Towards Autonomous Railway Operations: A Semi-Hierarchical Deep Reinforcement Learning Approach to the Vehicle Rescheduling Problem
Managing disruptions in railway traffic management is a major challenge. Rising traffic density and infrastructure limits increase complexity, making the Vehicle Routing and Scheduling Problem (VRSP) difficult to solve reliably and in real time. While Operational Research (OR) methods are widely used, most dispatching still relies on human expertise due to the problem's exponential combinatorial complexity. Reinforcement Learning (RL) has gained attention for its potential in multi-agent coordination, but existing RL approaches often underperform OR methods and struggle to scale in dense rail networks. This paper addresses this gap from a machine learning perspective by introducing a semi...
| Authors | Alberto Castagna, Stefan Zahlner, Adrian Egli et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
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