STUDY OF DESIGNING ROAD TRAFFIC CONTROL SYSTEM USING MACHINE LEARNING AND IMPLEMENTATION WILL BE IN PYTHON

Abstract

The increasing complexity and volume of road traffic necessitate the development of advanced traffic control systems to ensure efficient traffic flow and reduce congestion. This study proposes a novel road traffic control system leveraging machine learning techniques, with implementation carried out in Python. The system aims to optimize traffic signal timings, predict traffic patterns, and manage dynamic traffic conditions in real-time. Machine learning models, including neural networks and reinforcement learning algorithms, are employed to analyze historical and real-time traffic data. These models predict traffic volumes and optimize signal control strategies to minimize waiting times and improve overall traffic efficiency. Python, with its robust libraries such as TensorFlow, Keras, and Scikit-learn, is utilized for model development, training, and deployment. The proposed system is validated through simulations using real-world traffic data from urban areas. Key performance metrics such as average wait time, throughput, and congestion levels are measured to evaluate the system's effectiveness. Initial results indicate significant improvements in traffic flow and reductions in congestion compared to traditional traffic control methods. This research demonstrates the potential of integrating machine learning into traffic management systems, offering a scalable and adaptive solution for modern urban traffic challenges. The implementation in Python showcases the practicality and flexibility of using open-source tools for developing intelligent traffic control systems. Future work will focus on enhancing model accuracy, expanding the system to larger networks, and incorporating additional traffic parameters for more comprehensive traffic management.