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Understanding valuation of travel time changes: are preferences different under different stated choice design settings?

机译：了解旅行时间变化的评估：在不同的陈述式选择设计设置下，偏好是否有所不同？

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摘要

Stated choice (SC) experiments are the most popular method to estimate the value of travel time changes (VTTC) of a population. In the simplest VTTC experiment, the SC design variables are time changes and cost changes. The levels of these variables create a particular setting from which preferences are inferred. This paper tries to answer the question "do preferences vary with SC settings?". For this, we investigate the role of the variables used in the SC experiment on the estimation of the set of VTTC (i.e. mean and covariates). Ideally, one would like to observe the same individuals completing different SC experiments. Since that option is not available, an alternative approach is to use a large dataset of responses, and split it according to different levels of the variable of interest. We refer to this as partial data analysis. The estimation of the same model on each sub-sample provides insights into potential effects of the variable of interest. This approach is applied in relation to three design variables on the data for the last national VTTC study in the UK, using state-of-the-art model specifications. The results show several ways in which the estimated set of VTTC can be affected by the levels of SC design variables. We conclude that model estimates (including the VTTC and covariates) are different in different settings. Hence by focussing the survey on specific settings, sample level results will be affected accordingly. Our findings have implications for appraisal and can inform the construction of future SC experiments.
机译：状态选择（SC）实验是估计人群旅行时间变化（VTTC）值的最流行方法。在最简单的VTTC实验中，SC设计变量是时间变化和成本变化。这些变量的级别创建一个特定的设置，从中可以推断出首选项。本文试图回答“偏好设置是否随SC设置而变化？”的问题。为此，我们调查了SC实验中使用的变量对VTTC集的估计（即均值和协变量）的作用。理想情况下，人们希望观察同一个人完成不同的SC实验。由于该选项不可用，因此另一种方法是使用较大的响应数据集，并根据关注变量的不同级别对其进行拆分。我们将此称为部分数据分析。在每个子样本上对相同模型的估计可提供有关感兴趣变量的潜在影响的见解。使用最新的模型规范，针对英国上一次国家VTTC研究的数据，针对三种设计变量应用了此方法。结果表明，通过SC设计变量的水平可以影响VTTC的估计集的几种方式。我们得出结论，模型估计值（包括VTTC和协变量）在不同的设置下是不同的。因此，通过将调查重点放在特定的设置上，样本水平的结果将受到相应的影响。我们的发现对评估具有重要意义，并且可以为将来的SC实验构建提供参考。

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