Estimating spatio-temporal variations of taxi ridership caused by Hurricanes Irene and Sandy: A case study of New York City

Abstract

Extreme weather days were usually removed as outliers in past studies of taxi ridership. However, taxis play an important role during extreme weather events when other public transportation service is suspended. Thus, estimating spatio-temporal variations of taxi ridership during extreme weather conditions can provide valuable information on heretofore relatively unknown behavior of taxi riders and help identify areas with unusual taxi demands. In this study, New York City (NYC) taxi ridership shortly before the landfall of Hurricanes Irene and Sandy was analyzed. It was found that taxi ridership began to drop about 24 h before each hurricane made landfall. Six multisource regression models were estimated to explain the variation of taxi ridership in the last 24 h. Characteristics of the approaching hurricane, local weather conditions, and zonal socio-demographic variables were entered as explanatory variables. It was found that taxi ridership during hurricane-affected periods has a strong linear association with the ridership in unaffected periods but the proportion decreases as the storm approaches; a storm has the greatest impact on taxi ridership during weekend and at night, and the least impact on a weekday during the day; and taxi users make fewer trips during conditions of heavy rain or strong wind.