method, the CBTC Simulation System can calculation the headway for every particular position to produce the position-headway profile. The transfer track between depot/yard and the mainline is the typical area of the mixed mode train operation that is difficult to calculate the headway in the real project. By using the PBAM method in the CBTC Simulation System, the headway of the transfer track in every position can be precisely calculated and the position-headway profile can be provided to prove the current system design is acceptable or not.
The precision of the energy consumption during the train movement is the basis of the algorithms for energy consumption optimization. This thesis studied the train energy consumption algorithm from the aspect of the CBTC train speed regulation. Since CBTC system calculates the train tracking/braking status in the train speed regulation algorithm and sends the current tracking/braking command and the effort to the train tracking/braking system, which determines the train energy consumption in the operation, this algorithm can have more precision and controllability. The regenerative braking energy is specifically described in the algorithm for the further study of the regenerative braking energy absorption.
Satisfied the train speed regulation, energy saving and pollution reduction is another important study in the urban rail transit train control system. In this thesis, a genetic algorithm based train traction energy saving method is studied. The object is to reduce the train traction energy by the combinatorial optimization of the train speed profile in the inter-station section. The energy saving optimized speed profiles will be generated for each performance level. By the test of simulation, energy optimized speed profile can make the best use of the energy saving civil grade and avoid the frequent braking and tracking due to the inter-section speed restriction. The effective energy saving can be achieved for each performance level except in the rush hour that imposed the maximum train speed by the timetable. The energy optimized speed profile can make the best use of the energy saving civil grade and avoid the frequent braking and tracking due to the inter-section speed restriction.
The CBTC simulation system design and the implementation are described in the last of the thesis. In order to avoid the dramatically modifying the original system structure and provide an open testing platform for the future advanced algorithm study, an ITDS model, Intelligent Train Dispatching System is designed in the system design level.
引文
[1]Step-by-step Introduetion to the Railsim[EB/OL].http://www.railsim.com.
[2]Hirao Y., Bond L. Development of a Universal Train Simulator(UTRAS) and Evaluation of Signaling System[J].TRRI,1995,336(4):180-185.
[3]Railroad and Transit Systems [EB/OL]. http://www.orthstar.com.
[4]Erofeyev E. Calculation of optimum train control using dynamic Programming mehtod[J].In Proceedings of Moscow Railway Engineering Institute(Trudy MIIT), Moscow,issue 811,1967:16-30.
[5]Petar K., Gurdial S. Minimum-Energy Control of a Traction Motor[J]. Automatic Control, IEEE Transactions on,1971,17(1):92-94.
[6]I.P.Milroy.Aspects of Automatic train control[D]. Anstralia:Loughborough University, 1980.
[7]Asnis A., Dmitruk A., Osmolovski N. Using the maximum principle to solve the problem of energy-optimal control of the motion of the trains[J]. Vychisl. Mat. Mat Fiz,1985,25(11):1644-1656.
[8]Golovitcher I. Train Control algorithm for energy consumption optimization[C]. In Proceedings of All-Union Railway Research Institute,Vestnik VNllZHT,vol.8 1982:18-23.
[9]Golovitcher I. An analytical method for optimum train control computation[C]. Proceedings of State Universities, Electro-mechanics (Izvestiya VUZov Seriya Electromehanika),1986:59-66.
[10]Goloviteher I. Control algorithms for automatic operation of rail vehicles[C]. Automated and Remote Contorl.Journal of Russian Academy of Science (Automatika I Telemekhanika),1986:118-126.
[11]Goloviteher I. Optimum control of electric locomotives with regenerative braking[C]. In Proceedings of Moscow Railway Engineering Institue (Trudy MIIT), Moseow, 1989:19-24.
[12]Golovitcher I. An analytical method for computation of optimum train speed profile considering variable efficiency of locomotive[C]. Proeeedings of State University.Electro-mechnaics (Izvestiya VUZov Seriya Electro-mehnaika), 1989:72-81.
[13]Eroefyev E., Golovitcher E. I., Shmdrik M., Akulov M., Solomatin A. Using computers for speed profile calculations and regime cards developing[J]. Electric and Diesel Fleet (Electricheskaja I Teplovoznaya Tiaga),1991,6(414):15-17.
[14]Gill D. C. and Goodman C. J. Computer-based optimization techniques for mass transit railway signalling design[J]. IEE Proceedings B:Electric Power Applications, 1992,139(3):261-275.
[15]Chang C. S., Du D. Improved optimisation method using genetic algorithm for mass transit signalling block-layout design[J], IEE Proceeding-Electr. Power Appl.,1998, 145 (3):266-272.
[16]Milroy I. P. Aspect of automatic train control[D]. Leicestershire. Loughborough University,1980.
[17]Lee D. H., Milroy I. P., Tyler K. Application of pontryagin's maximum principle to the semi-automatic control of rail vehicles[C]. Proceedings of Second Conference on Control Engineering, Newcastle,1982:233-236.
[18]Guan J. F., Yang H., Wirasinglhe S. C. Simultaneous optimization of transit line configuration and passenger line assignment[J]. Transportation research Part B,2006, 40(10):885-902.
[19]Asnis I. A., Dmitruk A. V., Osmolovskii N. P. Using the maximum principle to solve the problem energy-optimal control of the motion of the train[J]. Zh.Vychisl.Mat.Mat.Fiz,1985,25(11):1644-1656.
[20]Howlett P.G., Pudney P.J. Energy-efficient train control[M]. London:Springer Press, 1995.
[21]Miyamoto Shoji, Seiji Yasunobu, Ihara Hirokazu. Predictive fuzzy control and its application to automatic train operation systems[C]. Proceeding of the Anal of Fuzz Inf.,1987:59-72.
