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Accounting for scale heterogeneity within and between pooled data sources

机译：考虑合并数据源内部和之间的规模异质性

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There is growing interest in incorporating both preference heterogeneity and scale heter ogeneity in choice models, as a way of capturing an increasing number of sources of utility amongst a set of alternatives. The extension of mixed logit to incorporate scale heteroge neity in a generalised mixed logit (GMXL) model provides a way to accommodate these sources of influence, observed and unobserved. The small but growing number of applica tions of the GMXL model have parameterized scale heterogeneity as a single estimate; however it is often the case that analysts pool data from more than one source, be it revealed preference (RP) and stated preference (SP) sources, or multiple SP sources, induc ing the potential for differences in the scale factor between the data sources. Existing prac tice has developed ways of accommodating scale differences between data sources by adopting a scale homogeneity assumption within each data source (e.g., the nested logit trick) that varies between data sources. This paper extends the state of the art by incorpo rating data-source specific scale differences in scale heterogeneity setting across pooled RP and SP data set. An example of choice amongst RP and SP transport modes (including two 'new' SP modes) is used to obtain values of travel time savings that vary significantly between a model that accounts for scale heterogeneity differences within pooled RP and SP data, and the other where differences in scale heterogeneity is also accommodated between RP and SP data.
机译：将偏好异质性和规模异质性并入选择模型的兴趣日益浓厚，以此作为在一组替代方案中获取越来越多的效用来源的方法。扩展混合Logit以在广义混合Logit（GMXL）模型中合并尺度异质性提供了一种方式来容纳这些已观察到和未观察到的影响源。 GMXL模型的应用程序数量虽小但在不断增长，已经将参数化规模异质性作为单个估计值；但是，通常情况下，分析人员会从多个来源收集数据，无论是揭示偏好（RP）还是陈述偏好（SP）来源，还是多个SP来源，都可能导致数据源之间的比例因子存在差异。现有实践已经通过在每个数据源之间采用比例均一性假设（例如，嵌套的logit技巧），开发了一种适应数据源之间比例差异的方法，该假设在数据源之间是不同的。本文通过在汇总的RP和SP数据集中对规模异质性设置中的数据源特定规模差异进行合并评估，扩展了现有技术。在RP和SP传输模式（包括两个“新” SP模式）之间进行选择的示例用于获得节省的旅行时间值，该值在考虑汇总的RP和SP数据内的规模异质性差异的模型与其他模型之间存在显着差异。 RP和SP数据之间也存在尺度异质性差异。

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