Timetabling for strategic passenger railway planning

Polinder, Gert-Jaap; Schmidt, Marie; Huisman, Dennis

doi:10.1016/j.trb.2021.02.006

Cited by 11 publications

(18 citation statements)

References 30 publications

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“…While the model allows to group passengers into (predefined) time slices and penalize deviation from the respective time slice, a heuristic to include adaption time, only one time slice (that spans the whole period) is used in the numerical experiments reported in Gattermann et al [2016]. Polinder et al [2021] consider timetabling in the strategic railway planning phase. Like in the POT problem, they aim at finding a periodic timetable that minimizes perceived travel time (a weighted sum of in-train, transfer, and adaption time and transfer penalties) under the assumption that passenger demand is uniformly distributed over the period.…”

Section: Related Workmentioning

confidence: 99%

“…However, in a dense line network, where many different lines share (part of) their physical route, and in addition many passenger routes require a transfer, it is far from obvious which trains should be synchronized to obtain an ideal timetable. Polinder et al [2021] proposes a model that includes adaption time (the time from desired departure time until the scheduled departure) to be able to drop pre-imposed regularity constraints in the construction of an ideal timetable. Polinder et al [2021] approach periodic timetabling from a strategic perspective and therefore do not consider any infrastructure constraints.…”

Section: Introductionmentioning

confidence: 99%

“…Polinder et al [2021] proposes a model that includes adaption time (the time from desired departure time until the scheduled departure) to be able to drop pre-imposed regularity constraints in the construction of an ideal timetable. Polinder et al [2021] approach periodic timetabling from a strategic perspective and therefore do not consider any infrastructure constraints.…”

Section: Introductionmentioning

confidence: 99%

“…In the context of strategic timetabling, Polinder et al [2021] combine a Mixed Integer Linear Program (MILP) formulation of a PESP model with an approach for modelling average perceived travel time. A similar approach would be possible for the POT problem, adding infrastructure constraints (which are not considered in Polinder et al [2021]) as PESP constraints, and using indicator variables to model the cancelling of trains (see Appendix A.4).…”

Section: Introductionmentioning

confidence: 99%

“…For this reason, in this paper we propose an iterative approach that combines (extended versions of) two existing approaches. First, we compute an ideal timetable, that is: a timetable that does not need to respect infrastructure constraints, using the method proposed in Polinder et al [2021] for strategic timetabling. Secondly, we transform this ideal timetable into a feasible timetable, that is: let it satisfy the infrastructure constraints, using an extension of the Lagrangian heuristic (LH) proposed in Cacchiani et al [2010] with the goal to find a feasible timetable that stays as close as possible to the ideal timetable.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An iterative heuristic for passenger-centric train timetabling with integrated adaption times

Polinder

Cacchiani

Schmidt

et al. 2022

Computers & Operations Research

Self Cite

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An iterative heuristic for passenger-centric train timetabling with integrated adaption times

Polinder

Cacchiani

Schmidt

et al. 2022

Computers & Operations Research

Self Cite

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The complexity of the timetable‐based railway network design problem

Friesen,

Sander,

Büsing

et al. 2023

Networks

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Because of the long planning periods and their long life cycle, railway infrastructure has to be outlined long ahead. At the present, the infrastructure is designed while only little about the intended operation is known. Hence, the timetable and the operation are adjusted to the infrastructure. Since space, time and money for extension measures of railway infrastructure are limited, each modification has to be done carefully and long lasting and should be appropriate for the future unknown demand. To take this into account, we present the robust network design problem for railway infrastructure under capacity constraints and uncertain timetables. Here, we plan the required expansion measures for an uncertain long‐term timetable. We show that this problem is NP‐hard even when restricted to bipartite graphs and very simple timetables and present easier solvable special cases. This problem corresponds to the fixed‐charge network design problem where the expansion costs are minimized such that the timetable is conductible. We model this problem by an integer linear program using time expanded networks. To incorporate the uncertainty of the future timetable, we use a scenario‐based approach. We define scenarios with individual departure and arrival times and optional trains. The network is then optimized such that a given percentage of the scenarios can be operated while minimizing the expansion costs and potential penalty costs for not scheduled optional trains.

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