城市交通与居住地选择之间关系
详细信息    本馆镜像全文|  推荐本文 |  |   获取CNKI官网全文
摘要
我国土地利用的半市场化、高密度开发、人口密集和快速城市化等现状特征与欧美等国的发展状况存在较大的差异,国外的土地利用模型大多数不完全适用于我国国情,且国外不同模型开发的目的和侧重点不同,对于我国而言,交通与土地利用一体化模型构思和实用化还有很长的路要走,需要研究适合高密度开发和快速城市化发展的机理模型,积累本土化研究经验,在理论上或规划模型方法上必须要获得突破。本论文立足我国城市发展实际,研究城市居民居住地选择与交通系统相互关系的理论与方法,为城市交通规划制定、交通政策分析以及城市空间结构调整提供理论基础。
     1为了检验交通与活动场所之间关系,通过建立房主与移动家庭之间的谈判模型,得到并证明了城市每个小区每期房价的NASH均衡解,每个移动家庭根据均衡房价、位置引力、广义交通费用得到各个小区的效用,通过模糊效用估计,获得候选小区集,并在候选集上采用非协作博弈的混合策略。在此基础上,提出了谈判机制混合策略选择居住地一定存在均衡解,并通过构造函数,利用Brouwer不动点定理进行证明。通过设计求解谈判机制模型的算法,并基于一个算例,分析了道路改善前后居民移动居住地的不同,给出了引导居民对居住地选择方法,针对交通影响居住地选择的特点,对城市结构完善提出了规划与管理建议。
     2建立住房估价模型,并通过建模探讨轨道交通与其它交通方式之间的相互衔接,获得家庭交通阻抗。每个移动家庭根据各小区的住房价格、享乐成本和家庭广义交通费用,获得各小区对自己的吸引力,并由贴近度得到各小区的满意度,移动家庭根据适当的满意度和价格,获得家庭剩余最大的居住地。
     3根据住房价格、家庭的广义交通阻抗和小区享乐设施的完备性,建立对各小区居住地进行满意度评估模型,并结合家庭收入和房价建立家庭消费剩余模型。通过对路网实行区域或警戒线拥挤征收政策,由家庭每个成员不同出行目的的旅行链特征和相应的出行方式,得到家庭的通勤广义交通阻抗和节假日娱乐出行的广义交通阻抗,并根据家庭消费剩余模型获得家庭剩余最大的居住地。最后,通过例证不同拥挤征收方式和征收水平,观察拥挤征收对居民居住地选择的影响。
     4为了在交叉口合理地分配通行权,通过代理人技术,获得各个相邻的代理人之间相互博弈模型,并建立衡量双方谈判效果的Nash均衡模型,证明了双方谈判的Nash均衡解的存在性,得到了各个相邻代理人之间相互博弈的均衡解,从而获得路网的均衡价格(费用)。根据路网的均衡价格,各个驾驶者采取费用总和最小的路径策略或模糊对比策略,选择对自己最有利的路径。结果表明:合同模式算法使得各交叉口的信号控制进行协调统一,充分发挥交通管理控制的主观能动性,并把管理者的主动性和驾驶者的个人理性行为结合起来,使得路网系统效用提高55.58%,是研究路网系统最优和用户最优之间相互协调有效方法。为了进一步提高整个路网的运行效率,采取对交叉口的信号控制方案进行优化策略。根据相邻交叉口流出交通流大小和方向不同,通过动态模糊控制手段,采用弹性信号周期和最大隶属度原则,合理地动态分配交叉口信号相位的绿灯时间。驾驶者通过对路径察觉,获得最小察觉阻抗路径,并与智能交通系统提供的各路径阻抗进行对比,选择最大满意度的路径。结果表明,动态模糊控制和决策法与最短路径法的交通流分配结果基本一致,模糊最优控制使得信号交叉口的控制效用进一步提高50%以上,且动态模糊控制和决策法更能反映人的行为,是一种有效的交通控制方法。
China's land use is half market, high-density development, population density and rapid urbanization the current characteristics which is a big different from Europe and the United States and other countries. Most of the foreign land-use models are not fully applicable to the conditions of our country, and the purpose and focus of the foreign development different models are different. For China, ideas and practical are still a long way to go about transport and land use the integration model. The mechanism model of high-density development and the rapid urbanization need to be studied. So localization experience is accumulated, and theory or planning model approach must be to a breakthrough. In this dissertation, some theories and methodologies about the relationship between dynamic traffic and residential location choice were proposed in accordance with the special characteristics of Chinese cities. This dissertation lays a solid theoretical foundation for urban transportation planning, traffic policy analysis, and urban spatial structure alteration.
