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 COVID-19 的重创，疫情给纽约市交通带来了前所未有的挑战。本文介绍了在以前可靠的来源突然变得不可用（例如，当地公交车支付数据、人工现场检查）时用于估计急剧变化的乘客量的技术。该文件描述了乘客模型的改变，以及自动乘客柜台的扩展使用，包括新技术的验证和扩展以考虑部分数据可用性。然后，本文研究了地铁和公共汽车乘客的趋势。与一天中的其他时间相比，高峰期随着一天中的时间和相对强度的变化而变化，但在工作日和周末的变化方式不同。平均而言，地铁和当地公交路线的行程距离变长，但由于快速巴士使用率下降，总体平均巴士行程距离有所减少。将地铁乘客量的变化与邻里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、种族和民族的相关性。其他因素，例如医院的存在，被发现并不重要。