Data-driven spatial modeling for quantifying networkwide resilience in the aftermath of hurricanes Irene and Sandy

Yuan Zhu, Kun Xie, Kaan Ozbay, Fan Zuo, Hong Yang

Research output: Contribution to journalArticlepeer-review

Abstract

In recent years, the New York City metropolitan area was hit by two major hurricanes, Irene and Sandy. These extreme weather events dis rupted and devastated the transportation infrastructure, including road and subway networks. As an extension of the authors' recent research on this topic, this study explored the spatial patterns of infrastructure resilience in New York City with the use of taxi and subway ridership data. Neighborhood tabulation areas were used as the units of analysis. The recovery curve of each neighborhood tabulation area was modeled with the logistic function to quantify the resilience of road and subway systems. Moran's I tests confirmed the spatial correlation of recovery patterns for taxi and subway ridership. To account for this spatial correlation, citywide spatial models were estimated and found to out perform linear models. Factors such as the percentage of area influenced by storm surges, the distance to the coast, and the average elevation are found to affect the infrastructure resilience. The findings in this study provide insights into the vulnerability of transportation networks and can be used for more efficient emergency planning and management.

Original languageEnglish (US)
Pages (from-to)9-18
Number of pages10
JournalTransportation Research Record
Volume2604
DOIs
StatePublished - 2017

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'Data-driven spatial modeling for quantifying networkwide resilience in the aftermath of hurricanes Irene and Sandy'. Together they form a unique fingerprint.

Cite this