     1 In order to test the relationship between transport and residential location, the negotiation model was proposed between the homeowner and mobile home, and NASH equilibrium solution of city each district housing prices was got and proven. The effectiveness of the various districts of each mobile home was got by a balanced price, the location gravity and the generalized transportation imprecise. The candidate cell set was received by the fuzzy effectiveness estimation, and a mixed strategy of non-cooperative game was adopted in the candidate cell set. The existence of the balanced solution was proposed to choose their residential location about hybrid strategy negotiation mechanism on the basis of this mixed strategy. A function was constructed, and the existence of the balanced solution was proven by Brouwer's fixed point theorem. The algorithm of solving the negotiation model was proposed. Base on a numerical example, residential location choice was analyzed by the road improvement, and guidance method of the resident choice location is given. According to traffic impact residential choice features, structural perfection of urban planning and management suggestion is proposed.
     2 A housing valuation models is established. The interface link is explored through the establishment of model between the rail transit and the other transport modes, and family transportation impedance is acquired. Each mobile home gets the attractiveness of the various districts for their own in accordance with a balanced price, hedonic cost and the generalized transportation impedance, and satisfaction of all residential is received by the close degree. In accordance with appropriate satisfaction and price, the mobile home obtains the greatest residence of consumer surplus.
     3 First, according to house prices, the family generalized transport impedance and the completeness of community hedonic facilities, the satisfaction assessment model is proposed to assess the residential location, and the household consumption remaining model is established by the satisfaction, household income and house prices. Second, cordon or area-road congestion charging regimes is implemented, and the generalized commuting traffic impedance and generalized holiday entertainment traffic impedance are got by different trip purpose travel chain characteristics and the corresponding travel mode of family members. The residence of the largest family remaining is got by the household consumption remaining model. Last, according to the different congestion charging methods and different charging levels, residential location choice is observed by a numerical example.
     4 In order to allocation the right-of-way of intersection reasonably, the game model was obtained between each adjacent agent by agent technology. The Nash equilibrium model of measurable negotiations effects was proposed, and the existence of Nash equilibrium solution was proven. The game equilibrium solution was got between each adjacent agent, so the equilibrium strategy price (cost) was obtained in the network. According to the equilibrium strategy price, travelers take the smallest prices path strategy or fuzzy comparison hybrid strategy in transportation networks, so their own best path was chosen. The results indicate that contract model algorithm makes the intersection signal control harmonize and shows subjective initiative of traffic control and management fully. The contract-base dynamic traffic game model combines with the management initiatives and the driver's personal rational behavior, and the control effectiveness of road network system increases by 55.58% based on contract model algorithm, so it is an effective measurement for studying coordination between system optimal and user optimal. In order to improve the operational efficiency of the whole traffic network, intersection signal control program was optimized. According to magnitude and direction of the adjacent intersection traffic flow, intersection signal dynamic phase was reasonably allocated by the different elastic signal period and the maximum membership degree principle. Based on the observation path of the driver, the minimum detection impedance path was obtained, and the impedance was compared with the all path impedance of intelligent transportation system, so the greatest satisfaction path was chosen. The results indicate that traffic assignment of the dynamic fuzzy control and decision-making method is consistent with the shortest path method, and that signal intersection control effectiveness is futher increased 50% or more by the fuzzy optimal control, and dynamic fuzzy control and decision making could more accurately reflect human behavior and could be an effective measurement for that signal intersection control.
引文
[1]Zhang Lin, Du Wen, Zhao Liyuan. OD allocation model and solution algorithm in transportation networks with the capacity [C]. International Conference on Transportation Engineering, American:American Society of Civil Engineers,2009: 788-793.
    [2]张邻,杜文.基于动态模糊控制和模糊决策的交通分配方法[J].交通运输工程学报.2010,10(3):100-107.
