Decision-making based on sensordata from rail traffic

Important results from the project

The project met its initial objectives. A data-driven decision-support model was developed and applied using real operational sensor data, enabling the identification and assessment of energy-efficiency measures. In addition, the project provided important methodological insights by combining technical analysis with economic business cases, supporting informed decision-making and future implementation.

Expected long term effects

In the long term, the project is expected to support a more systematic and data-driven approach to energy efficiency in rail transport. The decision-support model can be integrated into regular planning, monitoring, and investment processes, enabling reduced energy use, more stable power demand, and increased system capacity. The results are scalable and applicable to other transport authorities and operators.

Approach and implementation

The project was carried out through close collaboration between the Transport Administration, MTR, and KTH, but was affected by external changes. Limited data access and the transfer of metro operations from MTR to SJ required the project to be extended and partly reoriented. This led to valuable insights and a stronger focus on processes, practical data use, and understanding different stakeholder drivers. The collaboration was constructive and based on good dialogue.