A parallel genetic algorithm-based TSK-Fuzzy system for dynamic car-following modeling

This paper presents the application of Parallel Genetic Algorithm (PGA)-based Takagi Sugeno Kang (TSK)-Fuzzy approach for dynamic car-following modeling in the traffic simulation software. It differs from the usual car-following model significantly as the proposed model provides a more dynamic car m...

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Main Authors:	Purnomo, Muhammad Ridwan Andi, Abdul Wahab, Dzuraidah, Hassan, Azmi, Rahmat, Riza Atiq
Format:	Article
Language:	English
Published:	EuroJournals Publishing, Inc. 2009
Subjects:	QA75 Electronic computers. Computer science T175 Industrial research. Research and development
Online Access:	http://irep.iium.edu.my/37310/ http://irep.iium.edu.my/37310/1/a_parallel_genetic_algorithm.pdf

id	iium-37310
recordtype	eprints
spelling	iium-373102014-07-11T07:51:46Z http://irep.iium.edu.my/37310/ A parallel genetic algorithm-based TSK-Fuzzy system for dynamic car-following modeling Purnomo, Muhammad Ridwan Andi Abdul Wahab, Dzuraidah Hassan, Azmi Rahmat, Riza Atiq QA75 Electronic computers. Computer science T175 Industrial research. Research and development This paper presents the application of Parallel Genetic Algorithm (PGA)-based Takagi Sugeno Kang (TSK)-Fuzzy approach for dynamic car-following modeling in the traffic simulation software. It differs from the usual car-following model significantly as the proposed model provides a more dynamic car movement and realistic headway by considering the driver progressive level factor. These two advantages could make further traffic analysis become more accurate. The proposed model is used for the tire-road slippage index determination which influences the car's speed. Since the car interact with each other on the road and the driver progressive level is different, three interaction variables, that are current car speed, distance to the car ahead and driver progressive level, are defined and an indication of their influence on the tire-road slippage index is analysed. PGA is included in the TSK-Fuzzy system to determine the optimum parameters in the Fuzzy sets and Fuzzy rules so as to improve the accuracy of the tire-road slippage index estimation. A set of data in a size of 38 × 4 and 22 × 4 were used for training and testing the performance of the model. The study shows that TSK-Fuzzy system combined with PGA is effective and accurate in estimating the tire-road slippage index EuroJournals Publishing, Inc. 2009-03 Article PeerReviewed application/pdf en http://irep.iium.edu.my/37310/1/a_parallel_genetic_algorithm.pdf Purnomo, Muhammad Ridwan Andi and Abdul Wahab, Dzuraidah and Hassan, Azmi and Rahmat, Riza Atiq (2009) A parallel genetic algorithm-based TSK-Fuzzy system for dynamic car-following modeling. European Journal of Scientific Research, 28 (4). pp. 628-642. ISSN 1450-216X, 1450-202X
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science T175 Industrial research. Research and development
spellingShingle	QA75 Electronic computers. Computer science T175 Industrial research. Research and development Purnomo, Muhammad Ridwan Andi Abdul Wahab, Dzuraidah Hassan, Azmi Rahmat, Riza Atiq A parallel genetic algorithm-based TSK-Fuzzy system for dynamic car-following modeling
description	This paper presents the application of Parallel Genetic Algorithm (PGA)-based Takagi Sugeno Kang (TSK)-Fuzzy approach for dynamic car-following modeling in the traffic simulation software. It differs from the usual car-following model significantly as the proposed model provides a more dynamic car movement and realistic headway by considering the driver progressive level factor. These two advantages could make further traffic analysis become more accurate. The proposed model is used for the tire-road slippage index determination which influences the car's speed. Since the car interact with each other on the road and the driver progressive level is different, three interaction variables, that are current car speed, distance to the car ahead and driver progressive level, are defined and an indication of their influence on the tire-road slippage index is analysed. PGA is included in the TSK-Fuzzy system to determine the optimum parameters in the Fuzzy sets and Fuzzy rules so as to improve the accuracy of the tire-road slippage index estimation. A set of data in a size of 38 × 4 and 22 × 4 were used for training and testing the performance of the model. The study shows that TSK-Fuzzy system combined with PGA is effective and accurate in estimating the tire-road slippage index
format	Article
author	Purnomo, Muhammad Ridwan Andi Abdul Wahab, Dzuraidah Hassan, Azmi Rahmat, Riza Atiq
author_facet	Purnomo, Muhammad Ridwan Andi Abdul Wahab, Dzuraidah Hassan, Azmi Rahmat, Riza Atiq
author_sort	Purnomo, Muhammad Ridwan Andi
title	A parallel genetic algorithm-based TSK-Fuzzy system for dynamic car-following modeling
title_short	A parallel genetic algorithm-based TSK-Fuzzy system for dynamic car-following modeling
title_full	A parallel genetic algorithm-based TSK-Fuzzy system for dynamic car-following modeling
title_fullStr	A parallel genetic algorithm-based TSK-Fuzzy system for dynamic car-following modeling
title_full_unstemmed	A parallel genetic algorithm-based TSK-Fuzzy system for dynamic car-following modeling
title_sort	parallel genetic algorithm-based tsk-fuzzy system for dynamic car-following modeling
publisher	EuroJournals Publishing, Inc.
publishDate	2009
url	http://irep.iium.edu.my/37310/ http://irep.iium.edu.my/37310/1/a_parallel_genetic_algorithm.pdf
first_indexed	2023-09-18T20:53:32Z
last_indexed	2023-09-18T20:53:32Z
_version_	1777410152463335424

A parallel genetic algorithm-based TSK-Fuzzy system for dynamic car-following modeling

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