    [3]吴兵,李桦.交通管理与控制[M].北京:人民交通出版社,2005.
    [4]陆化普,王建伟,李江平等.城市交通管理评价体系[M].北京:人民交通出版社2003.
    [5]毛保华,李夏苗,王明生.城市轨道交通规划与设计[M].北京:人民交通出版社,2008.
    [6]陆化普,黄海军.交通规划理论研究前沿[M].北京:清华大学出版社,2007.
    [7]王炜等.城市交通管理规划指南[M].北京:人民交通出版社,2003.
    [8]邵春福,韩印.城市公共交通规划的理论与实践[M].北京:中国铁道出版社,2007.
    [9]杨励雅.城市交通与土地利用相互关系的基础理论与方法研究[D].博士学位论文.北京:北京交通大学,2007.
    [10]钟华.轨道交通与土地利用协调发展的法律障碍分析[J].城市公用事业,2009,23(4):11-13.
    [11]王缉宪.国外城市土地利用与交通一体规划的方法与实践[J].国际城市规划,2009,增刊:205-209.
    [12]徐永健,阎小培.西方国家城市交通与土地利用关系研究[J].城市规划,1999.23(11):38-43.
    [13]杨纶标,高英仪.模糊数学原理及应用[M].中山:华南理工大学出版社,2002.
    [14]李荣军.模糊多准则决策理论与应用[M].北京:科学出版社,2002.
    [15]Hernan M G. The interaction between distance to work and vehicle miles traveled [D]. Maryland, England:The Faculty of the Graduate School of the University of Maryland,2007
    [16]Wilson A. G. Land-use and transport interaction models[J]. Past and Future:Journal of Transport Economics and Policy,1998,32(1):3-26.
    [17]Wilson A G. Entropy in urban and regional modelling[M]. London:Pion,1971.
    [18]韩渭敏,陈晓良.变分不等式简介[M].北京:高等教育出版社,2007.
    [19]McFadden, Daniel. The measurement of urban travel demand [J]. Journal of Public Economics,1974,3:303-328.
    [20]Domencich, McFadden T D. Urban travel demand:a behavioral analysis [M]. Holland: North-Holland Publishing Co,1975.
    [21]Eliasson, Mattsson L G. A model for integrated analysis of household location and travel choices [J]. Transportation Research Part A,2000,34:375-394.
    [22]Wie B W, Tobin R L, On the relationship between dynamic nash and instantaneous user equilibria [J]. Transport Science.1998,2:141-163.
    [23]朱.费登博格,让.梯若尔.博弈论[M].北京:中国人民大学出版社,2003.
    [24]施锡铨.博弈论[M].上海:上海财经大学出版社,2003.
    [25]张盛开,张亚东.现代对策(博弈)论与工程决策方法[M].大连:东北财经大学出版社,2005.
    [26]岳超源.决策理论与方法[M].北京:科学出版社,2003.
    [27]Tan H N, Gershwin S, Athans M. Hybrid optimization in urban traffic networks [R]. American:Research and special programs administration, U.S department of transportation,1979.
    [28]Beckmann M, Mcguire C B, Winsten C B. Studies in the Economics of Transportation [M]. American:Yale University Press,1956.
    [29]Dougherty M. Investigation of network performance prediction literature review [R]. England:Institute for Transport Studies, University of Leeds,1993.
    [30]Fisk C S. Game theory and transportation systems modeling[J]. Transportation Research,1984,18B:301-313.
    [31]Braess D. Uberein paradoxen der verkehrsp lanung [J]. Unternehmenforschung,1968, 12:258-268.
    [32]Bell M G H. A game theory approach to measuring the performance reliability of transport networks [J]. Transportation Research Part B:Methodological,2000,34(6): 533-545.
    [33]Bell M G H, Cassir C. Risk averseness in user equilibrium traffic assignment:an application of game theory [C]. Beijing:Proceedings of Conference on Traffic and Transportation Studies,2000:9-16.
    [34]Mesterton-Gibbons M. Game-theoretic analysis of a motorist's dilemma [J]. Mathematical and Computer Modeling,1990,13(2):9-14.
