Abstract
Energy-efficient train operation can reduce operating costs and contribute to a reduction in CO\(_2\) emissions. To utilise the full potential of energy-efficient driving, energy-efficient timetabling is crucial. To address this problem, we propose a decision support tool to give timetable planners insight into energy consumption for a given timetable. The decision support tool uses a recommendation based on quadratic optimisation of a given timetable. Differently to previous work, the optimisation uses actual data from the train operation, which is pre-processed by data reduction, outlier detection, and second-degree regression modelling. With this approach, our results show that the optimised timetables can save up to \(33.07\%\) energy on a single section and up to \(6.23\%\) for a complete timetable. Solutions are computed in less than a microsecond.
This research is conducted in collaboration with Cubris - A Thales Company.
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Notes
- 1.
Examples are engine type, length, and weight.
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Madsen, M.B., Als, M.V.H., Jensen, R.M., Gram, S.E. (2019). A Decision Support Tool for Energy-Optimising Railway Timetables Based on Behavioural Data. In: Paternina-Arboleda, C., Voß, S. (eds) Computational Logistics. ICCL 2019. Lecture Notes in Computer Science(), vol 11756. Springer, Cham. https://doi.org/10.1007/978-3-030-31140-7_25
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