基于规则的出行路径和出发时间选择行为研究
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摘要
出行行为分析一直是交通科学领域研究的前沿课题，个体出行者在出行过程中的决策行为及其相互影响的结果表现为系统层面的流量、速度、可靠性等属性，对出行行为的分析和研究是诊断城市交通问题、制定交通管理政策的重要基础。路径选择和出发时间选择是出行行为的重要维度，为许多交通问题的研究提供了理论基础。传统的路径选择和出发时间选择行为的分析多是在规范假设下展开的，认为出行者是绝对理性的，面对临时决策时总有清晰的认识、明确的目标、无限的认知能力、稳定的偏好等等，并总是遵循效用最大化准则，较少考虑出行者的有限理性问题。
本文围绕出行者的路径选择和出发时间选择问题，以出行者的出行决策行为研究为切入点，在有限理性的框架下构建基于规则的出行路径和出发时间选择行为模型，通过引入空间知识获取、学习及认知更新和方案搜索等关键行为要素，研究在认知局限约束下的出行决策行为。论文成果丰富了出行行为分析理论，为更合理、科学地制定交通需求管理政策提供理论指导和决策支持。论文进行了以下几个方面的研究：
（1）以活动-出行理论为基础，分析了出行行为的三种决策理论。界定了有限理性决策过程的关键行为要素，解释了出行行为的形成和各关键要素之间的关系，为基于规则的出行行为分析提供了研究框架。
（2）介绍了出行调查的两种方法：RP调查和SP调查，重点分析了SP调查的属性选择和情境设计原理，结合出行路径和出发时间选择行为对调查数据的具体要求，设计了基于序列正交设计法的出行路径和出发时间选择行为意愿调查方案。调查所得数据为后续研究提供数据支持。
（3）将出行者的信息搜索、学习、认知更新和方案搜索等出行决策关键要素关联在一起，提出了基于RIPPER规则的出行路径搜索和选择行为模型。建模方法包括空间知识的呈现、搜索模型规范的定义、连续属性的离散化、属性的选择、路径搜索和选择规则的计算等。提出了主观搜索收益和感知搜索成本的概念，用以确定方案搜索开始和停止的条件。
（4）构建了基于后悔最小化理论的出行者路径选择行为模型，详细分析了隐含在后悔最小化模型和效用最大化模型数学公式之内的决策机理，并结合出行路径的SP调查数据，分别建立采用完全理性和有限理性决策理论的路径选择模型，并分析了预测结果之间的差异。
（5）将基于规则的出行行为分析框架用于出发时间选择行为的研究，提出基于PART决策树规则的出发时间行为模型，模型结合精心设计的出发时间调查方案，得到出行者出发时间搜索和选择的规则。
论文的创新点主要体现在以下几个方面：
（1）在有限理性框架下，提出了基于规则的出行路径和出发时间选择行为分析理论框架；在这个理论框架中，界定了有限理性决策过程的关键要素，包括出行预期、信息获取、学习和认知更新、方案生成和搜索过程，并将搜索理论应用在选择集的生成阶段；
（2）定义了主观搜索收益和感知搜索成本，提出了基于出行预期的搜索过程启动和终止规则，构建了基于成本收益分析的出行选择方案搜索过程模型；
（3）设计了基于序列正交设计法的出行路径和出发时间选择行为意愿调查方案，为基于规则的出行行为研究调查方案的设计提供研究基础；
（4）分析了后悔最小化理论和效用最大化理论所蕴含的决策机理，解释了两个模型在决策过程和选择概率方面的差异。
Travel behavior analysis is one of the frontiers of transportation science. Thesystem-level properties of transportation emerge from the behaviors of and theinteractions among a large number of individual decision makers. The researches on thetravel behavior analysis are the foundation of diagnosing urban traffic problems andmaking travel demand management policies. Route and departure time choice are theimportant dimensions of travel behavior. Most of the traditional route and departure timechoice behavior analyses are under the hypothesis of specific assumptions that traveler arecompletely rational, they always follow the utility maximization criterion, and seldomconsider bounded rationality in their decision-making.
Based on the travel decision-making behavior as the point of research, this paper isintended to build the rule-based route and departure time choice behavior models in theframework of bounded rationality, and introduce the key subjective factors of behavior,such as spatial knowledge acquisition, learning, cognition updating, and alternativesearching. Travel decision-making behavior is analyzed under the restriction of cognitivelimitations. The main research contents are summarized as follows:
(1) This paper defines and illustrates subjective factors related to the traveldecision-making process, and provides the research framework for the rule-based travelbehavior analysis.
(2) Making comparative analysis between revealed preference and stated preferenceof travel behavior survey method, this paper focuses on the attribute selection andsituation design principle of SP survey. Combined with the specific requirements of routeand departure time choice behavior on survey data, the paper proposes and designs theroute and departure time choice behavior intention survey scheme based on the sequentialorthogonal design.
(3) Making the subjective factors, such as the information search, learning, cognitiveupdating and alternative acquisition, related with each other, the paper proposes the route search and choice behavior models based on Ripper-rules. It also puts forward theconcepts of subjective search gain and perceived search cost, and uses these concepts todetermine the starting and ending conditions of alternative acquisition.
(4) The paper constructs the route choice behavior model based on regretminimization theory, analyzes the underlying decision-making mechanism within themathematical formulas of regret minimization model and utility maximization model.Combined with the SP survey data of travel route, it also establishes the route choicemodel based on the perfect rationality and bounded rationality decision-making theoryrespectively, and analyzes the different between the estimation results.
(5) Taking the rule-based travel behavior analysis framework as the basis foranalyzing departure time choice behavior, the paper proposes the rule-based departuretime choice behavior model, and builds the search and choice model of departure time.
The main contributions of this dissertation research are as follows:
(1) Under the framework of bounded rationality, this paper proposes the analysistheory framework of rule-based route and departure time choice behavior.
(2) The concepts of subjective search gain and perceived search cost are defined, andthe starting and ending rules of expectation based search process are proposed. It alsobuilds the travel choice scheme search process model based on cost-benefit analysis.
(3) The paper proposes and designs the route and departure time choice behaviorintention survey scheme based on the sequential orthogonal design, and provides researchfoundation for other rule-based travel behavior survey scheme.
