Traﬃc congestion reduction in SUMO using reinforcement learning method

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Détails bibliographiques
Auteurs principaux:	Mouly, Radia Rahman, Rini, Puja Roy, Ethic, Ahsan Habib, Ayon, Mahdi Islam
Autres auteurs:	Md. Khalilur Rahman
Format:	Thèse
Langue:	English
Publié:	Brac University 2021
Sujets:	Traﬃc congestion Reinforcement learning Q-learning Greedy approach SARSA Bias-Q-learning MDP SUMO Reinforcement learning.
Accès en ligne:	http://hdl.handle.net/10361/15728

id	10361-15728
record_format	dspace
spelling	10361-157282022-01-26T10:23:14Z Traﬃc congestion reduction in SUMO using reinforcement learning method Mouly, Radia Rahman Rini, Puja Roy Ethic, Ahsan Habib Ayon, Mahdi Islam Md. Khalilur Rahman Department of Computer Science and Engineering, Brac University Traﬃc congestion Reinforcement learning Q-learning Greedy approach SARSA Bias-Q-learning MDP SUMO Reinforcement learning. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 33-35). The exemplary traﬃc controlling system is getting helpless because of urbanization and a consistently expanding populace. Living in a cutting-edge time of science and innovation, an advanced arrangement is a beggar description. Reinforcement learning appears to be the advanced promising answer for this endless issue. Thus, proposing a ﬁtting and dynamic methodology to meet the excessive necessity is a signiﬁcant part of the traﬃc control system. Our main objective is to using diﬀerent algorithms in an environment to get the best possible result in order to reducing traﬃc congestion. Our algorithm ensured the best possible result by comparing diﬀerent parameters in a SUMO(Simulation of Urban MObility) generated dataset. Firstly, we obtained a result by performing a normal simulation and then performed Q-Learning, Greedy Approach, SARSA, and Bias Q-Learning algorithms. We compared the results from the performed algorithms afterwards. The research is expected to improve productivity in bustling cities by eﬀectively reducing traﬃc congestion. Radia Rahman Mouly Puja Roy Rini Ahsan Habib Ethic Mahdi Islam Ayon B. Computer Science 2021-12-13T05:04:00Z 2021-12-13T05:04:00Z 2021 2021-02 Thesis ID 16101136 ID 18201213 ID 16301200 ID 16301168 http://hdl.handle.net/10361/15728 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 35 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Traﬃc congestion Reinforcement learning Q-learning Greedy approach SARSA Bias-Q-learning MDP SUMO Reinforcement learning.
spellingShingle	Traﬃc congestion Reinforcement learning Q-learning Greedy approach SARSA Bias-Q-learning MDP SUMO Reinforcement learning. Mouly, Radia Rahman Rini, Puja Roy Ethic, Ahsan Habib Ayon, Mahdi Islam Traﬃc congestion reduction in SUMO using reinforcement learning method
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Md. Khalilur Rahman
author_facet	Md. Khalilur Rahman Mouly, Radia Rahman Rini, Puja Roy Ethic, Ahsan Habib Ayon, Mahdi Islam
format	Thesis
author	Mouly, Radia Rahman Rini, Puja Roy Ethic, Ahsan Habib Ayon, Mahdi Islam
author_sort	Mouly, Radia Rahman
title	Traﬃc congestion reduction in SUMO using reinforcement learning method
title_short	Traﬃc congestion reduction in SUMO using reinforcement learning method
title_full	Traﬃc congestion reduction in SUMO using reinforcement learning method
title_fullStr	Traﬃc congestion reduction in SUMO using reinforcement learning method
title_full_unstemmed	Traﬃc congestion reduction in SUMO using reinforcement learning method
title_sort	traﬃc congestion reduction in sumo using reinforcement learning method
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15728
work_keys_str_mv	AT moulyradiarahman trafficcongestionreductioninsumousingreinforcementlearningmethod AT rinipujaroy trafficcongestionreductioninsumousingreinforcementlearningmethod AT ethicahsanhabib trafficcongestionreductioninsumousingreinforcementlearningmethod AT ayonmahdiislam trafficcongestionreductioninsumousingreinforcementlearningmethod
_version_	1814309638938558464

Traﬃc congestion reduction in SUMO using reinforcement learning method

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