Microscopic control and optimization of traffic network with Q-Learning algorithm

The main objective of this research is to optimize the traffic flow within a traffic network using microscopic level control. With the increment of on-road vehicles, modern traffic networks have more complicated topologies dealing with the higher traffic demands. Fixed-time traffic signal timing pla...

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Main Author:	Chin, Yit Kwong
Format:	Thesis
Language:	English English
Published:	2013
Subjects:	TE210-228.3 Construction details Including foundations maintenance equipment
Online Access:	https://eprints.ums.edu.my/id/eprint/38168/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/38168/2/FULLTEXT.pdf
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id	my-ums-ep.38168
record_format	uketd_dc
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English English
topic	TE210-228.3 Construction details Including foundations maintenance equipment
spellingShingle	TE210-228.3 Construction details Including foundations maintenance equipment Chin, Yit Kwong Microscopic control and optimization of traffic network with Q-Learning algorithm
description	The main objective of this research is to optimize the traffic flow within a traffic network using microscopic level control. With the increment of on-road vehicles, modern traffic networks have more complicated topologies dealing with the higher traffic demands. Fixed-time traffic signal timing plan (FTSTP) management system and Webster's model are the most common practice used by Public Works Department of Malaysia (JKR). The current efforts made by JKR should be improved to meet the increasing traffic demands within the traffic network. In this work, Q-learning traffic signal timing plan management system (QLTSTP) is developed with the attributes of Q-learning (QL). QL is able to search for the best traffic signal timing plan through the learning process from the past experiences. In order to test the viability of the proposed system, level of vehicle in queue has been chosen as the performance indicator, whilst level of traffic flow is used to determine the traffic condition of the traffic network. QLTSTP is designed as a multi-agent system with every intersection in the developed traffic network is managed by an individual QLTSTP agent towards a mutual objective. The individual QLTSTP agent has shown better performance against the FTSTP management system with the improvement of 2.61 % - 10.40% under various traffic conditions at single intersection traffic model. The performance of the multi-QLTSTP agent system is then tested in different topologies of traffic networks. The results show that there are about 2.90 % - 18.99 % of improvements in the total vehicles pass through every intersection in the traffic network for the proposed multi-QLTSP agent system. Therefore, it can be concluded that under different traffic conditions of traffic network, the proposed multi-QLTSTP agent system is able to optimize the traffic flow with the developed traffic model.The main objective of this research is to optimize the traffic flow within a traffic network using microscopic level control. With the increment of on-road vehicles, modern traffic networks have more complicated topologies dealing with the higher traffic demands. Fixed-time traffic signal timing plan (FTSTP) management system and Webster's model are the most common practice used by Public Works Department of Malaysia (JKR). The current efforts made by JKR should be improved to meet the increasing traffic demands within the traffic network. In this work, Q-learning traffic signal timing plan management system (QLTSTP) is developed with the attributes of Q-learning (QL). QL is able to search for the best traffic signal timing plan through the learning process from the past experiences. In order to test the viability of the proposed system, level of vehicle in queue has been chosen as the performance indicator, whilst level of traffic flow is used to determine the traffic condition of the traffic network. QLTSTP is designed as a multi-agent system with every intersection in the developed traffic network is managed by an individual QLTSTP agent towards a mutual objective. The individual QLTSTP agent has shown better performance against the FTSTP management system with the improvement of 2.61 % - 10.40% under various traffic conditions at single intersection traffic model. The performance of the multi-QLTSTP agent system is then tested in different topologies of traffic networks. The results show that there are about 2.90 % - 18.99 % of improvements in the total vehicles pass through every intersection in the traffic network for the proposed multi-QLTSP agent system. Therefore, it can be concluded that under different traffic conditions of traffic network, the proposed multi-QLTSTP agent system is able to optimize the traffic flow with the developed traffic model.
format	Thesis
qualification_level	Master's degree
author	Chin, Yit Kwong
author_facet	Chin, Yit Kwong
author_sort	Chin, Yit Kwong
title	Microscopic control and optimization of traffic network with Q-Learning algorithm
