Even before the start of the COVID-19 pandemic, bus ridership in the United States 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 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. 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 frequency change. 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.
翻译:甚至在COVID-19大流行开始之前,美国公共汽车骑手就达到了1973年以来的最低水平。如果过境机构希望扭转这一趋势,它们必须了解其服务分配政策如何影响骑手。本文是最早在超地方一级模拟骑手趋势的第一批文件之一。Poisson固定效应模型用来利用来自Portland、迈阿密、Minneapolis/St-Paul和亚特兰大的乘客计票数据评估骑手对频率的弹性。2012年至2018年之间,美国公共汽车骑手达到了最低水平。在每个机构,在观察单个路段之间的差异时,驾驶员被认为具有弹性到频率的频率。换句话说,最频繁的路线已经是最具生产力的。在观察每个路段的变异时,骑手是无弹性的;预计每增加一辆汽车的车轮车会产生比在这条路线上的平均车轮车更低的骑手。在四个机构中的3个机构,在观察之前的频率上发现骑手的弹性是频率越来越弱的功能,这意味着低频路段路段的频率是最具有最敏感的频率的路线,因此可以改进整个运输结构的运输结构。