Peak-hour rail demand shifting with discrete optimisation

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

In this work we consider an information-based system to reduce metropolitan rail congestion in Melbourne, Australia. Existing approaches aim to reduce congestion by asking commuters to travel outside of peak times. We propose an alternative approach where congestion is reduced by enabling commuters to make an informed trade-off between travel time and ride comfort. Our approach exploits the differences in train frequency and stopping patterns between stations that results in trains, arriving within a short time of each other, to have markedly different levels of congestion, even during peak travel periods. We show that, in such cases, commuters can adjust their departure and arrival time by a small amount (typically under 10 min) in exchange for more comfortable travel. We show the potential benefit of making this trade-off with a discrete optimisation model which attempts to redistribute passenger demand across neighbouring services to improve passenger ride comfort overall. Computational results show that even at low to moderate levels of passenger take-up, our method of demand shifting has the potential to significantly reduce congestion across the rail corridor studied, with implications for the metropolitan network more generally.
Original languageEnglish
Title of host publicationPrinciples and Practice of Constraint Programming
Subtitle of host publication25th International Conference, CP 2019 Stamford, CT, USA, September 30 – October 4, 2019 Proceedings
EditorsThomas Schiex, Simon de Givry
Place of PublicationCham Switzerland
Pages748-763
Number of pages16
ISBN (Electronic)9783030300487
DOIs
Publication statusPublished - 2019

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11802
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Cite this

Betts, J. M., Dowe, D. L., Guimarans, D., Harabor, D., Kumarage, H., Stuckey, P. J., & Wybrow, M. (2019). Peak-hour rail demand shifting with discrete optimisation. In T. Schiex, & S. de Givry (Eds.), Principles and Practice of Constraint Programming: 25th International Conference, CP 2019 Stamford, CT, USA, September 30 – October 4, 2019 Proceedings (pp. 748-763). (Lecture Notes in Computer Science; Vol. 11802). Cham Switzerland. https://doi.org/10.1007/978-3-030-30048-7_43
Betts, John M. ; Dowe, David L. ; Guimarans, Daniel ; Harabor, Daniel ; Kumarage, Heshah ; Stuckey, Peter J. ; Wybrow, Michael. / Peak-hour rail demand shifting with discrete optimisation. Principles and Practice of Constraint Programming: 25th International Conference, CP 2019 Stamford, CT, USA, September 30 – October 4, 2019 Proceedings. editor / Thomas Schiex ; Simon de Givry. Cham Switzerland, 2019. pp. 748-763 (Lecture Notes in Computer Science).
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Betts, JM, Dowe, DL, Guimarans, D, Harabor, D, Kumarage, H, Stuckey, PJ & Wybrow, M 2019, Peak-hour rail demand shifting with discrete optimisation. in T Schiex & S de Givry (eds), Principles and Practice of Constraint Programming: 25th International Conference, CP 2019 Stamford, CT, USA, September 30 – October 4, 2019 Proceedings. Lecture Notes in Computer Science, vol. 11802, Cham Switzerland, pp. 748-763. https://doi.org/10.1007/978-3-030-30048-7_43

Peak-hour rail demand shifting with discrete optimisation. / Betts, John M.; Dowe, David L.; Guimarans, Daniel; Harabor, Daniel; Kumarage, Heshah; Stuckey, Peter J.; Wybrow, Michael.

Principles and Practice of Constraint Programming: 25th International Conference, CP 2019 Stamford, CT, USA, September 30 – October 4, 2019 Proceedings. ed. / Thomas Schiex; Simon de Givry. Cham Switzerland, 2019. p. 748-763 (Lecture Notes in Computer Science; Vol. 11802).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AB - In this work we consider an information-based system to reduce metropolitan rail congestion in Melbourne, Australia. Existing approaches aim to reduce congestion by asking commuters to travel outside of peak times. We propose an alternative approach where congestion is reduced by enabling commuters to make an informed trade-off between travel time and ride comfort. Our approach exploits the differences in train frequency and stopping patterns between stations that results in trains, arriving within a short time of each other, to have markedly different levels of congestion, even during peak travel periods. We show that, in such cases, commuters can adjust their departure and arrival time by a small amount (typically under 10 min) in exchange for more comfortable travel. We show the potential benefit of making this trade-off with a discrete optimisation model which attempts to redistribute passenger demand across neighbouring services to improve passenger ride comfort overall. Computational results show that even at low to moderate levels of passenger take-up, our method of demand shifting has the potential to significantly reduce congestion across the rail corridor studied, with implications for the metropolitan network more generally.

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Betts JM, Dowe DL, Guimarans D, Harabor D, Kumarage H, Stuckey PJ et al. Peak-hour rail demand shifting with discrete optimisation. In Schiex T, de Givry S, editors, Principles and Practice of Constraint Programming: 25th International Conference, CP 2019 Stamford, CT, USA, September 30 – October 4, 2019 Proceedings. Cham Switzerland. 2019. p. 748-763. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-030-30048-7_43