Intelligent traffic lights using Q-learning

Mohd Yusop, Muhammad Aminuddin and Mansor, Hasmah and Gunawan, Teddy Surya and Nasir, Haidawati and UNSPECIFIED (2022) Intelligent traffic lights using Q-learning. In: 2022 IEEE 8th International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 26-28 September 2022, Melaka.

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Abstract

Globally, traffic congestion is one of the most significant issues in many cities around the world. It frequently occurs in urban areas due to the increasing number of vehicles that clog the road. It contributes to the aforementioned issue because as the number of vehicles increases, so does traffic congestion. The conventional traffic light system contributes to the safety of the road but is not an effective method of traffic control. The failure of traffic intersections to learn from their past mistakes has rendered them incapable of adapting to the dynamic changes in traffic flow. The Q-learning algorithm has been applied to the traffic lights system in this study. Q-learning derives benefits from past experiences and determines the optimal course of action based on them. The performance of the proposed system has been measured against that of the traditional system, which is a fixed time cycle system. Simulated results indicated that the proposed system could efficiently and effectively manage traffic control when measured using cumulative delay, average queue length, and cumulative negative reward.

Item Type:	Conference or Workshop Item (Plenary Papers)
Uncontrolled Keywords:	traffic intersection, traffic simulation, Q-learning, cumulative delay, average queue length.
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr Hasmah Mansor
Date Deposited:	07 Nov 2022 10:16
Last Modified:	07 Nov 2022 10:17
URI:	http://irep.iium.edu.my/id/eprint/100958

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