Mapping Influential Nodes for Transportation Network Post-Disaster Restoration Planning Using Real-World Data

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

Second Research Center/Lab

Intelligent Systems 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

01 Apr 2018

Link to Full Text

Share

 
COinS