城市公共交通价格联动策略研究
|
摘要
城市公共交通价格策略整合是提高交通资源配置效率的有效手段，利用公交价格联动策略的协同效应调控公交需求与供给之间的平衡，已成为公交运营实践和公交价格理论研究的热点问题。公交系统运营中，由于公交价格结构体系不合理或公交资源市场配置的“失灵”，导致公交需求分配与公交资源配置出现偏差，影响公交主体效用和公交服务的社会福利水平。本文从公交价格主体行为决策出发，利用公交价格联动策略调控公交需求与供给的平衡。
针对我国城市公交系统中公交价格策略与公交主体行为、公交价格策略时间演化规律和公交价格策略效果评价存在的问题，本文从公交价格主体间递阶结构关系和公交价格传导机制出发，分析公交运营者间竞争与合作的价格博弈策略和策略收益，通过优化不同公交价格间比价关系，影响公交需求结构；结合公交运营主体、公交价格策略集合，确定价格联动策略和独立策略收益函数基础上，建立公交价格联动策略演化博弈模型，揭示公交价格联动策略演化路径和演化稳定策略。此外，围绕公交价格联动策略，采用系统动力学方法仿真公交价格联动策略对公交需求和供给变量的时间累积效果，选取机动车大气排放物指标衡量公交价格联动策略对城市大气环境的改善作用。
以交通行为理论为基础，分析公交出行者方式选择行为、运营者之间的竞争与合作行为，以及公交管理者对运营者公交运营准入、服务水平、票价的市场管制行为，形成公交主体间的层次关系，表现为公交管理者位于上层约束公交运营者竞争与合作行为，并考虑和引导公交出行者主体行为；公交运营者与公交出行者之间为相互影响、相互制约的递阶层次关系。由此给出公交运营者价格策略集合和价格策略收益函数的定义。此外，进一步分析公交价格传导机制，揭示公交价格联动策略的生成机理，包括公交价格传导路径和传导方式。
针对公交运营者与出行者之间领导者-追随者的博弈关系，采用双层规划理论，以公交系统运营社会福利最大化为目标，公交管理者对公交价格管制为约束条件作为上层规划模型；下层模型出行者效用最大为目标，公交企业服务能力、服务水平管制和公交系统需求均衡为约束条件，建立双层规划模型优化公交系统价格结构，明晰公交价格联动策略对公交系统社会福利和公交出行者行为决策的影响，以及对公交需求均衡分配调控的效果。采用遗传算法和模拟退火算法设计启发式算法求解公交价格联动体系优化模型，以广州市常规公交、BRT、地铁三种公交方式相平行的线路为例，验证模型的实用性，以及算法的准确性和计算效率。
基于演化博弈理论，综合公交价格联动和独立策略集合，以及各策略收益模型，构建公交价格联动策略演化博弈模型，刻画采用价格联动策略与价格独立策略公交运营者群体双方相互学习的速度和方向，分析不同公交价格策略演化博弈结果中稳定点的存在性。以广州市常规公交和BRT运营者群体为例，应用公交价格联动策略演化博弈模型，分析不同公交运营者群体初始规模和需求条件下公交价格策略演化路径和演化稳定策略。
采用系统动力学方法，以公交价格联动体系优化对公交系统供需静态均衡的影响结果为基础，围绕公交价格联动策略建立公交需求子系统和公交供给子系统间正、负反馈回路，利用广州市交通调查数据标定模型参数和校核模型的准确性，仿真公交价格联动策略对公交需求和公交供给影响的动态变化趋势和时间累积效果。通过公交供、需变量与小客车数量之间的函数关系，获得公交价格联动策略对机动车主要排放物减排的效果，衡量公交价格联动策略对城市大气环境改善的作用。
本文针对城市公交价格策略，利用效用理论、博弈理论和系统科学的研究成果，以公交主体决策行为分析为基础，对公交价格联动体系进行了优化，通过建立公交价格联动策略演化博弈模型，描述采用不同价格策略公交运营者群体双方相互学习的速度和方向，应用系统动力学方法仿真与评价了公交价格联动策略对城市大气环境改善的作用。以上研究成果不仅能够完善公交价格策略优化和演化博弈理论研究的不足，同时对城市公交价格策略整合、公交资源的有效配置和实现公交优先发展具有重要的理论意义和实践价值。
The integration of public transport price strategies is an effective way to improveallocative efficiency of traffic resource. It is transit operating and price theory researchfocuses that using transit price linkage strategy is to maintain equilibrium betweentransit demand and supply. In operating, the inrratonality of transit price structuresystem and market failure of transit resource allocation may lead to the incoordinationbetween transit demand and supply, or transit participator utility and social welfare. Thispaper aims to gain equilibrium between transit demand and supply by transit pricelinkage strategy in an angle of behaviors of transit participator.
This paper models the game strategies and strategies’ payoff between partners oftransit system and optimizes the transit price linkage system to balance transit demandon modes with the problems in researches of transit paticipators’ behavior, strategyevolution game and strategy evaluation. Based on price strategy set and strategy payoff,evolution game model is advanced to identify the evolution process and results of pricestrategies. Furthemore, transit price stretegies are extended to transit system to developthe System Dynamic model for measuring the atmospheric environment improvementof transit price linkage strategies.
Analysising the behavior of traveller, competing and coorperating between transitoperators, control policies in transit market is to present the hierarchical relationshipamong paticipators with traffic behavior theory. In other words, with the travellerbehaviors, the optimization of transit operating strategies can achieve the maximizationof operators under transit price control and service level control conditions. Moreover,transit price transmission machanism is defined to represent the generation mechanismof price linkage strategy, including transit price transmission routes and transmissionmodes.
The bi-level programing theory is used to describe the leader-follower relationshipbetween operators and travellers. The upper level aims to for maximize the socialwelfare of transit operating with constrains on price control; constrains of lower levelare service capacity, service level and demand equilibrium with utility maximization oftraveller. The effect of price linkage strategy on social welfare, demand distribution andtraveller behavior is determined by bi-level model. Genetic Algorithm and SimulatingAnnealing algorithm are adopted to solve the optimization model. The experience studyof Guangzhou bus, BRT and metro, verifies the practical applicability of model andaccuracy and computational efficiency of algorithms.
Evolution game model of transit price strategy is developed to present the learningspeed and direction between operators with differ price strategies and find the evolutionstability strategy in transit strategy set by evolution game theory. The volution game model is used on bus and BRT network of Guangzhou to discussing the evolutionprocess and results of price strategies under different demand conditons.
