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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Innovation Information Technology and Application (JINITA)
Muhammad Wahyudi
Politeknik Caltex Riau
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Design and Implementation of Intelligent Traffic Lights for One-Way Open and Close Roads Based on Reinforcement Learning Yoanda Alim Syahbana; Sugeng Purwantoro E.S.G.S; Muhammad Wahyudi; Muhammad Imam Akbar; Ibnu Zachri Baihaqi
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2691

Abstract

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
Reward Scheme Analysis for DQN-Based Adaptive Traffic Signal Control in Road Repair Zones Yoanda Alim Syahbana; Muhammad Wahyudi; Fikri Muhaffizh Imani; Ardianto Wibowo; Fatur Rizky Al-Farisz
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7381

Abstract

Traffic congestion caused by temporary road repairs often forces bidirectional traffic to alternate through a single lane, leading to increased delays and imbalanced traffic flow. This study investigates the impact of reward scheme design on the performance of a Deep Q-Network (DQN)-based adaptive traffic signal control system in such constrained environments. Using the Simulation of Urban Mobility (SUMO), a traffic scenario involving 1,656 vehicles over 1,800 seconds was modeled to evaluate six reward scheme configurations combining Traffic Flow (TF), Waiting Time (WT), and Average Speed (AS): TF-TF, TF-WT, WT-TF, WT-WT, AS-TF, and AS-WT. The DQN agent, implemented with a two-layer neural network and trained for 50 epochs, dynamically adjusted signal timing to balance traffic from opposing directions. Experimental results indicate that the AS-WT configuration achieved the most balanced performance, producing the best fairness index (1.04) while maintaining stable traffic flow. In contrast, schemes with misaligned or redundant metrics showed significantly poorer performance. These findings highlight the importance of reward design in reinforcement learning-based traffic signal control and suggest that carefully selected reward schemes can improve fairness and efficiency in temporary road repair zones.
Co-Authors Ardianto Wibowo Fatur Rizky Al-Farisz Fikri Muhaffizh Imani Ibnu Zachri Baihaqi Muhammad Imam Akbar Sugeng Purwantoro E.S.G.S Yoanda Alim Syahbana