(4) The underlying decision-making mechanism within the regret minimizationmodel and utility maximization model are analyzed. The paper explains the difference indecision-making and choice probability.
本文围绕出行者的路径选择和出发时间选择问题，以出行者的出行决策行为研究为切入点，在有限理性的框架下构建基于规则的出行路径和出发时间选择行为模型，通过引入空间知识获取、学习及认知更新和方案搜索等关键行为要素，研究在认知局限约束下的出行决策行为。论文成果丰富了出行行为分析理论，为更合理、科学地制定交通需求管理政策提供理论指导和决策支持。论文进行了以下几个方面的研究：
（1）以活动-出行理论为基础，分析了出行行为的三种决策理论。界定了有限理性决策过程的关键行为要素，解释了出行行为的形成和各关键要素之间的关系，为基于规则的出行行为分析提供了研究框架。
（2）介绍了出行调查的两种方法：RP调查和SP调查，重点分析了SP调查的属性选择和情境设计原理，结合出行路径和出发时间选择行为对调查数据的具体要求，设计了基于序列正交设计法的出行路径和出发时间选择行为意愿调查方案。调查所得数据为后续研究提供数据支持。
（3）将出行者的信息搜索、学习、认知更新和方案搜索等出行决策关键要素关联在一起，提出了基于RIPPER规则的出行路径搜索和选择行为模型。建模方法包括空间知识的呈现、搜索模型规范的定义、连续属性的离散化、属性的选择、路径搜索和选择规则的计算等。提出了主观搜索收益和感知搜索成本的概念，用以确定方案搜索开始和停止的条件。
（4）构建了基于后悔最小化理论的出行者路径选择行为模型，详细分析了隐含在后悔最小化模型和效用最大化模型数学公式之内的决策机理，并结合出行路径的SP调查数据，分别建立采用完全理性和有限理性决策理论的路径选择模型，并分析了预测结果之间的差异。
（5）将基于规则的出行行为分析框架用于出发时间选择行为的研究，提出基于PART决策树规则的出发时间行为模型，模型结合精心设计的出发时间调查方案，得到出行者出发时间搜索和选择的规则。
论文的创新点主要体现在以下几个方面：
（1）在有限理性框架下，提出了基于规则的出行路径和出发时间选择行为分析理论框架；在这个理论框架中，界定了有限理性决策过程的关键要素，包括出行预期、信息获取、学习和认知更新、方案生成和搜索过程，并将搜索理论应用在选择集的生成阶段；
（2）定义了主观搜索收益和感知搜索成本，提出了基于出行预期的搜索过程启动和终止规则，构建了基于成本收益分析的出行选择方案搜索过程模型；
（3）设计了基于序列正交设计法的出行路径和出发时间选择行为意愿调查方案，为基于规则的出行行为研究调查方案的设计提供研究基础；
（4）分析了后悔最小化理论和效用最大化理论所蕴含的决策机理，解释了两个模型在决策过程和选择概率方面的差异。
Travel behavior analysis is one of the frontiers of transportation science. Thesystem-level properties of transportation emerge from the behaviors of and theinteractions among a large number of individual decision makers. The researches on thetravel behavior analysis are the foundation of diagnosing urban traffic problems andmaking travel demand management policies. Route and departure time choice are theimportant dimensions of travel behavior. Most of the traditional route and departure timechoice behavior analyses are under the hypothesis of specific assumptions that traveler arecompletely rational, they always follow the utility maximization criterion, and seldomconsider bounded rationality in their decision-making.
Based on the travel decision-making behavior as the point of research, this paper isintended to build the rule-based route and departure time choice behavior models in theframework of bounded rationality, and introduce the key subjective factors of behavior,such as spatial knowledge acquisition, learning, cognition updating, and alternativesearching. Travel decision-making behavior is analyzed under the restriction of cognitivelimitations. The main research contents are summarized as follows:
(1) This paper defines and illustrates subjective factors related to the traveldecision-making process, and provides the research framework for the rule-based travelbehavior analysis.
(2) Making comparative analysis between revealed preference and stated preferenceof travel behavior survey method, this paper focuses on the attribute selection andsituation design principle of SP survey. Combined with the specific requirements of routeand departure time choice behavior on survey data, the paper proposes and designs theroute and departure time choice behavior intention survey scheme based on the sequentialorthogonal design.
(3) Making the subjective factors, such as the information search, learning, cognitiveupdating and alternative acquisition, related with each other, the paper proposes the route search and choice behavior models based on Ripper-rules. It also puts forward theconcepts of subjective search gain and perceived search cost, and uses these concepts todetermine the starting and ending conditions of alternative acquisition.
(4) The paper constructs the route choice behavior model based on regretminimization theory, analyzes the underlying decision-making mechanism within themathematical formulas of regret minimization model and utility maximization model.Combined with the SP survey data of travel route, it also establishes the route choicemodel based on the perfect rationality and bounded rationality decision-making theoryrespectively, and analyzes the different between the estimation results.
(5) Taking the rule-based travel behavior analysis framework as the basis foranalyzing departure time choice behavior, the paper proposes the rule-based departuretime choice behavior model, and builds the search and choice model of departure time.
The main contributions of this dissertation research are as follows:
(1) Under the framework of bounded rationality, this paper proposes the analysistheory framework of rule-based route and departure time choice behavior.
(2) The concepts of subjective search gain and perceived search cost are defined, andthe starting and ending rules of expectation based search process are proposed. It alsobuilds the travel choice scheme search process model based on cost-benefit analysis.
(3) The paper proposes and designs the route and departure time choice behaviorintention survey scheme based on the sequential orthogonal design, and provides researchfoundation for other rule-based travel behavior survey scheme.
(4) The underlying decision-making mechanism within the regret minimizationmodel and utility maximization model are analyzed. The paper explains the difference indecision-making and choice probability.
引文
[1]李志瑶.基于活动的出行需求预测模型研究[D].长春:吉林大学,2006.
[2] Meyer M D. Demand management as an element of transportation policy: using carrots and sticks toinfluence travel behavior[J]. Transportation Research Part A: Policy and Practice,1999,33(7):575-599.