Based on static equilibrium between transit demand and supply by the optimizationof transit price linkage system, system dynamic is used to simulate the vriation trendand accumulative effect of transit demand and supply after calibrating parameters andchecking model accuracy with traffic survey data of Guangzhou. The relationshipfunction, among transit demand, supply and car flow, obtains the emmission dropped ofvehicle major pollutions as results of price linkage strategy to evaluate the improvementof atmospherics evironment.
This paper aims to optimize transit price structure system and identify the course ofevolution and evolutionary stability strategy of transit price strategies with the researchachievements of utility theory, game theory and system science. Furthermore, theimprovement of atmospheric environment is measured by system dynamic analysis oftransit price linkage strategy. The researches can not only make up for the deficiency ofthe transit price optimization theory, but also provide the theory and experience forintegrating transit price strategies, the allocating transit resource and developing transtpreferentially.
针对我国城市公交系统中公交价格策略与公交主体行为、公交价格策略时间演化规律和公交价格策略效果评价存在的问题，本文从公交价格主体间递阶结构关系和公交价格传导机制出发，分析公交运营者间竞争与合作的价格博弈策略和策略收益，通过优化不同公交价格间比价关系，影响公交需求结构；结合公交运营主体、公交价格策略集合，确定价格联动策略和独立策略收益函数基础上，建立公交价格联动策略演化博弈模型，揭示公交价格联动策略演化路径和演化稳定策略。此外，围绕公交价格联动策略，采用系统动力学方法仿真公交价格联动策略对公交需求和供给变量的时间累积效果，选取机动车大气排放物指标衡量公交价格联动策略对城市大气环境的改善作用。
以交通行为理论为基础，分析公交出行者方式选择行为、运营者之间的竞争与合作行为，以及公交管理者对运营者公交运营准入、服务水平、票价的市场管制行为，形成公交主体间的层次关系，表现为公交管理者位于上层约束公交运营者竞争与合作行为，并考虑和引导公交出行者主体行为；公交运营者与公交出行者之间为相互影响、相互制约的递阶层次关系。由此给出公交运营者价格策略集合和价格策略收益函数的定义。此外，进一步分析公交价格传导机制，揭示公交价格联动策略的生成机理，包括公交价格传导路径和传导方式。
针对公交运营者与出行者之间领导者-追随者的博弈关系，采用双层规划理论，以公交系统运营社会福利最大化为目标，公交管理者对公交价格管制为约束条件作为上层规划模型；下层模型出行者效用最大为目标，公交企业服务能力、服务水平管制和公交系统需求均衡为约束条件，建立双层规划模型优化公交系统价格结构，明晰公交价格联动策略对公交系统社会福利和公交出行者行为决策的影响，以及对公交需求均衡分配调控的效果。采用遗传算法和模拟退火算法设计启发式算法求解公交价格联动体系优化模型，以广州市常规公交、BRT、地铁三种公交方式相平行的线路为例，验证模型的实用性，以及算法的准确性和计算效率。
基于演化博弈理论，综合公交价格联动和独立策略集合，以及各策略收益模型，构建公交价格联动策略演化博弈模型，刻画采用价格联动策略与价格独立策略公交运营者群体双方相互学习的速度和方向，分析不同公交价格策略演化博弈结果中稳定点的存在性。以广州市常规公交和BRT运营者群体为例，应用公交价格联动策略演化博弈模型，分析不同公交运营者群体初始规模和需求条件下公交价格策略演化路径和演化稳定策略。
采用系统动力学方法，以公交价格联动体系优化对公交系统供需静态均衡的影响结果为基础，围绕公交价格联动策略建立公交需求子系统和公交供给子系统间正、负反馈回路，利用广州市交通调查数据标定模型参数和校核模型的准确性，仿真公交价格联动策略对公交需求和公交供给影响的动态变化趋势和时间累积效果。通过公交供、需变量与小客车数量之间的函数关系，获得公交价格联动策略对机动车主要排放物减排的效果，衡量公交价格联动策略对城市大气环境改善的作用。
本文针对城市公交价格策略，利用效用理论、博弈理论和系统科学的研究成果，以公交主体决策行为分析为基础，对公交价格联动体系进行了优化，通过建立公交价格联动策略演化博弈模型，描述采用不同价格策略公交运营者群体双方相互学习的速度和方向，应用系统动力学方法仿真与评价了公交价格联动策略对城市大气环境改善的作用。以上研究成果不仅能够完善公交价格策略优化和演化博弈理论研究的不足，同时对城市公交价格策略整合、公交资源的有效配置和实现公交优先发展具有重要的理论意义和实践价值。
The integration of public transport price strategies is an effective way to improveallocative efficiency of traffic resource. It is transit operating and price theory researchfocuses that using transit price linkage strategy is to maintain equilibrium betweentransit demand and supply. In operating, the inrratonality of transit price structuresystem and market failure of transit resource allocation may lead to the incoordinationbetween transit demand and supply, or transit participator utility and social welfare. Thispaper aims to gain equilibrium between transit demand and supply by transit pricelinkage strategy in an angle of behaviors of transit participator.
This paper models the game strategies and strategies’ payoff between partners oftransit system and optimizes the transit price linkage system to balance transit demandon modes with the problems in researches of transit paticipators’ behavior, strategyevolution game and strategy evaluation. Based on price strategy set and strategy payoff,evolution game model is advanced to identify the evolution process and results of pricestrategies. Furthemore, transit price stretegies are extended to transit system to developthe System Dynamic model for measuring the atmospheric environment improvementof transit price linkage strategies.
Analysising the behavior of traveller, competing and coorperating between transitoperators, control policies in transit market is to present the hierarchical relationshipamong paticipators with traffic behavior theory. In other words, with the travellerbehaviors, the optimization of transit operating strategies can achieve the maximizationof operators under transit price control and service level control conditions. Moreover,transit price transmission machanism is defined to represent the generation mechanismof price linkage strategy, including transit price transmission routes and transmissionmodes.
The bi-level programing theory is used to describe the leader-follower relationshipbetween operators and travellers. The upper level aims to for maximize the socialwelfare of transit operating with constrains on price control; constrains of lower levelare service capacity, service level and demand equilibrium with utility maximization oftraveller. The effect of price linkage strategy on social welfare, demand distribution andtraveller behavior is determined by bi-level model. Genetic Algorithm and SimulatingAnnealing algorithm are adopted to solve the optimization model. The experience studyof Guangzhou bus, BRT and metro, verifies the practical applicability of model andaccuracy and computational efficiency of algorithms.
