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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Series: | Journal of Applied Mathematics |
Online Access: | http://dx.doi.org/10.1155/2013/249726 |
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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 |
work_keys_str_mv |
AT nshahsavaripour fuzzymultiobjectivetrafficlightsignaloptimization AT hasadi fuzzymultiobjectivetrafficlightsignaloptimization AT mpourkheradmand fuzzymultiobjectivetrafficlightsignaloptimization |
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