Maximizing Section Throughput Using AI‐Powered Precise Train Traffic Control

Abstract

Railway networks transport millions of passengers and vast quantities of freight daily, yet a substantial share of operational delays arises from manual or semi-automated traffic management systems that cannot adapt swiftly enough to evolving track conditions. This paper proposes an Artificial Intelligence-driven framework for maximizing section throughput—the number of trains traversing a defined track segment per unit time—while enforcing safe headways and minimizing schedule deviation. The system pairs real-time sensor telemetry with a Multi-Agent Reinforcement Learning (MARL) controller that issues movement authorities and adjusts speed profiles at block boundaries, supported by an LSTM-based predictive layer that forecasts congestion bottlenecks up to 40 minutes ahead. Simulation experiments on a 187 km single-track corridor with 14 intermediate stations show the AI controller raises section capacity by up to 23% under normal conditions and 39% under severe disturbance compared to a rule-based baseline, while reducing mean delay propagation by 41%. The architecture deploys above legacy interlocking systems via a standardized API bridge, preserving existing safety certifications, and recorded zero safety violations across all evaluation episodes.