Evolution game model of transit price strategy is developed to present the learningspeed and direction between operators with differ price strategies and find the evolutionstability strategy in transit strategy set by evolution game theory. The volution game model is used on bus and BRT network of Guangzhou to discussing the evolutionprocess and results of price strategies under different demand conditons.
Based on static equilibrium between transit demand and supply by the optimizationof transit price linkage system, system dynamic is used to simulate the vriation trendand accumulative effect of transit demand and supply after calibrating parameters andchecking model accuracy with traffic survey data of Guangzhou. The relationshipfunction, among transit demand, supply and car flow, obtains the emmission dropped ofvehicle major pollutions as results of price linkage strategy to evaluate the improvementof atmospherics evironment.
This paper aims to optimize transit price structure system and identify the course ofevolution and evolutionary stability strategy of transit price strategies with the researchachievements of utility theory, game theory and system science. Furthermore, theimprovement of atmospheric environment is measured by system dynamic analysis oftransit price linkage strategy. The researches can not only make up for the deficiency ofthe transit price optimization theory, but also provide the theory and experience forintegrating transit price strategies, the allocating transit resource and developing transtpreferentially.
引文
[1] Van Vugt M., Meertens R. M., Van Lange P. A. M.. Car versus publictransportation? The role of social value orientations in a real-life socialdilemma[J]. Journal of Applied Social Psychology,1995,25:258-278.
[2] Louis de Grange, Rodrigo Troncoso, Felipe González. An empirical evaluation ofthe impact of three urban transportation policies on transit use[J]. Transport Policy,2012,22:11-19.
[3] Juan Carlos Mu oz, Louis de Grange. On the development of public transit inlarge cities[J]. Research in Transportation Economics,2010,29(1):379-386.
[4] Yos Sunitiyoso, Shoji Matsumoto. Modelling a social dilemma of mode choicebased on commuters’ expectations and social learning[J]. European Journal ofOperational Research,2009,193:904–914.
[5]陆化普.交通规划理论与方法[M].北京:清华大学出版社,1998:137-181.
[6] Daniel McFadden. A Method of Smiulated Moments for Estimation of DiscreteResponse Models Without Numerical Integration[J]. Econometria,1989,57(5):995-1026.
[7] Caspar G. Chorus, Joan L. Walker, Moshe Ben-Akiva. A joint model of travelinformation acquisition and response to received messages[J]. TransportationResearch Part C: Emerging Technologies,2013,26:61-77.
[8] Dilum Dissanayake, Takayuki Morikawa. Investigating household vehicleownership, mode choice and trip sharing decisions using a combined revealedpreference/stated preference Nested Logit model: case study in BangkokMetropolitan Region[J]. Journal of Transport Geography,2010,18(3):402-410.
[9] Chu C.. A paired combinatorial LOGIT model for travel demand analysis[C].Proceedings of the Fifth World Conference on Transportation Researeh,1989,4:295-309.
[10] Bhat C. R.. A nested LOGIT model with covariance heterogeneity[J].Transportation Research Part B,1997,31:11-21.
[11] Wang J. Y. T., Yang H.. A game-theoretic analysis of competition in a deregulatedbus market[J]. Transportation Research Part E,2005,41:329-355.
[12] Marcotte P., Savard G, Zhu D.. Mathematical structure of a bi-level strategicPricing model[J]. European Journal of Operational Research,2009,193:552-566.
[13] Fisk C. S.. Game theory and transportation systems modeling[J]. TransportationResearch Part B,1984,18B(4/5):301-313.
[14] Gilbert Laporte, Juan A. Mesa, Federico Perea. A game theoretic framework forthe robust railway transit network design problem[J]. Transportation ResearchPart B: Methodological,2010,44(4):447-459.
[15] Konstantina Gkritza, Matthew G. Karlaftis, Fred L. Mannering. Estimatingmultimodal transit ridership with a varying fare structure[J]. TransportationResearch Part A,2011,45:148-160.
[16] Camille Kamga, M. An l Yaz c, Abhishek Singhal. Implementation of interactivetransit information kiosks at New York City transit facilities: Analysis of userutilization and lessons learned[J]. Transportation Research Part C: EmergingTechnologies,2013,35:218-231.
[17] Kenyon, S., Lyons, G.. The value of integrated multimodal traveler informationand its potential contribution to modal change[J]. Transportation Research Part F,2003,6(1):1-21.
[18] Juan de O a, Rocio de O a, Francisco J. Calvo. A classification tree approach toidentify key factors of transit service quality[J]. Expert Systems with Applications,2012,39(12):11164-11171.
[19] Abrate, G., Piacenza,M., Vannoni, D. The impact of integrated tariff systems onpublic transport demand: evidence from Italy[J]. Regional Science and UrbanEconomics,2009,39(2):120-127.
[20] Matas, A. Demand and revenue implications of an integrated public transportpolicy: the case of Madrid[J]. Transport Reviews,2004,24(2):195-217.
[21] Oliver Sch ller-Schwedes. The failure of integrated transport policy in Germany:a historical perspective[J]. Journal of Transport Geography,2010,18(1):85-96.
[22] Pedler, A., Paulley, N. Transland: the integration of transport and land useplanning[C]. In Paper presented at the European Transport conference2000.www.etcproceedings.org/paper/download/2593.
[23] European Commission. First thematic research summary: Economic aspects ofsustainable mobility[J]. DG Energy and Transport.2008,http://www.transport-research.info/Upload/Documents/200810/20081003_230251_18361_TRS%20Economic%20Aspects%20i1.2%20011008.pdf.
[24] Parry, I. W. H., Bento, A. M. Tax deductions, environmental policy, and the“double dividend” hypothesis[J]. Journal of Environmental Economics andManagement,2000,39(1):67-96.
[25] UNEP. Class of2006: Industry report cards on environment and socialresponsibility[M]. United Nations Environment Programme,2006.
[26] Timothy F. Welch, Sabyasachee Mishra. A measure of equity for public transitconnectivity[J]. Journal of Transport Geography,2013,33:29-41.
[27] Cebollada, A. Mobility and labour market exclusion in the BarcelonaMetropolitan Region[J]. Journal of Transport Geography,2009,17(3):226-233.
[28] Inge Mayere S, Stef Proost. Reforming transport pricing: an economist’sperspective on equity[M]. Efficiency and acceptability Center for EconomicStudies-K. U. Leuven.2002,9: l-2.
