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

Mathias Bejlegaard Madsen, Matthias Villads Hinsch Als, Rune Møller Jensen, Sune Edinger Gram

Publikation: Konference artikel i Proceeding eller bog/rapport kapitelKonferencebidrag i proceedingsForskningpeer review

Abstract

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.
OriginalsprogEngelsk
TitelProceedings of the 10th International Conference on Computational Logistics (ICCL19)
Antal sider16
ForlagSpringer
Publikationsdato30 sep. 2019
Sider397-412
ISBN (Trykt)978-3-030-31139-1
DOI
StatusUdgivet - 30 sep. 2019
Begivenhed10th International Conference on Computational Logistics - Barranquilla, Colombia
Varighed: 30 sep. 20192 okt. 2019
Konferencens nummer: 10

Konference

Konference10th International Conference on Computational Logistics
Nummer10
Land/OmrådeColombia
ByBarranquilla
Periode30/09/201902/10/2019
NavnLecture Notes in Computer Science
Vol/bind11756
ISSN0302-9743

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