[3] Hensher D A, Rose J M. Development of commuter and non-commuter mode choice models for theassessment of new public transport infrastructure projects: a case study[J]. Transportation ResearchPart A: Policy and Practice,2007,41(5):428-443.
[4] Allais P M. The behavior of rational man in risk situations-a critique of the axioms and postulates ofthe American School [J]. Econometrica,1953,21(4):503-546.
[5] Ellsberg D. Risk, ambiguity, and the Savage axioms[J]. The Quarterly Journal of Economics,1961:643-669.
[6] Simon H A. Theories of bounded rationality[J]. Decision and organization,1972,1:161-176.
[7] Quiggin J. A theory of anticipated utility[J]. Journal of Economic Behavior&Organization,1982,3(4):323-343.
[8] Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk[J]. Econometrica:Journal of the Econometric Society,1979:263-291.
[9]隽志才,宗芳,栾琨.活动-出行决策行为与TDM策略互动关系研究的贝叶斯方法[J].交通运输系统工程与信息,2009,9(4):97-102.
[10] Kitamura R. A model of daily time allocation to discretionary out-of-home activities and trips[J].Transportation Research Part B: Methodological,1984,18(3):255-266.
[11] Jones P M, Koppelman F, Orfeuil J. Activity analysis: State-of-the-art and future directions[J].Developments in dynamic and activity-based approaches to travel analysis,1990:34-55.
[12] Recker W W, McNally M G, Root G S. A model of complex travel behavior: Part I—Theoreticaldevelopment[J]. Transportation Research Part A: General,1986,20(4):307-318.
[13] Kawakami S, Isobe T. Development of a one-day travel-activity scheduling model for workers[J].Developments in dynamic and activity-based approaches to travel analysis,1990:184-205.
[14] Bowman J L. The day activity schedule approach to travel demand analysis[D]. MassachusettsInstitute of Technology,1998.
[15] Bowman J L, Bradley M. Disaggregate treatment of purpose, time of day and location in anactivity-based regional travel forecasting model: European Transport Conference, Strasbourg, France,October,2005[C].
[16] Newell A, Simon H A. Human problem solving[M]. Prentice-Hall Englewood Cliffs, NJ,1972.
[17]孙晓端.一种新兴的美国城市交通流量需求预测模型系统[J].城市交通,2002,1:37-38.
[18] Recker W W, McNally M G, Root G S. A model of complex travel behavior: Part II—an operationalmodel[J]. Transportation Research Part A: General,1986,20(4):319-330.
[19] Pendyala R M, Kitamura R, Prasuna Reddy D. Application of an activity-based travel-demand modelincorporating a rule-based algorithm[J]. Environment and Planning B,1998,25:753-772.
[20] Kitamura R, Pas E I, Lula C V, et al. The sequenced activity mobility simulator (SAMS): an integratedapproach to modeling transportation, land use and air quality[J]. Transportation,1996,23(3):267-291.
[21] Kitamura R, Lula C V, Pas E I. AMOS: An activity-based, flexible and truly behavioral tool forevaluation of TDM measures: PTRC Summer Annual Meeting,21st,1993, University of Manchester,United Kingdom,1993[C].
[22] Stopher P R, Hartgen D T, Li Y. SMART: simulation model for activities, resources and travel[J].Transportation,1996,23(3):293-312.
[23] Kitamura R, Fujii S. Two computational process models of activity-travel behavior[J]. Theoreticalfoundations of travel choice modeling,1998:251-279.
[24] Arentze T A, Timmermans H J. A learning-based transportation oriented simulation system[J].Transportation Research Part B: Methodological,2004,38(7):613-633.
[25] Pendyala R M, Kitamura R, Kikuchi A, et al. Florida activity mobility simulator: overview andpreliminary validation results[J]. Transportation Research Record: Journal of the TransportationResearch Board,2005,1921(1):123-130.
[26] Fellendorf M T H U. PTV VISION: Activity based demand forecasting in daily practice: paperpresented at the Travel Model Improvement Program Conference[R].1995.
[27] Bhat C R, Guo J Y, Srinivasan S, et al. Comprehensive econometric microsimulator for dailyactivity-travel patterns[J]. Transportation Research Record: Journal of the Transportation ResearchBoard,2004,1894(1):57-66.
[28]邵昀泓.基于活动的出行需求分析及信息影响研究[D].东南大学博士学位论文,2006.
[29] Avineri E, Prashker J N. Violations of expected utility theory in route-choice stated preferences:Certainty effect and inflation of small probabilities[J]. Transportation Research Record: Journal of theTransportation Research Board,2004,1894(1):222-229.
[30] Avineri E. A cumulative prospect theory approach to passengers behavior modeling: waiting timeparadox revisited: Intelligent Transportation Systems,2004[C]. Taylor&Francis.
[31] Erev I, Bereby-Meyer Y, Roth A E. The effect of adding a constant to all payoffs: experimentalinvestigation, and implications for reinforcement learning models[J]. Journal of Economic Behavior&Organization,1999,39(1):111-128.
[32] Stathopoulos A, Dimitriou L, Tsekeris T. Fuzzy modeling approach for combined forecasting of urbantraffic flow[J]. Computer‐Aided Civil and Infrastructure Engineering,2008,23(7):521-535.
[33] Noland R B, Small K A, Koskenoja P M, et al. Simulating travel reliability[J]. Regional Science andUrban Economics,1998,28(5):535-564.
[34] Yin Y, Ieda H. Assessing performance reliability of road networks under nonrecurrent congestion[J].Transportation Research Record: Journal of the Transportation Research Board,2001,1771(1):148-155.
[35] de Palma A, Picard N. Route choice decision under travel time uncertainty[J]. Transportation ResearchPart A: Policy and Practice,2005,39(4):295-324.
[36]石小法,王炜.不确定意义下的路径选择研究[J].运筹学学报,2001,5(4):90-96.
[37] Zhang J, Timmermans H, Borgers A, et al. Modeling traveler choice behavior using the concepts ofrelative utility and relative interest[J]. Transportation Research Part B: Methodological,2004,38(3):215-234.
[38] Tversky A, Kahneman D. Advances in prospect theory: Cumulative representation of uncertainty[J].Journal of Risk and uncertainty,1992,5(4):297-323.