[22]Hiroyasu Oshima, Seiji Yasunobu, Shin-ichi Sekino. Automatic train operation system based on predictive fuzzy control[C]. Proceeding of the International workshop on Artifitial Intelligence for Industrial Applications,1998:485-489.
[23]Chang C. S., Sim S. S. Optimizing train movements through coast control using genetic algorithms[C]. Proceedings of Electric Power Application, IEEE,1997:65-73.
[24]Chang C. S., Xu D.Y. Differential evolution based tuning of fuzzy automatic train operation for mass rapid transit system[C]. Proceedings of Eleetric Power Applieation, IEEE,2000,147(3):206-212.
[25]Han S. H., Byen Y. S., Baek J. H., et. An optimal automatic train operation (ATO) control using genetic algorithm (GA)[C]. Proceedings of the IEEE Region 10 Conference, TENCON 99,1999 (1):360-362.
[26]Wong K. K., Ho T. K. Dynamic coast control of train movement with genetic algorithm[C]. International Journal of Systems Science,2004:835-846.
[27]Wong K. K., Ho T. K. Coast control for mass rapid transit railways with searehing methods[C]. IEE Proc-Electr.Power Appl.,2004,151(3):365-376.
[28]Chen R., Guo J. Development of the new CBTC system simulation and performance analysis[C]. Proceedings of 12th International Conference on Computer System Design and Operation in Railways and Other Transit Systems. Beijing:WIT Press, 2010:497-507.
[29]刘云.列车运行仿真系统的建模与实现[J].铁道学报,1995,17(专辑)：20-26.
[30]赵明,汪希时.移动自动闭塞条件下列车追踪运行控制研究[J].铁道学报,1997,19(3)：61-68.
[31]郭佑民,王志伟,武福等.列车操纵与运行仿真系统[J].兰州铁道学院学报,2002,21(6)：125-127.
[32]毛保华,何天健,袁振洲,刘海东,赵立宁,陈志英.通用列车运行模拟软件系统研究[J].铁道学报,2000,22(1)：1-6.
[33]张波,马大伟.中高速列车共线运行的仿真研究[J].中国铁道科学,2003,24(3)：119-124.
[34]刘炜,李群湛.地铁牵引仿真计算中的牵引策略研究[J].机车电传动,2006,1(1)：46-49.
[35]唐涛,郜春海,李开成,燕飞.基于通信的列车运行控制技术发展战略探讨[J].都市快轨交通,2005,18(6)：25-29.
[36]燕飞,唐涛.IEC61508及其在铁路安全相关系统研制开发中的应用研究[J].铁道学报.2005,27(6)：124-128.
[37]郜春海,燕飞,唐涛.轨道交通信号系统安全评估方法研究[J].中国安全科学.2005,15(10)：74-79.
[38]陈荣武,刘莉,诸昌钤.基于CBTC的列车自动驾驶控制算法[J].计算机应用,2007,27(11)：2649-2651.
[39]王卓,王艳辉,贾利民等.基于ANFIS的高速列车制动控制仿真研究[J].中国铁道学报,2005,27(3)：113-117.
[40]Kirk, P.M. Automated personal transit control systems[C]. American Society of Mechanical Engineers (Paper),1973
[41]McAulay, Alastair D. Progress in signaling for track guided systems, American Society of Civil Engineers[J]. Transportation Engineering Journal,1975,101(4): 621-637.
[42]Hinman E. J., Pitts G. L. Practical headway limitations for personalised automated-transit systems[C]. Proceedings of the Institution of Electrical Engineers, 1975,122(7):756-759.
[43]Renfrew R. M. Technology selection and development for an intermediate capacity transit system [C]. Conference Record - IAS Annual Meeting (IEEE Industry Applications Society),1977:939-945.
[44]Bousman, William G., Winkler, Diana J. Application of the moving-block analysis[C]. Collection of Technical Papers-AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Pt 2,1981:755-763.
[45]Watanabe, Ikuo, Takashige, Tetuo. Moving block system with continuous train detection utilizing train shunting impedance of track circuit[J]. Quarterly Report of RTRI (Railway Technical Research Institute),1989,3(4):190-197.
[46]Gill D. C., Goodman C. J. Computer-based optimisation techniques for mass transit railway signalling design[C]. IEE Proceedings B:Electric Power Applications,1992, 139(3):261-275.
[47]Pascoe R. D., Rossi C., Savio S., Sciullo, G. Comparison between fixed and moving block signalling systems performance by digital simulation[C]. Proceedings of the International Conference on Computer Aided Design, Manufacture and Operation in the Railway and other Advaced Mass Transit, Computers in Railways: Management, 1992.
[48]Hill R., John, Bond, Louisa J. Modelling moving-block railway signalling systems using discrete-event simulation[C]. Proceedings of the IEEE/ASME Joint Railroad Conference,1995:105-111.
[49]Hirao, Yuji, Hasegawa, Yutaka. Development of a universal train simulator (UTRAS) and evaluations of signalling systems[C]. Quarterly Report of RTRI (Railway Technical Research Institute) [J].1995,36(4):180-185.
[50]Lockyear M. J. Changing track moving-block railway signaling, IEE Review[J].1996, 42(1):21-25.
[51]Gill D.C. Impact of moving block train control on heavy metros[C]. IEE Conference Publication,1998:235-242.
[52]Lockyear M. J. Application of a transmission based moving block Automatic Train Control system on Docklands Light Railway[C]. IEE Conference Publication,1998: 51-61.
