<![CDATA[This study aims to design and implement an intelligent traffic light system for one-way open and close road conditions, commonly encountered during road repair projects. These situations often cause congestion due to alternating vehicle flow in a single lane. To address this issue, the system utilizes a Reinforcement Learning (RL) algorithm to dynamically adjust the traffic light timing based on real-time traffic conditions. The research was conducted in three main stages: (1) designing the network topology and IoT devices using Raspberry Pi, ESP modules, and Access Points (APs), (2) implementing the intelligent traffic light system, and (3) conducting a functional evaluation. A key performance metric evaluated was the response time of the system. Experimental results showed that the traffic light system achieved an average response time of 0.51 seconds, indicating that it is responsive and suitable for real-time operation. The successful integration of RL and MQTT-based communication also demonstrates the feasibility of deploying this system in dynamic traffic environments. Further research is recommended for field testing with additional sensor integration and advanced RL models to enhance system accuracy and efficiency]]>
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