title_short	Microscopic control and optimization of traffic network with Q-Learning algorithm
title_full	Microscopic control and optimization of traffic network with Q-Learning algorithm
title_fullStr	Microscopic control and optimization of traffic network with Q-Learning algorithm
title_full_unstemmed	Microscopic control and optimization of traffic network with Q-Learning algorithm
title_sort	microscopic control and optimization of traffic network with q-learning algorithm
granting_institution	Universiti Malaysia Sabah
granting_department	Sekolah Kejuruteraan dan Teknologi Maklumat
publishDate	2013
url	https://eprints.ums.edu.my/id/eprint/38168/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/38168/2/FULLTEXT.pdf
_version_	1794022708011335680
spelling	my-ums-ep.381682024-02-09T03:10:25Z Microscopic control and optimization of traffic network with Q-Learning algorithm 2013 Chin, Yit Kwong TE210-228.3 Construction details Including foundations, maintenance, equipment The main objective of this research is to optimize the traffic flow within a traffic network using microscopic level control. With the increment of on-road vehicles, modern traffic networks have more complicated topologies dealing with the higher traffic demands. Fixed-time traffic signal timing plan (FTSTP) management system and Webster's model are the most common practice used by Public Works Department of Malaysia (JKR). The current efforts made by JKR should be improved to meet the increasing traffic demands within the traffic network. In this work, Q-learning traffic signal timing plan management system (QLTSTP) is developed with the attributes of Q-learning (QL). QL is able to search for the best traffic signal timing plan through the learning process from the past experiences. In order to test the viability of the proposed system, level of vehicle in queue has been chosen as the performance indicator, whilst level of traffic flow is used to determine the traffic condition of the traffic network. QLTSTP is designed as a multi-agent system with every intersection in the developed traffic network is managed by an individual QLTSTP agent towards a mutual objective. The individual QLTSTP agent has shown better performance against the FTSTP management system with the improvement of 2.61 % - 10.40% under various traffic conditions at single intersection traffic model. The performance of the multi-QLTSTP agent system is then tested in different topologies of traffic networks. The results show that there are about 2.90 % - 18.99 % of improvements in the total vehicles pass through every intersection in the traffic network for the proposed multi-QLTSP agent system. Therefore, it can be concluded that under different traffic conditions of traffic network, the proposed multi-QLTSTP agent system is able to optimize the traffic flow with the developed traffic model.The main objective of this research is to optimize the traffic flow within a traffic network using microscopic level control. With the increment of on-road vehicles, modern traffic networks have more complicated topologies dealing with the higher traffic demands. Fixed-time traffic signal timing plan (FTSTP) management system and Webster's model are the most common practice used by Public Works Department of Malaysia (JKR). The current efforts made by JKR should be improved to meet the increasing traffic demands within the traffic network. In this work, Q-learning traffic signal timing plan management system (QLTSTP) is developed with the attributes of Q-learning (QL). QL is able to search for the best traffic signal timing plan through the learning process from the past experiences. In order to test the viability of the proposed system, level of vehicle in queue has been chosen as the performance indicator, whilst level of traffic flow is used to determine the traffic condition of the traffic network. QLTSTP is designed as a multi-agent system with every intersection in the developed traffic network is managed by an individual QLTSTP agent towards a mutual objective. The individual QLTSTP agent has shown better performance against the FTSTP management system with the improvement of 2.61 % - 10.40% under various traffic conditions at single intersection traffic model. The performance of the multi-QLTSTP agent system is then tested in different topologies of traffic networks. The results show that there are about 2.90 % - 18.99 % of improvements in the total vehicles pass through every intersection in the traffic network for the proposed multi-QLTSP agent system. Therefore, it can be concluded that under different traffic conditions of traffic network, the proposed multi-QLTSTP agent system is able to optimize the traffic flow with the developed traffic model. 2013 Thesis https://eprints.ums.edu.my/id/eprint/38168/ https://eprints.ums.edu.my/id/eprint/38168/1/24%20PAGES.pdf text en public https://eprints.ums.edu.my/id/eprint/38168/2/FULLTEXT.pdf text en validuser masters Universiti Malaysia Sabah Sekolah Kejuruteraan dan Teknologi Maklumat

Microscopic control and optimization of traffic network with Q-Learning algorithm

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