Mr. Rama Chandra Rao Nampalli

Enhancing Rail and Road Transformation: Leveraging Generative AI for Improved Coordination, Risk Mitigation, and Passenger Safety

Abstract

The integration of rail and road networks presents unique challenges in transportation coordination, risk management, and passenger safety. Generative AI offers transformative potential to address these challenges by providing advanced analytics, real-time data processing, and predictive capabilities. This research investigates the application of Generative AI in optimizing rail and road coordination, reducing risks associated with multi-modal transport, and enhancing overall passenger safety. By analyzing vast datasets from both rail and road systems, Generative AI models can identify potential hazards, optimize traffic flow, and provide proactive risk management solutions, significantly reducing the likelihood of incidents. Furthermore, these models enable personalized safety features, dynamic scheduling adjustments, and adaptive responses to environmental and operational conditions, thus ensuring a safer and more efficient travel experience for passengers. The research draws on case studies, pilot programs, and simulations to demonstrate the real-world impact of Generative AI on enhancing passenger safety and operational efficiency. This study outlines a roadmap for incorporating Generative AI into transportation networks, aiming to facilitate a more resilient, coordinated, and passenger-centric approach to rail and road transformation.