[29] Todd Litman. Evaluating Transportation Equity[M]. Victoria Transport policyinstitute,2006,3:3-4.
[30] Praveen Maghelal. Investigating the relationships among rising fuel prices,increased transit ridership, and CO2emissions[J]. Transportation Research Part D:Transport and Environment,2011,16(3):232-235.
[31] Eddington, R. The Eddington transport-study Main report: Transport’s role insustaining the UK’s productivity and competitiveness,2006,12.
[32] Gross, R., Heptonstall, P., Anable, J., Greenacre, P. What policies are effective atreducing carbon missions from surface passenger transport?[OL]. UK EnergyResearch Centre.2009, http://www.ukerc.ac.uk/ResearchProgrammes/TechnologyandPolicyAssessment/0904TransportReport.aspx.
[33] Kemp, R. Traction energy metrics. Research programme[M]. Rail Safety&Standards Board,2007, http://www.rssb.co.uk/pdf/reports/research/T618_tracti-on-energy-metrics_nal.pdf.
[34] Dominic Stead. Institutional aspects of integrating transport, environment andhealth policies[J]. Transport Policy,2008,15(3):139-148.
[35] Hanna Armelius. An Integrated Approach to Urban Road Pricing[J]. Journal ofTransport Economics and Policy Part I,2005,39:75-92.
[36] Graham Qarkhurst, Jeremy Richardson. Modal integration of bus and car in UKlocal transport policy: the case for strategic environmental assessment[J]. Journalof Transport Geography,2002,10:195-206.
[37] Maria Berrittella, Antonella Certa, Mario Enea, PietroZito. Transport policy andclimate change: How to decide when experts disagree[J]. Environmental science&policy,2008,11:307-314.
[38] Shiyong Liu, Konstantinos P. Triantis, Sudipta Sarangi. A framework forevaluating the dynamic impacts of a congestion pricing policy for a transportationsocioeconomic system[J]. Transportation Research Part A,2010,doi:10.1016/j.tra.2010.04.001.
[39] Qiping Shen, Qing Chen, Bo-sin Tang, Stanley Yeung, Yucun Hu, Gordon Cheung.A system dynamics model for the sustainable land use planning anddevelopment[J]. Habitat International,2009,33:15-25.
[40]徐循初.关于确定城市交通方式结构的研究[J].城市规划汇刊,2003,1:13-15.
[41]王召强,李彬.道路网容量约束下的交通结构忧化方法研究[J].交通与运输,2006,12:18-22.
[42]陈星光,周晶,朱振涛.城市交通出行方式选择的演化博弈分析[J].管理工程学报,2009,2(23):140-142.
[43]黄海军, Michael G. H. Bell,杨海.公共与个体竞争交通系统的定价研究[J].管理科学学报,1998,1(2):17-23.
[44]李树彬,陈峰.城市间多模式交通旅客票价制定的博弈分析[J].城市交通,2006,28(3):8-10.
[45]卞长志,陆化普.城市轨道交通和常规公交的票价博弈研究.公路工程,2009,34(1):45-49.
[46]周晶,徐晏.公共交通网络系统的广义Nash经营博弈模型[J].系统工程学报,2001,4(16):261-266.
[47]陈宽民,罗小强.城市快速轨道交通合理票价的博弈分析[J].长安大学学报(自然科学版),2005,25(4):52-55.
[48]周晶,盛昭瀚,何建敏.公交网络车费设定问题的Stackelberg博弈模型[J].系统工程学报,2000,3(15):231-237.
[49]张秋萍,陈义华.基于非集计模型的交通方式选择研究[J].铁道运输与经济,2010,32(1):75-78.
[50] Su B. B., Chang H., Chen Y. Z., He D. R.. A game theory model of urban publictraffic networks[J]. Physic A,2007,379:291-297.
[51]肖海燕,王先甲.政府参与模式下出行者出行方式选择行为的演化博弈分析[J].管理工程学报,2010,24(2):115-118.
[52]张迦南,赵鹏.综合运输通道规划方法研究[J].北京交通大学学报,2010,34(3):142-147.
[53]邱玉琢,陈森发.运输方式互利共生的非线性动力学模型[J].系统工程,2006,24(7):8-12.
[54]范文婷,黄中祥.城市交通方式间的互利合作模型[J].铁道科学与工程学报,2009,6(2):69-73.
[55]马国荣.城市公共交通系统发展问题研究[D].西安:长安大学,2003:32-38.
[56]杨明,谢辉.基于循环经济的城市客运交通结构优化模型[J].长沙理工大学学报(自然科学版),2006,4(3):24-27.
[57]郭钰.对城市公交服务价格免费政策的理性思考——从湖南省常宁市“免费公交”谈起[J].价格理论与实践,2011:21-22.
[58]杨文银.试论交通公平[M].综合运输,2006:13-16.
[59]李硕,杨运平.试论城市交通应遵循公平原则[J].城市公共交通,2006,5:31.
[60]陆丹丹,张生瑞,郭勐.城市交通公平性分析及对策[J].交通科技与经济,2008(2):103-105.
[61]石京,杨朗,黄谦,应习文.交通公平性的衡量角度与模型描述[J].铁道工程学报,2009,1:97-101.
[62]张小琴,范东旭.基于交通环境承载力的城市人口容量预测研究——以太原市为例[J].科学技术与工程,2012,12(9):2229-2233.
[63]金力.公交资源的整合思路与建议[J].资源节约与环保,2012,6:17-19.
[64]李晓燕,陈红,胡晗.交通环境承载力及其定量化方法初探[J].公路交通科技,2008,1:151-154.
[65]向睿.交通能耗在城市绿色交通规划中的应用[D].成都:西南交通大学,2011:82-88.
[66]贾顺平,彭宏勤,刘爽,张笑杰.交通运输与能源消耗相关研究综述[J].交通运输系统工程与信息,2009,9(3):6-16.
[67]郭李梅.基于环保约束的城市客运交通结构优化方法研究[D].长春:吉林大学,2009:28-35.
[68]马荣国,刘洪营,梁国华.城市客运交通结构评价指标[J].交通运输工程学报,2004,4(1):86-91.
[69]苏城元.基于低碳发展模式的城市交通结构优化研究[D].上海:上海交通大学,2012:27-31.
[70]王炜,杨新苗,陈学武.城市公共交通系统规划方法与管理技术[M].北京:科学出版社,2002,2.
[71]张生瑞,周伟,姜彩良,李绍岩.城市客运结构评价体系及评价方法[J].长安大学学报,2004,24(4):59-62.
