城市轨道交通系统能耗影响因素的量化分析
|
摘要
城市轨道交通是城市公共交通体系的重要组成部分,具有运量大、速度快、准点率高、占地少、污染小等特点,可较好地满足城市内部与城郊之间大规模的出行需求。随着我国城市轨道交通网络建设进程的加快,系统运营能耗快速增长,导致运营成本激增。在能源相对稀缺、环境保护日益受到重视的我国,研究城市轨道交通系统能耗结构与影响因素,为系统规划设计与组织管理的节能优化提供参考,对于促进轨道交通可持续发展具有重要的现实意义。
在借鉴国内外已有研究成果的基础上,论文主要从固定/移动设备技术条件、运输组织模式两个方面分析城市轨道轨道交通能耗影响因素的灵敏度,基于相关规范标准和能耗统计数据进一步研究城市轨道交通能耗构成与影响因素的关联度。论文的具体工作包括以下几个方面：
(1)通过总结既有研究成果及分析统计数据,系统研究了城市轨道交通系统能耗构成。城市轨道交通系统能耗主要由列车运行能耗和车站运营能耗两部分构成,其中,列车运行能耗约占总能耗的50%,基础设施和运输组织对其影响显著,是本文研究的重点。
(2)总结提出了城市轨道交通系统列车运行能耗和车站运营能耗的估算思路与框架。针对列车实际的运行过程,总结了两种城市轨道交通列车运行能耗的计算思路：即基于能耗曲线的测算思路和基于牵引做功的估算思路,分别可用于能耗曲线已知和未知的情形下的列车运行能耗估算。另外,对车站运营能耗进行了初步的总结与分析。
(3)定量分析了列车选型和基础设施中坡道、曲线以及站间距等要素对列车运行能耗的影响。研究结果表明：列车选型对运行能耗的影响与列车目标速度密切相关,目标速度和其他条件相同的情况下,不同列车的能耗差异可达19%；设置节能坡可显著降低列车的运行能耗,利用纵断面的凸型条件设置10‰的节能坡,可节约6.7%的列车运行能耗；曲线半径对列车运行能耗的也有较大影响,小曲线半径导致列车运行能耗额外增加,且增加量与列车运行速度呈正相关性,最大可达30%以上,当曲线半径在800m以上时,对列车运行能耗的影响几乎可忽略；在相同停站次数的情况下,列车单位距离运行能耗随着站间距的增加而递减,站间距从400m至2600m平均每增加200m,单位距离运行能耗降低约13%,当站间距大于2600m时,对运行能耗的影响不再显著。
(4)定量分析了运输组织模式中技术速度、停站方案、满载率及编组方案对城市轨道交通列车运行能耗的影响。研究结果表明：列车运行能耗与技术速度的关系呈U形,技术速度为30km/h时,列车运行能耗最低,大于30km/h时,技术速度每增加10%,能耗则增加约15%。在其他条件一定的情况下,列车运行能耗随满载率提高增加缓慢,但单位旅客周转量的运行能耗随着满载率的上升呈显著下降趋势,且在技术速度较大时尤为敏感,计算实验案例表明城市轨道交通列车节能满载率在45%左右。
(5)在运输组织模式中,选择合理、可持续的目标速度不但要考虑列车的运行时分,还应考虑列车运行过程中的能耗。因此,本文基于模糊综合评判模型,提出了同时考虑能源消耗和运行时分的列车目标速度选择方法,并通过案例验证了模型的有效性。
(6)提出了轨道交通能耗构成及其影响因素关联度的概念,通过构建灰色关联层次分析模型研究城市轨道交通系统能耗构成及影响因素的关联度。研究结果表明：列车自重、牵引动力传递效率、车载辅助设备对系统总能耗影响最为显著;列车运行控制技术如速度均衡性和预判距离、编组方案、技术速度对系统总能耗也有重要影响；最大正线坡度、车站照明设备能耗、平均停站间距、车站动力设备对系统能耗有一定影响；最小曲线半径对系统能耗影响相对轻微。相关结论可为制定城市轨道交通系统宏观节能管理策略提供参考。
Characterized by high volume traffic, high rapid, high delay quality, using less land and lower pollution, urban rail transit plays a critical role in the public transportation system. With the rapidly growing economy and social developments in China, urban rail transportation has expanded drastically and the demands on energy consumption escalate accordingly. The huge cost makes efficient energy utilization in urban railway transportation an urgent and desirable issue. It is also benefit to the sustainable development of the urban rail transit system.
This paper mainly focuses on the impacts of train characteristics and track profile design on the energy consumption of the urban rail transit system, and then further analysis the structure importance of the whole system energy consumption. The main contents include:
(1) By summarizing the exit research results and analyzing the statistical data, the energy consumption of urban rail transit system contains two parts:train movement energy consumtion and station operating energy onsumption, the former of which is about 50%. The infrastructure and transport organization mode play a significant role on the train movement enegy consumption, which is the key point of this paper.
(2) The paper proposes the estimation methods of train movement energy consumption and station facility energy consumption. As for train movement energy consumption, a model based on the meritorious electric current curve is developed. Additional, another method by transforming the power to the energy is proposed as well to estimate the energy consumption of train movement when the meritorious electric current curve is unavailable.
(3) Quantity analysis the impacts of train characteristics and track profile scheme on train movement energy consumption, according to analytical analysis and simulation case studies. The results are concluded as follows:the impact of train characteristics on its energy consumption is related to the train target speed, while the difference of energy consumption among different trains is up to 19% even with the same target speed; Energy-optimized track profiles is always designed at the advantage sections which saves 6.7% energy consumption of train movement. Curve radius of the track profile also effects the energy consumption greatly, the smaller the curve radius is, the much more the energy will be consumed, furthermore. The increment of the energy consumption is positive correlated to the train target speed, which might up to 30%. The impact of curve radius on the train movement energy consumption can be neglected when the curve radius is larger than 800 meters. With the same stop scheme, train movement energy consumption per kilometers is decreased with the increment of inter-station distance, and the energy consumption is reduced by 13% for every 200 meters increment of the inter-station distance, from 400 meters to 2600 meters. The impact of inter-station distance on the train movement energy consumption can be neglected when the inter-station distance is larger than 2600 meters.
(4) Quantity analysis the impacts of transportation operation mode on train movement energy consumption, according to analytical analysis and simulation case studies. The results are concluded as follows:the absolute train energy consumption is slightly increased with the passenger load-ratio. The energy consumption per passenger, however, is drastically decreased with the increment of passenger load-ratio. It is more sensitive when the technical speed is higher and the passenger load-ratio is relative lower. This decrease of energy consumption per passenger is becoming relative slight when the passenger load-ratio is larger than 60%. The relationship between the energy consumption of train movement and its technical speed is in the shape of U. The energy consumption of train movement is minimal when the technical speed is at 30 km/h. While the energy consumption is increased every 15% when there is a 10% increment of technical speed.
(5) Selecting a reasonable and sustainable target speed should consider train operating time and train movemet energy consumption in the transport organization mode. Thus the paper propose a mothed of selecting target speed considering both train operating time and train movement energy consumption based on fuzzy comprehensive evaluation model, and a case in conducted to demonstrate the effectiveness of the method.
(6) Analysis the structure importance of energy consumption in urban rail transit system and the correlative degree of the energy factors, by applying the combination of Grey correlative degree and Analytic Hierarchy Process (AHP). The results are described as follows:Train weight, traction power transmission efficiency, train auxiliary device is the most important factors which impact the energy consumption of the whole urban rail transit system drastically; and train control strategy such as train speed uniformity and looking forward distance, train formation as well as train technical speed impacts the system energy consumption greatly; The maximum grades of the track profile, light devices in the station, average inter-station distance and the station power equipment is also impacts on the system energy consumption; Minimum radius of the track curve also impacts on the system energy consumption of urban railways, which is relatively slight.