    [35]Kite H. Merging-giveway interaction model of cars in a merging section:A game theoretic analysis[J]. Transportation Research A,1999,33(3):305-312.
    [36]Chen O J, Ben-Akiva M E. Game-theoretic formulations of interaction between dynamic traffic control and dynamic traffic assignment [J]. Transportation Research Record,1998,1617:179-188.
    [37]Wie B W. A differential game approach to the dynamic nixed behavior traffic network equilibrium problem [J]. European Journal of Operational Research,1995, 83(1):117-136.
    [38]Owen J C. Game-theoretic formulations interaction between dynamic traffic control and dynamic traffic assignment [J]. Transportation Research Record,1998,1617:153-169,.
    [39]Alonso, William. Location and land use[M]. Cambridge:Harvard University Press. 1964.
    [40]Muth, Richard E. Cities and housing:The spatial pattern of urban residential land use[M]. Chicago:The University of Chicago Press,1969.
    [41]Herbert J D, Stevens B H. A model for the distribution of residential activity in urban areas [J]. Journal of Regional Science,1960,2:21-36.
    [42]徐永健,阎小培.西方国家城市交通与土地利用关系研究.城市规划[J],1999,23(11):38-43
    [43]Herrin, William E, Clifford R. Testing the standard urban model of residential choice: an implicit markets approach [J]. Journal of Urban Economics,1992,31(2):145-63.
    [44]Henderson, Vernon J. Economic theory and the cities[M]. Orlando:Academic Press,1985.
    [45]White, Michelle J. Location choice and commuting behavior in cities with decentralized employment [J]. Journal of Urban Economics,1988,24(2):129-152.
    [46]DeSalvo, Joseph. Theory of locally employed urban household [J]. Journal of Regional Science,1977,17:345-356.
    [47]DeSalvo, Joseph. A model of urban household behavior with leisure choice [J]. Journal of Regional Science,1985.25:159-174.
    [48]Ben-Akiva M, dePalma A. Analysis of a dynamic residential location choice model with transactions costs[J]. Journal of Regional Science,1986,26:321-341.
    [49]Abraham J E, Hunt J.D. Specification and estimation of nested logit model of home, workplaces, and commuter mode choices by multiple-worker households [J]. Transportation Research Record,1997,1606:17-24.
    [50]Train, Kenneth E. Qualitative choice analysis:Theory, econometrics and an application to automobile demand [M]. America:MIT Press,1986.
    [51]Kockelman, Kara M. Travel behavior as a function of accessibility,land use mixing, and land use balance:Evidence from the san francisco bay area[J]. Transportation Research Record,1997,1607:117-125.
    [52]De Jong G C, Fox J, Daly A, et al. A comparison of car ownership models [J]. Transport Reviews,2004,24(4):379-408.
    [53]Berkowitz, Michael K, Nancy T, et al. Disaggregate analysis of the demand for gasoline [J]. Canadian Journal of Economics,1990,23(2):253-275.
    [54]Kayser, Hilke. Gasoline demand and car choice:estimating gasoline demand using household information[J], Energy Economics,2000,22:331-348.
    [55]Schimek, Paul. Household motor vehicle ownership and use:How much does residential density matter[J].Transportation Research Record,1996,1552:120-125.
    [56]Mannering, Fred, Winston C J. A dynamic empirical analysis of household vehicle ownership and utilization[J]. RAND Journal of Economics,1985,16(2):215-235.
    [57]Cervero. Jobs-housing balancing and regional mobility [J].American Planning Association Journal, Spring,1989,4:136-150.
    [58]Bento, Antonio M, Maureen L, et al. The impact of urban spatial structure on travel demand in the United States[J]. The Review of Economics and Statistics,2005, 87:23-45.
    [59]Dunphy, Robert T, Fisher K. Transportation congestion and density:New insights [J]. Transportation Research Record,1996,1552:89-96.
    [60]Levinson D, Kumar A. Density and the journey to work[J]. Growth and Change,1997, 28:147-72.
    [61]Giuliano, Genevieve, Kenneth S. Is the journey to work explained by urban structure[J], Urban Studies,1993,30(9):1485-1500.