[53]Ning, B. Absolute braking and relative distance braking-train operation control modes in moving block systems[C]. Proceedings of the International Conference on Computer Aided Design, Manufacture and Operation in The Railway and Other Advanced Mass Transit Systems,1998:991-1001.
[54]Ho M. T. K. Multi-train movement simulator with moving block signaling[C]. Proceedings of the International Conference on Computer Aided Design, Manufacture and Operation in The Railway and Other Advanced Mass Transit Systems,1998: 783-792.
[55]Nakamura H. Analysis of minimum train headway on a moving block system by genetic algorithm[C]. Proceedings of the International Conference on Computer Aided Design, Manufacture and Operation in The Railway and Other Advanced Mass Transit Systems,1998:1013-1022.
[56]Ji Jialun. Headway and carrying capacity of railway moving block system', Proceedings of the Conference on Traffic and Transportation Studies, ICTTS 2000, 2000:279-284.
[57]Whitwam F. Moving block does work. Railway Gazette International[J].2001, 157(11).
[58]Gillan D. Distance-to-go signalling rivals moving block benefits[J]. Railway Gazette International,2001,157(10):689-692.
[59]Zafar, Nazir A., Araki, Keijiro. Formalizing moving block railway interlocking system for directed network[C]. Research Reports on Information Science and Electrical Engineering of Kyushu University, September 2003,8(2):109-114.
[60]Lindqvist L., Jadhav R. Application of communication based Moving Block systems on existing metro lines[C]. WIT Transactions on the Built Environment, Computers in Railways Ⅹ: Computer System Design and Operation in the Railway and Other Transit Systems,2006:391-400.
[61]Mazzarello Maura, Ottaviani Ennio. A traffic management system for real-time traffic optimisation in railways[J]. Transportation Research Part B:Methodological,2007, 41(2):246-274.
[62]纪加伦,杨肇夏.移动闭塞方式下列车运行组织及区间通过能力计算方法的探讨,铁道学报,1992,14(01)：38-46.
[63]刘海东,袁振洲,朱钰,李巍屹.移动自动闭塞仿真系统列车追踪过程的探讨,交通与计算机,1999,17(01)：46-48.
[64]刘云,张振江.MAS下区间列车追踪的研究和仿真[J].系统仿真学报,1999,11(1)：29-54.
[65]张勇,赵明,汪希时.基于移动自动闭塞条件的列车运行仿真研究[J].系统仿真学报,1999,11(3)：195-204.
[66]宋瑞,何世伟,朱松年.铁路系统线路通过能力分析模拟模型的研究[J].铁道学报,1999,21(2)：2-7.
[67]刘英.移动自动闭塞条件下的能力利用分析[D].北京：北方交通大学博士学位论文,1998.
[68]刘海东,毛保华,何天健,丁勇,王璇.不同闭塞方式下城轨列车追踪运行过程及其仿真系统的研究[J],铁道学报,2003,8(2)：109-114.
[69]路飞,宋沐民,田国会.地铁列车的追踪间隔控制模型与仿真[J],信息与控制,2006,35(5)：641-646.
[70]金娟,杨梅,王长林.基于移动闭塞原理的地铁列车线路通过能力的研究[J],铁路计算机应用,2008,17(6)：7-10.
[71]李本刚CBTC移动闭塞和准移动闭塞列车运行安全间隔时间的计算[J],铁路通信信号工程技术,2008,5(6)：8-11.
[72]Chang C.S., Sim, S.S. Optimising train movements through coast control using genetic algorithm[J]. IEE Proceeding of Electric Power Applications,1997,144(1):65-73.
[73]Hwang H. S. Control strategy for optimal compromise between trip time and energy consumption in a high-speed railway [J]. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans,1998,28(6):791-802.
[74]Chang C. S., Xu D. Y., Quek H. B. Pareto-optimal set based multiobjective tuning of fuzzy automatic train operation for mass transit system[J]. IEE Proceedings:Electric Power Applications,1999,146(5):577-583.
[75]Chang C. S., Xu D.Y. Differential evolution based tuning of fuzzy automatic train operation for mass rapid transit system, IEE Proceedings:Electric Power Applications[J],2000,147(3):206-212.
[76]Khmelnitsky E. On an Optimal control problem of train operation[J], IEEE Transactions on Automatic Control,2000,45(7):1257-1266.
[77]Cheng J., Davydova Y., Howlett P., Pudney P. Optimal driving strategies for a train journey with non-zero track gradient and speed limits[J]. IMA Journal of Mathematics Applied in Business and Industry,1999,10(2):89-115.
[78]Cheng J., Howlett P. Application of critical velocities to the minimization of fuel consumption in the control of trains [J]. Automatical,1992,28(1):165-169.
[79]Van Breusegem V., Campion G., Bastin G. Traffic modeling and state feedback control for metro lines[J]. IEEE Transactions on Automatic Control,1991,36(7): 770-784.
[80]Haitl R. F., Sethi S. P., Vickson R. G. Survey of the maximum principles for optimal control problems with state constraints [J]. SIAM Review,1995,37(2):181-218.
[81]Gen M., Runwei C. Genetic algorithms and engineering design[M], New York:Wiley, 1997.
[82]Yu J., Qian Q. Q., He Z. Genetic algorithms with application to optimize high speed train ATO[C], International Conference on Transportation Engineering 2007, ICTE 2007,2007:2512-2517.
[83]Wegele S., Schnieder E. Automated dispatching of train operations using genetic algorithms Advances in Transport[C]. Ninth International Conference on Computers in Railways, COMPRAIL IX,2004:775-784.
[84]Hani Y., Chehade H. Amodeo L., Yalaoui F. Simulation based optimization of a train maintenance facility model using genetic algorithms[C]. Proceedings of ICSSSM'06, 2006 International Conference on Service Systems and Service Management,2007: 513-518.