[39] Katsikopoulos K V, Duse-Anthony Y, Fisher D L, et al. Risk attitude reversals in drivers' route choicewhen range of travel time information is provided[J]. Human Factors: The Journal of the HumanFactors and Ergonomics Society,2002,44(3):466-473.
[40] Bogers E, Van Zuylen H J. The importance of reliability in route choices in freight transport forvarious actors on various levels: Proceedings of the European Transport Conference,2004[C].
[41] Schwanen T, Ettema D. Coping with unreliable transportation when collecting children: examiningparents’ behavior with cumulative prospect theory[J]. Transportation Research Part A: Policy andPractice,2009,43(5):511-525.
[42]赵凛,张星臣.基于“前景理论”的路径选择行为建模及实例分析[J].土木工程学报,2007,40(7):82-86.
[43]徐红利,周晶,陈星光.基于前景理论的路径选择行为规则分析与实证[J].交通运输系统工程与信息,2007(06):95-101.
[44]刘玉印,刘伟铭,吴建伟.基于累积前景理论的出行者路径选择模型[J].华南理工大学学报(自然科学版),2010(07):84-89.
[45]杨志勇,颜贵云.基于前景理论的动态路径选择模型[J].大连交通大学学报,2010,31(001):53-58.
[46]张波.基于前景理论的动态路径选择行为研究[D].上海交通大学,2012.
[47] Small K A. The scheduling of consumer activities: work trips[J]. The American Economic Review,1982,72(3):467-479.
[48] Polak J, Jones P. A tour-based model of journey scheduling under road pricing[M]. University ofOxford, Transport Studies Unit,1993.
[49] Bowman J L, Bradley M, Shiftan Y, et al. Demonstration of an activity based model system forPortland:8th World Conference on Transport Research, Antwerp, Belgium,1998[C].
[50] Steed J L, Bhat C R. On modeling departure-time choice for home-based social/recreational andshopping trips[J]. Transportation Research Record: Journal of the Transportation Research Board,2000,1706(1):152-159.
[51] Koppelman F S, Wen C. The paired combinatorial logit model: properties, estimation andapplication[J]. Transportation Research Part B: Methodological,2000,34(2):75-89.
[52] Wen C, Koppelman F S. The generalized nested logit model[J]. Transportation Research Part B:Methodological,2001,35(7):627-641.
[53] Ben-Elia E, Ettema D. Rewarding rush-hour avoidance: A study of commuters’travel behavior[J].Transportation Research Part A: Policy and Practice,2011,45(7):567-582.
[54] Bhat C R. Accommodating flexible substitution patterns in multi-dimensional choice modeling:formulation and application to travel mode and departure time choice[J]. Transportation Research PartB: Methodological,1998,32(7):455-466.
[55] de Jong G, Daly A, Pieters M, et al. A model for time of day and mode choice using error componentslogit[J]. Transportation Research Part E: Logistics and Transportation Review,2003,39(3):245-268.
[56] Greene W H, Hensher D A. A latent class model for discrete choice analysis: contrasts with mixedlogit[J]. Transportation Research Part B: Methodological,2003,37(8):681-698.
[57] Senbil M, Kitamura R. Reference points in commuter departure time choice: a prospect theoretic testof alternative decision frames: Intelligent Transportation Systems,2004[C]. Taylor&Francis.
[58] Timmermans H. On the (ir) relevance of prospect theory in modelling uncertainty in travel decisions[J].EJTIR,2010,4(10).
[59]杨励雅,邵春福, A HAGHANI.出行方式与出发时间联合选择的分层Logit模型[J].交通运输工程学报,2012(02):76-83.
[60]杨励雅,李霞,邵春福.居住地、出行方式与出发时间联合选择的交叉巢式Logit模型[J].同济大学学报(自然科学版),2012(11):1647-1653.
[61]张春勤,姜桂艳,吴正言.机动车出行者出发时间选择的影响因素[J].吉林大学学报(工学版),2013(03):626-632.
[62]张波,隽志才,林徐勋.基于累积前景理论的出发时间选择SDUO模型[J].管理工程学报,2013(01):68-76.
[63] Von Neumann J, Morgenstern O. The theory of games and economic behavior[J].1947.
[64] Samuelson P A. FOUNDATIONS OF ECONOMIC ANALYSIS..[J].1947.
[65]西蒙,杨砾,决策,等.现代决策理论的基石:有限理性说[M].北京经济学院出版社,1989.
[66] Simon H A. A behavioral model of rational choice[J]. The quarterly journal of economics,1955,69(1):99-118.
[67]西蒙,黄涛.西蒙选集[M].首都经济贸易大学出版社,2002.
[68] Tversky A, Kahneman D. Judgment under uncertainty: Heuristics and biases[M]. Springer,1975.
[69] Slovic P, Fischhoff B, Lichtenstein S. Behavioral decision theory[J]. Annual review of psychology,1977,28(1):1-39.
[70] Ben-Aakiva M, Lerman S R. Discrete choice analysis: theory and application to travel demand[J].1985.
[71] Svenson O. Process descriptions of decision making[J]. Organizational behavior and humanperformance,1979,23(1):86-112.
[72] Stigler G J. The economics of information[J]. The journal of political economy,1961,69(3):213-225.
[73] Timmermans H. Consumer spatial choice strategies: a comparative study of some alternativebehavioural spatial shopping models[J]. Geoforum,1980,11(2):123-131.
[74] Williams H, Ortúzar J D D. Behavioural theories of dispersion and the mis-specification of traveldemand models[J]. Transportation Research Part B: Methodological,1982,16(3):167-219.
[75] Richardson A. Search models and choice set generation[J]. Transportation Research Part A: General,1982,16(5):403-419.
[76] Thompson R G, Richardson A J. A parking search model[J]. Transportation Research Part A: Policyand Practice,1998,32(3):159-170.
[77]徐健锐,李星毅.主动停车诱导系统的模型研究:搜索算法[J].计算机工程与设计,2010(007):1627-1630.
[78] Kitamura R, Chen C, Pendyala R M, et al. Micro-simulation of daily activity-travel patterns for traveldemand forecasting[J]. Transportation,2000,27(1):25-51.
