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Modeling the Impact of Smart Growth on Travel Choices: An Enhanced Travel Demand Forecasting Approach

机译:建模智能增长对差旅选择的影响:增强型差旅需求预测方法

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This paper intends to develop an enhanced travel demand forecasting framework that is sensitive to assess the impacts of smart growth strategies on travel behavior. The refinement focuses on intrazonal trips which are often neglected by the traditional fourstep travel demand forecasting method. In the two major steps of trip generation and mode split, separate models are developed to quantify the impact of land use on intrazonal trip making. The modelling results indicate that the balanced and diverse land use in terms of population and employment distributions will encourage more intrazonal trips, shorter travels, and the usage of transit and non-motorized modes. In addition, the enhanced modelling framework is applied to evaluate several smart growth scenarios for the Greater Buffalo-Niagara area in New York. As found, the overall Vehicle Miles Travelled (VMT) and Vehicle Hours Travelled (VHT) will be reduced with the improvement of balance and diversity of land use.
机译:本文旨在开发一个增强的旅行需求预测框架,该框架可以敏感地评估智能增长策略对旅行行为的影响。改进的重点是通常由传统的四步旅行需求预测方法忽略的区域内旅行。在行程生成和模式拆分的两个主要步骤中,开发了单独的模型以量化土地使用对区域内行程产生的影响。模拟结果表明,就人口和就业分布而言,土地使用的平衡和多样化将鼓励更多的区域内旅行,较短的旅行以及过境和非机动模式的使用。此外,增强的建模框架用于评估纽约州大布法罗-尼亚加拉地区的几种智能增长方案。可以发现,随着土地利用的平衡性和多样性的改善,总的行车里程数(VMT)和行车小时数(VHT)将减少。

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