在借鉴国内外已有研究成果的基础上,论文主要从固定/移动设备技术条件、运输组织模式两个方面分析城市轨道轨道交通能耗影响因素的灵敏度,基于相关规范标准和能耗统计数据进一步研究城市轨道交通能耗构成与影响因素的关联度。论文的具体工作包括以下几个方面：
(1)通过总结既有研究成果及分析统计数据,系统研究了城市轨道交通系统能耗构成。城市轨道交通系统能耗主要由列车运行能耗和车站运营能耗两部分构成,其中,列车运行能耗约占总能耗的50%,基础设施和运输组织对其影响显著,是本文研究的重点。
(2)总结提出了城市轨道交通系统列车运行能耗和车站运营能耗的估算思路与框架。针对列车实际的运行过程,总结了两种城市轨道交通列车运行能耗的计算思路：即基于能耗曲线的测算思路和基于牵引做功的估算思路,分别可用于能耗曲线已知和未知的情形下的列车运行能耗估算。另外,对车站运营能耗进行了初步的总结与分析。
(3)定量分析了列车选型和基础设施中坡道、曲线以及站间距等要素对列车运行能耗的影响。研究结果表明：列车选型对运行能耗的影响与列车目标速度密切相关,目标速度和其他条件相同的情况下,不同列车的能耗差异可达19%；设置节能坡可显著降低列车的运行能耗,利用纵断面的凸型条件设置10‰的节能坡,可节约6.7%的列车运行能耗；曲线半径对列车运行能耗的也有较大影响,小曲线半径导致列车运行能耗额外增加,且增加量与列车运行速度呈正相关性,最大可达30%以上,当曲线半径在800m以上时,对列车运行能耗的影响几乎可忽略；在相同停站次数的情况下,列车单位距离运行能耗随着站间距的增加而递减,站间距从400m至2600m平均每增加200m,单位距离运行能耗降低约13%,当站间距大于2600m时,对运行能耗的影响不再显著。
(4)定量分析了运输组织模式中技术速度、停站方案、满载率及编组方案对城市轨道交通列车运行能耗的影响。研究结果表明：列车运行能耗与技术速度的关系呈U形,技术速度为30km/h时,列车运行能耗最低,大于30km/h时,技术速度每增加10%,能耗则增加约15%。在其他条件一定的情况下,列车运行能耗随满载率提高增加缓慢,但单位旅客周转量的运行能耗随着满载率的上升呈显著下降趋势,且在技术速度较大时尤为敏感,计算实验案例表明城市轨道交通列车节能满载率在45%左右。
(5)在运输组织模式中,选择合理、可持续的目标速度不但要考虑列车的运行时分,还应考虑列车运行过程中的能耗。因此,本文基于模糊综合评判模型,提出了同时考虑能源消耗和运行时分的列车目标速度选择方法,并通过案例验证了模型的有效性。
(6)提出了轨道交通能耗构成及其影响因素关联度的概念,通过构建灰色关联层次分析模型研究城市轨道交通系统能耗构成及影响因素的关联度。研究结果表明：列车自重、牵引动力传递效率、车载辅助设备对系统总能耗影响最为显著;列车运行控制技术如速度均衡性和预判距离、编组方案、技术速度对系统总能耗也有重要影响；最大正线坡度、车站照明设备能耗、平均停站间距、车站动力设备对系统能耗有一定影响；最小曲线半径对系统能耗影响相对轻微。相关结论可为制定城市轨道交通系统宏观节能管理策略提供参考。
Characterized by high volume traffic, high rapid, high delay quality, using less land and lower pollution, urban rail transit plays a critical role in the public transportation system. With the rapidly growing economy and social developments in China, urban rail transportation has expanded drastically and the demands on energy consumption escalate accordingly. The huge cost makes efficient energy utilization in urban railway transportation an urgent and desirable issue. It is also benefit to the sustainable development of the urban rail transit system.
This paper mainly focuses on the impacts of train characteristics and track profile design on the energy consumption of the urban rail transit system, and then further analysis the structure importance of the whole system energy consumption. The main contents include:
(1) By summarizing the exit research results and analyzing the statistical data, the energy consumption of urban rail transit system contains two parts:train movement energy consumtion and station operating energy onsumption, the former of which is about 50%. The infrastructure and transport organization mode play a significant role on the train movement enegy consumption, which is the key point of this paper.
(2) The paper proposes the estimation methods of train movement energy consumption and station facility energy consumption. As for train movement energy consumption, a model based on the meritorious electric current curve is developed. Additional, another method by transforming the power to the energy is proposed as well to estimate the energy consumption of train movement when the meritorious electric current curve is unavailable.
(3) Quantity analysis the impacts of train characteristics and track profile scheme on train movement energy consumption, according to analytical analysis and simulation case studies. The results are concluded as follows:the impact of train characteristics on its energy consumption is related to the train target speed, while the difference of energy consumption among different trains is up to 19% even with the same target speed; Energy-optimized track profiles is always designed at the advantage sections which saves 6.7% energy consumption of train movement. Curve radius of the track profile also effects the energy consumption greatly, the smaller the curve radius is, the much more the energy will be consumed, furthermore. The increment of the energy consumption is positive correlated to the train target speed, which might up to 30%. The impact of curve radius on the train movement energy consumption can be neglected when the curve radius is larger than 800 meters. With the same stop scheme, train movement energy consumption per kilometers is decreased with the increment of inter-station distance, and the energy consumption is reduced by 13% for every 200 meters increment of the inter-station distance, from 400 meters to 2600 meters. The impact of inter-station distance on the train movement energy consumption can be neglected when the inter-station distance is larger than 2600 meters.
(4) Quantity analysis the impacts of transportation operation mode on train movement energy consumption, according to analytical analysis and simulation case studies. The results are concluded as follows:the absolute train energy consumption is slightly increased with the passenger load-ratio. The energy consumption per passenger, however, is drastically decreased with the increment of passenger load-ratio. It is more sensitive when the technical speed is higher and the passenger load-ratio is relative lower. This decrease of energy consumption per passenger is becoming relative slight when the passenger load-ratio is larger than 60%. The relationship between the energy consumption of train movement and its technical speed is in the shape of U. The energy consumption of train movement is minimal when the technical speed is at 30 km/h. While the energy consumption is increased every 15% when there is a 10% increment of technical speed.
(5) Selecting a reasonable and sustainable target speed should consider train operating time and train movemet energy consumption in the transport organization mode. Thus the paper propose a mothed of selecting target speed considering both train operating time and train movement energy consumption based on fuzzy comprehensive evaluation model, and a case in conducted to demonstrate the effectiveness of the method.
(6) Analysis the structure importance of energy consumption in urban rail transit system and the correlative degree of the energy factors, by applying the combination of Grey correlative degree and Analytic Hierarchy Process (AHP). The results are described as follows:Train weight, traction power transmission efficiency, train auxiliary device is the most important factors which impact the energy consumption of the whole urban rail transit system drastically; and train control strategy such as train speed uniformity and looking forward distance, train formation as well as train technical speed impacts the system energy consumption greatly; The maximum grades of the track profile, light devices in the station, average inter-station distance and the station power equipment is also impacts on the system energy consumption; Minimum radius of the track curve also impacts on the system energy consumption of urban railways, which is relatively slight.
引文
[1]Acampora G, Landi C, Lusio M, et al. Optimization of energy consumption in a railway traction system [C]. International Symposium on Power Electronics, Electrical Drives, Automation and Motion,2006.
[2]Albrecht T. Energy-efficient train operation in Hansen [M]. Germany: Railway Timetable & Traffic, Eurailpress/DVV Media, Eurail Press,2008.
[3]Albrecht T. The influence of anticipating train driving on the dispatching process in railway conflict situations [J]. Networks and Spatial Economics,2009,9(l):85-101.
[4]Andersson E, Lukaszewicz P. Energy consumption and related air pollution for Scandinavian electric passenger train [R]. Stockholm, Sweden:Department of Aeronautical and Vehicle Engineering Royal Institute of Technology, KTH,2006.
[5]Anthony R N. Planning and control systems: a framework for analysis [D]. Boston: Harvard University,1965.
[6]Assisa W O, Milani B E A. Generation of optimal schedules for metro lines using model predictive control [J]. Automatic,2004,40(8): 1397-1404.
[7]Bai Y, Mao B H, Zhou F M, et al. An onboard optimal control system for freight trains [C]. The Sixth International Conference on Traffic and Transportation Studies,2008.
[8]Bai Y, Mao B H, Zhou F M, et al. Energy-efficient driving strategy for freight trains based on power consumption analysis [J]. Journal of Transportation Systems Engineering and Information Technology,2009,9(3):43-50.
[9]Blum J, Eskandarian A. Enhancing intelligent agent collaboration for flow optimization of railroad traffic [J]. Transportation Research Part A,2002,36(10):919-930.
