International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
www.irjet.net
Traffic Congestion Control through Adaptive Signaling System using Machine Learning K Sandhya Rani1, SK Haniya2, SK Hafizunnisa3, P Venkata Srinivasa Reddy4, P Vijay Kumar5, K Prathyusha6, 1Assistant Professor, Dept of AI&DS, VVIT, Andhra Pradesh, India 2Student, Dept of AI&DS, VVIT, Andhra Pradesh, India 3Student, Dept of AI&DS, VVIT, Andhra Pradesh, India 4Student, Dept of AI&DS, VVIT, Andhra Pradesh, India 5Student, Dept of AI&DS, VVIT, Andhra Pradesh, India 6Student, Dept of AI&DS, VVIT, Andhra Pradesh, India
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Abstract - The escalating urbanization and subsequent
edge machine learning technologies into a Smart Traffic Congestion Control System, leveraging the power of advanced object detection algorithms, predictive modelling, and dynamic signal optimization.
surge in vehicular traffic have exacerbated the challenge of traffic congestion, resulting in substantial economic losses, environmental degradation, and widespread commuter frustration. Traditional traffic management systems have struggled to cope with the evolving demands, often failing to deliver effective and sustainable solutions. In response, we propose a Smart Traffic Congestion Control System that integrates cutting-edge machine learning technologies. At its core is YOLOv8, a state-of-the-art object detection algorithm adept at swiftly identifying and classifying vehicles, pedestrians, cyclists, and other road elements within live traffic camera feeds. By accurately detecting and tracking these objects, the system can make informed decisions about traffic flow, signal timings, and safety measures, thereby enhancing overall efficiency and effectiveness in urban traffic management. This proposed system embodies a comprehensive strategy for addressing traffic congestion, leveraging Convolutional Neural Networks (CNNs) for congestion detection, Reinforcement Learning with Proximal Policy Optimization (PPO) for dynamic signal timing, and Long Short-Term Memory (LSTM) networks for predictive modeling. Through the synergistic integration of these advanced algorithms, the system can adapt to real-time traffic conditions, minimize congestion, and optimize the utilization of existing infrastructure.
At the core of this innovative system lies YOLOv8, a state-ofthe-art object detection algorithm that excels at rapidly identifying and classifying vehicles, pedestrians, cyclists, and other road elements within live traffic camera feeds. By accurately detecting and tracking these objects, the system can make informed decisions about traffic flow, signal timings, and safety measures, thereby enhancing the overall efficiency and effectiveness of urban traffic management. The proposed system represents a holistic approach to tackling traffic congestion, combining the capabilities of Convolutional Neural Networks (CNNs) for congestion detection, Reinforcement Learning with Proximal Policy Optimization (PPO) for dynamic signal timing, and Long Short-Term Memory (LSTM) networks for predictive modeling. This synergistic integration of advanced algorithms enables the system to adapt to real-time traffic conditions, minimize congestion, and optimize the utilization of existing infrastructure.
2. LITERATURE REVIEW The enduring issue of urban traffic congestion has prompted a concerted effort among researchers and engineers to devise innovative remedies. Incorporating Machine Learning (ML) and artificial intelligence (AI) methodologies into traffic management systems has surfaced as a promising approach. This review surveys pertinent studies and advancements in ML-based urban traffic management, aiming to address its multifaceted impacts on society, the environment, and the economy.
Key Words: Machine learning, YOLOv8, LSTM, PPO, CNNs, Traffic congestion
1.INTRODUCTION The relentless growth of urban populations and the consequent increase in vehicular traffic have exacerbated the challenge of traffic congestion, leading to significant economic losses, environmental degradation, and widespread frustration among commuters. Traditional traffic management systems have struggled to keep pace with the ever-evolving demands, often falling short in providing effective and sustainable solutions. In response to this pressing issue, we propose the integration of cutting-
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2.1 Commuter Congestion Challenges: Urban traffic congestion has long been a pervasive issue, with far-reaching consequences that extend beyond mere inconvenience. Prolonged travel times due to congestion
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