[85]Chang C. S., Wand W., Liew A. C., Wen F. S., Srinivasan D. Genetic algorithm based bicriterion optimisation for traction substations in DC railway system[C]. Proceedings of the IEEE Conference on Evolutionary Computation,1995:11-16.
[86]Cox E. Fuzzy modeling and genetic algorithms for data mining and exploration[M], Morgan: Caufmann,2005.
[87]Ke B. R., Chen N. Signalling blocklayout and strategy of train operation for saving energy in mass rapid transit systems[J]. IEE Proc.-Electr. Power Appl,2005,152(2): 129-140.
[88]Ke B. R., Signaling system for saving energy on mass rapid transit systems[D], Taipei: Ph.D. dissertation, Nat. Taiwan Univ. Sci. Technol.,2006.
[89]Dorigo M., Stutzle T., Ant Colony Optimization. Cambridge[M], MA:MIT Press, 2004.
[90]Dorigo M., Gambardella L. M. Ant colony system: A cooperative leaning approach to the traveling salesman problem[J]. IEEE Trans. Evol. Comput.,1997,1(1):53-66.
[91]Maniezzo V., Colorni A. The ant system applied to the quadratic assignment problem. [J] IEEE Trans. Knowl. Data Eng.,1999,11(5):769-778.
[92]Merkle D., Middendorf M., Schmeck H. Ant colony optimization for resource-constrained project scheduling[J]. IEEE Transactions on Evolutionary Computation,2002,6(4):333-346.
[93]Dorigo M., Birattari M., Stutzle T. Ant colony optimization. IEEE Comput. Intell. Mag.[J],2006,1(4):28-39.
[94]Nock O. S., Railway Signalling: A Treatiose on the Recent Practice of British Railways. London, U.K.:A. & C. Black,1980.
[95]Dorigo M., Maniezzo V., Colorni A., Ant system:Optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,1996,26(1):29-41.
[96]Stutzle T., Hoos H. H. MAX-MIN ant system and local search for the traveling salesman problem[C]. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC,1997:309-314.
[97]Stutzle T., Hoos H. H. MAX-MIN ant system[J]. Future Gener. Comput. Syst.,2000, 16(9):889-914.
[98]Bocharnikov Y. V., Tobias A. M., Roberts C., Hillmansen S., Goodman C. J. Optimal driving strategy for traction energy saving on DC suburban railways[J]. IET Electr. Power Appl.,2007, 1(5):675-682.
[99]王白力.列车节能运行优化操纵的研究[J].西南交通大学学报,1994,29(3)：275-280.
[100]金炜东,靳蕃,李崇维等.列车优化操纵速度模式曲线生成的智能计算研究[J].铁道学报,1998,20(5)：47-52.
[101]冯晓云.模糊预测控制及其在列车自动驾驶中的应用研究[D].成都：西南交通大学博士学位论文,2001.
[102]石红国.列车运行过程仿真及优化研究[D].成都：西南交通大学博士学位论文,2006.
[103]余进.多目标列车运行过程优化及控制策略研究[D].成都：西南交通大学博士学位论文,2009.
[104]路飞.移动闭塞条件下地铁列车的运行优化[D].济南：山东大学博士学位论文,2007.
[105]付印平.列车追踪运行于节能优化建模及模拟研究[D].北京：北京交通大学博士学位论文,2009.
[106]IEEE STD 1474.1-2004, "IEEE Standard for Communications-Based Train Control (CBTC) Performance and Functional Requirements", IEEE Vehicular Technology Society, February 2005.
[107]IEEE STD 1698TM-2009, "IEEE Guide for the Calculation of Braking Distances for Rail Transit Vehicles", IEEE Vehicular Technology Society, November 2009.
[108]YOUNG-CHAN KIM, YOUNG-GER SEO. A study of the train running simulation for train propulsion system performance analysis[C]. The 7th International Conference on Power Electronics,2007,15(3):192-202.
[109]HUI-JEN CHUANG, CHAO-SHUN CHEN. Design of Optimal Coasting Speed for MRT Systems Using ANN Models[J]. IEEE Transactions on Industry Applications, 2009,45(6):2090-2097.
[110]刘炜,李群湛.地铁牵引仿真计算中的牵引策略研究[J].机车电传动,2006,1(1)：46-49.
[111]刘海东,毛保华.城市轨道交通列车节能问题及方案研究[J].交通运输系统工程与信息,2007,7(5)：68-73.
[112]李政,潘孟春,胡楷.城市轻轨再生制动能量吸收的仿真研究[J].系统仿真学报,2007,21(15)：4916-4919.
[113]荀径,宁滨,郜春海.列车追踪运行仿真系统的研究与实现[J].北京交通大学学报,2007,31(2)：34-37.
[114]薛艳冰,马大炜,王烈.列车牵引能耗计算方法[J].中国铁道科学,2007,28(3)：84-87.
[115]Francois Ruelland, Kamal Al-Haddad. Reducing Subway's Energy[C]. Proceedings of 2007 IEEE Canada Electrical Power Conference. Canada: IEEE Press,2007:261-267.
[116]李玉生,侯忠生.基于遗传算法的列车节能控制研究[J].系统仿真学报,2007,19(2)：384-387.
[117]李波.基于遗传算法的列车操纵曲线寻优[D].成都：西南交通大学,2007.
[118]陈荣武,诸昌钤,刘莉.基于CBTC的城市轨道交通列车能耗算法及仿真[J].计算机应用研究,2011,28(6)：2126-2129.
[119]陈荣武,诸昌钤,刘莉CBTC系统列车追踪间隔计算及优化[J].西南交通大学学报,2011,46(4)：579-585.
