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Dissertation/Thesis Abstract

Transportation Network Analysis for Reliability Based on Path Failure Strategies
by Tadikamalla, Veera Venkata Gopi Sai Krishna, M.S., California State University, Long Beach, 2017, 48; 10639442
Abstract (Summary)

The transportation network is an essential component to provide a better transport service for both people and goods. Reliability, vulnerability, and robustness are the primary characteristics that can be used to analyze a transport network. The reliability of a transport network is defined as the possibility of moving people or goods from one place to another successfully. Exploring the reliability of a road network has attracted significant attention in the recent times due to increase in natural disasters. Such natural disasters not only damage the connections of the roadways but paralyze the transportation system for a remarkable period. However, the reliability of a transport network can be maintained by closely monitoring and ensuring the safety of critical paths in the network.

Critical paths of a transportation network are the most frequently used paths of that network. Road maintenance, car accidents that cause failure of independent paths or sub-networks have a severe impact on the reliability of the transportation network. Several methods exist to analyze the reliability of the transport network such as the concepts of connect reliability, and network reliability technique to monitor the current traffic status.

In this thesis, a robust network model is proposed;the implementation of this network model includes two approaches: selective path failure strategy, and random path failure strategy using the Betweenness Centrality Index as a metric. These strategies can successfully calculate the reliability and find the critical paths of the transportation network in California.UCINET, a simulation tool, is used to calculate reliability and find the critical paths of a transportation network. UCINET is a software package developed for the analysis of social network data. It comes with the NetDraw network visualization tool.

Indexing (document details)
Advisor: Wu, Xiaolong
Commitee: He, Min, Wang, Sen
School: California State University, Long Beach
Department: Computer Engineering and Computer Science
School Location: United States -- California
Source: MAI 57/01M(E), Masters Abstracts International
Subjects: Computer science
Publication Number: 10639442
ISBN: 978-0-355-52949-4
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