[10]Bocharnikov Y V, Tobias A M, Roberts C, et al. Optimal driving strategy for traction energy saving on DC suburban railways [J]. Electric Power Applications,2007,1(5):675-682.
[11]Brerma M, Foiadelli F, Tironi E, et al. Ultra capacitors application for energy saving in subway transportation systems [C]. International Conference on Clean Elecrical Power, 2007.
[12]Chang C S, Chua C S, Quek H B. Development of train movement simulator for analysis and optimization of railway signaling system [C]. International Conference on Developments in Mass Transit Systems,1998.
[13]Chang C S, Sim S S. Optimizing train movements through coast control using genetic algorithms [J]. Electric Power Applications,1997,144(1):65-73.
[14]Chang C S, Xu D Y. Differential evolution based tuning of fuzzy automatic train operation for mass transit system [J]. Electric Power Applications,2000,147(3):206-212.
[15]Chen J F, Lin R L, Liu Y C. Optimization of an MRT train schedule:reducing maximum traction power by using genetic algorithms [J]. Power Systems,2005,20(3):1366-1372.
[16]Cheng J X, Howlett P G. A note on the calculation of optimal strategies for the minimization of fuel consumption in the control of trains [J]. Automatic Control,1993, 38(11):1730-1734.
[17]Cheng J X, Howlett P G. Application of critical velocities to the minimization of fuel consumption in the control of trains [J]. Automatic,1992,28(1):165-169.
[18]Cheng Y. Hybrid simulation for resolving resource conflicts in train traffic rescheduling [J]. Computers in Industry,1998,35(3):233-246.
[19]Corbo P, Corcione F E, Migliardini F, et al. Energy management in fuel cell power trains [J]. Energy Conversion and Management,2006,47(18-19):3255-3271.
[20]Danziger N H. Energy optimization for rail public transit system of Washington DC [J]: Transportation Research Record,1975,15(4):31-39
[21]Ding Y, Mao B H, Liu H D, et al. Train movement simulation system for saving energy [C]. The Fourth International Conference on Traffic and Transportation Studies,2004.
[22]Ding Y, Zhou F M, Bai Y, et al. A correction model of loaded train's grade resistance calculation [C]. The Fifth Advanced Forum on Transportation of China,2009.
[23]EC. Methodology for calculating transport emissions and energy consumption [R]. Luxemburg: European Commission (EC),1999.
[24]Effati S, Roohparvar H. The minimization of the fuel costs in the train transportation [J]. Applied Mathematics Computer,2006,175(2):1415-1431.
[25]Fay A. A fuzzy knowledge-based system for railway traffic control [J]. Engineering Applications of Artificial Intelligence,2000,13:719-729.
[26]Federici M, Ulgiati S, Basosi R. Air versus terrestrial transport modalities:an energy and environmental comparison [J]. Energy,2009,34(10):1493-1503.
[27]Fong K F, Hanby V I, Chow T T. Optimization of MVAC system for energy management by evolutionary algorithm [J]. Facilities,2003,21(10):223-232.
[28]Fu Y P, Gao Z Y, Li K P. Optimization method of energy saving train operation for railway network [J]. Journal of Transportation Systems Engineering and Information Technology, 2009,9(4):90-96.
[29]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.
[30]Han S H, Byen Y S, Baek J H, et al. An optimal automatic train operation (ATO) control using genetic algorithm (GA) [J]. Tencon,1999,25(2):67-71.
[31]Helms H. Fuel saving by light-weighting for European articulated trucks [R]. Heidelberg, Brussels: Insititute for Energy and Environmental Research (IFEU),2005.
[32]Heung T H, Ho T K. Hierarchical fuzzy logic traffic control at a road junction using genetic algorithms [C]. International Conference of Fuzzy Systems,1998.
[33]Hoc H, Polis M, Haurie A. Reducing energy consumption through trajecory optimization for a metro network [J]. Automatic Control,1975,20(5):590-595.
[34]Hong W H, Kim S D. A study on the energy consumption unit of subway stations in Korea [J]. Building and Environment,2004,39(12):1497-1503.
[35]Howlett P G, Pudney P J. Energy-efficient train control [M]. London:Springer Press,1995.
[36]Howlett P G. An optimal strategy for the control of a train [J]. Australian Mathematics Society, Series B,1990,31(4): 454-471.
[37]Howlett P G. Optimal strategies for the control of a train [J]. Automatica,1996,32(4): 519-532.
[38]Howlett P G. The optimal control of a train [C]. Annals of Operation Research,2000.
[39]Hoyt E V, Levay R R. Assessing the effects of several variables on freight train fuel consumption and performance using a train performance simulator [J]. Transportation Research Part A,1990,24(2):99-112
[40]Hwang H S. Control strategy for optimal compromise between trip time and energy consumption in a high-speed railway [J]. Systems, Man and Cybernetics, Part A,1998, 28(6):791-802.
[41]IFEU, SGKV. Comparative analysis of energy consumption and CO2 emissions of road transport and combined transport road/rail [R]. Institute for Energy and Environmental Research (IFEU) and Association for Study of Combined Transport (SGKV),2002.
[42]Ignacio L, Rodriguez J, Buron J M, Garcia, Alberto. A methodology for evaluating environmental impacts of railway freight transportation policies [J]. Energy Policy,2009, 37(12):5393-5398.
[43]Jia W Z, Chen S K, Ho T K, et al. A heuristic algorithm for fixed train runtime [C]. International Conference on Railway Engineering: Challengers for Railway Transportation in Information Age,2008.
[44]Jong J C, Chang E F. Models for estimating energy consumption of electric trains [J]. Journal of Eastern Asia Society for Transportation Studies,2005,6:278-291.
[45]Jong J C, Chang S. Algorithm for generating train speed profiles [J]. Journal of the Eastern Asia Society for Transportation Studies,2005,6:356-371.
[46]Kanayama M, Miyoshi M, Seki Y, et al. Development of train control simulator [C]. International Conference on Developments in Mass Transit Systems,1998.
[47]Kawakami T. Dynamic power saving strategy and data system for future Shinkansen traffic control [C]. The Fourth International Symposium on Integration of Heterogeneous Systems, 1999.
[48]Ke B R, Chen N. Signaling block layout and strategy of train operation for saving energy in mass rapid transit systems [J]. Electrial Power Application,2005,152(2):129-140.
[49]Khanbaghi M, Malhame R P, Ecole P, et al. Reducing travel energy costs for a subway train via fuzzy logic controls [C]. The 1994 International Symposium on Intelligent Control, 1994.
[50]Khmelnitsky E. On an optimal control problem of train operation [J]. Automatic Control, 2000,45(7):1257-1266.
[51]Kraay D, Harker P T, Chen B. Optimal pacing of trains in freight railroads: model formulation and solution [J]. Operations Research,1991,39(1):82-99.
[52]Lee C K, Kim Y K, Pruitichaiwiboon P, et al. Assessing environmentally friendly recycling methods for composite bodies of railway rolling stock using life-cycle analysis [J]. Transportation Research Part D,2010,15(4):197-203
[53]Lee C K, Kim Y K. Assessing environmentally friendly recycling methods for composite bodies of railway rolling stock using life-cycle analysis [J]. Transportation Research Part D, 2010,15(4):197-203.
[54]Li K P, Gao Z Y, Ning B. Cellular automaton model for railway traffic [J]. Journal of Computational Physics,2005,209(1):179-192.
[55]Li K P, Gao Z Y, Ning B. Modelling the railway traffic using cellular automation model [J]. International Journal of Modern Physics C,2005,16(6):921-932.
[56]Liu H D, Mao B H, Ding Y, et al. Train energy-saving scheme with evaluation in urban mass transit systems [J]. Journal of Transportation Systems Engineering and Information Technology,2007,7(5):68-73.
[57]Liu R F, Golovitcher M I. Energy-efficient operation of rail vehicles [J]. Transportation Research Part A,2003,37(10):917-932.
[58]London Underground. London Underground Environment Report 2008 [R/OL]. www. tfl.gov.uk/corporate/modesoftransport/londonunderground/performance/1594.aspx.
[59]Lukaszewicz P. Energy consumption and running time for trains: Modelling of running resistance and driver behavior based on full scale testing [D]. Stockholm: Royal Institute of Technology,2001.
[60]Meibom P. Technology analysis of public transport modes [R]. Lyngby: Technical University of Denmark,2001.
