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Developing Restoration Schemes for a...

Antony, Ebin.

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Developing Restoration Schemes for a Road Transportation Network in the Event of a Disaster.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Developing Restoration Schemes for a Road Transportation Network in the Event of a Disaster./
Author:	Antony, Ebin.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	57 p.
Notes:	Source: Masters Abstracts International, Volume: 79-06.
Contained By:	Masters Abstracts International79-06.
Subject:	Civil engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10286789
ISBN:	9780355254396

Developing Restoration Schemes for a Road Transportation Network in the Event of a Disaster.
Antony, Ebin.

Developing Restoration Schemes for a Road Transportation Network in the Event of a Disaster. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 57 p.

Source: Masters Abstracts International, Volume: 79-06.

Thesis (M.S.)--Missouri University of Science and Technology, 2017.

This item must not be sold to any third party vendors.

Transportation systems such as rail, road, and waterways are key component of critical infrastructure systems, providing connectivity between other components to enable the production and distribution of goods and services. During large scale disasters such as earth quakes and floods, this connectivity is disrupted, restricting or completely halting the flow of goods and services. To ensure that the connectivity between the different modes of transportation are restored in an aftermath of these disruptions, the interdependence between them and the importance of individual elements to the overall connectivity have to be studied and formulated to develop a system-level restoration plan. This paper presents a framework to develop efficient restoration schemes for a road transportation network in an aftermath of a disruption. The road transportation network is modelled using graph theory analytics. Using a system oriented parameter such as the Eigen-vector centrality measure associated with the road transportation, it is possible to understand the importance of different network components. This model captures the interdependence between the different elements in the road transportation network critical in understanding failure effects by identifying the important nodes in the network using the Eigen-vector centrality measure. The model is constructed from publically available data for Saint-Louis, Missouri. By performing a sensitivity analysis, we have found that the node with the highest Eigen-vector centrality measures are shown to provide a higher value within a ninety-five percent confidence level, indicating low sensitivity to changes in input parameters. This provides a measure to determine the most important nodes to place back into service to assist in restoring an urban center's supply chain in the wake of an extreme event.

ISBN: 9780355254396Subjects--Topical Terms:

860360
Civil engineering.

Developing Restoration Schemes for a Road Transportation Network in the Event of a Disaster.
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520 $a Transportation systems such as rail, road, and waterways are key component of critical infrastructure systems, providing connectivity between other components to enable the production and distribution of goods and services. During large scale disasters such as earth quakes and floods, this connectivity is disrupted, restricting or completely halting the flow of goods and services. To ensure that the connectivity between the different modes of transportation are restored in an aftermath of these disruptions, the interdependence between them and the importance of individual elements to the overall connectivity have to be studied and formulated to develop a system-level restoration plan. This paper presents a framework to develop efficient restoration schemes for a road transportation network in an aftermath of a disruption. The road transportation network is modelled using graph theory analytics. Using a system oriented parameter such as the Eigen-vector centrality measure associated with the road transportation, it is possible to understand the importance of different network components. This model captures the interdependence between the different elements in the road transportation network critical in understanding failure effects by identifying the important nodes in the network using the Eigen-vector centrality measure. The model is constructed from publically available data for Saint-Louis, Missouri. By performing a sensitivity analysis, we have found that the node with the highest Eigen-vector centrality measures are shown to provide a higher value within a ninety-five percent confidence level, indicating low sensitivity to changes in input parameters. This provides a measure to determine the most important nodes to place back into service to assist in restoring an urban center's supply chain in the wake of an extreme event.
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