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An enhanced travel demand forecasting framework to evaluate smart growth strategies.

机译:增强的旅行需求预测框架,用于评估智能增长策略。

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摘要

In recent years, there is an increasing trend to promote smart growth strategies that aim to revitalize land-use and transportation patterns to avoid "sprawl" and to replace it by safe, livable, healthy, environmentally-sound and green-mode-oriented communities. All these interests point to the genuine need for travel demand forecasting methods and traffic analysis tools sensitive enough to reflect the benefits of smart growth strategies. In this context, this paper takes the initiative to develop an enhanced travel demand forecasting method to evaluate the impact of smart growth strategies on travel demand.;The enhanced travel demand forecasting framework is tested by using the Greater Buffalo-Niagara Area as the study case. In this framework, several behavior choice models are developed in order to capture the impact of smart growth land use on individual travellers' various travel decisions such as intrazonal trip making, destination choices, and mode choices. As found, dense and diverse land use will encourage the usage of non-motorized modes such as bicycle and walking while reducing automobile travels. In addition, diverse land uses and transit-oriented designs play an important role in reducing the average trip length and vehicle miles travelled.
机译:近年来,越来越多的趋势提出了明智的增长战略,旨在振兴土地使用和运输方式,避免“蔓延”,并以安全,宜居,健康,环保和绿色模式为导向的社区取代“蔓延” 。所有这些利益都表明了对旅行需求预测方法和交通分析工具的真正需求,这些方法和敏感度足以反映智能增长策略的优势。在这种情况下,本文主动开发了一种增强型旅行需求预测方法,以评估智能增长策略对旅行需求的影响。;以大布法罗-尼亚加拉大区为研究案例,对增强型旅行需求预测框架进行了测试。在此框架中,开发了几种行为选择模型,以捕获明智的增长土地利用对个人旅行者的各种旅行决策(如区域内旅行,目的地选择和方式选择)的影响。如发现的那样,密集而多样的土地利用将鼓励使用非机动模式,例如自行车和步行,同时减少汽车出行。此外,多样化的土地利用和面向交通的设计在减少平均出行时间和行进的车辆里程方面也起着重要作用。

著录项

  • 作者

    Su, Peng.;

  • 作者单位

    State University of New York at Buffalo.;

  • 授予单位 State University of New York at Buffalo.;
  • 学科 Engineering Civil.;Urban and Regional Planning.;Transportation.
  • 学位 M.S.
  • 年度 2011
  • 页码 122 p.
  • 总页数 122
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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