[61]Mellit B, Sujitjorn S, Goodman C J, et al. Energy minimisation using expert system for dynamic coast control in rapid transit trains [C]. Conference on Railways Engineering, 1987.
[62]Missikoff M. An object-oriented approach to an information and decision support system for railway traffic control [J]. Engineering Applications of Artifficial Intelligence.1998, 11(1):25-40.
[63]MTR Corporation Ltd.2009 Sustainability Report [R/OL]. www.mtr.com.hk/eng/ sustainability.
[64]Oshima H, Yasunobu S, Sekino S I. Automatic train operation system based on predictive fuzzy control [C]. International Working on Artifical Intelligence for Industrial Applications, 1988.
[65]Owen R E. A systems approach to energy management and policy in commuter rail transportation [D]. Pennsylvania, United States: University of Pennsylvania,1998.
[66]Pachl J. Railway Operation and Control (2nd edition) [M]. Mountlake Terrace, Washington: VTD Rail Publishing,2009.
[67]Profillidis V A. Railway Management and Engineering (3rd Edition) [M]. Burlington, USA: Ashgate Publishing Limited,2006.
[68]Ransome E O. A analysis approach to energy management and policy in commuter rail transportation [D]. Philadelphia: University of Pennsylvania,2005.
[69]Richardson M B. Flywheel energy storage system for traction applications [J]. Power Electronics, Machines and Drives,2002:275-279.
[70]Thompson J A, Maidment G G, Missenden J F. Modeling low-energy cooling strategies for underground railways [J]. Applied Energy,2006,83(10):1152-1162.
[71]Wong K K, Ho T K. Coast control for mass rapid transit railways with searching methods [J]. Electric Power Application,2004,151(3):365-376.
[72]Wong K K, Ho T K. Coast control of train movement with genetic algorithm [J]. Evolutionary Computation,2003(2):1280-1287.
[73]Wong K K, Ho T K. Dwell-time and run-time control for DC mass rapid transit railways [J]. Electric Power Application,2007, (6):956-966.
[74]Wong K K, Ho T K. Energy reduction for train operation by coasting in metro systems [C]. The 9th International Conference on Enhancement and Promotion of Computational Methods in Engineering and Science,2003.
[75]Wong W G, Han B M, Ferreira L, et al. Evaluation of management strategies for the operation of high-speed railway in china [J]. Transportation Research Part A,2002,36(3): 277-289.
[76]Xuan V. Analysis of necessary conditions for the optimal control of a train [D]. Adelaide: University of South Australia,2006.
[77]Yeh S T. Integrated analysis of vertical alignments and speed profiles for rail transit routes [D]. Maryland: University of Maryland,2003.
[78]白雪.世界油船订单量的波动及其预测研究[D].哈尔滨：哈尔滨工程大学,2010.
[79]柏贇,毛保华,周方明,等.基于功耗分析的货物列车节能运行控制方法研究[J].交通运输系统工程与信息,2009,9(3)：43-51.
[80]柏赞.内燃牵引货物列车节能操纵模型与实时优化算法[D].北京：北京交通大学,2010.
[81]北方交通大学交通运输学院.城市列车运行计算系统[R].北京：北方交通大学交通运输学院,2002.
[82]北京交通大学中国综合交通研究中心.不同交通方式能耗与排放因子及其可比性研究[R].北京：北京交通大学中国综合研究中心,2009.
[83]北京交通大学中国综合交通研究中心.轨道交通技术规范及其发展规划评估[R].北京：北京交通大学中国综合交通研究中心,2010.
[84]北京市城建设计研究总院.GB50157-2003地铁设计规范[S].北京：中国计划出版社,2003.
[85]曹海利.基于耐久性的半刚性基层与垫层研究[D].西安：长安大学,2008.
[86]曹为理.基于灰色控制的永磁无刷直流电机调速系统研究[D].天津：天津大学,2008.
[87]陈立齐.地铁车站环控系统设计中的一些想法[J].铁道工程学报,2005,6：4547.
[88]陈曼英.建设项目前期造价控制的方法与应用[D].长沙：中南大学,2008.
[89]陈勇明.基于统计视角的灰色系统的儿个基本问题研究[D].成都：西南财经大学,2008.
[90]邓聚龙.灰理论基础[M].武汉：华中科技大学出版社,2002.
[91]邓聚龙.灰色系统基本方法(第一版)[M].武汉：华中科技大学出版社,2005.
[92]邓连波,史峰,周文梁.旅客列车开行方案停站设置优化[J/OL]. http://www.paper.edu.cn,2008.
[93]丁超,森和俊.地铁列车节能运营速度的探讨[J].铁道机车车辆,2009,29(3)：48-50.
[94]丁勇,刘海东,柏赞,等.地铁列车节能运行的两阶段优化模型算法研究[J].交通运输系统工程与信息,2011,11(1)：96-101.
[95]丁勇.列车运行计算与操纵优化模拟系统的研究[D].北京：北京交通大学,2005.
[96]付印平.列车追踪运行与节能优化建模及模拟研究[D].北京：北京交通大学,2009.
[97]高春宏,屈海洋.城市轨道交通车辆总体设计研究[J].城市轨道交通研究,2009：4448.
[98]广州地下铁道总公司.广州地铁2009年度年报[R/OL].www.gzmtr.com.
[99]广州地下铁道总公司.全国城市轨道交通能耗调查[R].广州：广州地下铁道总公司,2010.
[100]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.
[101]郭东.考虑再生制动的城市轨道交通牵引供电仿真的研究[D].成都：西南交通大学,2008.
[102]韩立岩,汪培庄.应用模糊数学[M].北京：首都经济贸易大学出版社,1998.
[103]韩长虎,郝建杰,徐强.列车制动耗能一般意义公式及机车优化操纵侧重点问题[J].铁道机车车辆,2004,24(2)：47-49.
[104]韩长虎.应用开放式和保守式操纵方法的探讨[J].内燃机车,1993,10：5-9.
[105]贺仲雄.模糊数学及其应用[M].天津：天津科学出版社,1983.
[106]胡荣,庄琼剑,朱兰,等.缓冲算子修正的DGM(1,1)模型在负荷预测中的应用[J].华东电力,2009,37(11)：1835-1838.
[107]黄磊.灰色系统理论在城市轨道交通自动驾驶的应用研究[D].北京：北京交通大学,2008.
[108]黄留兵,张宁,杨小宝.SUE问题中几种常用路线选择模型的比较研究[J].交通运输系统工程与信息,2006,6(5)：87-91.
[109]贾顺平,毛保华,刘爽,等.中国交通运输能源消耗水平测算与分析[J].交通运输系统工程与信息,2010,10(1),22-27.
[110]江家骅,王金波.城市轨道交通规划环境影响评价指标体系探讨[J].上海环境科学,2008,27(1)：33-36.
[111]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.
[112]孔令洋,梁青槐,张岩,等.直线电机轮轨交通系统牵引能耗研究[J].铁道学报,2007,27(6)：106-112.
[113]李刚.模糊神经网络与灰色理论在已有建筑可靠性鉴定中的应用研究[D].上海：同济大学,2004.
[114]李会超.城市轨道交通列车运行于供电联合仿真研究[D].成都：西南交通大学,2005.
[115]李鲲鹏,张振生.直线电机轨道交通系统能耗分析[J].都市快轨交通,2008,21(1)：31-33.
[116]李连成,吴文化.我国交通运输业能源利用效率及发展趋势[J].综合运输,2008,3：16-20.
[117]李明亮.基于模块化设计方法的城市轨道交通牵引计算软件研制[D].北京：北京交通大学,2008.
[118]李平.浅谈降低地铁建设工程投资[J].西部探矿工程,2009,21(4)：233-237.
[119]李延杰.基于AHP与FOEM的信息安全风险评估研究[D].天津：天津财经大学,2010.
[120]刘宝林.地铁列车能耗分析[J].电力机车与城轨车辆,2007,30(4)：65-68,70.
[121]刘海东,毛保华,丁勇,等.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.
[122]刘建强.直线电机轨道交通牵引传动系统研究[D].北京：北京交通大学,2008.
[123]刘开国.轨道交通供电系统节能措施与经济运行[J].现代城市轨道交通,2009,1：13-14.
[124]刘庆超,张振刚,焦云朋.变化风速下的风电场经济效益分析[J].电力建设,2010,31(1)：77-79.
