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.
翻译:即使在COVID-19大流行开始之前,美国公共汽车骑手就达到了自1973年以来的最低水平;如果转运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驾驶员;本文是最早在超地方一级模拟驾驶员趋势的第一批文件之一;Poisson固定效应模型是用来利用来自Portland、迈阿密、Minneapolis/St-Paul和亚特兰大等地的乘客计票数据,评估骑手在周日的弹性;2012年至2018年之间,美国公共汽车骑手达到了其最低水平;在每个机构,在观察单个路线间段之间的变异时,其服务分配政策被认为与频率有弹性;换句话说,最常见的路线已经是每辆汽车的乘客。在观察每个路线的变异时,骑手是无弹性的;预计每多辆汽车的车轮车会比在路线上的平均数少。在四个机构中,在观察单个路段之间的变换位时,驾驶员在观察每个车流的频率上,这种变换最易变的路线将意味着整个运输机的频率的频率上,可以改进路况。