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On bus ridership and frequency

机译:在公交车乘坐和频率上

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Even before the start of the COVID-19 pandemic, bus ridership in the United States had attained its lowest level since 1973. If transit agencies hope to reverse this trend, they must understand how their service allocation policies affect ridership. This paper is among the first to model ridership trends on a hyper-local level over time. A Poisson fixed-effects model is developed to evaluate the ridership elasticity to frequency on weekdays using passenger count data from Portland, Miami, Minneapolis/St-Paul, and Atlanta between 2012 and 2018. In every agency, ridership is found to be elastic to frequency when observing the variation between individual route-segments at one point in time. In other words, the most frequent routes are already the most productive in terms of passengers per vehicle-trip. When observing the variation within each route-segment over time, however, ridership is inelastic; each additional vehicle-trip is expected to generate less ridership than the average bus already on the route. In three of the four agencies, the elasticity is a decreasing function of prior frequency, meaning that low-frequency routes are the most sensitive to changes in frequency. This paper can help transit agencies anticipate the marginal effect of shifting service throughout the network. As the quality and availability of passenger count data improve, this paper can serve as the methodological basis to explore the dynamics of bus ridership.
机译:即使在Covid-19大流行开始之前,美国的巴士乘坐自1973年以来就获得了最低水平。如果过境机构希望扭转这一趋势,他们必须了解他们的服务分配政策如何影响乘客。本文是第一个在超级局部水平上模拟乘积趋势之一。开发了泊松固定效果模型,以评估工作日频率的频率,使用波特兰,迈阿密,明尼阿波利斯/圣保罗和亚特兰大之间的乘客计数数据。在每个机构中,发现乘客被弹性在一个时间点观察各个路线段之间的变化时的频率。换句话说,最常见的路线在每辆车旅行中的乘客方面都是最富有成效的。然而,当观察每个路线段内的变化随时间的推移时,乘坐缺陷是无弹性的;每次额外的车程都预计会产生比路线上的平均公交车更少的乘客。在四个机构中的三个中,弹性是现有频率的降低,这意味着低频路线对频率变化最敏感。本文可以帮助运输机构预期在整个网络中转换服务的边际效果。作为乘客计数数据的质量和可用性,本文可以作为探索巴士乘客动态的方法基础。

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