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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
https://eprints.ums.edu.my/id/eprint/38168/
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record_format eprints
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
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
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
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
https://eprints.ums.edu.my/id/eprint/38168/
_version_ 1792152905630875648
spelling my.ums.eprints.381682024-02-09T03:10:25Z https://eprints.ums.edu.my/id/eprint/38168/ Microscopic control and optimization of traffic network with Q-Learning algorithm 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 NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/38168/1/24%20PAGES.pdf text en https://eprints.ums.edu.my/id/eprint/38168/2/FULLTEXT.pdf Chin, Yit Kwong (2013) Microscopic control and optimization of traffic network with Q-Learning algorithm. Masters thesis, Universiti Malaysia Sabah.
score 13.211869

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