[72]郑晨辉,杨国利,王大淼.城市公交系统动态模拟[J].哈尔滨商业大学学报(自然科学版),2004,2(20):255-257.
[73]刘惠玲,关伟.基于系统动力学的城市公交系统模型[J].城市公共交通,2007,6:24-31.
[74]申金升,雷黎,徐一飞.城市交通可持续发展的动力学机制研究[J].系统工程,1997,15(5):8-12.
[75] M J LIGHTHILL, G B WHITHAM. On kinematic waves II: A traffic flow theoryon long crowded roads[J]. Proceedings of the Royal Society of London,1955,229:317-345.
[76] Pareto. Cours d’Economic Politique2Vols. Lausanne: F. Rouge,1896.
[77]邵祖峰.博弈论在道路交通管理中的应用[J].道路交通与安全,2006,6(03):8-11.
[78]卢雪,孙英隽.城市优惠公交的博弈分析[J].武汉理工大学学报(社会科学版),2013,26(1):21-25.
[79]贾子若,宋守信.基于博弈论的城市交通拥堵问题分析[J].物流技术,2012,31(3):56-59.
[80]关宏志.非集计模型—交通行为分析的工具[M].北京:人民交通出版社,2004.
[81] McFadden D. Conditional logit analysis of qualitative choice behavior[M].Zaremmbka P.(Ed.), Frontiers in econometrics, Academic Press, New York,1973:24-54.
[82]周小梅.我国公交补贴政策及其改革——从桂林免费公交谈起[J].价格理论与实践,2002,(8):25-27.
[83]张志哲,张晓春,赵再先.公交行业发展模式比较研究[J].北方交通,2006,(9):64-68.
[84] Gwilliam, K. M., Nash, C. A., Mackie, P. J. Deregulating the bus industry inBritain; the case against[J]. Transport Reviews,1985,5(2):105-132.
[85] Judith Y. T. Wang, Hai Yang. A game-theoretic analysis of competition in aderegulated bus market[J]. Transportation Research Part E,2005,41:329-355.
[86]冷淑莲,江野军.价格传导机制的分析[J].价格天地,2004,12:10-13.
[87]王永志.关于价格传导机制的若干问题研究[J].价格理论与实践,2005,(2):10-13.
[88]孙胜祥,李振宇.海军装备价格传导机理研究[J].武汉理工大学学报·信息与管理工程版,2008,6(30):955-958.
[89] Beno t Colson, Patrice Marcotte, Gilles Savard. An overview of bileveloptimization[J]. Ann Oper Res,2007,153:235-256.
[90] Kolstad, C. D. A review of the literature on bi-level mathematical programming[J].Technical Report,1985, LA-10284-MS, Los Alamos National Laboratory, LosAlamos, New Mexico, USA.
[91] Colson, B. Mathematical programs with equilibrium constraints and nonlinearbilevel programming problems[D]. Numur: University of Namur,1999.
[92] Colson, B., Marcotte, P.,&Savard, G. A trust-region method for nonlinearprogramming: algorithm and computational experience[J]. ComputationalOptimization and Applications,2005,30.
[93] S. Dempe, H. Schmidt. On an algorithm solving two-level programming problemswith nonunique lower-level solutions[J]. Computational Optimization andApplications,1996,6:227-249.
[94] P. Hansen, B. Jaumard, G. Savard. New branch-and-bound rules for linear bilevelprogramming[J]. SIAM Journal on Scientific and Statistical Computing1992,13:1194-1217.
[95] X. Deng, Complexity issues in bilevel linear programming, in: A. Migdalas, P.M.Pardalos, P. Varbrand(Eds.), Multilevel Optimization: Algorithms andApplications[M]. Kluwer Academic Publishers, Dordrecht,1998:149-164.
[96] Case, L. M. An.1penalty function approach to the nonlinear bilevelprogramming problem[D]. Canada: University of Waterloo,1999.
[97] Surabhi Sinha, S.B. Sinha. KKT transformation approach for multi-objectivemulti-level linear programming problems[J]. European Journal of OperationalResearch,2002,143:19-31.
[98] Ben-Ayed, O.,&Blair, C. Computational difficulties of bilevel linearprogramming[J]. Operations Research,1990,38:556-560.
[99] Horowitz, A., Sheth, J. Ride sharing to work, an attitudinal analysis[J].Transportation Research Record,1977,637:1-8.
[100] Younes Hamdouch, H. W. Ho, Agachai Sumalee, Guodong Wang. Schedule-basedtransit assignment model with vehicle capacity and seat availability[J].Transportation Research Part B,2011,45:1805-1830.
[101]李和成.非线性双层规划问题的遗传算法研究[D].西安:西安电子科技大学,2009:21-22.
[102] Goldberg, D. E. Genetic Algorithms in Search, Optimization and MachineLearning, Reading[M]. United States: Addison-Wesley,1989.
[103] A. Vasan, Komaragiri Srinivasa Raju. Comparative analysis of SimulatedAnnealing, Simulated Quenching and Genetic Algorithms for optimal reservoiroperation[J]. Applied Soft Computing,2009,9:274-281.
[104]康立山,谢云.非数值并行计算——模拟退火算法[M].北京:科学出版社,1998,22-38:195-215.
[105] Maynard Smith. Game theory and the evolution of fighting, in: John MaynardSmith (Ed.), On Evolution[M]. UK: Edinburgh University Press,1972:8-28.
[106] Josef Hofbauer, William H. Sandholm. Evolution in games with randomlydisturbed payoffs[J]. Journal of Economic Theory,2007,132(1):47-69.
[107] Binmore K, L Samuelson. Evolution and mixed strategies[J]. Games andEconomic Behavior,2001,34:200-260.
[108] Van Damme E.. Stability and Perfection of Nash Equilibria[M]. Berlin:Springer-Berlag Press,1991.
[109] Weibull W.. Evolutionary Game Theory [M]. Cambridge: MIT Press,1995.
[110]吴昊,杨梅英,陈良猷.合作竞争博弈中的复杂性与演化均衡的稳定性分析[J].系统工程理论与实践,2004,2:90-94.
[111]徐建华.现代地理学中的数学方法[M].北京:高等教育出版社,1996:230-232.
[112] Ford A. Modeling the environment: An introduction to system dynamics modelingof environmental systems[M]. Washington, D.C.: Island Press,1999.