[120]冯晓云,何鸿云.列车优化操纵原则及其优化操纵策略的数学描述[J].机车电传动,2001,7(4)：13-16.
[121]崔世文,冯晓云.列车优化操纵与自动驾驶模式的研究与仿真[J].铁道机车车辆,2005,25(5)：9-12.
[122]龙胜,冯晓云,曲健伟等.地铁动车牵引特性设计[J].铁路计算机应用,2010：22-25.
[123]雷德明,严新平.多目标智能优化算法及其应用[M].北京：科学出版社,2009：3-10.
[124]玄光男,程润伟.遗传算法与工程优化[M].北京：清华大学出版社,2004：3-10.
[125]饶中.列车制动[M].北京：中国铁道出版社,2006：128-134.
[126]桂翔.城市轨道交通牵引计算仿真系统的研究和开发[D].北京：北京交通大学,2008.
[2]Hirao Y., Bond L. Development of a Universal Train Simulator(UTRAS) and Evaluation of Signaling System[J].TRRI,1995,336(4):180-185.
[3]Railroad and Transit Systems [EB/OL]. http://www.orthstar.com.
[4]Erofeyev E. Calculation of optimum train control using dynamic Programming mehtod[J].In Proceedings of Moscow Railway Engineering Institute(Trudy MIIT), Moscow,issue 811,1967:16-30.
[5]Petar K., Gurdial S. Minimum-Energy Control of a Traction Motor[J]. Automatic Control, IEEE Transactions on,1971,17(1):92-94.
[6]I.P.Milroy.Aspects of Automatic train control[D]. Anstralia:Loughborough University, 1980.
[7]Asnis A., Dmitruk A., Osmolovski N. Using the maximum principle to solve the problem of energy-optimal control of the motion of the trains[J]. Vychisl. Mat. Mat Fiz,1985,25(11):1644-1656.
[8]Golovitcher I. Train Control algorithm for energy consumption optimization[C]. In Proceedings of All-Union Railway Research Institute,Vestnik VNllZHT,vol.8 1982:18-23.
[9]Golovitcher I. An analytical method for optimum train control computation[C]. Proceedings of State Universities, Electro-mechanics (Izvestiya VUZov Seriya Electromehanika),1986:59-66.
[10]Goloviteher I. Control algorithms for automatic operation of rail vehicles[C]. Automated and Remote Contorl.Journal of Russian Academy of Science (Automatika I Telemekhanika),1986:118-126.
[11]Goloviteher I. Optimum control of electric locomotives with regenerative braking[C]. In Proceedings of Moscow Railway Engineering Institue (Trudy MIIT), Moseow, 1989:19-24.
[12]Golovitcher I. An analytical method for computation of optimum train speed profile considering variable efficiency of locomotive[C]. Proeeedings of State University.Electro-mechnaics (Izvestiya VUZov Seriya Electro-mehnaika), 1989:72-81.
[13]Eroefyev E., Golovitcher E. I., Shmdrik M., Akulov M., Solomatin A. Using computers for speed profile calculations and regime cards developing[J]. Electric and Diesel Fleet (Electricheskaja I Teplovoznaya Tiaga),1991,6(414):15-17.
[14]Gill D. C. and Goodman C. J. Computer-based optimization techniques for mass transit railway signalling design[J]. IEE Proceedings B:Electric Power Applications, 1992,139(3):261-275.
[15]Chang C. S., Du D. Improved optimisation method using genetic algorithm for mass transit signalling block-layout design[J], IEE Proceeding-Electr. Power Appl.,1998, 145 (3):266-272.
[16]Milroy I. P. Aspect of automatic train control[D]. Leicestershire. Loughborough University,1980.
[17]Lee D. H., Milroy I. P., Tyler K. Application of pontryagin's maximum principle to the semi-automatic control of rail vehicles[C]. Proceedings of Second Conference on Control Engineering, Newcastle,1982:233-236.
[18]Guan J. F., Yang H., Wirasinglhe S. C. Simultaneous optimization of transit line configuration and passenger line assignment[J]. Transportation research Part B,2006, 40(10):885-902.
[19]Asnis I. A., Dmitruk A. V., Osmolovskii N. P. Using the maximum principle to solve the problem energy-optimal control of the motion of the train[J]. Zh.Vychisl.Mat.Mat.Fiz,1985,25(11):1644-1656.
[20]Howlett P.G., Pudney P.J. Energy-efficient train control[M]. London:Springer Press, 1995.
[21]Miyamoto Shoji, Seiji Yasunobu, Ihara Hirokazu. Predictive fuzzy control and its application to automatic train operation systems[C]. Proceeding of the Anal of Fuzz Inf.,1987:59-72.
[22]Hiroyasu Oshima, Seiji Yasunobu, Shin-ichi Sekino. Automatic train operation system based on predictive fuzzy control[C]. Proceeding of the International workshop on Artifitial Intelligence for Industrial Applications,1998:485-489.
[23]Chang C. S., Sim S. S. Optimizing train movements through coast control using genetic algorithms[C]. Proceedings of Electric Power Application, IEEE,1997:65-73.
[24]Chang C. S., Xu D.Y. Differential evolution based tuning of fuzzy automatic train operation for mass rapid transit system[C]. Proceedings of Eleetric Power Applieation, IEEE,2000,147(3):206-212.
[25]Han S. H., Byen Y. S., Baek J. H., et. An optimal automatic train operation (ATO) control using genetic algorithm (GA)[C]. Proceedings of the IEEE Region 10 Conference, TENCON 99,1999 (1):360-362.
