Fuzzy Multiobjective Traffic Light Signal Optimization

Traffic congestion is a major concern for many cities throughout the world. In a general traffic light controller, the traffic lights change at a constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers in current use are based on the “time-of-the-day” schem...

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Main Authors: N. Shahsavari Pour, H. Asadi, M. Pour Kheradmand
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2013/249726
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spelling doaj-be076c79e4a442f1a946f930f19eaa912020-11-24T22:04:17ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/249726249726Fuzzy Multiobjective Traffic Light Signal OptimizationN. Shahsavari Pour0H. Asadi1M. Pour Kheradmand2Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Kerman, IranDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad University, Kerman, IranDepartment of Industrial Engineering, Science and Research Branch, Islamic Azad University, Kerman, IranTraffic congestion is a major concern for many cities throughout the world. In a general traffic light controller, the traffic lights change at a constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers in current use are based on the “time-of-the-day” scheme, which use a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. In this paper, the fuzzy traffic light controller is used to optimize the control of fluctuating traffic volumes such as oversaturated or unusual load conditions. The problem is solved by genetic algorithm, and a new defuzzification method is introduced. The performance of the new defuzzification method (NDM) is compared with the centroid point defuzzification method (CPDM) by using ANOVA. Finally, an illustrative example is presented to show the competency of proposed algorithm.http://dx.doi.org/10.1155/2013/249726
collection DOAJ
language English
format Article
sources DOAJ
author N. Shahsavari Pour
H. Asadi
M. Pour Kheradmand
spellingShingle N. Shahsavari Pour
H. Asadi
M. Pour Kheradmand
Fuzzy Multiobjective Traffic Light Signal Optimization
Journal of Applied Mathematics
author_facet N. Shahsavari Pour
H. Asadi
M. Pour Kheradmand
author_sort N. Shahsavari Pour
title Fuzzy Multiobjective Traffic Light Signal Optimization
title_short Fuzzy Multiobjective Traffic Light Signal Optimization
title_full Fuzzy Multiobjective Traffic Light Signal Optimization
title_fullStr Fuzzy Multiobjective Traffic Light Signal Optimization
title_full_unstemmed Fuzzy Multiobjective Traffic Light Signal Optimization
title_sort fuzzy multiobjective traffic light signal optimization
publisher Hindawi Limited
series Journal of Applied Mathematics
issn 1110-757X
1687-0042
publishDate 2013-01-01
description Traffic congestion is a major concern for many cities throughout the world. In a general traffic light controller, the traffic lights change at a constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers in current use are based on the “time-of-the-day” scheme, which use a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. In this paper, the fuzzy traffic light controller is used to optimize the control of fluctuating traffic volumes such as oversaturated or unusual load conditions. The problem is solved by genetic algorithm, and a new defuzzification method is introduced. The performance of the new defuzzification method (NDM) is compared with the centroid point defuzzification method (CPDM) by using ANOVA. Finally, an illustrative example is presented to show the competency of proposed algorithm.
url http://dx.doi.org/10.1155/2013/249726
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