TrafSched: Integrating Bayesian Adaptation with LLMs for Traffic Scheduling Optimization

Abstract

Railway timetabling requires resolving complex scheduling conflicts arising from shared tracks, station capacity limits, and strict safety intervals. Existing optimization or learning-based approaches often struggle to scale or generalize across diverse operational scenarios. We present TrafSched, a novel hybrid decision framework that combines a curated strategy library, multi-dimensional conflict prioritization, Bayesian strategy adaptation and an optional Large Language Models (LLMs) integration module. TrafSched iteratively detects and resolves conflicts through adaptive strategy selection and backtracking, enabling robust exploration of feasible timetables without costly model retraining. Experiments on real-world-scale datasets involving 50–120 trains show that TrafSched consistently outperforms heuristic and reinforcement learning baselines, achieving up to 85.05% conflict-resolution success in the most challenging cases. These results demonstrate TrafSched’s effectiveness and scalability for modern railway scheduling operations.