[26]Wong K. K., Ho T. K. Dynamic coast control of train movement with genetic algorithm[C]. International Journal of Systems Science,2004:835-846.
[27]Wong K. K., Ho T. K. Coast control for mass rapid transit railways with searehing methods[C]. IEE Proc-Electr.Power Appl.,2004,151(3):365-376.
[28]Chen R., Guo J. Development of the new CBTC system simulation and performance analysis[C]. Proceedings of 12th International Conference on Computer System Design and Operation in Railways and Other Transit Systems. Beijing:WIT Press, 2010:497-507.
[29]刘云.列车运行仿真系统的建模与实现[J].铁道学报,1995,17(专辑)：20-26.
[30]赵明,汪希时.移动自动闭塞条件下列车追踪运行控制研究[J].铁道学报,1997,19(3)：61-68.
[31]郭佑民,王志伟,武福等.列车操纵与运行仿真系统[J].兰州铁道学院学报,2002,21(6)：125-127.
[32]毛保华,何天健,袁振洲,刘海东,赵立宁,陈志英.通用列车运行模拟软件系统研究[J].铁道学报,2000,22(1)：1-6.
[33]张波,马大伟.中高速列车共线运行的仿真研究[J].中国铁道科学,2003,24(3)：119-124.
[34]刘炜,李群湛.地铁牵引仿真计算中的牵引策略研究[J].机车电传动,2006,1(1)：46-49.
[35]唐涛,郜春海,李开成,燕飞.基于通信的列车运行控制技术发展战略探讨[J].都市快轨交通,2005,18(6)：25-29.
[36]燕飞,唐涛.IEC61508及其在铁路安全相关系统研制开发中的应用研究[J].铁道学报.2005,27(6)：124-128.
[37]郜春海,燕飞,唐涛.轨道交通信号系统安全评估方法研究[J].中国安全科学.2005,15(10)：74-79.
[38]陈荣武,刘莉,诸昌钤.基于CBTC的列车自动驾驶控制算法[J].计算机应用,2007,27(11)：2649-2651.
[39]王卓,王艳辉,贾利民等.基于ANFIS的高速列车制动控制仿真研究[J].中国铁道学报,2005,27(3)：113-117.
[40]Kirk, P.M. Automated personal transit control systems[C]. American Society of Mechanical Engineers (Paper),1973
[41]McAulay, Alastair D. Progress in signaling for track guided systems, American Society of Civil Engineers[J]. Transportation Engineering Journal,1975,101(4): 621-637.
[42]Hinman E. J., Pitts G. L. Practical headway limitations for personalised automated-transit systems[C]. Proceedings of the Institution of Electrical Engineers, 1975,122(7):756-759.
[43]Renfrew R. M. Technology selection and development for an intermediate capacity transit system [C]. Conference Record - IAS Annual Meeting (IEEE Industry Applications Society),1977:939-945.
[44]Bousman, William G., Winkler, Diana J. Application of the moving-block analysis[C]. Collection of Technical Papers-AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Pt 2,1981:755-763.
[45]Watanabe, Ikuo, Takashige, Tetuo. Moving block system with continuous train detection utilizing train shunting impedance of track circuit[J]. Quarterly Report of RTRI (Railway Technical Research Institute),1989,3(4):190-197.
[46]Gill D. C., Goodman C. J. Computer-based optimisation techniques for mass transit railway signalling design[C]. IEE Proceedings B:Electric Power Applications,1992, 139(3):261-275.
[47]Pascoe R. D., Rossi C., Savio S., Sciullo, G. Comparison between fixed and moving block signalling systems performance by digital simulation[C]. Proceedings of the International Conference on Computer Aided Design, Manufacture and Operation in the Railway and other Advaced Mass Transit, Computers in Railways: Management, 1992.
[48]Hill R., John, Bond, Louisa J. Modelling moving-block railway signalling systems using discrete-event simulation[C]. Proceedings of the IEEE/ASME Joint Railroad Conference,1995:105-111.
[49]Hirao, Yuji, Hasegawa, Yutaka. Development of a universal train simulator (UTRAS) and evaluations of signalling systems[C]. Quarterly Report of RTRI (Railway Technical Research Institute) [J].1995,36(4):180-185.
[50]Lockyear M. J. Changing track moving-block railway signaling, IEE Review[J].1996, 42(1):21-25.
[51]Gill D.C. Impact of moving block train control on heavy metros[C]. IEE Conference Publication,1998:235-242.
[52]Lockyear M. J. Application of a transmission based moving block Automatic Train Control system on Docklands Light Railway[C]. IEE Conference Publication,1998: 51-61.
[53]Ning, B. Absolute braking and relative distance braking-train operation control modes in moving block systems[C]. Proceedings of the International Conference on Computer Aided Design, Manufacture and Operation in The Railway and Other Advanced Mass Transit Systems,1998:991-1001.
[54]Ho M. T. K. Multi-train movement simulator with moving block signaling[C]. Proceedings of the International Conference on Computer Aided Design, Manufacture and Operation in The Railway and Other Advanced Mass Transit Systems,1998: 783-792.
[55]Nakamura H. Analysis of minimum train headway on a moving block system by genetic algorithm[C]. Proceedings of the International Conference on Computer Aided Design, Manufacture and Operation in The Railway and Other Advanced Mass Transit Systems,1998:1013-1022.
[56]Ji Jialun. Headway and carrying capacity of railway moving block system', Proceedings of the Conference on Traffic and Transportation Studies, ICTTS 2000, 2000:279-284.
[57]Whitwam F. Moving block does work. Railway Gazette International[J].2001, 157(11).
[58]Gillan D. Distance-to-go signalling rivals moving block benefits[J]. Railway Gazette International,2001,157(10):689-692.
