【作者】 罗京；

【导师】 王元庆；

【作者基本信息】 长安大学 ， 交通运输规划与管理， 2009， 硕士

【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。更多还原

【Abstract】 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.更多还原