International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI Signal OptiSense: A Real-Time Simulation Based Intelligent Traffic Signal Optimization System |
|---|---|
| ABSTRACT | Urban traffic congestion has become a major challenge in rapidly growing smart cities, leading to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal systems operate on fixed timing mechanisms, which are inefficient in handling dynamic traffic conditions [1][4]. This research presents AI Signal OptiSense, an intelligent traffic management system designed to optimize signal timings using real-time traffic data and simulation-based analysis. The proposed system integrates SUMO (Simulation of Urban Mobility) with TraCI interface to dynamically monitor vehicle density, waiting time, and junction congestion levels. A real-time web dashboard is developed to visualize traffic conditions, signal phases, and vehicle activity. The system also uses JSON-based data exchange for seamless communication between simulation and dashboard modules [2][5]. Experimental results show that adaptive signal timing significantly reduces congestion and improves traffic flow efficiency compared to traditional systems. |
| AUTHOR | SARITA KRUSHNA PARTE, CHETAN MUKUND PATIL, SANIKA KASHINATH SURYAVANSHI, HARSHADA BHIMARAO SAWANT, PRIYA ANIL MORE, SAMINDAR VIBHUTE Department of Computer Science, Sanjeevan Group of Institute, Panhala, Maharashtra, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406030 |
| pdf/30_AI Signal OptiSense A Real-Time Simulation Based Intelligent Traffic Signal Optimization System.pdf | |
| KEYWORDS | |
| References | [1] S. Wei, Y. Zheng, and Y. Li, “Intelligent traffic signal control using deep reinforcement learning,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 11, pp. 4567–4578, 2020. [2] M. A. Almannaa, M. Elhenawy, and H. Rakha, “Traffic signal control using machine learning: A comprehensive review,” IEEE Access, vol. 8, pp. 102897–102913, 2020. [3] Z. Liang, X. Du, G. Wang, and Z. Han, “Deep reinforcement learning for traffic light control in vehicular networks,” IEEE Transactions on Vehicular Technology, vol. 69, no. 2, pp. 1249–1259, 2020. [4] J. Chen, L. Liu, and H. Zhang, “Adaptive traffic signal control based on multi-agent reinforcement learning,” Applied Sciences, vol. 11, no. 2, pp. 1–15, 2021. [5] Y. Ma, K. Wang, and Y. Wang, “Urban traffic signal optimization using SUMO and deep Q-learning,” Simulation Modelling Practice and Theory, vol. 108, pp. 102–118, 2021. [6] A. Belletti, A. Gkoumas, and M. S. Mahmoud, “Real-time traffic management using data-driven analytics and simulation tools,” Transportation Research Part C, vol. 129, pp. 103–121, 2021. [7] H. Liang, F. Zhang, and Y. Li, “Smart traffic signal control system using IoT and AI-based prediction models,” Sensors, vol. 22, no. 3, pp. 1–20, 2022. [8] R. Zhang, J. Wu, and Q. Chen, “A scalable traffic signal control framework based on multi-intersection reinforcement learning,” IEEE Access, vol. 11, pp. 24567–24580, 2023. [9] P. Kumar and S. Singh, “Simulation-based intelligent traffic management system for smart cities,” Journal of Intelligent Transportation Systems, vol. 28, no. 1, pp. 55–72, 2024. [10] Y. Zhao, H. Xu, and L. Wang, “Deep multi-agent reinforcement learning for adaptive traffic signal control,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 4, pp. 2456–2467, 2021. [11] X. Tan, B. Li, and J. Wu, “A survey on reinforcement learning for traffic signal control,” Transportation Research Part C: Emerging Technologies, vol. 125, pp. 103–118, 2021. [12] M. Wei, Y. Chu, and Z. Liu, “Real-time traffic congestion prediction and signal optimization using machine learning,” IEEE Access, vol. 9, pp. 150321–150334, 2021. [13] K. Abdelghany and A. Abdelghany, “Simulation-based optimization of traffic signal systems in urban networks,” Simulation Modelling Practice and Theory, vol. 112, pp. 102–120, 2022. [14] R. G. Dowling, J. Skabardonis, and V. Alexiadis, “Performance evaluation of adaptive traffic control systems using microsimulation,” Journal of Transportation Engineering, vol. 148, no. 5, pp. 1–14, 2022. |