    [62]Molin S, Tolker-Nielsen T. Gene transfer occurs with enhanced efficiency in biofilms and induces enhanced stabilisation of the biofilm structure [J]. Current Opinion in Biotechnology,2003,14(3):255-261.
    [63]Matihew A, Tumer. A simple theory of smart growth and sprawl[J]. Journal of Urban Eeononnes,2007,61:2144.
    [64]Bhat C R, Guo J Y. A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels[J]. Transportation Research, 2007,41B(5):506-526.
    [65]Mokhtarian P.L., and Cao X. Examining the impacts of residential self-selection on travel behavior:A focus on methodologies[J]. Transportation Research Part B,2007, 42(3):204-228.
    [66]Kitamura R, Mokhtarian P L, Laidet L. A micro-analysis of land use and travel in five neighborhoods in the san francisco bay area[J]. Transportation 1997,24:125-158.
    [67]Schwanen T, Mokhtarian P L. The extent and determinants of dissonance between actual and preferred residential neighborhood type[J]. Environment And Planning B, 2004,31(5):759-784.
    [68]Handy S L, Clifton K J. Local shopping as a strategy for reducing automobile travel[JJ. Transportation,2001,28(4):317-346.
    [69]Boarnet M G, Sarmiento S. Can land-use policy really affect travel behavior? A study of the link between non-work travel and land-use characteristics [J]. Urban Studies, 1998,35(7):1155-1169.
    [70]Boarnet M G, Crane R. The influence of land use on travel behavior:Specification and estimation strategies [J]. Transportation Research Part A,2001,35:823-845.
    [71]Greenwald M J, Boarnet M G. Built environment as determinant of walking behavior-analyzing nonwork pedestrian travel in portland, oregon[J]. Transportation Research Record,2001,1780:33-42.
    [72]Berry S, Levinsohn J, Pakes A. Automobile prices in market equilibrium [J]. Econometrica,1995,63:841-889.
    [73]Louviere J, Train K, Ben-Akiva M, et al. Recent progress on endogeneity in choice modeling [J]. Marketing Letters, Special Issue:Sixth Invitational Choice Symposium, 2005,16(3):255-265.
    [74]Pinjari A R. Modeling residential self-selection in activity-travel behavior models: Integrated models of multidimensional choice processes[D]. Texas, U.S.A:The University of Texas at Austin,2008.
    [75]Bagley M N, Mokhtarian P L. The impact of residential neighborhood type on travel behavior:A structural equations modeling approach[J]. Annals of Regional Science, 2002,36(2):279-297.
    [76]Cao, X, Handy S L, Mokhtarian P L. The influences of the built environment and residential self-selection on pedestrian behavior:Evidence from Austin, TX [J]. Transportation 2006,33(1):1-20.
    [77]Zhao Y, Kockelman K M. On-line marginal-cost pricing across networks:Incorporating heterogeneous users and stochastic equilibria[J]. Transportation Research Part B: Methodological,2006,40(5):424-435.
    [78]Heckman J J. Sample selection bias as a specification error[J]. Econometrica,1979, 47(1):153-161.
    [79]Dubin J, McFadden D. An econometric analysis of residential electric appliance holdings and consumption[J]. Econometrica,1984,52:345-62.
    [80]Lee B A, Guest A M. Determinants of neighborhood satisfaction:A metropolitan-level analysis[J]. The Sociological Quarterly,1983,24:287-303.
    [81]Bhat C R. A model of post-home arrival activity participation behavior[J]. Transportation Research Part B,1998,32(6):387-400.
    [82]Lerman S R. Location, housing, automobile ownership and mode to work:A joint choice model[J]. Transportation Research Record,1976,610:6-11.
    [83]Daly A. Estimating choice models containing attraction variables[J]. Transportation Research Part B,1982,16:5-15.
    [84]Williams H C. On the formation of travel demand models and economic evaluation measures of user benefit[J]. Environment and Planning A,1977,9:285-344.
    [85]Ben-Akiva M, DePalma A. Analysis of a dynamic residential location choice model with transactions costs[J]. Journal of Regional Science,1986,26:321-341.
    [86]Cervero R. Walking, bicycling, and urban landscapes:Evidence from the San Francisco Bay Area final summary report[D]. Berkeley:University of California Transportation Center, University of California,2003.
