Simulation of track infrastructure and train traffic
To enable reinforcement learning, a realistic rail simulation was created based on infrastructure data and timetables.
Python package for parsing the infrastructure description
Python package for parsing the RailML timetable
- In order to be able to generate decision recommendations for train scheduling, reinforcement learning is to be applied.
- As a basis for the training of RL models, a realistic, track-focused simulation of train traffic is to be created. SUMO is to be used as the simulation software
- Track plan data and timetable must be translated into a format that can be used for the simulation. The resulting track graph should represent the actual location and position of the operating points
- Understanding structure and documentation of the data formats used
- Evaluation of the realism of the simulation based on simulated times and distances using target data
- Modular software structure to allow the use of different data sources (e.g. timetable) or simulation tools
- Realistic simulation that can be used to generate states for reinforcement learning
- Python package for parsing the infrastructure description and generating the track graph for the simulation
- Python package for parsing the RailML timetable and generating route files for the simulation
Are you interested in your own use cases?
An automotive company would like to visualise various market-specific data in order to create a Competitive analysis for the US market.
There will be a interactive and Flexible application, including of different maps with two different views implemented.
Relevant markets are identifies, analyses and visualises. The dealer or the respective sales department have the possibility to compare the direct competition with their own product and to visualise the relevant data.