四阶段交通需求预测模型不确定性传递分析
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摘要
交通运输系统是包含许多不确定性因素的复杂大系统,在进行交通需求预测时应充分考虑不确定性因素的影响,以便做出正确决策。交通需求预测的不确定性引起了国外较多规划人员的重视,研究表明不确定性是需求预测模型的本质属性,而国内对它的研究还非常少。本文为分析和定量交通需求预测模型的不确定性提供了分析框架。
本文首先回顾了常用的四阶段交通需求预测模型及模型的影响因素、模型参数的选取等问题,接着针对特定模型,对四阶段交通需求预测模型的不确定性问题进行了深入分析,界定了四阶段模型不确定性的来源,将预测模型的不确定性主要归为两类:输入的不确定性和模型的不确定性。
针对四阶段模型不确定性问题的特点,选取二维蒙特卡罗仿真分析方法,该方法能够将模型总的不确定性中由输入变量引起的不确定性和由模型参数引起的不确定性定量区分,可帮助决策者定量选择对模型结果不确定性影响较大的因素,从而有针对性的进行改善。
采用算例研究,针对四阶段模型的不确定性问题,考虑人口数量、方式分担、分布阻抗系数的不确定性,定量分析各阶段的输出变化情况,认为输入变量、模型参数都是影响模型不确定性的主要因素。在对本文算例进行分析后得出,相较模型输入来说,模型参数对模型输出不确定性的影响更大,在进行预测和制定决策方案时应重点考虑。算例也对四阶段模型中不确定性在各阶段的传递进行了量化研究,得出四阶段模型前三阶段不确定性是累积发生的,而最后交通分配阶段采用均衡分配法能够减少路段流量分配的不确定性。
The influence of uncertainty in transportation demand forecasts should be studied in order to make correct decision since the transportation system is a complicated system comprising of much uncertainty. Uncertainty in traffic demand forecast models caused more attention by the foreign planners. Research shows that demand uncertainty is the essential attribute of the prediction model, and its research is very few internally. This study provides a framework for analyzing and quantifying the uncertainty involved in travel demand forecasting models.
This article first reviewed the commonly used four-step travel demand forecasting models and model the impact of factors, the selection of model parameters and so on, then for specific four-step travel demand forecasting models, analysised the uncertainty in-depth, defined the sources of uncertainty in the four-step model, and classified the uncertainty of prediction models into two categories mainly: input uncertainty and model uncertainty.
For four-step model uncertainties features, selected the two dimensions Monte Carlo simulation analysis method. This method is able to quantitatively distinguish the general uncertainty caused by the input variable uncertainty and the model parameters uncertainty, in order to help decision makers choose the factors that have significant impact on the model results , targeted to improve the situation.
Used a simple network as study case, for the four-step model uncertainty problem, consider the number of people, mode choice, distribution impendence coefficient as the risk variable, quantitative analysised the output change of each stage. After analysised the example network, found that compared with the model input, the model parameters contribute major model output uncertainty. When predict the future situation and decision making should pay more attention to the four-step model's parameters. The example also quantitative analysised the uncertainty propagation of the four-step model. The conclusion was that the uncertainty of the first three steps of a four-step model is cumulative occurrence, but if the final assignment stage uses equilibrium distribution method would reduce the uncertainty of the link flow.
本文首先回顾了常用的四阶段交通需求预测模型及模型的影响因素、模型参数的选取等问题,接着针对特定模型,对四阶段交通需求预测模型的不确定性问题进行了深入分析,界定了四阶段模型不确定性的来源,将预测模型的不确定性主要归为两类:输入的不确定性和模型的不确定性。
针对四阶段模型不确定性问题的特点,选取二维蒙特卡罗仿真分析方法,该方法能够将模型总的不确定性中由输入变量引起的不确定性和由模型参数引起的不确定性定量区分,可帮助决策者定量选择对模型结果不确定性影响较大的因素,从而有针对性的进行改善。
采用算例研究,针对四阶段模型的不确定性问题,考虑人口数量、方式分担、分布阻抗系数的不确定性,定量分析各阶段的输出变化情况,认为输入变量、模型参数都是影响模型不确定性的主要因素。在对本文算例进行分析后得出,相较模型输入来说,模型参数对模型输出不确定性的影响更大,在进行预测和制定决策方案时应重点考虑。算例也对四阶段模型中不确定性在各阶段的传递进行了量化研究,得出四阶段模型前三阶段不确定性是累积发生的,而最后交通分配阶段采用均衡分配法能够减少路段流量分配的不确定性。
The influence of uncertainty in transportation demand forecasts should be studied in order to make correct decision since the transportation system is a complicated system comprising of much uncertainty. Uncertainty in traffic demand forecast models caused more attention by the foreign planners. Research shows that demand uncertainty is the essential attribute of the prediction model, and its research is very few internally. This study provides a framework for analyzing and quantifying the uncertainty involved in travel demand forecasting models.