    [87]Salon D. Cars and the City:An investigation of transportation and residential location choices in new york city[D]. Berkeley:Department of Agricultural and Resource Economics, University of California,2006.
    [88]Walker J. The mixed logit model:Dispelling the misconceptions of identification [J]. Transportation Research Record,2002,1805:86-99.
    [89]Train K. Discrete choice methods with simulation[M]. Cambridge:Cambridge University Press,2003.
    [90]Bhat C R, Srinivasan S, Sen S. A joint model for the perfect and imperfect substitute goods case:Application to activity time-use decisions[J]. Transportation Research Part B,2006,40(10):827-850.
    [91]杨兆升,姜桂艳.基于高阶神经网络的城市交通诱导理论模型[J].公路交通科技,1998,15(2):16-19。
    [92]李振龙.基于Stackelberg博弈的动态用户最优配流和信号控制[J].控制理论与应用,2005,22(3):353-358.
    [93]吴兵,李林波.交通拥挤的进化动态分析[J].中国公路学报.2006,19(3):106-110。
    [94]李振龙,陈德望.交通信号区域协调优化的多智能体博弈模型[J].公路交通科技,2004,21(1):85-93.
    [95]鲁丛林.诱导条件下的驾驶员反应行为的博弈模型[J].交通运输系统工程与信息,2005,5(1):58-61.
    [96]殷亚峰,陆化普.动态网络交通信号配时模型研究[J].公路交通科技,1997,14(3):11-16.
    [97]马寿峰.智能交通系统中控制与诱导问题的研究[D].天津:天津大学,1999.
    [98]徐建闽,许伦辉,撒元功.交叉口有交通信号控制时用户最优动态配流模型[J].控制理论与应用,2000,17(1):117-120.
    [99]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,2009,23(2):140-142.
    [100]袁长伟,蔚欣欣,陆化普,等.基于斯塔克尔伯格博弈的路网均衡交通分配方法[J].中国公路学报,2009,22(5):89-93.
    [101]李振龙,钱海峰,刘喆.交叉口处驾驶员排队与插队的演化博弈分析[J].北京工业大学学报,2010,36(1):46-50.
    [102]张邻,杜文,向红艳.基于合同模式交通分配模型和求解算法[J].公路交通科技,2010,27(2):97-103.
    [103]陆大道.区位论和区域研究方法[M].北京:科学出版社,1991.
    [104]范炳全,张燕萍.城市土地利用和交通综合规划研究的进展[J].系统工程,1993,11(2):1-5.
    [105]王殿海.开发区土地利用与交通规划模型研究[D].北京:北方交通大学,1995.
    [106]田继敏,赵纯均,黄京炜,等.城市土地利用规划的交通影响评价建模研究[J].中国管理科学,1998,6(3):16-26.
    [107]杨明,曲大义,王炜,等.城市土地利用与交通需求相关关系模型研究[J].公路交通科技.2004,19(1):72-75.
    [108]陆建,王炜.城市居民出行时耗特征分析研究[J].公路交通科技,2004,21(10):102-104.
    [109]陈新.城市用地形态与城市交通布局模式研究[J].经济经纬,2005,4(3):64-67.
    [110]杨敏,王炜,陈学武等.基于DEIAHP方法的大运量快速交通方式选择决策[J].公路交通科技,2006,23(7):111-115.
    [111]陆化普.基于交通效率的大城市合理土地利用形态研究[J].中国公路学报,2005,18(7):109-113.
    [112]陈峰,吴奇兵.轨道交通对房地产增值的定量研究[J].城市轨道交通研究.2006,3:12-17,
    [113]卢建锋.城市新区交通生成预测模型[J].广东工业大学学报,2008,25(4):98-100.
    [114]郝记秀.城市公共交通与土地利用一体化发展研究[D].博士学位论文.长安:长安大学,2007.
    [115]ZHANG Lin, DU Wen, ZHAO Liyuan. OD allocation model and solution algorithm in transportation networks with the capacity [C]. International conference on transportation engineering, American:American Society of Civil Engineers, 2009:788-793.
    [116]甘应爱,田丰,钱颂迪,等.运筹学[M].北京:清华大学出版社,2006.
