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首页> 外文期刊>Journal of Transport Geography >Accommodating spatial correlation across choice alternatives in discretechoice models: an application to modeling residential location choice behavior
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Accommodating spatial correlation across choice alternatives in discretechoice models: an application to modeling residential location choice behavior

机译：在离散选择模型中适应选择选择之间的空间相关性：对住宅区位选择行为建模的应用

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摘要

This paper presents a modeling methodology capable of accounting for spatial correlation across choice alternatives in discrete choice modeling applications. Many location choice (e.g., residential location, workplace location, destination location) modeling contexts involve choice sets where alternatives are spatially correlated with one another due to unobserved factors. In the presence of such spatial correlation, traditional discrete choice modeling methods that are often based on the assumption of independence among choice alternatives are not appropriate. In this paper, a Generalized Spatially Correlated Logit (GSCL) model that allows one to represent the degree of spatial correlation as a function of a multi-dimensional vector of attributes characterizing each pair of location choice alternatives is formulated and presented. The formulation of the GSCL model allows one to accommodate alternative correlation mechanisms rather than pre-imposing restrictive correlation assumptions on the location choice alternatives. The model is applied to the analysis of residential location choice behavior using a sample of households drawn from the 2000 San Francisco Bay Area Travel Survey (BATS) data set. Model estimation results obtained from the GSCL are compared against those obtained using the standard multinomial logit (MNL) model and the spatially correlated logit (SCL) model where only correlations across neighboring (or adjacent) alternatives are accommodated. Model findings suggest that there is significant spatial correlation across alternatives that do not share a common boundary, and that the GSCL offers the ability to more accurately capture spatial location choice behavior.
机译：本文提出了一种建模方法，该方法能够解决离散选择建模应用程序中跨选择备选方案的空间相关性。许多位置选择（例如，住宅位置，工作场所位置，目的地位置）建模上下文都涉及选择集，其中由于未观察到的因素，替代方案在空间上相互关联。在存在这种空间相关性的情况下，通常基于选择备选方案之间的独立性假设的传统离散选择建模方法是不合适的。在本文中，提出并提出了一种通用的空间相关Logit（GSCL）模型，该模型可以表示空间相关程度作为表征每个位置选择对的属性的多维矢量的函数。 GSCL模型的制定允许人们适应替代性的相关机制，而不是在限制性选择假设上预先施加限制性的相关假设。该模型使用从2000年旧金山湾区旅行调查（BATS）数据集中抽取的家庭样本来分析住宅区位选择行为。从GSCL获得的模型估计结果与使用标准多项式logit（MNL）模型和空间相关logit（SCL）模型获得的模型估计结果进行比较，其中仅容纳相邻（或相邻）替代方案之间的相关性。模型发现表明，在不具有共同边界的替代方案之间存在显着的空间相关性，并且GSCL提供了更准确地捕获空间位置选择行为的能力。

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