[125]刘思峰,党耀国,方志耕,等.灰色系统理论及其应用[J].北京：科学出版社,2004.
[126]刘万明.我国高速铁路客运专线主要技术经济问题研究[D].成都：西南交通大学,2002.
[127]刘炜.城市轨道交通供电系统仿真[D].成都：西南交通大学,2006.
[128]柳世辉.客货共线运行铁路线路平面圆曲线半径标准的制订[J].铁道标准设计,2005(10)：103-107.
[129]马大炜.高速列车及其速度目标值的探讨[J].中国铁道科学,2003,24(5)：1-8.
[130]马凌飞.基于灰关联与人工神经网络的混凝土中钢筋腐蚀量预测[D].北京：北京交通大学,2009.
[131]马卫武,李立清,胡蔷,等.铁路客运站空调能耗影响因素分析[J].建筑热能通风空调,2009,28(3)：20-23.
[132]马卫武,孙政,周谦,等.夏热冬冷地区客运站候车室夏季热舒适性[J].土木建筑与环境工程,2009,31(5)：100-105.
[133]马奕.上海轨道交通能耗评价体系及动力系统建模研究[D].上海：同济大学,2009.
[134]马志华.现代船舶电气设备维修决策方法研究[D].上海：上海海事大学,2005.
[135]毛保华,何天键,袁振洲,等.通用列车运行模拟软件系统研究[J].铁道学报,2000,22(1)：1-6.
[136]毛保华,彭宏勤,贾顺平.2009年中国综合交通体系发展趋势研究[J].交通运输系统工程与信息,2010,10(2)：17-22.
[137]毛保华.列车运行计算与设计[M].北京：人民交通出版社,2008.
[138]穆广友,李晓龙,尹力明,等.地铁车站照明系统能耗分析及节能对策[J].城市轨道交通研究,2010,13(8)：35-39.
[139]倪恒.滑坡稳定性分析中非确定性理论的应用研究[D].武汉：中国地质大学,2003.
[140]潘健英.城市轨道交通环控系统节能途径分析[J].城市建设,2010,4：112-113.
[141]潘志敏，基于灰色理论的振动钻削仿真与参数优化[D].长春：吉林大学,2002.
[142]彭其渊，客运专线运输组织[M].北京：科学出版社,2007.
[143]冉茂平,杨光旭,罗忠文.城市轨道交通系统节能措施研究[J].城市公共交通,2010,(10)：45-47.
[144]饶忠.列车牵引计算(第二版)[M].北京：中国铁道出版社,1998.
[145]上海铁路局合肥铁路机务段,北京交通大学交通运输学院.列车节能优化操纵指导装置技术报告[R].合肥：上海铁路局合肥铁路机务段；北京：北京交通大学交通运输学院,2009.
[146]沈柳.连徐高速公路后评价交通量预测研究[D].南京：南京航空航天大学,2006.
[147]石静雅,苏永清,岳继光.轨道交通能耗影响因素分析及能耗评价体系的建立[J].铁道运输与经济,2008,30(9)：46-49.
[148]石静雅.关于上海城市轨道交通能耗指标体系的建立与分析[D].上海：同济大学,2009.
[149]史茂.铁路运输中机车能耗的浪费[J].上海铁道科技,2004,5：42-43.
[150]宋敏华.城市轨道交通节能技术发展趋势研究[J].工程建设与设计,2009,1：15-19.
[151]孙宝鑫.关于地铁环控系统节能的探讨[J].中国集体经济,2009(7)：170-171.
[152]孙启鹏,王庆云,毛保华.不同交通方式能耗因子及其可比性研究框架设计[J].交通运输系统工程与信息,2009,9(4)：10-14.
[153]田晓琳.基于利益相关者理论的企业社会责任指标体系研究[D].北京：北京交通大学,2010.
[154]王丁.哈尔滨市商品住宅价格研究[D].哈尔滨：东北林业大学,2010.
[155]王华.深圳市教育与经济协调发展的灰关联分析[D].天津：天津大学,2005.
[156]王晓.工程项目风险管理一基于灰色系统理论的应用[D].南昌：南昌大学,2009.
[157]王晓军,韩长虎.重载货运列车在困难上坡道起停优化操纵方法探讨[J].内燃机车,1999,6：47-48.
[158]吴晓.城市轨道车辆辅助供电系统的仿真[D].成都：西南交通大学,2009.
[159]伍勇,刘思宁.基于节能和面向旅客服务的列车编组方案研究[J].城市轨道交通研究,2004,6：27-31.
[160]谢乃明.灰色系统建模技术研究[D].南京：南京航空航天大学,2008.
[161]徐波.地铁安全门系统和屏蔽门系统舒适性与能耗性研究[D].天津：天津大学,2007.
[162]薛艳冰,马大炜.列车牵引能耗计算方法[J].中国铁道科学,2007,28(3)：84-87.
[163]杨春波.基于灰色模型与人工神经网络的改进组合预测模型及其应用研究[D].济南：山东师范大学,2009.
[164]杨厚学.应用统计分析[M].成都：西南交通大学出版社,2009.
[165]杨俭,黄厚明,方宇,等.上海轨道交通二号线列车运行能耗分析[J].内燃机车,2009,4：23-25,47.
[166]杨雪峰.地市轨道交通列车节能运行模式的研究[J].城市轨道交通研究,2010,8：68-42.
[167]叶芹禄.试论城市轨道交通车辆的编组[J].铁道勘测与设计,2004,6：31-33.
[168]于书翰.道路工程[M].武汉：武汉工业大学出版社,2000.
[169]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,2009.
[170]张俊峰.单轨车辆牵引计算与仿真[D].重庆：重庆交通大学,2009.
[171]张三慧.大学物理(第二版)[M].北京：清华大学出版社,2002.
[172]张文丽.城市轨道交通供电再生制动反馈能量利用仿真研究[D].成都：西南交通大学，2008.
[173]张燕燕.城市轨道交通系统牵引及车站能耗研究[D].北京：北京交通大学,2008.
[174]张越.模糊数学方法及其应用[M].北京：煤炭工业出版社,1992.
[175]中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.城市轨道交通照明[S].北京：中国标准出版社,2009.
[176]中华人民共和国国家质量监督检验检疫总局.地铁车辆通用技术条件[S].北京：中国计划出版社,2004.
[177]中华人民共和国建设部.GB50157-2003地铁设计规范[S].北京：中国计划出版社,2003.
[178]中华人民共和国铁道部.GB50090-99铁路线路设计规范[S].北京：中国计划出版社,1999.
[179]中华人民共和国铁道部.TB/T1407-1998列车牵引计算规程[S].北京：中国标准出版社，1998.
[180]中华人民共和国住房和城乡建设部,国家质量监督检验检疫总局.城市轨道交通项目建设管理规范(征求意见稿)[S].2010.
[181]钟素银.广州地铁车站照明系统节能研究[J].都市快轨交通,2008,21(4)：81-84.
[182]周锋.动车组牵引计算建模软件仿真[D].成都：西南交通大学,2005.
[183]周广科.东平湖水库移民后期扶持与库区可持续发展评价研究[D].济南：山东大学,2010.
[184]周萍.我国东部十城市环境质量评价与预警研究[D].南京：南京信息工程大学,2008.
[185]周素萍.我国高科技中小企业集群生成机制研究[D].天津：天津大学,2008.
[186]朱国华.灰色系统理论在电信行业中的应用[D].广州：中山大学,2005.
[187]朱江.地下铁道动力照明设计中降损节能措施的探讨[J].建筑电气,2002,(04)：39-41.
[188]庄金迅,张九红,刘圆圆.城市地铁站照明节能研究[J].铁道标准设计,2010,4：110-112.
[2]Albrecht T. Energy-efficient train operation in Hansen [M]. Germany: Railway Timetable & Traffic, Eurailpress/DVV Media, Eurail Press,2008.
[3]Albrecht T. The influence of anticipating train driving on the dispatching process in railway conflict situations [J]. Networks and Spatial Economics,2009,9(l):85-101.
[4]Andersson E, Lukaszewicz P. Energy consumption and related air pollution for Scandinavian electric passenger train [R]. Stockholm, Sweden:Department of Aeronautical and Vehicle Engineering Royal Institute of Technology, KTH,2006.
[5]Anthony R N. Planning and control systems: a framework for analysis [D]. Boston: Harvard University,1965.
