Revolutionizing Railways: An AI-Powered Approach for Enhanced Monitoring and Optimization

Abstract

The relentless progress of Artificial Intelligence (AI) has ushered in transformative possibilities across industries, notably impacting the landscape of transportation. This paper introduces an AI-based optimized railway monitoring system, a pioneering approach that integrates machine learning algorithms such as Support Vector Machines, Random Forest, Recurrent Neural Networks, Gradient Boosting Machines, Convolutional Neural Networks, Long Short-Term Memory Networks, and K-Means Clustering. Complemented by computer vision and data analytics, this system represents a comprehensive framework poised to revolutionize traditional railway monitoring practices. In the global expanse of transportation, railways serve as vital conduits, facilitating the seamless movement of both passengers and goods. However, ensuring the unwavering safety and operational reliability of these intricate networks demands constant vigilance and upkeep. Traditional monitoring systems, while effective, grapple with challenges related to real-time analysis, predictive maintenance, and the nuanced optimization of resources. It is within this backdrop that the integration of AI into railway monitoring emerges as a promising solution, presenting novel avenues for heightened efficiency and performance. Key Words: Artificial Intelligence, Railway Monitoring, Machine Learning, Computer Vision, Predictive Maintenance, Adaptive Scheduling, Transportation Infrastructure.