A Decision Support Tool for Energy-Optimising Railway Timetables Based on Behavioural Data

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.