AI-Based Disposition Using a Reinforcement Learning Approach | Skip to main content
10 Apr 2019 | 16:10 - 16:30 | Big-Data.AI Summit

AI-Based Disposition Using a Reinforcement Learning Approach

Grace Hopper Stage
Stage BAS
During the last decade the number of passengers in Germany’s rail environment has been steadily growing, reaching 144 million passengers in 2017. The increasing demand on capacity requires new methods for the planning and efficient management of trains and infrastructure. This work presents an approach that aims at solving future dispatching decisions by Reinforcement Learning. The transfer of this approach from today’s computer games to real-world problems remains a core challenge that is tackled in this work for a suburban train network in two steps. First, an approach on anomaly detection is presented, which covers an initial analysis on present and upcoming anomalies. The second step introduces our current work on modelling a suburban train network as a network of related entities. Based on this approach different prediction models are used to achieve a data-driven perspective on the current status quo of the network, possible future states and actions that could lead to these states.
Programmes & Topics
10 Apr 2019 | 12:30 - 12:50
Big-Data.AI Summit
11 Apr 2019 | 15:20 - 15:40
Big-Data.AI Summit
10 Apr 2019 | 17:10 - 17:30
Big-Data.AI Summit
11 Apr 2019 | 13:30 - 14:00
Big-Data.AI Summit