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

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

Emneord

Energy-efficient train operation
Timetabling optimisation
Decision support tool
Quadratic optimisation
Data preprocessing

Adgang til dokumentet

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

MatthiasForlagets udgivne version, 1,2 MB

Citationsformater

@inproceedings{7d20627376a748fda8a9fb99887672c7,

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.",

keywords = "Energy-efficient train operation, Timetabling optimisation, Decision support tool, Quadratic optimisation, Data preprocessing, Energy-efficient train operation, Timetabling optimisation, Decision support tool, Quadratic optimisation, Data preprocessing",

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

year = "2019",

month = sep,

day = "30",

doi = "10.1007/978-3-030-31140-7_25",

language = "English",

isbn = "978-3-030-31139-1",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "397--412",

booktitle = "Proceedings of the 10th International Conference on Computational Logistics (ICCL19)",

address = "Germany",

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

}

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

PY - 2019/9/30

Y1 - 2019/9/30

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.

KW - Energy-efficient train operation

KW - Timetabling optimisation

KW - Decision support tool

KW - Quadratic optimisation

KW - Data preprocessing

KW - Energy-efficient train operation

KW - Timetabling optimisation

KW - Decision support tool

KW - Quadratic optimisation

KW - Data preprocessing

U2 - 10.1007/978-3-030-31140-7_25

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

M3 - Article in proceedings

SN - 978-3-030-31139-1

T3 - Lecture Notes in Computer Science

SP - 397

EP - 412

BT - Proceedings of the 10th International Conference on Computational Logistics (ICCL19)

PB - Springer

T2 - 10th International Conference on Computational Logistics

Y2 - 30 September 2019 through 2 October 2019

ER -

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

Abstract

Konference

Emneord

Adgang til dokumentet

Fingeraftryk

Citationsformater