Halvorsen Anne, Wood Daniel, Jefferson Darian, Stasko Timon, Hui Jack, Reddy Alla
Data Research and Development, Operations Planning, New York City Transit Authority, New York, NY.
Transp Res Rec. 2023 Apr;2677(4):51-64. doi: 10.1177/03611981211028860. Epub 2021 Sep 8.
The New York City metropolitan area was hard hit by COVID-19, and the pandemic brought with it unprecedented challenges for New York City Transit. This paper addresses the techniques used to estimate dramatically changing ridership, at a time when previously dependable sources suddenly became unavailable (e.g., local bus payment data, manual field checks). The paper describes alterations to ridership models, as well as the expanding use of automated passenger counters, including validation of new technology and scaling to account for partial data availability. The paper then examines the trends in subway and bus ridership. Peak periods shifted by both time of day and relative intensity compared with the rest of the day, but not in the same way on weekdays and weekends. On average, trip distances became longer for subway and local bus routes, but overall average bus trip distances decreased owing to a drop in express bus usage. Subway ridership changes were compared with neighborhood demographic statistics and numerous correlations were identified, including with employment, income, and race and ethnicity. Other factors, such as the presence of hospitals, were not found to be significant.
纽约市大都市区受到新冠疫情的重创,这场大流行给纽约市交通局带来了前所未有的挑战。本文探讨了在以前可靠的数据源突然无法获取(例如,当地公交支付数据、人工现场检查)的情况下,用于估算急剧变化的客流量的技术。本文描述了客流量模型的变更,以及自动乘客计数器的广泛使用,包括新技术的验证和为考虑部分数据可用性而进行的扩展。本文接着研究了地铁和公交客流量的趋势。与一天中的其他时段相比,高峰时段在时间和相对强度上都发生了变化,但工作日和周末的变化方式不同。平均而言,地铁和当地公交线路的出行距离变长了,但由于特快巴士使用量下降,公交总体平均出行距离缩短了。将地铁客流量变化与社区人口统计数据进行了比较,并确定了许多相关性,包括与就业、收入以及种族和族裔的相关性。未发现其他因素(如医院的存在)具有显著影响。