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A joint model of destination and mode choice for urban trips: a disaggregate approach

机译:城市旅行的目的地和方式选择的联合模型:分类方法

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摘要

Trip destination and mode choice are highly influenced by travelers' perceptions and behaviors; selecting a destination and a vehicle for a trip are two interdependent problems. This paper presents and applies a disaggregate joint model for traveler destination and mode choice. The choice model uses fuzzy set and probability theory to deal with the uncertainty embedded in travelers' perceptions and behaviors. The model is structured as a decision tree in which the fuzzy and non-fuzzy classification of influential variables regarding destination selection and mode choice expand the tree. The most influential explanatory variables among all the variables categorized for travelers' household, trip, and living zone specifications are selected based on the maximizing information. An aggregation method is designed to provide aggregate estimates for transportation planning based on the suggested disaggregate choice model. A data-set of over 9000 home-based morning peak-hour trips in Shiraz, a large city in Iran, is used for model construction and evaluation. When compared with a multinomial logit (MNL) model, the suggested models' estimates are more accurate than the traditional MNL model.
机译:出行目的地和方式选择在很大程度上受旅行者的感知和行为影响;选择目的地和旅行的车辆是两个相互依赖的问题。本文提出并应用了一个分解的联合模型,用于旅行者的目的地和方式选择。选择模型使用模糊集和概率论来处理旅行者的感知和行为中嵌入的不确定性。该模型被构造为决策树,其中关于目的地选择和模式选择的影响变量的模糊和非模糊分类扩展了该树。根据最大化的信息,在针对旅行者的家庭,旅行和居住区规格分类的所有变量中,最具影响力的解释变量将被选择。设计一种汇总方法,以根据建议的分类选择模型为运输计划提供汇总估算。伊朗大城市设拉子(Shiraz)的9000多个家庭上午高峰时段出行数据集用于模型构建和评估。与多项式logit(MNL)模型相比,建议模型的估计值比传统MNL模型更准确。

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