Eliminating Problems And Shortcomings Encountered In Optimizing Train Operating Modes
Keywords:
Train operation optimization, Operational shortcomings, Railway efficiency, Real-time control systems, Predictive train algorithms, Data latency in rail networks, Energy-efficient transport, AI in railway systemsAbstract
Optimizing train operating modes is essential for achieving energy efficiency, punctuality, and operational sustainability in modern rail systems. However, the implementation of optimization strategies often encounters critical challenges such as data latency, hardware limitations, inconsistent real-time decision-making, and limited adaptability to unpredictable conditions. This paper examines the most common problems and shortcomings associated with current train optimization efforts and proposes targeted solutions to eliminate or mitigate them. Emphasis is placed on improving system scalability, enhancing real-time responsiveness through AI and IoT technologies, ensuring reliable communication infrastructure, and adopting multi-objective optimization frameworks. By addressing these limitations, rail operators can unlock the full potential of intelligent train operations and achieve more robust, efficient, and resilient railway systems.