[59]Zafar, Nazir A., Araki, Keijiro. Formalizing moving block railway interlocking system for directed network[C]. Research Reports on Information Science and Electrical Engineering of Kyushu University, September 2003,8(2):109-114.
[60]Lindqvist L., Jadhav R. Application of communication based Moving Block systems on existing metro lines[C]. WIT Transactions on the Built Environment, Computers in Railways Ⅹ: Computer System Design and Operation in the Railway and Other Transit Systems,2006:391-400.
[61]Mazzarello Maura, Ottaviani Ennio. A traffic management system for real-time traffic optimisation in railways[J]. Transportation Research Part B:Methodological,2007, 41(2):246-274.
[62]纪加伦,杨肇夏.移动闭塞方式下列车运行组织及区间通过能力计算方法的探讨,铁道学报,1992,14(01)：38-46.
[63]刘海东,袁振洲,朱钰,李巍屹.移动自动闭塞仿真系统列车追踪过程的探讨,交通与计算机,1999,17(01)：46-48.
[64]刘云,张振江.MAS下区间列车追踪的研究和仿真[J].系统仿真学报,1999,11(1)：29-54.
[65]张勇,赵明,汪希时.基于移动自动闭塞条件的列车运行仿真研究[J].系统仿真学报,1999,11(3)：195-204.
[66]宋瑞,何世伟,朱松年.铁路系统线路通过能力分析模拟模型的研究[J].铁道学报,1999,21(2)：2-7.
[67]刘英.移动自动闭塞条件下的能力利用分析[D].北京：北方交通大学博士学位论文,1998.
[68]刘海东,毛保华,何天健,丁勇,王璇.不同闭塞方式下城轨列车追踪运行过程及其仿真系统的研究[J],铁道学报,2003,8(2)：109-114.
[69]路飞,宋沐民,田国会.地铁列车的追踪间隔控制模型与仿真[J],信息与控制,2006,35(5)：641-646.
[70]金娟,杨梅,王长林.基于移动闭塞原理的地铁列车线路通过能力的研究[J],铁路计算机应用,2008,17(6)：7-10.
[71]李本刚CBTC移动闭塞和准移动闭塞列车运行安全间隔时间的计算[J],铁路通信信号工程技术,2008,5(6)：8-11.
[72]Chang C.S., Sim, S.S. Optimising train movements through coast control using genetic algorithm[J]. IEE Proceeding of Electric Power Applications,1997,144(1):65-73.
[73]Hwang H. S. Control strategy for optimal compromise between trip time and energy consumption in a high-speed railway [J]. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans,1998,28(6):791-802.
[74]Chang C. S., Xu D. Y., Quek H. B. Pareto-optimal set based multiobjective tuning of fuzzy automatic train operation for mass transit system[J]. IEE Proceedings:Electric Power Applications,1999,146(5):577-583.
[75]Chang C. S., Xu D.Y. Differential evolution based tuning of fuzzy automatic train operation for mass rapid transit system, IEE Proceedings:Electric Power Applications[J],2000,147(3):206-212.
[76]Khmelnitsky E. On an Optimal control problem of train operation[J], IEEE Transactions on Automatic Control,2000,45(7):1257-1266.
[77]Cheng J., Davydova Y., Howlett P., Pudney P. Optimal driving strategies for a train journey with non-zero track gradient and speed limits[J]. IMA Journal of Mathematics Applied in Business and Industry,1999,10(2):89-115.
[78]Cheng J., Howlett P. Application of critical velocities to the minimization of fuel consumption in the control of trains [J]. Automatical,1992,28(1):165-169.
[79]Van Breusegem V., Campion G., Bastin G. Traffic modeling and state feedback control for metro lines[J]. IEEE Transactions on Automatic Control,1991,36(7): 770-784.
[80]Haitl R. F., Sethi S. P., Vickson R. G. Survey of the maximum principles for optimal control problems with state constraints [J]. SIAM Review,1995,37(2):181-218.
[81]Gen M., Runwei C. Genetic algorithms and engineering design[M], New York:Wiley, 1997.
[82]Yu J., Qian Q. Q., He Z. Genetic algorithms with application to optimize high speed train ATO[C], International Conference on Transportation Engineering 2007, ICTE 2007,2007:2512-2517.
[83]Wegele S., Schnieder E. Automated dispatching of train operations using genetic algorithms Advances in Transport[C]. Ninth International Conference on Computers in Railways, COMPRAIL IX,2004:775-784.
[84]Hani Y., Chehade H. Amodeo L., Yalaoui F. Simulation based optimization of a train maintenance facility model using genetic algorithms[C]. Proceedings of ICSSSM'06, 2006 International Conference on Service Systems and Service Management,2007: 513-518.
[85]Chang C. S., Wand W., Liew A. C., Wen F. S., Srinivasan D. Genetic algorithm based bicriterion optimisation for traction substations in DC railway system[C]. Proceedings of the IEEE Conference on Evolutionary Computation,1995:11-16.
[86]Cox E. Fuzzy modeling and genetic algorithms for data mining and exploration[M], Morgan: Caufmann,2005.
[87]Ke B. R., Chen N. Signalling blocklayout and strategy of train operation for saving energy in mass rapid transit systems[J]. IEE Proc.-Electr. Power Appl,2005,152(2): 129-140.
[88]Ke B. R., Signaling system for saving energy on mass rapid transit systems[D], Taipei: Ph.D. dissertation, Nat. Taiwan Univ. Sci. Technol.,2006.
[89]Dorigo M., Stutzle T., Ant Colony Optimization. Cambridge[M], MA:MIT Press, 2004.
