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.
Boundary rigidity with partial data