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A Bi-Objective Optimization Model for Fare Structure Design in Public Transport

Authors Philine Schiewe , Anita Schöbel , Reena Urban

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Author Details

Philine Schiewe
  • Department of Mathematics and Systems Analysis, Aalto University, Finland
Anita Schöbel
  • Department of Mathematics, University of Kaiserslautern-Landau (RPTU), Germany
  • Fraunhofer Institute of Industrial Mathematics ITWM, Kaiserslautern, Germany
Reena Urban
  • Department of Mathematics, University of Kaiserslautern-Landau (RPTU), Germany

Funding

This work was partially funded by the European Union’s Horizon 2020 research and innovation programme [Grant 875022] and by the Federal Ministry of Education and Research [Project 01UV2152B] under the project sEAmless SustaInable EveRyday urban mobility (EASIER).

Cite AsGet BibTex

Philine Schiewe, Anita Schöbel, and Reena Urban. A Bi-Objective Optimization Model for Fare Structure Design in Public Transport. In 24th Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2024). Open Access Series in Informatics (OASIcs), Volume 123, pp. 15:1-15:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/OASIcs.ATMOS.2024.15

Abstract

Fare planning in public transport is important from the view of passengers as well as of operators. In this paper, we propose a bi-objective model that maximizes the revenue as well as the number of attracted passengers. The potential demand per origin-destination pair is divided into demand groups that have their own willingness how much to pay for using public transport, i.e., a demand group is only attracted as public transport passengers if the fare does not exceed their willingness to pay. We study the bi-objective problem for flat and distance tariffs and develop specialized algorithms to compute the Pareto front in quasilinear or cubic time, respectively. Through computational experiments on structured data sets we evaluate the running time of the developed algorithms in practice and analyze the number of non-dominated points and their respective efficient solutions.

Subject Classification

ACM Subject Classification
  • Applied computing → Transportation
Keywords
  • Public transport
  • fare structure design
  • modeling
  • bi-objective
  • algorithm

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References

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