Abstract This paper aims at better understanding passenger valuation of subway crowding in New York City. To this end, we conducted a stated preference survey with a discrete choice experiment where New Yorkers chose an alternative from a set of two hypothetical unlabeled subway routes based on occupancy levels and other attributes. We used the collected data to estimate crowding multipliers that quantify the trade-off between travel time and standee density. The previous studies have resorted to parametric heterogeneity distributions in analyzing preference variations in crowding multipliers, which can lead to misspecification issues. The contribution of this study is thus to estimate crowding multipliers using state-of-the-art semi-nonparametric models – logit-mixed logit (LML) and mixture of normals multinomial logit (MON-MNL), and compare them across different parameter spaces. The estimated distribution of crowding multiplier of LML and MON-MNL coincide below median, but the former underestimates and the latter overestimates above median. Even though these flexible logit models can be useful for a comprehensive economic analysis of transit service improvements, these differences in estimates make model selection an important avenue for future research.

中文翻译：

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灵活估算纽约市地铁拥挤估值中的异质性

摘要本文旨在更好地了解纽约市地铁拥挤的旅客价值。为此，我们通过离散选择实验进行了明示的偏好调查，其中纽约人根据占用水平和其他属性从两条假设的未标记地铁路线中选择了一条。我们使用收集到的数据来估算拥挤乘数，以量化出行时间与站立者密度之间的权衡。先前的研究在分析拥挤乘数中的偏好变化时采用了参数异质性分布，这可能会导致错误指定问题。因此，这项研究的贡献在于使用最新的半非参数模型-logit混合logit（LML）和法线多项式logit混合（MON-MNL）来估计拥挤乘数，并在不同的参数空间之间进行比较。LML和MON-MNL的拥挤乘数的估计分布在中位数以下一致，但前者低估了，后者高估了中位数。尽管这些灵活的logit模型可用于对公交服务改进进行全面的经济分析，但估计的这些差异使模型选择成为未来研究的重要途径。