[6]Assisa W O, Milani B E A. Generation of optimal schedules for metro lines using model predictive control [J]. Automatic,2004,40(8): 1397-1404.
[7]Bai Y, Mao B H, Zhou F M, et al. An onboard optimal control system for freight trains [C]. The Sixth International Conference on Traffic and Transportation Studies,2008.
[8]Bai Y, Mao B H, Zhou F M, et al. Energy-efficient driving strategy for freight trains based on power consumption analysis [J]. Journal of Transportation Systems Engineering and Information Technology,2009,9(3):43-50.
[9]Blum J, Eskandarian A. Enhancing intelligent agent collaboration for flow optimization of railroad traffic [J]. Transportation Research Part A,2002,36(10):919-930.
[10]Bocharnikov Y V, Tobias A M, Roberts C, et al. Optimal driving strategy for traction energy saving on DC suburban railways [J]. Electric Power Applications,2007,1(5):675-682.
[11]Brerma M, Foiadelli F, Tironi E, et al. Ultra capacitors application for energy saving in subway transportation systems [C]. International Conference on Clean Elecrical Power, 2007.
[12]Chang C S, Chua C S, Quek H B. Development of train movement simulator for analysis and optimization of railway signaling system [C]. International Conference on Developments in Mass Transit Systems,1998.
[13]Chang C S, Sim S S. Optimizing train movements through coast control using genetic algorithms [J]. Electric Power Applications,1997,144(1):65-73.
[14]Chang C S, Xu D Y. Differential evolution based tuning of fuzzy automatic train operation for mass transit system [J]. Electric Power Applications,2000,147(3):206-212.
[15]Chen J F, Lin R L, Liu Y C. Optimization of an MRT train schedule:reducing maximum traction power by using genetic algorithms [J]. Power Systems,2005,20(3):1366-1372.
[16]Cheng J X, Howlett P G. A note on the calculation of optimal strategies for the minimization of fuel consumption in the control of trains [J]. Automatic Control,1993, 38(11):1730-1734.
[17]Cheng J X, Howlett P G. Application of critical velocities to the minimization of fuel consumption in the control of trains [J]. Automatic,1992,28(1):165-169.
[18]Cheng Y. Hybrid simulation for resolving resource conflicts in train traffic rescheduling [J]. Computers in Industry,1998,35(3):233-246.
[19]Corbo P, Corcione F E, Migliardini F, et al. Energy management in fuel cell power trains [J]. Energy Conversion and Management,2006,47(18-19):3255-3271.
[20]Danziger N H. Energy optimization for rail public transit system of Washington DC [J]: Transportation Research Record,1975,15(4):31-39
[21]Ding Y, Mao B H, Liu H D, et al. Train movement simulation system for saving energy [C]. The Fourth International Conference on Traffic and Transportation Studies,2004.
[22]Ding Y, Zhou F M, Bai Y, et al. A correction model of loaded train's grade resistance calculation [C]. The Fifth Advanced Forum on Transportation of China,2009.
[23]EC. Methodology for calculating transport emissions and energy consumption [R]. Luxemburg: European Commission (EC),1999.
[24]Effati S, Roohparvar H. The minimization of the fuel costs in the train transportation [J]. Applied Mathematics Computer,2006,175(2):1415-1431.
[25]Fay A. A fuzzy knowledge-based system for railway traffic control [J]. Engineering Applications of Artificial Intelligence,2000,13:719-729.
[26]Federici M, Ulgiati S, Basosi R. Air versus terrestrial transport modalities:an energy and environmental comparison [J]. Energy,2009,34(10):1493-1503.
[27]Fong K F, Hanby V I, Chow T T. Optimization of MVAC system for energy management by evolutionary algorithm [J]. Facilities,2003,21(10):223-232.
[28]Fu Y P, Gao Z Y, Li K P. Optimization method of energy saving train operation for railway network [J]. Journal of Transportation Systems Engineering and Information Technology, 2009,9(4):90-96.
[29]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.
[30]Han S H, Byen Y S, Baek J H, et al. An optimal automatic train operation (ATO) control using genetic algorithm (GA) [J]. Tencon,1999,25(2):67-71.
[31]Helms H. Fuel saving by light-weighting for European articulated trucks [R]. Heidelberg, Brussels: Insititute for Energy and Environmental Research (IFEU),2005.
[32]Heung T H, Ho T K. Hierarchical fuzzy logic traffic control at a road junction using genetic algorithms [C]. International Conference of Fuzzy Systems,1998.
[33]Hoc H, Polis M, Haurie A. Reducing energy consumption through trajecory optimization for a metro network [J]. Automatic Control,1975,20(5):590-595.
[34]Hong W H, Kim S D. A study on the energy consumption unit of subway stations in Korea [J]. Building and Environment,2004,39(12):1497-1503.
[35]Howlett P G, Pudney P J. Energy-efficient train control [M]. London:Springer Press,1995.
[36]Howlett P G. An optimal strategy for the control of a train [J]. Australian Mathematics Society, Series B,1990,31(4): 454-471.
[37]Howlett P G. Optimal strategies for the control of a train [J]. Automatica,1996,32(4): 519-532.
[38]Howlett P G. The optimal control of a train [C]. Annals of Operation Research,2000.
[39]Hoyt E V, Levay R R. Assessing the effects of several variables on freight train fuel consumption and performance using a train performance simulator [J]. Transportation Research Part A,1990,24(2):99-112
[40]Hwang H S. Control strategy for optimal compromise between trip time and energy consumption in a high-speed railway [J]. Systems, Man and Cybernetics, Part A,1998, 28(6):791-802.
[41]IFEU, SGKV. Comparative analysis of energy consumption and CO2 emissions of road transport and combined transport road/rail [R]. Institute for Energy and Environmental Research (IFEU) and Association for Study of Combined Transport (SGKV),2002.
[42]Ignacio L, Rodriguez J, Buron J M, Garcia, Alberto. A methodology for evaluating environmental impacts of railway freight transportation policies [J]. Energy Policy,2009, 37(12):5393-5398.
[43]Jia W Z, Chen S K, Ho T K, et al. A heuristic algorithm for fixed train runtime [C]. International Conference on Railway Engineering: Challengers for Railway Transportation in Information Age,2008.
[44]Jong J C, Chang E F. Models for estimating energy consumption of electric trains [J]. Journal of Eastern Asia Society for Transportation Studies,2005,6:278-291.
[45]Jong J C, Chang S. Algorithm for generating train speed profiles [J]. Journal of the Eastern Asia Society for Transportation Studies,2005,6:356-371.
[46]Kanayama M, Miyoshi M, Seki Y, et al. Development of train control simulator [C]. International Conference on Developments in Mass Transit Systems,1998.
[47]Kawakami T. Dynamic power saving strategy and data system for future Shinkansen traffic control [C]. The Fourth International Symposium on Integration of Heterogeneous Systems, 1999.
[48]Ke B R, Chen N. Signaling block layout and strategy of train operation for saving energy in mass rapid transit systems [J]. Electrial Power Application,2005,152(2):129-140.
[49]Khanbaghi M, Malhame R P, Ecole P, et al. Reducing travel energy costs for a subway train via fuzzy logic controls [C]. The 1994 International Symposium on Intelligent Control, 1994.
[50]Khmelnitsky E. On an optimal control problem of train operation [J]. Automatic Control, 2000,45(7):1257-1266.
[51]Kraay D, Harker P T, Chen B. Optimal pacing of trains in freight railroads: model formulation and solution [J]. Operations Research,1991,39(1):82-99.
[52]Lee C K, Kim Y K, Pruitichaiwiboon P, et al. Assessing environmentally friendly recycling methods for composite bodies of railway rolling stock using life-cycle analysis [J]. Transportation Research Part D,2010,15(4):197-203
[53]Lee C K, Kim Y K. Assessing environmentally friendly recycling methods for composite bodies of railway rolling stock using life-cycle analysis [J]. Transportation Research Part D, 2010,15(4):197-203.
[54]Li K P, Gao Z Y, Ning B. Cellular automaton model for railway traffic [J]. Journal of Computational Physics,2005,209(1):179-192.
[55]Li K P, Gao Z Y, Ning B. Modelling the railway traffic using cellular automation model [J]. International Journal of Modern Physics C,2005,16(6):921-932.
[56]Liu H D, Mao B H, Ding Y, et al. Train energy-saving scheme with evaluation in urban mass transit systems [J]. Journal of Transportation Systems Engineering and Information Technology,2007,7(5):68-73.
