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 CO2 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 preprocessed 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.