Data-Driven Spatial Modeling for Quantifying Network-Wide Resilience in the Aftermath of Hurricanes Irene and Sandy

Details

• Creators:
  Zhu, Yuan ; Xie, Kun ; Ozbay, Kaan
• Corporate Creators:
  Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)
• Subject/TRT Terms:
  • Hurricanes
  • Spatial analysis
  • Data analysis
  • Infrastructure
  • Disaster resilience Hurricane
  • recovery curve
  • resilience
  • spatial analysis
  • taxi and subway data
• Resource Type:
  Journal Article
• Geographical Coverage:
  New York;United States
• Corporate Publisher:
  Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)
• Abstract:
  In recent years, New York City metropolitan area was hit by two major hurricanes, Irene and Sandy. These extreme weather events had major impacts on the transportation infrastructures, including road and subway networks. As an extension of our recent research on this topic, this study explores the spatial patterns of infrastructure resilience in New York City using taxi and subway ridership data. Neighborhood Tabulation Areas (NTAs) are used as units of analysis. The recovery curve of each NTA is modeled using the logistic function to quantify the resilience of road and subway systems. Moran’s I tests confirm the spatial correlation of recovery patterns for taxi and subway ridership. To account for this spatial correlation, citywide spatial models are estimated, and found to outperform 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 vulnerability of transportation networks and can be utilized for more efficient emergency planning and management.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/42410/dot_42410_DS1.pdf
• File Type:
  [PDF - 1.48 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:f5fab02eb8007917fac2e78197572dbbe7e6f0207c3dc4f9e2ddc76106ed5d6983ffe9fbe6da0653f60a85a88f71f55dca35e2d30739766029178fd4ffc41f83