[57]Liu R F, Golovitcher M I. Energy-efficient operation of rail vehicles [J]. Transportation Research Part A,2003,37(10):917-932.
[58]London Underground. London Underground Environment Report 2008 [R/OL]. www. tfl.gov.uk/corporate/modesoftransport/londonunderground/performance/1594.aspx.
[59]Lukaszewicz P. Energy consumption and running time for trains: Modelling of running resistance and driver behavior based on full scale testing [D]. Stockholm: Royal Institute of Technology,2001.
[60]Meibom P. Technology analysis of public transport modes [R]. Lyngby: Technical University of Denmark,2001.
[61]Mellit B, Sujitjorn S, Goodman C J, et al. Energy minimisation using expert system for dynamic coast control in rapid transit trains [C]. Conference on Railways Engineering, 1987.
[62]Missikoff M. An object-oriented approach to an information and decision support system for railway traffic control [J]. Engineering Applications of Artifficial Intelligence.1998, 11(1):25-40.
[63]MTR Corporation Ltd.2009 Sustainability Report [R/OL]. www.mtr.com.hk/eng/ sustainability.
[64]Oshima H, Yasunobu S, Sekino S I. Automatic train operation system based on predictive fuzzy control [C]. International Working on Artifical Intelligence for Industrial Applications, 1988.
[65]Owen R E. A systems approach to energy management and policy in commuter rail transportation [D]. Pennsylvania, United States: University of Pennsylvania,1998.
[66]Pachl J. Railway Operation and Control (2nd edition) [M]. Mountlake Terrace, Washington: VTD Rail Publishing,2009.
[67]Profillidis V A. Railway Management and Engineering (3rd Edition) [M]. Burlington, USA: Ashgate Publishing Limited,2006.
[68]Ransome E O. A analysis approach to energy management and policy in commuter rail transportation [D]. Philadelphia: University of Pennsylvania,2005.
[69]Richardson M B. Flywheel energy storage system for traction applications [J]. Power Electronics, Machines and Drives,2002:275-279.
[70]Thompson J A, Maidment G G, Missenden J F. Modeling low-energy cooling strategies for underground railways [J]. Applied Energy,2006,83(10):1152-1162.
[71]Wong K K, Ho T K. Coast control for mass rapid transit railways with searching methods [J]. Electric Power Application,2004,151(3):365-376.
[72]Wong K K, Ho T K. Coast control of train movement with genetic algorithm [J]. Evolutionary Computation,2003(2):1280-1287.
[73]Wong K K, Ho T K. Dwell-time and run-time control for DC mass rapid transit railways [J]. Electric Power Application,2007, (6):956-966.
[74]Wong K K, Ho T K. Energy reduction for train operation by coasting in metro systems [C]. The 9th International Conference on Enhancement and Promotion of Computational Methods in Engineering and Science,2003.
[75]Wong W G, Han B M, Ferreira L, et al. Evaluation of management strategies for the operation of high-speed railway in china [J]. Transportation Research Part A,2002,36(3): 277-289.
[76]Xuan V. Analysis of necessary conditions for the optimal control of a train [D]. Adelaide: University of South Australia,2006.
[77]Yeh S T. Integrated analysis of vertical alignments and speed profiles for rail transit routes [D]. Maryland: University of Maryland,2003.
[78]白雪.世界油船订单量的波动及其预测研究[D].哈尔滨：哈尔滨工程大学,2010.
[79]柏贇,毛保华,周方明,等.基于功耗分析的货物列车节能运行控制方法研究[J].交通运输系统工程与信息,2009,9(3)：43-51.
[80]柏赞.内燃牵引货物列车节能操纵模型与实时优化算法[D].北京：北京交通大学,2010.
[81]北方交通大学交通运输学院.城市列车运行计算系统[R].北京：北方交通大学交通运输学院,2002.
[82]北京交通大学中国综合交通研究中心.不同交通方式能耗与排放因子及其可比性研究[R].北京：北京交通大学中国综合研究中心,2009.
[83]北京交通大学中国综合交通研究中心.轨道交通技术规范及其发展规划评估[R].北京：北京交通大学中国综合交通研究中心,2010.
[84]北京市城建设计研究总院.GB50157-2003地铁设计规范[S].北京：中国计划出版社,2003.
[85]曹海利.基于耐久性的半刚性基层与垫层研究[D].西安：长安大学,2008.
[86]曹为理.基于灰色控制的永磁无刷直流电机调速系统研究[D].天津：天津大学,2008.
[87]陈立齐.地铁车站环控系统设计中的一些想法[J].铁道工程学报,2005,6：4547.
[88]陈曼英.建设项目前期造价控制的方法与应用[D].长沙：中南大学,2008.
[89]陈勇明.基于统计视角的灰色系统的儿个基本问题研究[D].成都：西南财经大学,2008.
[90]邓聚龙.灰理论基础[M].武汉：华中科技大学出版社,2002.
[91]邓聚龙.灰色系统基本方法(第一版)[M].武汉：华中科技大学出版社,2005.
[92]邓连波,史峰,周文梁.旅客列车开行方案停站设置优化[J/OL]. http://www.paper.edu.cn,2008.
[93]丁超,森和俊.地铁列车节能运营速度的探讨[J].铁道机车车辆,2009,29(3)：48-50.
[94]丁勇,刘海东,柏赞,等.地铁列车节能运行的两阶段优化模型算法研究[J].交通运输系统工程与信息,2011,11(1)：96-101.
[95]丁勇.列车运行计算与操纵优化模拟系统的研究[D].北京：北京交通大学,2005.
[96]付印平.列车追踪运行与节能优化建模及模拟研究[D].北京：北京交通大学,2009.
[97]高春宏,屈海洋.城市轨道交通车辆总体设计研究[J].城市轨道交通研究,2009：4448.
[98]广州地下铁道总公司.广州地铁2009年度年报[R/OL].www.gzmtr.com.
[99]广州地下铁道总公司.全国城市轨道交通能耗调查[R].广州：广州地下铁道总公司,2010.
[100]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.
[101]郭东.考虑再生制动的城市轨道交通牵引供电仿真的研究[D].成都：西南交通大学,2008.
[102]韩立岩,汪培庄.应用模糊数学[M].北京：首都经济贸易大学出版社,1998.
[103]韩长虎,郝建杰,徐强.列车制动耗能一般意义公式及机车优化操纵侧重点问题[J].铁道机车车辆,2004,24(2)：47-49.
[104]韩长虎.应用开放式和保守式操纵方法的探讨[J].内燃机车,1993,10：5-9.
[105]贺仲雄.模糊数学及其应用[M].天津：天津科学出版社,1983.
[106]胡荣,庄琼剑,朱兰,等.缓冲算子修正的DGM(1,1)模型在负荷预测中的应用[J].华东电力,2009,37(11)：1835-1838.
[107]黄磊.灰色系统理论在城市轨道交通自动驾驶的应用研究[D].北京：北京交通大学,2008.
[108]黄留兵,张宁,杨小宝.SUE问题中几种常用路线选择模型的比较研究[J].交通运输系统工程与信息,2006,6(5)：87-91.
[109]贾顺平,毛保华,刘爽,等.中国交通运输能源消耗水平测算与分析[J].交通运输系统工程与信息,2010,10(1),22-27.
[110]江家骅,王金波.城市轨道交通规划环境影响评价指标体系探讨[J].上海环境科学,2008,27(1)：33-36.
[111]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.
[112]孔令洋,梁青槐,张岩,等.直线电机轮轨交通系统牵引能耗研究[J].铁道学报,2007,27(6)：106-112.
[113]李刚.模糊神经网络与灰色理论在已有建筑可靠性鉴定中的应用研究[D].上海：同济大学,2004.
[114]李会超.城市轨道交通列车运行于供电联合仿真研究[D].成都：西南交通大学,2005.
[115]李鲲鹏,张振生.直线电机轨道交通系统能耗分析[J].都市快轨交通,2008,21(1)：31-33.
[116]李连成,吴文化.我国交通运输业能源利用效率及发展趋势[J].综合运输,2008,3：16-20.
[117]李明亮.基于模块化设计方法的城市轨道交通牵引计算软件研制[D].北京：北京交通大学,2008.
[118]李平.浅谈降低地铁建设工程投资[J].西部探矿工程,2009,21(4)：233-237.