[113] Loague K., VanderKwaak J.E.. Physics-based hydrologic response simulation:platinum bridge,1958Edsel, or useful tool[J]. Hydrological Processes,2004,18:2949-2956.
[114]王其藩.系统动力学[M].北京:清华大学出版社,1988:89-93.
[115]王其藩.高级系统动力学[M].北京:清华大学出版社,1995:30-33.
[116]蔡皓,谢绍东.中国不同排放标准机动车排放因子的确定[J].北京大学学报(自然科学版),2010,46(3):318-326.
[117] Cai H, Xie S D. Estimation of vehicular emission inventories in China from1980to2005[J]. Atmospheric Environment,2007,41(39):8963-8979.
[118]李国香,杨满宏.高等级公路机动车污染物排放因子的实验研究[J].交通环保,1998,2:8-16.
[2] Louis de Grange, Rodrigo Troncoso, Felipe González. An empirical evaluation ofthe impact of three urban transportation policies on transit use[J]. Transport Policy,2012,22:11-19.
[3] Juan Carlos Mu oz, Louis de Grange. On the development of public transit inlarge cities[J]. Research in Transportation Economics,2010,29(1):379-386.
[4] Yos Sunitiyoso, Shoji Matsumoto. Modelling a social dilemma of mode choicebased on commuters’ expectations and social learning[J]. European Journal ofOperational Research,2009,193:904–914.
[5]陆化普.交通规划理论与方法[M].北京:清华大学出版社,1998:137-181.
[6] Daniel McFadden. A Method of Smiulated Moments for Estimation of DiscreteResponse Models Without Numerical Integration[J]. Econometria,1989,57(5):995-1026.
[7] Caspar G. Chorus, Joan L. Walker, Moshe Ben-Akiva. A joint model of travelinformation acquisition and response to received messages[J]. TransportationResearch Part C: Emerging Technologies,2013,26:61-77.
[8] Dilum Dissanayake, Takayuki Morikawa. Investigating household vehicleownership, mode choice and trip sharing decisions using a combined revealedpreference/stated preference Nested Logit model: case study in BangkokMetropolitan Region[J]. Journal of Transport Geography,2010,18(3):402-410.
[9] Chu C.. A paired combinatorial LOGIT model for travel demand analysis[C].Proceedings of the Fifth World Conference on Transportation Researeh,1989,4:295-309.
[10] Bhat C. R.. A nested LOGIT model with covariance heterogeneity[J].Transportation Research Part B,1997,31:11-21.
[11] Wang J. Y. T., Yang H.. A game-theoretic analysis of competition in a deregulatedbus market[J]. Transportation Research Part E,2005,41:329-355.
[12] Marcotte P., Savard G, Zhu D.. Mathematical structure of a bi-level strategicPricing model[J]. European Journal of Operational Research,2009,193:552-566.
[13] Fisk C. S.. Game theory and transportation systems modeling[J]. TransportationResearch Part B,1984,18B(4/5):301-313.
[14] Gilbert Laporte, Juan A. Mesa, Federico Perea. A game theoretic framework forthe robust railway transit network design problem[J]. Transportation ResearchPart B: Methodological,2010,44(4):447-459.
[15] Konstantina Gkritza, Matthew G. Karlaftis, Fred L. Mannering. Estimatingmultimodal transit ridership with a varying fare structure[J]. TransportationResearch Part A,2011,45:148-160.
[16] Camille Kamga, M. An l Yaz c, Abhishek Singhal. Implementation of interactivetransit information kiosks at New York City transit facilities: Analysis of userutilization and lessons learned[J]. Transportation Research Part C: EmergingTechnologies,2013,35:218-231.
[17] Kenyon, S., Lyons, G.. The value of integrated multimodal traveler informationand its potential contribution to modal change[J]. Transportation Research Part F,2003,6(1):1-21.
[18] Juan de O a, Rocio de O a, Francisco J. Calvo. A classification tree approach toidentify key factors of transit service quality[J]. Expert Systems with Applications,2012,39(12):11164-11171.
[19] Abrate, G., Piacenza,M., Vannoni, D. The impact of integrated tariff systems onpublic transport demand: evidence from Italy[J]. Regional Science and UrbanEconomics,2009,39(2):120-127.
[20] Matas, A. Demand and revenue implications of an integrated public transportpolicy: the case of Madrid[J]. Transport Reviews,2004,24(2):195-217.
[21] Oliver Sch ller-Schwedes. The failure of integrated transport policy in Germany:a historical perspective[J]. Journal of Transport Geography,2010,18(1):85-96.
[22] Pedler, A., Paulley, N. Transland: the integration of transport and land useplanning[C]. In Paper presented at the European Transport conference2000.www.etcproceedings.org/paper/download/2593.
[23] European Commission. First thematic research summary: Economic aspects ofsustainable mobility[J]. DG Energy and Transport.2008,http://www.transport-research.info/Upload/Documents/200810/20081003_230251_18361_TRS%20Economic%20Aspects%20i1.2%20011008.pdf.
[24] Parry, I. W. H., Bento, A. M. Tax deductions, environmental policy, and the“double dividend” hypothesis[J]. Journal of Environmental Economics andManagement,2000,39(1):67-96.
[25] UNEP. Class of2006: Industry report cards on environment and socialresponsibility[M]. United Nations Environment Programme,2006.
[26] Timothy F. Welch, Sabyasachee Mishra. A measure of equity for public transitconnectivity[J]. Journal of Transport Geography,2013,33:29-41.
[27] Cebollada, A. Mobility and labour market exclusion in the BarcelonaMetropolitan Region[J]. Journal of Transport Geography,2009,17(3):226-233.
[28] Inge Mayere S, Stef Proost. Reforming transport pricing: an economist’sperspective on equity[M]. Efficiency and acceptability Center for EconomicStudies-K. U. Leuven.2002,9: l-2.
[29] Todd Litman. Evaluating Transportation Equity[M]. Victoria Transport policyinstitute,2006,3:3-4.
[30] Praveen Maghelal. Investigating the relationships among rising fuel prices,increased transit ridership, and CO2emissions[J]. Transportation Research Part D:Transport and Environment,2011,16(3):232-235.
[31] Eddington, R. The Eddington transport-study Main report: Transport’s role insustaining the UK’s productivity and competitiveness,2006,12.
[32] Gross, R., Heptonstall, P., Anable, J., Greenacre, P. What policies are effective atreducing carbon missions from surface passenger transport?[OL]. UK EnergyResearch Centre.2009, http://www.ukerc.ac.uk/ResearchProgrammes/TechnologyandPolicyAssessment/0904TransportReport.aspx.
