Ride-hailing has become an important part of the urban mobility landscape. The main contribution of this study is to estimate how travellers perceive time when using ride-hailing compared to using conventional public transport, to better understand ride-hailing mode choice. We combine two unique datasets containing actual, individual passenger behaviour for the Washington DC area from October 2018: a large set of almost 250,000 individual ride-hailing trips made using Uber, and more than 326,000 public transport trips obtained from automated ticketing data. Contrary to previous studies our model estimations rely on over half a million directly observed passenger choices between ride-hailing and public transport, based on which we estimate a discrete choice model to infer travel time perceptions for both modes using a binomial logit model. Our results show that on average ride-hailing in-vehicle time is perceived 35% less negative than public transport in-vehicle time. We also found that waiting time for ride-hailing is valued 1.3 times more negative than ride-hailing in-vehicle time, which is about 20% less negative than the ratio between waiting and in-vehicle time found for public transport. Our study enables a more accurate modelling of ride-hailing by using mode-specific travel time coefficients derived from large-scale empirical data, which can improve the accuracy of modelling outputs and thus improve decision-making processes.

中文翻译：

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网约车与公共交通：使用来自华盛顿特区的显示偏好数据比较出行时间感知

网约车已成为城市交通领域的重要组成部分。这项研究的主要贡献是估计旅行者在使用网约车与使用传统公共交通工具相比如何感知时间，以更好地了解网约车模式的选择。我们结合了两个独特的数据集，其中包含自 2018 年 10 月以来华盛顿特区地区的实际个人乘客行为：使用 Uber 进行的近 250,000 次个人叫车行程，以及从自动票务数据中获得的超过 326,000 次公共交通行程。与以前的研究相反，我们的模型估计依赖于超过五十万直接观察到的乘客在网约车和公共交通之间的选择，在此基础上，我们估计了一个离散选择模型，以使用二项式 logit 模型推断两种模式的旅行时间感知。我们的结果表明，平均而言，网约车的车内时间比公共交通的车内时间少 35%。我们还发现，网约车的等待时间比网约车的车内时间负值高 1.3 倍，这比公共交通的等待时间和车内时间之比的负值低约 20%。我们的研究通过使用从大规模经验数据中得出的特定模式的出行时间系数，可以更准确地对网约车进行建模，这可以提高建模输出的准确性，从而改善决策过程。