[119]李延杰.基于AHP与FOEM的信息安全风险评估研究[D].天津：天津财经大学,2010.
[120]刘宝林.地铁列车能耗分析[J].电力机车与城轨车辆,2007,30(4)：65-68,70.
[121]刘海东,毛保华,丁勇,等.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.
[122]刘建强.直线电机轨道交通牵引传动系统研究[D].北京：北京交通大学,2008.
[123]刘开国.轨道交通供电系统节能措施与经济运行[J].现代城市轨道交通,2009,1：13-14.
[124]刘庆超,张振刚,焦云朋.变化风速下的风电场经济效益分析[J].电力建设,2010,31(1)：77-79.
[125]刘思峰,党耀国,方志耕,等.灰色系统理论及其应用[J].北京：科学出版社,2004.
[126]刘万明.我国高速铁路客运专线主要技术经济问题研究[D].成都：西南交通大学,2002.
[127]刘炜.城市轨道交通供电系统仿真[D].成都：西南交通大学,2006.
[128]柳世辉.客货共线运行铁路线路平面圆曲线半径标准的制订[J].铁道标准设计,2005(10)：103-107.
[129]马大炜.高速列车及其速度目标值的探讨[J].中国铁道科学,2003,24(5)：1-8.
[130]马凌飞.基于灰关联与人工神经网络的混凝土中钢筋腐蚀量预测[D].北京：北京交通大学,2009.
[131]马卫武,李立清,胡蔷,等.铁路客运站空调能耗影响因素分析[J].建筑热能通风空调,2009,28(3)：20-23.
[132]马卫武,孙政,周谦,等.夏热冬冷地区客运站候车室夏季热舒适性[J].土木建筑与环境工程,2009,31(5)：100-105.
[133]马奕.上海轨道交通能耗评价体系及动力系统建模研究[D].上海：同济大学,2009.
[134]马志华.现代船舶电气设备维修决策方法研究[D].上海：上海海事大学,2005.
[135]毛保华,何天键,袁振洲,等.通用列车运行模拟软件系统研究[J].铁道学报,2000,22(1)：1-6.
[136]毛保华,彭宏勤,贾顺平.2009年中国综合交通体系发展趋势研究[J].交通运输系统工程与信息,2010,10(2)：17-22.
[137]毛保华.列车运行计算与设计[M].北京：人民交通出版社,2008.
[138]穆广友,李晓龙,尹力明,等.地铁车站照明系统能耗分析及节能对策[J].城市轨道交通研究,2010,13(8)：35-39.
[139]倪恒.滑坡稳定性分析中非确定性理论的应用研究[D].武汉：中国地质大学,2003.
[140]潘健英.城市轨道交通环控系统节能途径分析[J].城市建设,2010,4：112-113.
[141]潘志敏，基于灰色理论的振动钻削仿真与参数优化[D].长春：吉林大学,2002.
[142]彭其渊，客运专线运输组织[M].北京：科学出版社,2007.
[143]冉茂平,杨光旭,罗忠文.城市轨道交通系统节能措施研究[J].城市公共交通,2010,(10)：45-47.
[144]饶忠.列车牵引计算(第二版)[M].北京：中国铁道出版社,1998.
[145]上海铁路局合肥铁路机务段,北京交通大学交通运输学院.列车节能优化操纵指导装置技术报告[R].合肥：上海铁路局合肥铁路机务段；北京：北京交通大学交通运输学院,2009.
[146]沈柳.连徐高速公路后评价交通量预测研究[D].南京：南京航空航天大学,2006.
[147]石静雅,苏永清,岳继光.轨道交通能耗影响因素分析及能耗评价体系的建立[J].铁道运输与经济,2008,30(9)：46-49.
[148]石静雅.关于上海城市轨道交通能耗指标体系的建立与分析[D].上海：同济大学,2009.
[149]史茂.铁路运输中机车能耗的浪费[J].上海铁道科技,2004,5：42-43.
[150]宋敏华.城市轨道交通节能技术发展趋势研究[J].工程建设与设计,2009,1：15-19.
[151]孙宝鑫.关于地铁环控系统节能的探讨[J].中国集体经济,2009(7)：170-171.
[152]孙启鹏,王庆云,毛保华.不同交通方式能耗因子及其可比性研究框架设计[J].交通运输系统工程与信息,2009,9(4)：10-14.
[153]田晓琳.基于利益相关者理论的企业社会责任指标体系研究[D].北京：北京交通大学,2010.
[154]王丁.哈尔滨市商品住宅价格研究[D].哈尔滨：东北林业大学,2010.
[155]王华.深圳市教育与经济协调发展的灰关联分析[D].天津：天津大学,2005.
[156]王晓.工程项目风险管理一基于灰色系统理论的应用[D].南昌：南昌大学,2009.
[157]王晓军,韩长虎.重载货运列车在困难上坡道起停优化操纵方法探讨[J].内燃机车,1999,6：47-48.
[158]吴晓.城市轨道车辆辅助供电系统的仿真[D].成都：西南交通大学,2009.
[159]伍勇,刘思宁.基于节能和面向旅客服务的列车编组方案研究[J].城市轨道交通研究,2004,6：27-31.
[160]谢乃明.灰色系统建模技术研究[D].南京：南京航空航天大学,2008.
[161]徐波.地铁安全门系统和屏蔽门系统舒适性与能耗性研究[D].天津：天津大学,2007.
[162]薛艳冰,马大炜.列车牵引能耗计算方法[J].中国铁道科学,2007,28(3)：84-87.
[163]杨春波.基于灰色模型与人工神经网络的改进组合预测模型及其应用研究[D].济南：山东师范大学,2009.
[164]杨厚学.应用统计分析[M].成都：西南交通大学出版社,2009.
[165]杨俭,黄厚明,方宇,等.上海轨道交通二号线列车运行能耗分析[J].内燃机车,2009,4：23-25,47.
[166]杨雪峰.地市轨道交通列车节能运行模式的研究[J].城市轨道交通研究,2010,8：68-42.
[167]叶芹禄.试论城市轨道交通车辆的编组[J].铁道勘测与设计,2004,6：31-33.
[168]于书翰.道路工程[M].武汉：武汉工业大学出版社,2000.
[169]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,2009.
[170]张俊峰.单轨车辆牵引计算与仿真[D].重庆：重庆交通大学,2009.
[171]张三慧.大学物理(第二版)[M].北京：清华大学出版社,2002.
[172]张文丽.城市轨道交通供电再生制动反馈能量利用仿真研究[D].成都：西南交通大学，2008.
[173]张燕燕.城市轨道交通系统牵引及车站能耗研究[D].北京：北京交通大学,2008.
[174]张越.模糊数学方法及其应用[M].北京：煤炭工业出版社,1992.
[175]中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.城市轨道交通照明[S].北京：中国标准出版社,2009.
[176]中华人民共和国国家质量监督检验检疫总局.地铁车辆通用技术条件[S].北京：中国计划出版社,2004.
[177]中华人民共和国建设部.GB50157-2003地铁设计规范[S].北京：中国计划出版社,2003.
[178]中华人民共和国铁道部.GB50090-99铁路线路设计规范[S].北京：中国计划出版社,1999.
[179]中华人民共和国铁道部.TB/T1407-1998列车牵引计算规程[S].北京：中国标准出版社，1998.
[180]中华人民共和国住房和城乡建设部,国家质量监督检验检疫总局.城市轨道交通项目建设管理规范(征求意见稿)[S].2010.
[181]钟素银.广州地铁车站照明系统节能研究[J].都市快轨交通,2008,21(4)：81-84.
[182]周锋.动车组牵引计算建模软件仿真[D].成都：西南交通大学,2005.
[183]周广科.东平湖水库移民后期扶持与库区可持续发展评价研究[D].济南：山东大学,2010.
[184]周萍.我国东部十城市环境质量评价与预警研究[D].南京：南京信息工程大学,2008.
[185]周素萍.我国高科技中小企业集群生成机制研究[D].天津：天津大学,2008.
[186]朱国华.灰色系统理论在电信行业中的应用[D].广州：中山大学,2005.
[187]朱江.地下铁道动力照明设计中降损节能措施的探讨[J].建筑电气,2002,(04)：39-41.
[188]庄金迅,张九红,刘圆圆.城市地铁站照明节能研究[J].铁道标准设计,2010,4：110-112.