[33] Kemp, R. Traction energy metrics. Research programme[M]. Rail Safety&Standards Board,2007, http://www.rssb.co.uk/pdf/reports/research/T618_tracti-on-energy-metrics_nal.pdf.
[34] Dominic Stead. Institutional aspects of integrating transport, environment andhealth policies[J]. Transport Policy,2008,15(3):139-148.
[35] Hanna Armelius. An Integrated Approach to Urban Road Pricing[J]. Journal ofTransport Economics and Policy Part I,2005,39:75-92.
[36] Graham Qarkhurst, Jeremy Richardson. Modal integration of bus and car in UKlocal transport policy: the case for strategic environmental assessment[J]. Journalof Transport Geography,2002,10:195-206.
[37] Maria Berrittella, Antonella Certa, Mario Enea, PietroZito. Transport policy andclimate change: How to decide when experts disagree[J]. Environmental science&policy,2008,11:307-314.
[38] Shiyong Liu, Konstantinos P. Triantis, Sudipta Sarangi. A framework forevaluating the dynamic impacts of a congestion pricing policy for a transportationsocioeconomic system[J]. Transportation Research Part A,2010,doi:10.1016/j.tra.2010.04.001.
[39] Qiping Shen, Qing Chen, Bo-sin Tang, Stanley Yeung, Yucun Hu, Gordon Cheung.A system dynamics model for the sustainable land use planning anddevelopment[J]. Habitat International,2009,33:15-25.
[40]徐循初.关于确定城市交通方式结构的研究[J].城市规划汇刊,2003,1:13-15.
[41]王召强,李彬.道路网容量约束下的交通结构忧化方法研究[J].交通与运输,2006,12:18-22.
[42]陈星光,周晶,朱振涛.城市交通出行方式选择的演化博弈分析[J].管理工程学报,2009,2(23):140-142.
[43]黄海军, Michael G. H. Bell,杨海.公共与个体竞争交通系统的定价研究[J].管理科学学报,1998,1(2):17-23.
[44]李树彬,陈峰.城市间多模式交通旅客票价制定的博弈分析[J].城市交通,2006,28(3):8-10.
[45]卞长志,陆化普.城市轨道交通和常规公交的票价博弈研究.公路工程,2009,34(1):45-49.
[46]周晶,徐晏.公共交通网络系统的广义Nash经营博弈模型[J].系统工程学报,2001,4(16):261-266.
[47]陈宽民,罗小强.城市快速轨道交通合理票价的博弈分析[J].长安大学学报(自然科学版),2005,25(4):52-55.
[48]周晶,盛昭瀚,何建敏.公交网络车费设定问题的Stackelberg博弈模型[J].系统工程学报,2000,3(15):231-237.
[49]张秋萍,陈义华.基于非集计模型的交通方式选择研究[J].铁道运输与经济,2010,32(1):75-78.
[50] Su B. B., Chang H., Chen Y. Z., He D. R.. A game theory model of urban publictraffic networks[J]. Physic A,2007,379:291-297.
[51]肖海燕,王先甲.政府参与模式下出行者出行方式选择行为的演化博弈分析[J].管理工程学报,2010,24(2):115-118.
[52]张迦南,赵鹏.综合运输通道规划方法研究[J].北京交通大学学报,2010,34(3):142-147.
[53]邱玉琢,陈森发.运输方式互利共生的非线性动力学模型[J].系统工程,2006,24(7):8-12.
[54]范文婷,黄中祥.城市交通方式间的互利合作模型[J].铁道科学与工程学报,2009,6(2):69-73.
[55]马国荣.城市公共交通系统发展问题研究[D].西安:长安大学,2003:32-38.
[56]杨明,谢辉.基于循环经济的城市客运交通结构优化模型[J].长沙理工大学学报(自然科学版),2006,4(3):24-27.
[57]郭钰.对城市公交服务价格免费政策的理性思考——从湖南省常宁市“免费公交”谈起[J].价格理论与实践,2011:21-22.
[58]杨文银.试论交通公平[M].综合运输,2006:13-16.
[59]李硕,杨运平.试论城市交通应遵循公平原则[J].城市公共交通,2006,5:31.
[60]陆丹丹,张生瑞,郭勐.城市交通公平性分析及对策[J].交通科技与经济,2008(2):103-105.
[61]石京,杨朗,黄谦,应习文.交通公平性的衡量角度与模型描述[J].铁道工程学报,2009,1:97-101.
[62]张小琴,范东旭.基于交通环境承载力的城市人口容量预测研究——以太原市为例[J].科学技术与工程,2012,12(9):2229-2233.
[63]金力.公交资源的整合思路与建议[J].资源节约与环保,2012,6:17-19.
[64]李晓燕,陈红,胡晗.交通环境承载力及其定量化方法初探[J].公路交通科技,2008,1:151-154.
[65]向睿.交通能耗在城市绿色交通规划中的应用[D].成都:西南交通大学,2011:82-88.
[66]贾顺平,彭宏勤,刘爽,张笑杰.交通运输与能源消耗相关研究综述[J].交通运输系统工程与信息,2009,9(3):6-16.
[67]郭李梅.基于环保约束的城市客运交通结构优化方法研究[D].长春:吉林大学,2009:28-35.
[68]马荣国,刘洪营,梁国华.城市客运交通结构评价指标[J].交通运输工程学报,2004,4(1):86-91.
[69]苏城元.基于低碳发展模式的城市交通结构优化研究[D].上海:上海交通大学,2012:27-31.
[70]王炜,杨新苗,陈学武.城市公共交通系统规划方法与管理技术[M].北京:科学出版社,2002,2.
[71]张生瑞,周伟,姜彩良,李绍岩.城市客运结构评价体系及评价方法[J].长安大学学报,2004,24(4):59-62.
[72]郑晨辉,杨国利,王大淼.城市公交系统动态模拟[J].哈尔滨商业大学学报(自然科学版),2004,2(20):255-257.
[73]刘惠玲,关伟.基于系统动力学的城市公交系统模型[J].城市公共交通,2007,6:24-31.
[74]申金升,雷黎,徐一飞.城市交通可持续发展的动力学机制研究[J].系统工程,1997,15(5):8-12.
[75] M J LIGHTHILL, G B WHITHAM. On kinematic waves II: A traffic flow theoryon long crowded roads[J]. Proceedings of the Royal Society of London,1955,229:317-345.