This article first reviewed the commonly used four-step travel demand forecasting models and model the impact of factors, the selection of model parameters and so on, then for specific four-step travel demand forecasting models, analysised the uncertainty in-depth, defined the sources of uncertainty in the four-step model, and classified the uncertainty of prediction models into two categories mainly: input uncertainty and model uncertainty.
For four-step model uncertainties features, selected the two dimensions Monte Carlo simulation analysis method. This method is able to quantitatively distinguish the general uncertainty caused by the input variable uncertainty and the model parameters uncertainty, in order to help decision makers choose the factors that have significant impact on the model results , targeted to improve the situation.
Used a simple network as study case, for the four-step model uncertainty problem, consider the number of people, mode choice, distribution impendence coefficient as the risk variable, quantitative analysised the output change of each stage. After analysised the example network, found that compared with the model input, the model parameters contribute major model output uncertainty. When predict the future situation and decision making should pay more attention to the four-step model's parameters. The example also quantitative analysised the uncertainty propagation of the four-step model. The conclusion was that the uncertainty of the first three steps of a four-step model is cumulative occurrence, but if the final assignment stage uses equilibrium distribution method would reduce the uncertainty of the link flow.
引文
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[39]高连华,孙伟.变异系数在可靠性中的应用[J].装甲兵工程学院学报.Vol.18 No.4 Dec.2004
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[41]程琳,于春青,邱松林.随机型用户均衡网络的敏感度分析[J].中国公路学报.Mar.2008,Vol.21(2)
[42]徐丽群.交通拥挤控制的实时决策支持模型[J].控制与决策,2005,20(11):1221-1224.
[43]程琳,王炜.Dial交通量分配模型和选择概率问题的研究[J].交通运输工程与信息,2002,2(3):29-32.
[44]王十元,潘福全,张丽霞,等.基于交通限制的路网最优路径算法[J].交通运输工程学报,2005,5(1):92-95.
[45]郝勇,范君晖编著.系统工程方法与应用[M].北京:科学出版社,2007.
[46]李自良,黄惠明,王乃卫.基于蒙特卡罗方法的误差传播与误差分配计算[J].装备指挥技术学院学报.2003年6月,Vol.14(3)
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[2]王正武,罗大庸,谢永彰.交通需求预测中的不确定性的传播分析[J].系统工程,2005年7月,Vol.23,(7):81-84
[3]Horowitz,J.,Emslie,R.Comparison of measured and actual traffic volumes on urban interstate highways[J].Transportation Research,1978,Vol.12,Pergamon Press,pp.29-32.
[4]Ashley,D.Uncertainty in the context of highway appraisal[J].Transportation Research,1980,Vol.9,Springer,pp.249-267.
[5]Gilbert,D.,Jessop,A.Error and uncertainty in transport models,in Transportation models:proceedings of Seminar G[A],PTRC Summer Annual Meeting,University of Warwick,England,June 1977.
[6]Clarke,M.,Dix,M.,Jones,P.,Error and uncertainty in travel surveys[J],Transportation,1981,Vol.10,Springer,pp.105-125.
[7]Pell,C.,Meyburg,A.,Sources of error and their implications for uncertainty in urban transportation planning forecasts[J],1985,s.n.,S.1.
[8]Zhao,Y.,Kockelman,K.(2002),The propagation of uncertainty through travel demand models:An exploratory analysis[J],The Annals of regional science,Vol.36,Springer,pp.145-163.
[9]Hugosson M.,Quantifying uncertainties in a national forecasting model[J],Transportation research.Part A,Policy and practice.,2005,Vol.39A,Elsevier,pp.531-547.
[10]de Jong,G.,Daly,A.,Pieters,M.,Miller,S.,Plasmeijer,R.and Hofman,F.(2007),Uncertainty in traffic forecasts:literature review and new results for The Netherlands[J],Transportation,Vol.34,Springer,pp.375-395.
[11]Daly,A.J.,Ortzar,J.d.D.Forecasting and data aggregation:theory and practice[J],Traffic engineering & control.,1990,Vol.31,pp.632-643.
[12]Chang,K.-t.,Khatib,Z.,Ou,Y.Effects of zoning structure and network detail on traffic demand modeling[J],Environment and planning.B,Planning & design,2002,Vol.29,Pion Ltd.,London,pp.37-52.
[13]Rodier,C.Verifying accuracy of regional models used in transportation and air quality planning:case study in Sacramento,California,Region[J],Transportation Research Record,2004,Vol.1898,Trans Res Board,pp.45-51.
[14]Flyvbjerg,B.Measuring inaccuracy in travel demand forecasting:methodological considerations regarding ramp up and sampling[J],Transportation Research Part A,2005,Vol.39,Elsevier,pp.522-530.
[5]Flyvbjerg,B.,Holm,M.,Buhl,S.Inaccuracy in traffic forecasts,Transport Reviews,2006,Vol.26,Taylor & Francis,pp.1-24.
[6]Barton-Aschman Associates,Inc.,and Cambridge Systematics,Inc.Model Validation and Reasonableness Checking Manual[EB/OL].Federal Highway Administration Report,Washington,D.C.1997,http://www.ctre.iastate.edu/educweb/ce451/LECTURES/Validation/fmalval.pdf
[7]Alonso,W.Predicting Best with Imperfect Data[J],Journal of the American Institute of Planners.1968,pp.248-255.
