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A data-driven, variable-speed model for the train timetable rescheduling problem

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A data-driven, variable-speed model for the train timetable rescheduling problem. / Reynolds, E.; Maher, S.J.
In: Computers and Operations Research, Vol. 142, 105719, 30.06.2022.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Reynolds, E & Maher, SJ 2022, 'A data-driven, variable-speed model for the train timetable rescheduling problem', Computers and Operations Research, vol. 142, 105719. https://doi.org/10.1016/j.cor.2022.105719

APA

Reynolds, E., & Maher, S. J. (2022). A data-driven, variable-speed model for the train timetable rescheduling problem. Computers and Operations Research, 142, Article 105719. https://doi.org/10.1016/j.cor.2022.105719

Vancouver

Reynolds E, Maher SJ. A data-driven, variable-speed model for the train timetable rescheduling problem. Computers and Operations Research. 2022 Jun 30;142:105719. Epub 2022 Feb 15. doi: 10.1016/j.cor.2022.105719

Author

Reynolds, E. ; Maher, S.J. / A data-driven, variable-speed model for the train timetable rescheduling problem. In: Computers and Operations Research. 2022 ; Vol. 142.

Bibtex

@article{ed8eac69931b477c8f428d7ba4ab1e5b,
title = "A data-driven, variable-speed model for the train timetable rescheduling problem",
abstract = "Train timetable rescheduling — the practice of changing the routes and timings of trains in real-time to respond to delays — can help to reduce the impact of reactionary delay. There are a number of existing optimisation models that can be used to determine the best way to reschedule the timetable in any given traffic scenario. However, many of these models do not adequately account for the acceleration and deceleration required for trains to achieve the rescheduled timetable. The few models that do account for this are overly complex and cannot be solved to optimality in sufficiently short times. In this study, we propose a new model for train timetable rescheduling that uses statistical methods and historical data to parsimoniously take train speed into account. The model is tested using a new set of instances based on real data from Derby station in the UK. We show that the improved accuracy of the proposed model comes with little to no trade-off in terms of run time compared to fixed-speed timetable rescheduling models. ",
keywords = "Railway optimisation, Speed profile, Timetable rescheduling, Variable-speed, Economic and social effects, Railroad transportation, Speed, Acceleration and deceleration, Data driven, Railway optimizations, Real- time, Rescheduling problem, Speed models, Train timetables, Variable speed, Scheduling",
author = "E. Reynolds and S.J. Maher",
year = "2022",
month = jun,
day = "30",
doi = "10.1016/j.cor.2022.105719",
language = "English",
volume = "142",
journal = "Computers and Operations Research",
issn = "0305-0548",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - A data-driven, variable-speed model for the train timetable rescheduling problem

AU - Reynolds, E.

AU - Maher, S.J.

PY - 2022/6/30

Y1 - 2022/6/30

N2 - Train timetable rescheduling — the practice of changing the routes and timings of trains in real-time to respond to delays — can help to reduce the impact of reactionary delay. There are a number of existing optimisation models that can be used to determine the best way to reschedule the timetable in any given traffic scenario. However, many of these models do not adequately account for the acceleration and deceleration required for trains to achieve the rescheduled timetable. The few models that do account for this are overly complex and cannot be solved to optimality in sufficiently short times. In this study, we propose a new model for train timetable rescheduling that uses statistical methods and historical data to parsimoniously take train speed into account. The model is tested using a new set of instances based on real data from Derby station in the UK. We show that the improved accuracy of the proposed model comes with little to no trade-off in terms of run time compared to fixed-speed timetable rescheduling models.

AB - Train timetable rescheduling — the practice of changing the routes and timings of trains in real-time to respond to delays — can help to reduce the impact of reactionary delay. There are a number of existing optimisation models that can be used to determine the best way to reschedule the timetable in any given traffic scenario. However, many of these models do not adequately account for the acceleration and deceleration required for trains to achieve the rescheduled timetable. The few models that do account for this are overly complex and cannot be solved to optimality in sufficiently short times. In this study, we propose a new model for train timetable rescheduling that uses statistical methods and historical data to parsimoniously take train speed into account. The model is tested using a new set of instances based on real data from Derby station in the UK. We show that the improved accuracy of the proposed model comes with little to no trade-off in terms of run time compared to fixed-speed timetable rescheduling models.

KW - Railway optimisation

KW - Speed profile

KW - Timetable rescheduling

KW - Variable-speed

KW - Economic and social effects

KW - Railroad transportation

KW - Speed

KW - Acceleration and deceleration

KW - Data driven

KW - Railway optimizations

KW - Real- time

KW - Rescheduling problem

KW - Speed models

KW - Train timetables

KW - Variable speed

KW - Scheduling

U2 - 10.1016/j.cor.2022.105719

DO - 10.1016/j.cor.2022.105719

M3 - Journal article

VL - 142

JO - Computers and Operations Research

JF - Computers and Operations Research

SN - 0305-0548

M1 - 105719

ER -