A more attractive future railway system needs to offer more capacity in the railway network and improve quality and punctuality. A fundamental centerpiece of future digitized railway network operations is automated and optimized planning and dispatching. The sector initiative “Digitale Schiene Deutschland” (DSD) develops a holistic and intelligent Capacity & Traffic Management System (CTMS) that can automatically plan and continuously optimize railway traffic at scale. Both, planning and dispatching tasks, are highly complex and, today, require human expertise and oversight. Our main contribution is a multi-agent deep reinforcement learning approach at the core of the envisioned CTMS, which learns from interaction with a realistic, microscopic railway simulation. Our results demonstrate that the proposed approach flexibly solves planning and re-scheduling tasks in the realistic setting of a medium-sized part of the German railway network. It exhibits response times and scaling properties that make it a promising candidate for future applications in railway operations at scale.
Intelligent Railway Capacity and Traffic Management Using Multi-Agent Deep Reinforcement Learning
24.09.2024
554326 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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