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Algorithms and Hardness for Non-Pool-Based Line Planning

Authors Irene Heinrich , Philine Schiewe , Constantin Seebach

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Author Details

Irene Heinrich
  • TU Darmstadt, Germany
Philine Schiewe
  • TU Kaiserslautern, Germany
Constantin Seebach
  • TU Kaiserslautern, Germany

Funding

The research leading to these results has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (EngageS: grant agreement No. 820148) and by DFG under SCHO 1140/8-2.

Cite AsGet BibTex

Irene Heinrich, Philine Schiewe, and Constantin Seebach. Algorithms and Hardness for Non-Pool-Based Line Planning. In 22nd Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2022). Open Access Series in Informatics (OASIcs), Volume 106, pp. 8:1-8:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/OASIcs.ATMOS.2022.8

Abstract

Line planning, i.e. choosing paths which are operated by one vehicle end-to-end, is an important aspect of public transport planning. While there exist heuristic procedures for generating lines from scratch, most theoretical observations consider the problem of choosing lines from a predefined line pool. In this paper, we consider the complexity of the line planning problem when all simple paths can be used as lines. Depending on the cost structure, we show that the problem can be NP-hard even for paths and stars, and that no polynomial time approximation of sub-linear performance is possible. Additionally, we identify polynomially solvable cases and present a pseudo-polynomial solution approach for trees.

Subject Classification

ACM Subject Classification
  • Applied computing → Transportation
  • Mathematics of computing → Discrete optimization
  • Theory of computation → Problems, reductions and completeness
  • Theory of computation → Discrete optimization
  • Theory of computation → Design and analysis of algorithms
Keywords
  • line planning
  • public transport
  • discrete optimization
  • complexity
  • algorithm design

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