[79] Jones P M. New approaches to understanding travel behavior: the human activity approach[M]. OxfordUniversity,1977.
[80] H gerstraand T. What about people in regional science?[J]. Papers in regional science,1970,24(1):7-24.
[81] Bhat C R, Pendyala R M. Modeling intra-household interactions and group decision-making[J].Transportation,2005,32(5):443-448.
[82] Timmermans H J, Zhang J. Modeling household activity travel behavior: Examples of state of the artmodeling approaches and research agenda[J]. Transportation Research Part B: Methodological,2009,43(2):187-190.
[83] Bos L, Ettema D, Molin E. Modeling effect of travel time uncertainty and traffic information on use ofpark-and-ride facilities[J]. Transportation Research Record: Journal of the Transportation ResearchBoard,2004,1898(1):37-44.
[84] Joh C, Arentze T, Timmermans H. Modeling individuals' activity-travel rescheduling heuristics:Theory and numerical experiments[J]. Transportation research record,2002(1807):16-25.
[85]吴杨.通勤日活动—出行时间预测模型研究[D].吉林大学,2013.
[86]赵贝.居民出行方式选择与公交优先政策协同研究[D].吉林大学,2011.
[87]关宏志.非集计模型:交通行为分析的工具[M].人民交通出版社,2004.
[88] Manski C F, Lerman S R. The estimation of choice probabilities from choice based samples[J].Econometrica: Journal of the Econometric Society,1977:1977-1988.
[89]江涛,覃琼霞.有限理性下的个体决策:一种分析框架[J].南方经济,2007(12):24-31.
[90]张顺明,叶军.后悔理论述评Ξ[J].系统工程,2009.
[91] Loomes G, Sugden R. Regret theory: An alternative theory of rational choice under uncertainty[J]. TheEconomic Journal,1982,92(368):805-824.
[92] Bell D E. Regret in decision making under uncertainty[J]. Operations research,1982,30(5):961-981.
[93] Chorus C G, Arentze T A, Timmermans H J. A random regret-minimization model of travel choice[J].Transportation Research Part B: Methodological,2008,42(1):1-18.
[94] Hensher D A, Greene W H, Chorus C G. Random regret minimization or random utility maximization:an exploratory analysis in the context of automobile fuel choice[J]. Journal of AdvancedTransportation,2011.
[95] Chorus C, Arentze T A, Timmermans H J. Comparison of Regret Minimization and UtilityMaximization in the Context of Travel Mode Choices: Transportation Research Board87th AnnualMeeting,2008[C].
[96] Golledge R G. Spatial Behavoir: A Geographical Perspective[M]. Guilford Press,1997.
[97] Blades M. The reliability of data collected from sketch maps[J]. Journal of Environmental Psychology,1990,10(4):327-339.
[98] Tversky B. Distortions in memory for maps[J]. Cognitive psychology,1981,13(3):407-433.
[99] Gale N, Golledge R G, Pellegrino J W, et al. The acquisition and integration of route knowledge in anunfamiliar neighborhood[J]. Journal of Environmental Psychology,1990,10(1):3-25.
[100] McNamara T P. Mental representations of spatial relations[J]. Cognitive Psychology,1986,18(1):87-121.
[101] Péruch P, Giraudo M, Garling T. Distance cognition by taxi drivers and the general public[J]. Journalof Environmental Psychology,1989,9(3):233-239.
[102]王方,陈金川,陈艳艳.交通SP调查的均匀设计方法[J].城市交通,2005(03):69-72.
[103]王方.基于SP调查的行为时间价值研究[D].北京工业大学,2005.
[104]周伟.城市动态路况信息下交通行为的研究[D].大连理工大学,2012.
[105]邵洁,关宏志,王鑫.正交试验设计在交通意向调查中的应用[J].公路交通科技,2005(10):106-109.
[106] Péruch P, Giraudo M, Garling T. Distance cognition by taxi drivers and the general public[J]. Journalof Environmental Psychology,1989,9(3):233-239.
[107] Gelman A, Carlin J B, Stern H S, et al. Bayesian data analysis[M]. CRC press,2003.
[108] G rling T, Kwan M, Golledge R G. Computational-process modelling of household activityscheduling[J]. Transportation Research Part B: Methodological,1994,28(5):355-364.
[109]焦宁.连续属性离散化算法比较研究[D].合肥工业大学,2007.
[110] Fayyad U, Irani K. Multi-interval discretization of continuous-valued attributes for classificationlearning[J].1993.
[111] Dash M, Liu H. Feature selection for classification[J]. Intelligent data analysis,1997,1(3):131-156.
[112] John G H. Enhancements to the data mining process[D]. stanford university,1997.
[113]陈小莉.基于信息增益的中文特征提取算法研究[D].重庆大学,2008.
[114]王彦.中医证候的数据挖掘[D].上海交通大学,2009.
[115]刘华文.基于信息熵的特征选择算法研究[D].吉林大学,2010.
[116] Hall M A. Correlation-based feature selection for machine learning[D]. The University of Waikato,1999.
[117] Quinlan J R. Induction of decision trees[J]. Machine learning,1986,1(1):81-106.
[118] Cendrowska J. PRISM: An algorithm for inducing modular rules[J]. International Journal ofMan-Machine Studies,1987,27(4):349-370.
[119] Cohen W W. Fast effective rule induction: ICML,1995[C].
[120]赵蕊.基于WEKA平台的决策树算法设计与实现[D].中南大学,2007.
[121]栾琨,隽志才,倪安宁.出行路径选择的随机后悔最小化模型[J].交通信息与安全ISTIC,2012,30(6).
[122] Chorus C G. A new model of random regret minimization[J]. EJTIR,2010,2(10).
[123] Blackwell D, MacQueen J B. Ferguson distributions via Pólya urn schemes[J]. The annals ofstatistics,1973:353-355.
[124] Frank E, Witten I H. Generating accurate rule sets without global optimization[J].1998.
[125] Holmes G, Hall M, Prank E. Generating rule sets from model trees[M]. Springer,1999.
