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

doi:10.1007/978-3-030-31140-7_25

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

Computer Science

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer 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.

Originalsprog	Engelsk
Titel	Proceedings of the 10th International Conference on Computational Logistics (ICCL19)
Antal sider	16
Forlag	Springer
Publikationsdato	30 sep. 2019
Sider	397-412
ISBN (Trykt)	978-3-030-31139-1
DOI	https://doi.org/10.1007/978-3-030-31140-7_25
Status	Udgivet - 30 sep. 2019
Begivenhed	10th International Conference on Computational Logistics - Barranquilla, Colombia Varighed: 30 sep. 2019 → 2 okt. 2019 Konferencens nummer: 10

Konference

Konference	10th International Conference on Computational Logistics
Nummer	10
Land/Område	Colombia
By	Barranquilla
Periode	30/09/2019 → 02/10/2019

Navn	Lecture Notes in Computer Science
Vol/bind	11756
ISSN	0302-9743

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10.1007/978-3-030-31140-7_25

MatthiasForlagets udgivne version, 1,2 MB

Citationsformater

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title = "A Decision Support Tool for Energy-Optimising Railway Timetables Based on Behavioural Data",

abstract = "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.",

author = "Madsen, {Mathias Bejlegaard} and Als, {Matthias Villads Hinsch} and Jensen, {Rune M{\o}ller} and {Edinger Gram}, Sune",

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language = "English",

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series = "Lecture Notes in Computer Science",

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note = "10th International Conference on Computational Logistics, ICCL19 ; Conference date: 30-09-2019 Through 02-10-2019",

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Madsen, MB, Als, MVH, Jensen, RM & Edinger Gram, S 2019, A Decision Support Tool for Energy-Optimising Railway Timetables Based on Behavioural Data. i Proceedings of the 10th International Conference on Computational Logistics (ICCL19). Springer, Lecture Notes in Computer Science, bind 11756, s. 397-412, 10th International Conference on Computational Logistics, Barranquilla, Colombia, 30/09/2019. https://doi.org/10.1007/978-3-030-31140-7_25

A Decision Support Tool for Energy-Optimising Railway Timetables Based on Behavioural Data. / Madsen, Mathias Bejlegaard; Als, Matthias Villads Hinsch; Jensen, Rune Møller et al.
Proceedings of the 10th International Conference on Computational Logistics (ICCL19). Springer, 2019. s. 397-412 (Lecture Notes in Computer Science, Bind 11756).

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

TY - GEN

T1 - A Decision Support Tool for Energy-Optimising Railway Timetables Based on Behavioural Data

AU - Madsen, Mathias Bejlegaard

AU - Als, Matthias Villads Hinsch

AU - Jensen, Rune Møller

AU - Edinger Gram, Sune

N1 - Conference code: 10

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N2 - 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.

AB - 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.

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

Abstract

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