Maximizing Section Throughput Using AI‐Powered Precise Train Traffic Control

Manuscript submitted April 23, 2026; accepted June 8, 2026; published June 30, 2026

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 Intelligencedriven 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 Long Short-Term Memory (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 Application Programming Interface (API) bridge, preserving existing safety certifications, and recorded zero safety violations across all evaluation episodes.

keywords—train traffic control, section throughput, Multi-Agent Reinforcement Learning (MARL), railway automation, predictive scheduling, Long Short-Term Memory (LSTM), movement authority, real-time optimization, Application Programming Interface (API), Coordinated Universal Time (UTC)

Cite: Paras Bhandari, Dipali Bhusari, Omkar Bhasme, Tejas Pasalkar,"Maximizing Section Throughput Using AI‐Powered Precise Train Traffic Control," Journal of Advances in Artificial Intelligence, vol. 4, no. 2, pp. 143-151, 2026. doi: 10.18178/JAAI.2026.4.2.143-151

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).