The railway is the greenest and most sustainable means of transportation, especially in urban areas. Growing urbanization is putting a tremendous pressure on regional transportation systems worldwide, including Norway. Indeed, the railway network that serves the greater Oslo metropolitan area has recently experienced increasing difficulties and delays. Building new infrastructure is very costly and difficult in densely populated areas. It is hard to assess what the impact of specific investment decisions will be, and it takes several years from when these decisions are taken to the actual finalization. On the other hand, there is plenty of unused capacity which may be exploited with better control of the railway traffic. Indeed, trains are currently dispatched manually, with very little support from digital tools.
The GoTo main goal was to transfer the most recent advances in optimization and machine learning to railway traffic management. We developed the methodological groundwork for an Optimization-based Traffic Management System that integrates state-of-the-art mathematical optimization algorithms and advanced forecasting techniques to tackle the complex scenarios of train dispatching that are found in Norway and the rest of Europe.
The prototype tool developed in the project was tested in the greater Oslo area. Dispatchers at the Oslo control center are able to visualize the effects of each decision they make, up to few hours ahead. The system automatically suggests them a set of optimized dispatching decisions based on the current train positions and preferences. All in real-time.
Project Type: IPN – IKTPLUSS, funded by the Norwegian Research Council
Budget: 12.7 million NOK
