Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/25575
Title: Realibility enhancement of a traffic signal light system using a mean-variance approach
Authors: Shamshul Bahar, Yaakob
Watada, Junzo
Keywords: Boltzmann machine
Hopfield network
Mean-variance analysis
Structural learning
Issue Date: Aug-2012
Publisher: ICIC International
Citation: International Journal of Innovative Computing, Information and Control, vol.8 (8), 2012, pages 5835-5845.
Abstract: Traffic accidents cause tragic loss of life, property damage and substantial congestion to transportation systems. A large percentage of crashes occur at or near intersections. Therefore, traffic signals are often used to improve traffic safety and operations. The objective of this study is to present a significant and effective method of determining the optimal investment involved in retrofitting signals with light emitting diode (LED) units. In this study, the reliability and risks of each unit are evaluated using a variance-covariance matrix, and the effects and expenses of replacement are analyzed. The mean-variance analysis is formulated as a mathematical program with the objectives of minimizing the risk and maximizing the expected return. Finally, a structural learning model of a mutual connection neural network is proposed to solve problems defined by mixed-integer quadratic programming, and this model is employed in the mean-variance analysis. Our method is applied to an LED signal retrofitting problem. This method enables us to select results more effectively and enhance decision-making.
Description: Link to publisher's homepage at http://www.ijicic.org/home.htm
URI: http://www.ijicic.org/contents.htm
http://dspace.unimap.edu.my/123456789/25575
ISSN: 1349-4198
Appears in Collections:School of Electrical Systems Engineering (Articles)

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