[90]Dorigo M., Gambardella L. M. Ant colony system: A cooperative leaning approach to the traveling salesman problem[J]. IEEE Trans. Evol. Comput.,1997,1(1):53-66.
[91]Maniezzo V., Colorni A. The ant system applied to the quadratic assignment problem. [J] IEEE Trans. Knowl. Data Eng.,1999,11(5):769-778.
[92]Merkle D., Middendorf M., Schmeck H. Ant colony optimization for resource-constrained project scheduling[J]. IEEE Transactions on Evolutionary Computation,2002,6(4):333-346.
[93]Dorigo M., Birattari M., Stutzle T. Ant colony optimization. IEEE Comput. Intell. Mag.[J],2006,1(4):28-39.
[94]Nock O. S., Railway Signalling: A Treatiose on the Recent Practice of British Railways. London, U.K.:A. & C. Black,1980.
[95]Dorigo M., Maniezzo V., Colorni A., Ant system:Optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,1996,26(1):29-41.
[96]Stutzle T., Hoos H. H. MAX-MIN ant system and local search for the traveling salesman problem[C]. Proceedings of the IEEE Conference on Evolutionary Computation, ICEC,1997:309-314.
[97]Stutzle T., Hoos H. H. MAX-MIN ant system[J]. Future Gener. Comput. Syst.,2000, 16(9):889-914.
[98]Bocharnikov Y. V., Tobias A. M., Roberts C., Hillmansen S., Goodman C. J. Optimal driving strategy for traction energy saving on DC suburban railways[J]. IET Electr. Power Appl.,2007, 1(5):675-682.
[99]王白力.列车节能运行优化操纵的研究[J].西南交通大学学报,1994,29(3)：275-280.
[100]金炜东,靳蕃,李崇维等.列车优化操纵速度模式曲线生成的智能计算研究[J].铁道学报,1998,20(5)：47-52.
[101]冯晓云.模糊预测控制及其在列车自动驾驶中的应用研究[D].成都：西南交通大学博士学位论文,2001.
[102]石红国.列车运行过程仿真及优化研究[D].成都：西南交通大学博士学位论文,2006.
[103]余进.多目标列车运行过程优化及控制策略研究[D].成都：西南交通大学博士学位论文,2009.
[104]路飞.移动闭塞条件下地铁列车的运行优化[D].济南：山东大学博士学位论文,2007.
[105]付印平.列车追踪运行于节能优化建模及模拟研究[D].北京：北京交通大学博士学位论文,2009.
[106]IEEE STD 1474.1-2004, "IEEE Standard for Communications-Based Train Control (CBTC) Performance and Functional Requirements", IEEE Vehicular Technology Society, February 2005.
[107]IEEE STD 1698TM-2009, "IEEE Guide for the Calculation of Braking Distances for Rail Transit Vehicles", IEEE Vehicular Technology Society, November 2009.
[108]YOUNG-CHAN KIM, YOUNG-GER SEO. A study of the train running simulation for train propulsion system performance analysis[C]. The 7th International Conference on Power Electronics,2007,15(3):192-202.
[109]HUI-JEN CHUANG, CHAO-SHUN CHEN. Design of Optimal Coasting Speed for MRT Systems Using ANN Models[J]. IEEE Transactions on Industry Applications, 2009,45(6):2090-2097.
[110]刘炜,李群湛.地铁牵引仿真计算中的牵引策略研究[J].机车电传动,2006,1(1)：46-49.
[111]刘海东,毛保华.城市轨道交通列车节能问题及方案研究[J].交通运输系统工程与信息,2007,7(5)：68-73.
[112]李政,潘孟春,胡楷.城市轻轨再生制动能量吸收的仿真研究[J].系统仿真学报,2007,21(15)：4916-4919.
[113]荀径,宁滨,郜春海.列车追踪运行仿真系统的研究与实现[J].北京交通大学学报,2007,31(2)：34-37.
[114]薛艳冰,马大炜,王烈.列车牵引能耗计算方法[J].中国铁道科学,2007,28(3)：84-87.
[115]Francois Ruelland, Kamal Al-Haddad. Reducing Subway's Energy[C]. Proceedings of 2007 IEEE Canada Electrical Power Conference. Canada: IEEE Press,2007:261-267.
[116]李玉生,侯忠生.基于遗传算法的列车节能控制研究[J].系统仿真学报,2007,19(2)：384-387.
[117]李波.基于遗传算法的列车操纵曲线寻优[D].成都：西南交通大学,2007.
[118]陈荣武,诸昌钤,刘莉.基于CBTC的城市轨道交通列车能耗算法及仿真[J].计算机应用研究,2011,28(6)：2126-2129.
[119]陈荣武,诸昌钤,刘莉CBTC系统列车追踪间隔计算及优化[J].西南交通大学学报,2011,46(4)：579-585.
[120]冯晓云,何鸿云.列车优化操纵原则及其优化操纵策略的数学描述[J].机车电传动,2001,7(4)：13-16.
[121]崔世文,冯晓云.列车优化操纵与自动驾驶模式的研究与仿真[J].铁道机车车辆,2005,25(5)：9-12.
[122]龙胜,冯晓云,曲健伟等.地铁动车牵引特性设计[J].铁路计算机应用,2010：22-25.
[123]雷德明,严新平.多目标智能优化算法及其应用[M].北京：科学出版社,2009：3-10.
[124]玄光男,程润伟.遗传算法与工程优化[M].北京：清华大学出版社,2004：3-10.
[125]饶中.列车制动[M].北京：中国铁道出版社,2006：128-134.
[126]桂翔.城市轨道交通牵引计算仿真系统的研究和开发[D].北京：北京交通大学,2008.