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Alternative Approaches to Modeling the Travel-Demand Impacts of Smart Growth

机译:建模智能增长的出差需求影响的替代方法

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Four-step travel demand forecasting models were never meant to estimate the travel impacts of neighborhood-level smart growth initiatives like transit villages, but rather to guide regional highway and transit investments. While progress has been made in enhancing large-scale models, some analysts have turned to post-processing and direct models to reduce modeling time and cost, and to better capture the travel impacts of neighborhood-scale land use strategies. This paper presents examples of direct or off-line modeling of rail and transit-oriented land use proposals for greater Charlotte, the San Francisco Bay Area exurbs, and south St. Louis County. These alternative approaches provided a useful platform for scenario testing, and their results revealed that concentrating development near rail stations produced an appreciable ridership bonus. These alternative models are appropriate as sketch-planning supplements to, not substitutes for, traditional four-step models.
机译:四步旅行需求预测模型从来没有用来估计过境村庄等社区级智能增长计划对旅行的影响,而只是用来指导区域公路和过境投资。尽管在增强大型模型方面已取得进展,但一些分析人员已转向后处理和直接模型,以减少建模时间和成本,并更好地捕捉邻里规模土地使用策略对旅行的影响。本文介绍了针对更大的夏洛特,旧金山湾区郊区和南部圣路易斯县的铁路和公交导向的土地使用提案的直接或离线建模示例。这些替代方法为情景测试提供了有用的平台,其结果表明,集中开发靠近火车站可产生可观的载客量。这些替代模型适合作为草图规划的补充,而不是替代传统的四步模型。

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