The rapid expansion of Metro Rail networks in urban regions has created an urgent need for reliable, sustainable, and highly efficient transportation systems. This paper explores the integration of Artificial Intelligence into metro operations with a focus on service optimization and energy waste management. AI-driven predictive maintenance enhances system reliability by identifying equipment failures before they occur, while passenger flow forecasting enables dynamic scheduling and congestion control. Real-time surveillance analytics further strengthen safety and incident response. On the energy front, AI-based monitoring systems optimize power usage across trains and stations, leveraging techniques such as regenerative braking optimization, HVAC demand prediction, and automated idle- energy control. These interventions collectively reduce operational costs and environmental impact. The study highlights how AI transforms metro systems into adaptive, data-driven infrastructures capable of delivering improved passenger experience and long-term sustainability. The findings demonstrate that AI is not merely an add-on technology but a foundational component for next-generation smart mobility ecosystems.
Urmude et al. (Fri,) studied this question.