Introduction

Although ride-hailing and taxi trips can potentially reduce single-occupant vehicle trips and auto ownership, they can also replace pedestrian trips. Because physical activity is associated with improved health outcomes, the extent to which ride-hailing and taxi travel captures walking's mode share is of interest to policymakers.

Methods

Based on large-scale behavioral data from the 2017 U.S. National Household Travel Survey, this paper reports on the development of a full Bayesian logistic regression model for determining the mode split between (1) ride-hailing and taxi and (2) walk while accounting for unobserved heterogeneity. The results from the stand-alone model inform two longer-term travel forecasting scenarios: a) higher risk of walk trips converting to ride-hailing and taxi, specifically in the future with high prevalence of automated vehicles, b) higher probability of such trips remaining as walking.

Results

The results revealed that some of the important characteristics that increase the likelihood of a traveler using the ride-hailing and taxi mode versus walking include having a longer trip, using a smartphone to access the internet, having an interest in technologies, having a medical condition, and living in a metropolitan area with rail access. Further, the results from the first scenario suggest that an overall increase of up to 2.9% in the ride-hailing and taxi mode share may be expected. The second scenario shows that between 68% and 76% of ride-hailing and taxi trips could be diverted to walking if supportive pedestrian infrastructure were provided in the case study locations. The planning process can be adapted to consider not only congestion, crash, and emissions impacts of such shifts but also the effects of a loss of physical activity.

Conclusions

The study findings show how the ride-hailing and taxi mode competes with walking. Further, the findings enable planners to update their regional travel forecasting models; policy makers can thus encourage active travel by prioritizing pedestrian infrastructure investments that may divert ride-hailing and taxi trips to walking. However, equity should be a key consideration to ensure that addressing the competition between these two modal choices does not hinder the provision of pedestrian facilities in communities that depend on walking.

中文翻译：

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叫车和出租车与步行：大规模行为数据的长期预测和影响

介绍

尽管网约车和出租车出行可能会减少单人车辆出行和汽车拥有量，但它们也可以取代步行出行。由于身体活动与改善健康结果相关，因此政策制定者对网约车和出租车出行占步行模式份额的程度感兴趣。

方法

基于 2017 年美国全国家庭旅行调查的大规模行为数据，本文报告了一个完整的贝叶斯逻辑回归模型的开发，用于确定 (1) 打车和出租车和 (2) 步行同时记账对于未观察到的异质性。独立模型的结果为两个长期旅行预测场景提供了信息：a) 步行旅行转换为叫车和出租车的风险更高，特别是在自动驾驶汽车高度流行的未来，b) 此类旅行的概率更高剩下的就是走路。

结果

结果显示，与步行相比，增加旅行者使用叫车和出租车模式的可能性的一些重要特征包括长途旅行、使用智能手机访问互联网、对技术感兴趣、有健康状况，并居住在有铁路通道的大都市区。此外，第一种情景的结果表明，网约车和出租车模式的份额总体增长可能高达 2.9%。第二种情况表明，如果在案例研究地点提供支持性的步行基础设施，68% 到 76% 的网约车和出租车出行可以转为步行。规划过程不仅可以考虑这种转变的拥堵、碰撞和排放影响，还可以考虑体力活动损失的影响。

结论

研究结果显示了叫车和出租车模式如何与步行竞争。此外，调查结果使规划者能够更新其区域旅行预测模型；因此，政策制定者可以通过优先考虑步行基础设施投资来鼓励积极出行，这些投资可能会将网约车和出租车出行转向步行。然而，公平应该是一个关键考虑因素，以确保解决这两种模式选择之间的竞争不会阻碍在依赖步行的社区提供步行设施。