    [117]罗云峰,肖人彬.社会选择的理论与进展[M].北京:科学出版社,2002.
    [118]陈宝林.最优化理论与算法[M].北京:清华大学出版社,2007.
    [119]沈志云,邓学钧.交通运输工程学[M].北京:人民交通出版社,2003.
    [120]杨晓光等.城市道路交通设计指南[M].北京:人民交通出版社,2004.
    [121]王殿海.交通流理论[M].北京:人民交通出版社,2002.
    [122]陈宽民,严宝杰.道路通行能力分析[M].北京:人民交通出版社,2003.
    [123]姜启源,谢金星,叶俊.数学模型[M].北京:清华大学出版社,2004.
    [124]裴玉龙,张亚平.道路交通系统仿真[M].北京:人民交通出版社,2004.
    [125]赵静,但琦.数学建模与数学实验[M].北京:高等教育出版社,2003.
    [126]Chang J S, Mackett R L. A bi-level model of the relationship between transport and residential location [J]. Transportation Research Part B:Methodological,2006, 40(2):123-146.
    [127]Bravo M. Briceno L, Cominetti R, et al. An integrated behavioral model of the land-use and transport systems with network congestion and location externalities [J]. Transportation Research Part B:Methodological,2010,44(4):584-596.
    [128]Pinjari A R, Bhat C R, Hensher D A. Residential self-selection effects in an activity time-use behavior model [J]. Transportation Research Part B:Methodological,2009, 43(7):729-748.
    [129]Vega A, Aisling R F. A methodological framework for the study of residential location and travel-to-work mode choice under central and suburban employment destination patterns [J]. Transportation Research Part A:Policy and Practice,2009,43(4): 401-419.
    [130]Mokhtarian P L, Cao X. Examining the impacts of residential self-selection on travel behavior:A focus on methodologies [J]. Transportation Research Part B: Methodological,2008,42(3):204-228.
    [131]Earnhart D. Combining revealed and stated data to examine housing decisions using discrete choice analysis [J]. Journal of Urban Economics,2002,51(1):143-169.
    [132]Martinez F J, Henriquez R. A random bidding and supply land use equilibrium model [J]. Transportation Research Part B:Methodological,2007,41(6):632-651.
    [133]李晓燕,陈红.城市生态交通规划的理论框架[J].长安大学学报(自然科学版),2006,26(1):79-82.
    [134]裴玉龙,徐慧智.基于城市区位势能的路网密度规划方法[J].中国公路学报,2007,20(3):81-85.
    [135]Sheri M, Amadeo A, Deddy Koesrindartoto, et al. A smart market for passenger road transport(SMPRT) congestion:An application of computational mechanism design[J]. Journal of Economic Dynamics & Control,2007,31:2001-2032.
    [136]Lowry I. A model of metropolis[M]. Santa Monica:Rand Corporation,1964
    [137]Simmonds D, Still B. DELTA/START:adding land use analysis to integrated transport models[C]//Proceedings of the Eighth World Conference on Transport Research,1999,4:1-14.
    [138]Lundqvist L, Mattsson L. Transportation systems and residential location[J]. European Journal of Operations Research,1983,12:279-294.
    [139]Chu Y. Combined trip distribution and assignment model incorporating captive travel behavior [J]. Transportation Research Record,1990,1285:70-77.
    [140]Anas A. Discrete choice theory, information theory and the multinomial logit and gravity models [J]. Transportation Research Part B,1983,17:13-23.
    [141]Gross D. Estimating willingness to pay for housing characteristics:an application of the Ellickson bid rent model [J]. Journal of Urban Economics.1988,24:95-112.
    [142]Bhat C.R.. The Multiple Discrete-Continuous Extreme Value (MDCEV) model:role of utility function parameters, identification considerations, and model extensions [J]. Transportation Research Part B,2008,42 (3):274-303.
    [143]Pinjari A.R, Pendyala R.M, Bhat C.R, et al. Modeling residential sorting effects to understand the impact of the built environment on commute mode choice [J]. Transportation,2007,34 (5):557-573.
    [144]Khattak A.J, Rodriguez D. Travel behavior in neo-traditional neighborhood developments:a case study in USA [J]. Transportation Research Part A,2005, 39(6):481-500.
