Using Big Data to Study Resilience of Taxi and Subway Trips for Hurricanes Sandy and Irene

Abstract

Hurricanes Irene and Sandy had a significant impact on New York City; the result was devastating damage to the New York City transportation systems, which took days, even months to recover. This study explored posthurricane recovery patterns of the roadway and subway systems of New York City on the basis of data for taxi trips and for subway turnstile ridership. Both data sets were examples of big data with millions of individual ridership records per month. The spatiotemporal variations of transportation system recovery behavior were investigated by using neighborhood tabulation areas as units of analysis. Recovery curves were estimated for each evacuation zone category to model time-dependent recovery patterns of the roadway and subway systems. The recovery rate for Hurricane Sandy was found to be lower than that for Hurricane Irene. In addition, the results indicate a higher resilience of the road network compared with the subway network. The methodology proposed in this study can be used to evaluate the resilience of transportation systems with respect to natural disasters and the findings can provide government agencies with useful insights into emergency management.