AI-Driven Decision Support Systems for Managing Rail Traffic Flow and Safety
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-109Keywords:
Artificial Intelligence (AI), Decision Support Systems (DSS), Rail Traffic Flow, Railway Safety, Machine Learning (ML), Predictive Analytics, Predictive Maintenance, Real-Time Traffic Optimization, Traffic Prediction, Risk Management, Autonomous Trains, Smart Rail NetworksAbstract
The efficient management of rail traffic flow and safety remains a significant concern for railway operators globally. With growing urbanization and increased demand for reliable transportation, traditional rail traffic control methods often prove inadequate in addressing dynamic scheduling requirements, unforeseen incidents, and safety concerns. In response to these challenges, Artificial Intelligence (AI)-driven Decision Support Systems (DSS) are emerging as transformative tools in modern railway operations. These systems utilize machine learning algorithms, real-time data processing, and predictive analytics to provide actionable insights that support more informed and timely decisions. This paper explores the integration of AI within railway traffic management frameworks, focusing on key applications such as real-time traffic optimization, predictive maintenance, anomaly detection, and risk mitigation. Through the use of historical and real-time data, AI systems can forecast train delays, predict equipment failures before they occur, and suggest optimal routing strategies to minimize congestion and enhance throughput. Case studies from advanced railway networks in Europe, Asia, and North America demonstrate the tangible benefits of AI-enabled systems in improving both operational efficiency and passenger safety. However, despite the advantages, the deployment of AI in railway systems is not without challenges. Issues such as inconsistent data quality, the interpretability of complex AI models, regulatory compliance, and cybersecurity threats pose significant barriers to widespread adoption. Furthermore, integrating AI with legacy systems requires substantial investment and strategic planning. The paper concludes by discussing emerging trends, such as the use of digital twins, edge computing, and federated learning, which are poised to further enhance AI capabilities in rail traffic management. It also presents recommendations for overcoming existing challenges and advancing research and development in this field. Ultimately, AI-based DSS hold the potential to redefine the future of railway operations, making them safer, smarter, and more efficient
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