    [145]Kapur A. Bhat C.R. On modeling adults'daily time use by activity purpose and accompaniment arrangement [J]. Transportation Research Record,2007,2021:18-27.
    [146]Meloni I, Spissu E, Bez M. A model of the dynamic process of time allocation to discretionary activities [J]. Transportation Science,2007,41(1):15-28.
    [147]Sener I.N, Bhat C.R. An analysis of the social context of children's weekend discretionary activity participation [J]. Transportation,2007,34 (6):697-721.
    [148]Copperman R, Bhat C.R. An analysis of the determinants of children's weekend physical activity participation [J]. Transportation,2007,34 (1):67-87.
    [149]Wardrop J.G. Some theoretical of road traffic research [J]. Proceedings of the Institute of Civil Engineers, Part V1,1952,2:325-378.
    [150]Fernadez, J.E, Marcotte, P. Operators-users equilibrium model in a partially regulated transit system [J]. Transportation Science,1992,26 (2):95-105.
    [151]Bell M G H. A game theory approach to measuring the performance reliability of transport networks [J]. Transportation Research Part B:Methodological,2000,34(6): 533-545.
    [152]Bell M G H, Cassir C. Risk-averse equilibrium traffic assignment:An application of game theory [J]. Transportation Research Part B,2002,36 (8):671-681.
    [153]Zhou J, Lam W H K, Heydecker B G. The generalized nash equilibrium model for oligopolistic transit market with elastic demand [J]. Transportation Research Part B, 2005.39 (6):519-544.
    [154]李振龙,荣建,赵晓华.交通控制与诱导集成的关键问题评述[J].公路交通科技,2008,25(8):108-113.
    [155]Corte R, Paulo P. Essays in social choice theory [D]. Harvard:Harvard University,2002.
    [156]Biung G J. Continuous selections from the pareto correspondence and non-manipulability in exchange economies [J]. Journal of Mathematical Economics, 2004,540:73-592.
    [157]Ezzedine, Houcine, Trabelsi, et al. Modelling of an interactive system with an agent-based architecture using petri nets, application of the method to the supervision of a transport system [J]. Mathematics and Computers in Simulation,2006, 70(5):358-376.
    [158]Ezzedine, Houcine, Bonte, et al. Integration of traffic management and traveler information systems:basic principles and case study in inter-modal transport system management [J]. International Journal of Computers, Communications & Control, 2008,3(3):281-294.
    [159]Lotan T. Effects of familiarity on route choice behavior in the presence of information [J]. Transportation Research Part C:Emerging Technologies,1997,5(3):225-243.
    [160]Lotan T. Integration of fuzzy numbers corresponding to static knowledge and dynamic information [J]. Fuzzy Sets and Systems,1997,86(3):335-344.
    [161]Toledo T, Musicant O, Lotan T. In-vehicle data recorders for monitoring and feedback on drivers'behavior [J]. Transportation Research Part C:Emerging Technologies, 2008,16(3):320-331.
    [162]Prato C G, Toledo T, Lotan T, et al. Modeling the behavior of novice young drivers during the first year after licensure [J]. Accident Analysis and Prevention,2010, 42(2):480-486.
    [163]许伦辉,习利安,衷路生.孤立交叉口多相位自适应模糊控制及其神经网络实现[J].中国公路学报,2005,18(3):90-93.
    [164]张卫钢,刘亚萍,靳瑾,等.十字口4相位信号灯模糊控制模型设计与仿真[J].长安大学学报(自然科学版),2008,28(4):83-86.
    [165]温凯歌,曲仕茹,张玉梅.基于模糊逻辑的高速公路入口匝道控制方法[J].中国公路学报,2007,20(6):100-104.
    [166]Henn V. Fuzzy route choice model for traffic assignment [J]. Fuzzy Sets and Systems, 2000,116(1):77-101.
    [167]Henn V. What is the meaning of fuzzy costs in fuzzy traffic assignment models [J]. Transportation Research Part C:Emerging Technologies,2005,13 (2):107-119.
    [168]Henn V, Ottomanelli M. Handling uncertainty in route choice models:From probabilistic to possibilistic approaches [J]. European Journal of Operational Research,2006,175(3):1526-1538.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700