[2] Meyer M D. Demand management as an element of transportation policy: using carrots and sticks toinfluence travel behavior[J]. Transportation Research Part A: Policy and Practice,1999,33(7):575-599.
[3] Hensher D A, Rose J M. Development of commuter and non-commuter mode choice models for theassessment of new public transport infrastructure projects: a case study[J]. Transportation ResearchPart A: Policy and Practice,2007,41(5):428-443.
[4] Allais P M. The behavior of rational man in risk situations-a critique of the axioms and postulates ofthe American School [J]. Econometrica,1953,21(4):503-546.
[5] Ellsberg D. Risk, ambiguity, and the Savage axioms[J]. The Quarterly Journal of Economics,1961:643-669.
[6] Simon H A. Theories of bounded rationality[J]. Decision and organization,1972,1:161-176.
[7] Quiggin J. A theory of anticipated utility[J]. Journal of Economic Behavior&Organization,1982,3(4):323-343.
[8] Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk[J]. Econometrica:Journal of the Econometric Society,1979:263-291.
[9]隽志才,宗芳,栾琨.活动-出行决策行为与TDM策略互动关系研究的贝叶斯方法[J].交通运输系统工程与信息,2009,9(4):97-102.
[10] Kitamura R. A model of daily time allocation to discretionary out-of-home activities and trips[J].Transportation Research Part B: Methodological,1984,18(3):255-266.
[11] Jones P M, Koppelman F, Orfeuil J. Activity analysis: State-of-the-art and future directions[J].Developments in dynamic and activity-based approaches to travel analysis,1990:34-55.
[12] Recker W W, McNally M G, Root G S. A model of complex travel behavior: Part I—Theoreticaldevelopment[J]. Transportation Research Part A: General,1986,20(4):307-318.
[13] Kawakami S, Isobe T. Development of a one-day travel-activity scheduling model for workers[J].Developments in dynamic and activity-based approaches to travel analysis,1990:184-205.
[14] Bowman J L. The day activity schedule approach to travel demand analysis[D]. MassachusettsInstitute of Technology,1998.
[15] Bowman J L, Bradley M. Disaggregate treatment of purpose, time of day and location in anactivity-based regional travel forecasting model: European Transport Conference, Strasbourg, France,October,2005[C].
[16] Newell A, Simon H A. Human problem solving[M]. Prentice-Hall Englewood Cliffs, NJ,1972.
[17]孙晓端.一种新兴的美国城市交通流量需求预测模型系统[J].城市交通,2002,1:37-38.
[18] Recker W W, McNally M G, Root G S. A model of complex travel behavior: Part II—an operationalmodel[J]. Transportation Research Part A: General,1986,20(4):319-330.
[19] Pendyala R M, Kitamura R, Prasuna Reddy D. Application of an activity-based travel-demand modelincorporating a rule-based algorithm[J]. Environment and Planning B,1998,25:753-772.
[20] Kitamura R, Pas E I, Lula C V, et al. The sequenced activity mobility simulator (SAMS): an integratedapproach to modeling transportation, land use and air quality[J]. Transportation,1996,23(3):267-291.
[21] Kitamura R, Lula C V, Pas E I. AMOS: An activity-based, flexible and truly behavioral tool forevaluation of TDM measures: PTRC Summer Annual Meeting,21st,1993, University of Manchester,United Kingdom,1993[C].
[22] Stopher P R, Hartgen D T, Li Y. SMART: simulation model for activities, resources and travel[J].Transportation,1996,23(3):293-312.
[23] Kitamura R, Fujii S. Two computational process models of activity-travel behavior[J]. Theoreticalfoundations of travel choice modeling,1998:251-279.
[24] Arentze T A, Timmermans H J. A learning-based transportation oriented simulation system[J].Transportation Research Part B: Methodological,2004,38(7):613-633.
[25] Pendyala R M, Kitamura R, Kikuchi A, et al. Florida activity mobility simulator: overview andpreliminary validation results[J]. Transportation Research Record: Journal of the TransportationResearch Board,2005,1921(1):123-130.
[26] Fellendorf M T H U. PTV VISION: Activity based demand forecasting in daily practice: paperpresented at the Travel Model Improvement Program Conference[R].1995.
[27] Bhat C R, Guo J Y, Srinivasan S, et al. Comprehensive econometric microsimulator for dailyactivity-travel patterns[J]. Transportation Research Record: Journal of the Transportation ResearchBoard,2004,1894(1):57-66.
[28]邵昀泓.基于活动的出行需求分析及信息影响研究[D].东南大学博士学位论文,2006.
[29] Avineri E, Prashker J N. Violations of expected utility theory in route-choice stated preferences:Certainty effect and inflation of small probabilities[J]. Transportation Research Record: Journal of theTransportation Research Board,2004,1894(1):222-229.
[30] Avineri E. A cumulative prospect theory approach to passengers behavior modeling: waiting timeparadox revisited: Intelligent Transportation Systems,2004[C]. Taylor&Francis.
[31] Erev I, Bereby-Meyer Y, Roth A E. The effect of adding a constant to all payoffs: experimentalinvestigation, and implications for reinforcement learning models[J]. Journal of Economic Behavior&Organization,1999,39(1):111-128.
[32] Stathopoulos A, Dimitriou L, Tsekeris T. Fuzzy modeling approach for combined forecasting of urbantraffic flow[J]. Computer‐Aided Civil and Infrastructure Engineering,2008,23(7):521-535.
[33] Noland R B, Small K A, Koskenoja P M, et al. Simulating travel reliability[J]. Regional Science andUrban Economics,1998,28(5):535-564.
[34] Yin Y, Ieda H. Assessing performance reliability of road networks under nonrecurrent congestion[J].Transportation Research Record: Journal of the Transportation Research Board,2001,1771(1):148-155.
[35] de Palma A, Picard N. Route choice decision under travel time uncertainty[J]. Transportation ResearchPart A: Policy and Practice,2005,39(4):295-324.
[36]石小法,王炜.不确定意义下的路径选择研究[J].运筹学学报,2001,5(4):90-96.
[37] Zhang J, Timmermans H, Borgers A, et al. Modeling traveler choice behavior using the concepts ofrelative utility and relative interest[J]. Transportation Research Part B: Methodological,2004,38(3):215-234.
