摘要翻译：
本文对比了分层贝叶斯多项式logit模型中未观察到的异质性的参数和半参数表示，并利用这些方法来推断共享自动化车辆(SAV)服务特征的付费意愿分布。具体地，我们比较了多元正态(MVN)、有限混合正态（F-MON）和Dirichlet过程混合正态（DP-MON）混合分布。后者承诺在它可以假设的形状方面特别灵活，并且不像其他半参数方法，它不要求在估计之前它的复杂性是固定的。然而，它相对于简单混合分布的性质还不是很清楚。在本文中，我们使用模拟数据和来自纽约市SAV服务偏好选择研究的真实数据来评估MVN、F-MON和DP-MON混合分布的性能。我们的分析表明，DP-MON混合分布提供了更好的数据拟合，在样本外预测方面至少与竞争方法一样好。DP-MON混合分布还为采用SAVS提供了实质性的行为见解。我们发现带有平顺性分裂的SAV对车内旅行时间的偏好是强烈两极分化的。虽然三分之一的样本愿意支付10到80美元/小时，以避免与陌生人共用一辆车，但其余的样本要么对分车漠不关心，要么甚至渴望这样做。此外，我们估计，车辆自动化和电气化等新技术对出行者来说相对不重要。这表明，通过提高运营效率和降低运营成本，旅行者可能主要从这些新技术中获得间接而不是直接的好处。
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英文标题：
《Semi-Parametric Hierarchical Bayes Estimates of New Yorkers' Willingness
  to Pay for Features of Shared Automated Vehicle Services》
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作者：
Rico Krueger and Taha H. Rashidi and Akshay Vij
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
  In this paper, we contrast parametric and semi-parametric representations of unobserved heterogeneity in hierarchical Bayesian multinomial logit models and leverage these methods to infer distributions of willingness to pay for features of shared automated vehicle (SAV) services. Specifically, we compare the multivariate normal (MVN), finite mixture of normals (F-MON) and Dirichlet process mixture of normals (DP-MON) mixing distributions. The latter promises to be particularly flexible in respect to the shapes it can assume and unlike other semi-parametric approaches does not require that its complexity is fixed prior to estimation. However, its properties relative to simpler mixing distributions are not well understood. In this paper, we evaluate the performance of the MVN, F-MON and DP-MON mixing distributions using simulated data and real data sourced from a stated choice study on preferences for SAV services in New York City. Our analysis shows that the DP-MON mixing distribution provides superior fit to the data and performs at least as well as the competing methods at out-of-sample prediction. The DP-MON mixing distribution also offers substantive behavioural insights into the adoption of SAVs. We find that preferences for in-vehicle travel time by SAV with ride-splitting are strongly polarised. Whereas one third of the sample is willing to pay between 10 and 80 USD/h to avoid sharing a vehicle with strangers, the remainder of the sample is either indifferent to ride-splitting or even desires it. Moreover, we estimate that new technologies such as vehicle automation and electrification are relatively unimportant to travellers. This suggests that travellers may primarily derive indirect, rather than immediate benefits from these new technologies through increases in operational efficiency and lower operating costs.
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PDF链接：
https://arxiv.org/pdf/1907.09639