Efficient Machine Learning Methods for Real-Time Transport System Optimization and Predictive Maintenance

This research explores the application of machine learning techniques to enhance real-time transport system optimization and predictive maintenance. By leveraging advanced algorithms and data analysis methods, we aim to create high-performance applications capable of efficiently processing large volumes of traffic data. Our approach involves developing intelligent systems that can automatically adapt to changing conditions and predict potential issues on transport routes. We implement clustering methods, such as k-means, for effective data grouping and pattern recognition. Sophisticated data reduction techniques are employed to optimize dataset management and improve system performance. Specialized technologies like GPUs and cloud platforms are integrated to enhance computational capabilities. We focus on implementing efficient data cleaning processes to filter noisy data tracks and anomalies. The Davis-Boldin index is utilized to assess the quality of clustering in transport nodes. Application-level data transfer protocols are developed for distributed computing environments. TCP stream control algorithms are optimized for improved web service performance. Our methodology addresses challenges in real-time data processing, traffic flow prediction, and system stability. These approaches contribute to the development of more efficient and responsive transport infrastructure management solutions. The proposed methods demonstrate significant potential for improving transportation network performance and reducing operational costs through enhanced predictive maintenance capabilities.