[38] Tversky A, Kahneman D. Advances in prospect theory: Cumulative representation of uncertainty[J].Journal of Risk and uncertainty,1992,5(4):297-323.
[39] Katsikopoulos K V, Duse-Anthony Y, Fisher D L, et al. Risk attitude reversals in drivers' route choicewhen range of travel time information is provided[J]. Human Factors: The Journal of the HumanFactors and Ergonomics Society,2002,44(3):466-473.
[40] Bogers E, Van Zuylen H J. The importance of reliability in route choices in freight transport forvarious actors on various levels: Proceedings of the European Transport Conference,2004[C].
[41] Schwanen T, Ettema D. Coping with unreliable transportation when collecting children: examiningparents’ behavior with cumulative prospect theory[J]. Transportation Research Part A: Policy andPractice,2009,43(5):511-525.
[42]赵凛,张星臣.基于“前景理论”的路径选择行为建模及实例分析[J].土木工程学报,2007,40(7):82-86.
[43]徐红利,周晶,陈星光.基于前景理论的路径选择行为规则分析与实证[J].交通运输系统工程与信息,2007(06):95-101.
[44]刘玉印,刘伟铭,吴建伟.基于累积前景理论的出行者路径选择模型[J].华南理工大学学报(自然科学版),2010(07):84-89.
[45]杨志勇,颜贵云.基于前景理论的动态路径选择模型[J].大连交通大学学报,2010,31(001):53-58.
[46]张波.基于前景理论的动态路径选择行为研究[D].上海交通大学,2012.
[47] Small K A. The scheduling of consumer activities: work trips[J]. The American Economic Review,1982,72(3):467-479.
[48] Polak J, Jones P. A tour-based model of journey scheduling under road pricing[M]. University ofOxford, Transport Studies Unit,1993.
[49] Bowman J L, Bradley M, Shiftan Y, et al. Demonstration of an activity based model system forPortland:8th World Conference on Transport Research, Antwerp, Belgium,1998[C].
[50] Steed J L, Bhat C R. On modeling departure-time choice for home-based social/recreational andshopping trips[J]. Transportation Research Record: Journal of the Transportation Research Board,2000,1706(1):152-159.
[51] Koppelman F S, Wen C. The paired combinatorial logit model: properties, estimation andapplication[J]. Transportation Research Part B: Methodological,2000,34(2):75-89.
[52] Wen C, Koppelman F S. The generalized nested logit model[J]. Transportation Research Part B:Methodological,2001,35(7):627-641.
[53] Ben-Elia E, Ettema D. Rewarding rush-hour avoidance: A study of commuters’travel behavior[J].Transportation Research Part A: Policy and Practice,2011,45(7):567-582.
[54] Bhat C R. Accommodating flexible substitution patterns in multi-dimensional choice modeling:formulation and application to travel mode and departure time choice[J]. Transportation Research PartB: Methodological,1998,32(7):455-466.
[55] de Jong G, Daly A, Pieters M, et al. A model for time of day and mode choice using error componentslogit[J]. Transportation Research Part E: Logistics and Transportation Review,2003,39(3):245-268.
[56] Greene W H, Hensher D A. A latent class model for discrete choice analysis: contrasts with mixedlogit[J]. Transportation Research Part B: Methodological,2003,37(8):681-698.
[57] Senbil M, Kitamura R. Reference points in commuter departure time choice: a prospect theoretic testof alternative decision frames: Intelligent Transportation Systems,2004[C]. Taylor&Francis.
[58] Timmermans H. On the (ir) relevance of prospect theory in modelling uncertainty in travel decisions[J].EJTIR,2010,4(10).
[59]杨励雅,邵春福, A HAGHANI.出行方式与出发时间联合选择的分层Logit模型[J].交通运输工程学报,2012(02):76-83.
[60]杨励雅,李霞,邵春福.居住地、出行方式与出发时间联合选择的交叉巢式Logit模型[J].同济大学学报(自然科学版),2012(11):1647-1653.
[61]张春勤,姜桂艳,吴正言.机动车出行者出发时间选择的影响因素[J].吉林大学学报(工学版),2013(03):626-632.
[62]张波,隽志才,林徐勋.基于累积前景理论的出发时间选择SDUO模型[J].管理工程学报,2013(01):68-76.
[63] Von Neumann J, Morgenstern O. The theory of games and economic behavior[J].1947.
[64] Samuelson P A. FOUNDATIONS OF ECONOMIC ANALYSIS..[J].1947.
[65]西蒙,杨砾,决策,等.现代决策理论的基石:有限理性说[M].北京经济学院出版社,1989.
[66] Simon H A. A behavioral model of rational choice[J]. The quarterly journal of economics,1955,69(1):99-118.
[67]西蒙,黄涛.西蒙选集[M].首都经济贸易大学出版社,2002.
[68] Tversky A, Kahneman D. Judgment under uncertainty: Heuristics and biases[M]. Springer,1975.
[69] Slovic P, Fischhoff B, Lichtenstein S. Behavioral decision theory[J]. Annual review of psychology,1977,28(1):1-39.
[70] Ben-Aakiva M, Lerman S R. Discrete choice analysis: theory and application to travel demand[J].1985.
[71] Svenson O. Process descriptions of decision making[J]. Organizational behavior and humanperformance,1979,23(1):86-112.
[72] Stigler G J. The economics of information[J]. The journal of political economy,1961,69(3):213-225.
[73] Timmermans H. Consumer spatial choice strategies: a comparative study of some alternativebehavioural spatial shopping models[J]. Geoforum,1980,11(2):123-131.
[74] Williams H, Ortúzar J D D. Behavioural theories of dispersion and the mis-specification of traveldemand models[J]. Transportation Research Part B: Methodological,1982,16(3):167-219.
[75] Richardson A. Search models and choice set generation[J]. Transportation Research Part A: General,1982,16(5):403-419.
[76] Thompson R G, Richardson A J. A parking search model[J]. Transportation Research Part A: Policyand Practice,1998,32(3):159-170.
[77]徐健锐,李星毅.主动停车诱导系统的模型研究:搜索算法[J].计算机工程与设计,2010(007):1627-1630.
