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Mapping Influential Nodes for Transportation Network Post-Disaster Restoration Planning Using Real-World Data
Proceedings of the AAG Annual Meeting (2018, New Orleans, LA)
  • Bhanu Kanwar
  • Steven Corns, Missouri University of Science and Technology
  • Suzanna Long, Missouri University of Science and Technology
  • Thomas Shoberg
Abstract

Transportation networks are vital elements in modern economic and social systems. These networks are vulnerable to damage from the impact of extreme events. Such damage adversely affects network connectivity, as well as delaying relief and restoration operations. To better plan how to restore these infrastructure elements, this study develops network-analysis and graph theory based tools using real-world data for network restoration planning. Models are developed that identify the influential nodes to map the interdependencies between different modes of transportation and determine which network components contribute most to its connectivity. An efficient node ranking method is also proposed to aid in the restoration of the critical infrastructure network in the aftermath of a disaster. Weighting factors are used to rank and map influential nodes for prioritizing respective network regions by their actual use. This approach is applied to publicly available real-world data for St. Louis, Missouri.

Meeting Name
AAG Annual Meeting (2018: Apr. 3-7, New Orleans, LA)
Department(s)
Engineering Management and Systems Engineering
Research Center/Lab(s)
INSPIRE - University Transportation Center
Keywords and Phrases
  • Computational Intelligence,
  • Restoration,
  • Supply chain,
  • Infrastructure,
  • Interdependencies
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 American Association of Geographers (AAG), All rights reserved.
Publication Date
4-1-2018
Citation Information
Bhanu Kanwar, Steven Corns, Suzanna Long and Thomas Shoberg. "Mapping Influential Nodes for Transportation Network Post-Disaster Restoration Planning Using Real-World Data" Proceedings of the AAG Annual Meeting (2018, New Orleans, LA) (2018)
Available at: http://works.bepress.com/suzanna_long/112/