[76] Pareto. Cours d’Economic Politique2Vols. Lausanne: F. Rouge,1896.
[77]邵祖峰.博弈论在道路交通管理中的应用[J].道路交通与安全,2006,6(03):8-11.
[78]卢雪,孙英隽.城市优惠公交的博弈分析[J].武汉理工大学学报(社会科学版),2013,26(1):21-25.
[79]贾子若,宋守信.基于博弈论的城市交通拥堵问题分析[J].物流技术,2012,31(3):56-59.
[80]关宏志.非集计模型—交通行为分析的工具[M].北京:人民交通出版社,2004.
[81] McFadden D. Conditional logit analysis of qualitative choice behavior[M].Zaremmbka P.(Ed.), Frontiers in econometrics, Academic Press, New York,1973:24-54.
[82]周小梅.我国公交补贴政策及其改革——从桂林免费公交谈起[J].价格理论与实践,2002,(8):25-27.
[83]张志哲,张晓春,赵再先.公交行业发展模式比较研究[J].北方交通,2006,(9):64-68.
[84] Gwilliam, K. M., Nash, C. A., Mackie, P. J. Deregulating the bus industry inBritain; the case against[J]. Transport Reviews,1985,5(2):105-132.
[85] Judith Y. T. Wang, Hai Yang. A game-theoretic analysis of competition in aderegulated bus market[J]. Transportation Research Part E,2005,41:329-355.
[86]冷淑莲,江野军.价格传导机制的分析[J].价格天地,2004,12:10-13.
[87]王永志.关于价格传导机制的若干问题研究[J].价格理论与实践,2005,(2):10-13.
[88]孙胜祥,李振宇.海军装备价格传导机理研究[J].武汉理工大学学报·信息与管理工程版,2008,6(30):955-958.
[89] Beno t Colson, Patrice Marcotte, Gilles Savard. An overview of bileveloptimization[J]. Ann Oper Res,2007,153:235-256.
[90] Kolstad, C. D. A review of the literature on bi-level mathematical programming[J].Technical Report,1985, LA-10284-MS, Los Alamos National Laboratory, LosAlamos, New Mexico, USA.
[91] Colson, B. Mathematical programs with equilibrium constraints and nonlinearbilevel programming problems[D]. Numur: University of Namur,1999.
[92] Colson, B., Marcotte, P.,&Savard, G. A trust-region method for nonlinearprogramming: algorithm and computational experience[J]. ComputationalOptimization and Applications,2005,30.
[93] S. Dempe, H. Schmidt. On an algorithm solving two-level programming problemswith nonunique lower-level solutions[J]. Computational Optimization andApplications,1996,6:227-249.
[94] P. Hansen, B. Jaumard, G. Savard. New branch-and-bound rules for linear bilevelprogramming[J]. SIAM Journal on Scientific and Statistical Computing1992,13:1194-1217.
[95] X. Deng, Complexity issues in bilevel linear programming, in: A. Migdalas, P.M.Pardalos, P. Varbrand(Eds.), Multilevel Optimization: Algorithms andApplications[M]. Kluwer Academic Publishers, Dordrecht,1998:149-164.
[96] Case, L. M. An.1penalty function approach to the nonlinear bilevelprogramming problem[D]. Canada: University of Waterloo,1999.
[97] Surabhi Sinha, S.B. Sinha. KKT transformation approach for multi-objectivemulti-level linear programming problems[J]. European Journal of OperationalResearch,2002,143:19-31.
[98] Ben-Ayed, O.,&Blair, C. Computational difficulties of bilevel linearprogramming[J]. Operations Research,1990,38:556-560.
[99] Horowitz, A., Sheth, J. Ride sharing to work, an attitudinal analysis[J].Transportation Research Record,1977,637:1-8.
[100] Younes Hamdouch, H. W. Ho, Agachai Sumalee, Guodong Wang. Schedule-basedtransit assignment model with vehicle capacity and seat availability[J].Transportation Research Part B,2011,45:1805-1830.
[101]李和成.非线性双层规划问题的遗传算法研究[D].西安:西安电子科技大学,2009:21-22.
[102] Goldberg, D. E. Genetic Algorithms in Search, Optimization and MachineLearning, Reading[M]. United States: Addison-Wesley,1989.
[103] A. Vasan, Komaragiri Srinivasa Raju. Comparative analysis of SimulatedAnnealing, Simulated Quenching and Genetic Algorithms for optimal reservoiroperation[J]. Applied Soft Computing,2009,9:274-281.
[104]康立山,谢云.非数值并行计算——模拟退火算法[M].北京:科学出版社,1998,22-38:195-215.
[105] Maynard Smith. Game theory and the evolution of fighting, in: John MaynardSmith (Ed.), On Evolution[M]. UK: Edinburgh University Press,1972:8-28.
[106] Josef Hofbauer, William H. Sandholm. Evolution in games with randomlydisturbed payoffs[J]. Journal of Economic Theory,2007,132(1):47-69.
[107] Binmore K, L Samuelson. Evolution and mixed strategies[J]. Games andEconomic Behavior,2001,34:200-260.
[108] Van Damme E.. Stability and Perfection of Nash Equilibria[M]. Berlin:Springer-Berlag Press,1991.
[109] Weibull W.. Evolutionary Game Theory [M]. Cambridge: MIT Press,1995.
[110]吴昊,杨梅英,陈良猷.合作竞争博弈中的复杂性与演化均衡的稳定性分析[J].系统工程理论与实践,2004,2:90-94.
[111]徐建华.现代地理学中的数学方法[M].北京:高等教育出版社,1996:230-232.
[112] Ford A. Modeling the environment: An introduction to system dynamics modelingof environmental systems[M]. Washington, D.C.: Island Press,1999.
[113] Loague K., VanderKwaak J.E.. Physics-based hydrologic response simulation:platinum bridge,1958Edsel, or useful tool[J]. Hydrological Processes,2004,18:2949-2956.
[114]王其藩.系统动力学[M].北京:清华大学出版社,1988:89-93.
[115]王其藩.高级系统动力学[M].北京:清华大学出版社,1995:30-33.
[116]蔡皓,谢绍东.中国不同排放标准机动车排放因子的确定[J].北京大学学报(自然科学版),2010,46(3):318-326.
[117] Cai H, Xie S D. Estimation of vehicular emission inventories in China from1980to2005[J]. Atmospheric Environment,2007,41(39):8963-8979.
[118]李国香,杨满宏.高等级公路机动车污染物排放因子的实验研究[J].交通环保,1998,2:8-16.