ENHANCING RAILWAY TRANSPORTATION THROUGH DATA ANALYTICS: A COMPREHENSIVE REVIEW AND PREDICTIVE MODELING APPROACH

Abstract The integration of data analytics into railway transportation has emerged as a transformative approach to addressing long-standing challenges such as operational inefficiencies, unexpected delays, and costly maintenance practices. This paper presents a detailed examination of how predictive modeling and advanced data analytics can revolutionize railway systems by optimizing scheduling, enhancing safety protocols, and reducing downtime through proactive maintenance strategies. It begins with a systematic review of existing literature, highlighting key methodologies such as machine learning, artificial neural networks, and time-series forecasting as applied in global railway networks. Following this, it proposes a novel predictive modeling framework that leverages real-time data from sensors, historical maintenance logs, and environmental factors to forecast delays and infrastructure failures. Obtained experimental results demonstrate the efficacy of Random Forest classifiers in predicting delays with 89% accuracy, Long Short-Term Memory (LSTM) networks in forecasting track degradation with a mean absolute error of 0.12, and Isolation Forest algorithms in detecting signaling anomalies with 92% precision. These findings underscore the potential of data-driven decision-making in railway management. The discussion extends to practical challenges, including data interoperability, computational costs, and the need for scalable AI solutions in legacy systems. It concludes with recommendations for future research, emphasizing the role of federated learning for secure data sharing and digital twin technologies for real-time simulation. This study serves as a comprehensive guide for researchers and industry practitioners aiming to implement data analytics in railway transportation, offering both theoretical insights and actionable solutions.