[78] Kitamura R, Chen C, Pendyala R M, et al. Micro-simulation of daily activity-travel patterns for traveldemand forecasting[J]. Transportation,2000,27(1):25-51.
[79] Jones P M. New approaches to understanding travel behavior: the human activity approach[M]. OxfordUniversity,1977.
[80] H gerstraand T. What about people in regional science?[J]. Papers in regional science,1970,24(1):7-24.
[81] Bhat C R, Pendyala R M. Modeling intra-household interactions and group decision-making[J].Transportation,2005,32(5):443-448.
[82] Timmermans H J, Zhang J. Modeling household activity travel behavior: Examples of state of the artmodeling approaches and research agenda[J]. Transportation Research Part B: Methodological,2009,43(2):187-190.
[83] Bos L, Ettema D, Molin E. Modeling effect of travel time uncertainty and traffic information on use ofpark-and-ride facilities[J]. Transportation Research Record: Journal of the Transportation ResearchBoard,2004,1898(1):37-44.
[84] Joh C, Arentze T, Timmermans H. Modeling individuals' activity-travel rescheduling heuristics:Theory and numerical experiments[J]. Transportation research record,2002(1807):16-25.
[85]吴杨.通勤日活动—出行时间预测模型研究[D].吉林大学,2013.
[86]赵贝.居民出行方式选择与公交优先政策协同研究[D].吉林大学,2011.
[87]关宏志.非集计模型:交通行为分析的工具[M].人民交通出版社,2004.
[88] Manski C F, Lerman S R. The estimation of choice probabilities from choice based samples[J].Econometrica: Journal of the Econometric Society,1977:1977-1988.
[89]江涛,覃琼霞.有限理性下的个体决策:一种分析框架[J].南方经济,2007(12):24-31.
[90]张顺明,叶军.后悔理论述评Ξ[J].系统工程,2009.
[91] Loomes G, Sugden R. Regret theory: An alternative theory of rational choice under uncertainty[J]. TheEconomic Journal,1982,92(368):805-824.
[92] Bell D E. Regret in decision making under uncertainty[J]. Operations research,1982,30(5):961-981.
[93] Chorus C G, Arentze T A, Timmermans H J. A random regret-minimization model of travel choice[J].Transportation Research Part B: Methodological,2008,42(1):1-18.
[94] Hensher D A, Greene W H, Chorus C G. Random regret minimization or random utility maximization:an exploratory analysis in the context of automobile fuel choice[J]. Journal of AdvancedTransportation,2011.
[95] Chorus C, Arentze T A, Timmermans H J. Comparison of Regret Minimization and UtilityMaximization in the Context of Travel Mode Choices: Transportation Research Board87th AnnualMeeting,2008[C].
[96] Golledge R G. Spatial Behavoir: A Geographical Perspective[M]. Guilford Press,1997.
[97] Blades M. The reliability of data collected from sketch maps[J]. Journal of Environmental Psychology,1990,10(4):327-339.
[98] Tversky B. Distortions in memory for maps[J]. Cognitive psychology,1981,13(3):407-433.
[99] Gale N, Golledge R G, Pellegrino J W, et al. The acquisition and integration of route knowledge in anunfamiliar neighborhood[J]. Journal of Environmental Psychology,1990,10(1):3-25.
[100] McNamara T P. Mental representations of spatial relations[J]. Cognitive Psychology,1986,18(1):87-121.
[101] Péruch P, Giraudo M, Garling T. Distance cognition by taxi drivers and the general public[J]. Journalof Environmental Psychology,1989,9(3):233-239.
[102]王方,陈金川,陈艳艳.交通SP调查的均匀设计方法[J].城市交通,2005(03):69-72.
[103]王方.基于SP调查的行为时间价值研究[D].北京工业大学,2005.
[104]周伟.城市动态路况信息下交通行为的研究[D].大连理工大学,2012.
[105]邵洁,关宏志,王鑫.正交试验设计在交通意向调查中的应用[J].公路交通科技,2005(10):106-109.
[106] Péruch P, Giraudo M, Garling T. Distance cognition by taxi drivers and the general public[J]. Journalof Environmental Psychology,1989,9(3):233-239.
[107] Gelman A, Carlin J B, Stern H S, et al. Bayesian data analysis[M]. CRC press,2003.
[108] G rling T, Kwan M, Golledge R G. Computational-process modelling of household activityscheduling[J]. Transportation Research Part B: Methodological,1994,28(5):355-364.
[109]焦宁.连续属性离散化算法比较研究[D].合肥工业大学,2007.
[110] Fayyad U, Irani K. Multi-interval discretization of continuous-valued attributes for classificationlearning[J].1993.
[111] Dash M, Liu H. Feature selection for classification[J]. Intelligent data analysis,1997,1(3):131-156.
[112] John G H. Enhancements to the data mining process[D]. stanford university,1997.
[113]陈小莉.基于信息增益的中文特征提取算法研究[D].重庆大学,2008.
[114]王彦.中医证候的数据挖掘[D].上海交通大学,2009.
[115]刘华文.基于信息熵的特征选择算法研究[D].吉林大学,2010.
[116] Hall M A. Correlation-based feature selection for machine learning[D]. The University of Waikato,1999.
[117] Quinlan J R. Induction of decision trees[J]. Machine learning,1986,1(1):81-106.
[118] Cendrowska J. PRISM: An algorithm for inducing modular rules[J]. International Journal ofMan-Machine Studies,1987,27(4):349-370.
[119] Cohen W W. Fast effective rule induction: ICML,1995[C].
[120]赵蕊.基于WEKA平台的决策树算法设计与实现[D].中南大学,2007.
[121]栾琨,隽志才,倪安宁.出行路径选择的随机后悔最小化模型[J].交通信息与安全ISTIC,2012,30(6).
[122] Chorus C G. A new model of random regret minimization[J]. EJTIR,2010,2(10).
[123] Blackwell D, MacQueen J B. Ferguson distributions via Pólya urn schemes[J]. The annals ofstatistics,1973:353-355.
[124] Frank E, Witten I H. Generating accurate rule sets without global optimization[J].1998.
[125] Holmes G, Hall M, Prank E. Generating rule sets from model trees[M]. Springer,1999.
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