Numerous studies have suggested that land-use policies can reduce vehicle travel through mode shifting and reduced trip lengths and generation of fewer or more efficient trips. The findings from previous studies also suggest that the combined effect of two or more land-use policies can be significant, although the effects of individual policies appear to be modest. These studies present area-wide impacts of land-use policies on travel and suggest that their effects are additive. However, very little is known about how each land-use policy interacts with the others at different levels of development intensity to reduce vehicle travel. In this study, we explore how three well-known land-use strategies (densification, mixed-use development, and street network improvement) interact with each other by testing possible combinations of land-use factors and focus on how these interactive effects vary by the level of development intensity. Employing ordinary least squares regression analysis using a dataset created for the Austin metropolitan statistical area (MSA) (using 2006 Austin Travel Survey data), we examine the impact of land use on household vehicle travel. Our findings suggest that interaction effects occur, but they vary by development intensity. The results of this study show the importance of considering both threshold (development intensity) and interaction (combination of policies) effects in understanding how land-use factors do and do not affect travel (based on their interactive opposed to only their direct and additive effects). Though this paper uses data from just one MSA and thus is merely suggestive, it does point to a possibly more nuanced use of the commonly prescribed planning and design policy variable to account for variation in effectiveness based on differences in development intensity. For example, we find that greater land-use intensification has higher efficacy in changing vehicle travel behavior in areas with relatively higher development intensity. Future research should include data from a broader array of metropolitan areas and incorporate additional predictor variables that were unavailable for this analysis.

中文翻译：

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研究土地使用政策之间的互动影响以减少家用车辆的出行：探索性分析

大量研究表明，土地使用政策可以通过模式转换和缩短出行长度以及减少或提高出行效率来减少车辆出行。先前研究的结果还表明，尽管个别政策的影响似乎不大，但两个或多个土地使用政策的综合影响可能会很大。这些研究提出了土地使用政策对旅行的全域性影响，并表明其影响是相加的。但是，人们对每种土地利用政策如何与其他发展水平不同的政策相互影响以减少车辆行驶的知之甚少。在这项研究中，我们探索了三种著名的土地利用策略（致密化，混合用途开发，和街道网络改善）之间的互动，方法是测试土地使用因素的可能组合，并关注这些互动效应如何随开发强度水平而变化。使用为奥斯汀市统计区域（MSA）创建的数据集（使用2006年奥斯汀旅行调查数据）使用普通最小二乘回归分析，我们研究了土地使用对家用车辆旅行的影响。我们的研究结果表明发生相互作用的影响，但它们随发展强度而变化。这项研究的结果表明，在理解土地利用因素如何影响和不影响出行（基于互动而不是直接和累加效应）的基础上，同时考虑阈值（发展强度）和相互作用（政策组合）的重要性）。尽管本文仅使用来自一个MSA的数据，因此仅具有启发性，但它的确指出了可能更细微地使用通常指定的规划和设计策略变量来说明基于开发强度差异的有效性差异。例如，我们发现，在开发强度相对较高的地区，更大的土地利用集约化在改变车辆行驶行为方面具有更高的功效。未来的研究应包括来自更广泛的大都市地区的数据，并结合其他无法用于此分析的预测变量。我们发现，土地集约化程度越高，在开发强度相对较高的地区改变车辆出行行为的效果就越高。未来的研究应包括来自更广泛的大都市地区的数据，并结合其他无法用于此分析的预测变量。我们发现，土地集约化程度越高，在开发强度相对较高的地区改变车辆出行行为的效果就越高。未来的研究应包括来自更广泛的大都市地区的数据，并结合其他无法用于此分析的预测变量。