[8]Rice,J.Mathematical Statistics and Data Analysis[M],Second Edition.Belmont,California:Duxbury Press.1995
[9]Mackinder,I.H.,Evans,R.The Predictive Accuracy of British Transport Studies in Urban Areas.SR 699,Transport and Road Research Laboratory.1981,Crowthome,Berkshire.
[20]Robbins,J.Mathematical Modeling-the Error of Our Ways[J].Traffic Engineering and Control.1978,January,pp.32-35.
[21]Bonsall,P.W.,Champerowne,A.F.,Mason,A.C.,Wilson,A.G.Transport Modeling:Sensitivity Analysis and Policy Testing[J].1977,Progress in Planning.7(3).
[22]Leurent,F.Sensitivity and Error Analysis of the Dual Criteria Traffic Assignment Model[J].Transportation Research(B),1998,32(3),pp.189-204
[23]Surachet Pravinvongvuth,Anthony Chen,Simulating Variability and Uncertainty of A Combined Travel Demand Model[J],Journal of the Eastern Asia Society for Transportation Studies,October,2003,Vol.5,pp.1324-1338.
[24]王炜等.城市交通规划理论及其应用[M],南京:东南大学出版社.1998:43-52,222-224.
[25]陆化普.交通规划理论与方法[M].第二版,北京:清华大学出版社,2006:76-80,200-201
[26]刘洪丽.城市居民出行生成预测方法研究(D).河海大学硕士学位论文.南京:河海大学,2006
[27]杨涛.公路网规划[M].北京:人民交通出版社,1998:77-87.
[28]田志立,周海涛.引力模型中系数K的变化[J].公路交通科技.1995年6月,Vol.12(2)
[29]郭冠英.对出行分布模型的归纳和创新[J].同济大学学报.1994,22(3):312-316
[30]田志立,周海涛.重力模型预测交通分布量的误差分析[J].公路交通科技.1994年第2期.
[31]Michael D.Meyer,Eric J.Miller,Urban Transportation Planning[M],Second Edition,McGraw-Hill Companies,2001.
[32]叶霞飞,顾保南.城市轨道交通规划与设计[M].北京:中国铁道出版社,1999,48-62.
[33]龚勃文.交通方式划分的非集计模型及应用研究[D].吉林大学硕士学位论文.长春:吉林大学,2006
[34]郭凤香,等.交通分配模型研究及其应用[J].交通与计算机,2004年第4期,10-11.
[35]Sheffi Y.Urban transportation networks:equilibrium analysis with mathematical programming methods[M].Englewood Cliffs:Prentice-Hall Inc,1985.
[36]W.E.Walker,EHarremoes,etc.Defming Uncertainty A Conceptual Basis for Uncertainty Management in Model-Based Decision Support[J].Integrated Assessment.2003.Vol.4,No.1,pp.5-17
[37]Harremoes,P.and Madsen,H.Fiction and Reality in the Modeling World-Balance Between Simplicity and Complexity,Calibration and Identifiability[J].Verification and Falsification.Water Sci.Technol.1999,39(9)pp.1-8.
[38]孙庆辉,迟天河等.空间数据处理模型误差和不确定性分析[J].测绘科学技术学报.2007年2月,Vol.24(1).
[39]高连华,孙伟.变异系数在可靠性中的应用[J].装甲兵工程学院学报.Vol.18 No.4 Dec.2004
[40]蔡毅,邢岩,胡丹.敏感性分析综述[J].北京师范大学学报(自然科学版).2008年第2期,44(1)
[41]程琳,于春青,邱松林.随机型用户均衡网络的敏感度分析[J].中国公路学报.Mar.2008,Vol.21(2)
[42]徐丽群.交通拥挤控制的实时决策支持模型[J].控制与决策,2005,20(11):1221-1224.
[43]程琳,王炜.Dial交通量分配模型和选择概率问题的研究[J].交通运输工程与信息,2002,2(3):29-32.
[44]王十元,潘福全,张丽霞,等.基于交通限制的路网最优路径算法[J].交通运输工程学报,2005,5(1):92-95.
[45]郝勇,范君晖编著.系统工程方法与应用[M].北京:科学出版社,2007.
[46]李自良,黄惠明,王乃卫.基于蒙特卡罗方法的误差传播与误差分配计算[J].装备指挥技术学院学报.2003年6月,Vol.14(3)
[47]Vose,D.Risk Analysis a Quantitative Guide[M].England:John Wiley & Sons Ltd.,2000
[48]Brat-tin,W.J.,Barry,T.M.and Chiu,N.Monte Carlo modeling with uncertain probability density functions[J].Human and Ecological Risk Assessment.1996,Vol.2,(4):820-840.
[49]Burmaster,D.E.and Wilson,A.M.An introduction to second-order random variables in human health risk assessments[J].Human and Ecological Risk Assessment,1996,Vol.2(4):892-919.
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