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

Energy-efficient train operation can reduce operating costsand contribute to a reduction in CO2 emissions. To utilise the fullpotential of energy-efficient driving, energy-efficient timetabling is crucial.To address this problem, we propose a decision support tool togive timetable planners insight into energy consumption for a giventimetable. The decision support tool uses a recommendation based onquadratic 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 optimisedtimetables can save up to 33.07% energy on a single section and up to6.23% for a complete timetable. Solutions are computed in less than amicrosecond