License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.ATMOS.2021.3
URN: urn:nbn:de:0030-drops-148721
URL: https://drops.dagstuhl.de/opus/volltexte/2021/14872/
Go to the corresponding OASIcs Volume Portal


Müller-Hannemann, Matthias ; Rückert, Ralf ; Schiewe, Alexander ; Schöbel, Anita

Towards Improved Robustness of Public Transport by a Machine-Learned Oracle

pdf-format:
OASIcs-ATMOS-2021-3.pdf (4 MB)


Abstract

The design and optimization of public transport systems is a highly complex and challenging process. Here, we focus on the trade-off between two criteria which shall make the transport system attractive for passengers: their travel time and the robustness of the system. The latter is time-consuming to evaluate. A passenger-based evaluation of robustness requires a performance simulation with respect to a large number of possible delay scenarios, making this step computationally very expensive.
For optimizing the robustness, we hence apply a machine-learned oracle from previous work which approximates the robustness of a public transport system. We apply this oracle to bi-criteria optimization of integrated public transport planning (timetabling and vehicle scheduling) in two ways: First, we explore a local search based framework studying several variants of neighborhoods. Second, we evaluate a genetic algorithm. Computational experiments with artificial and close to real-word benchmark datasets yield promising results. In all cases, an existing pool of solutions (i.e., public transport plans) can be significantly improved by finding a number of new non-dominated solutions, providing better and different trade-offs between robustness and travel time.

BibTeX - Entry

@InProceedings{mullerhannemann_et_al:OASIcs.ATMOS.2021.3,
  author =	{M\"{u}ller-Hannemann, Matthias and R\"{u}ckert, Ralf and Schiewe, Alexander and Sch\"{o}bel, Anita},
  title =	{{Towards Improved Robustness of Public Transport by a Machine-Learned Oracle}},
  booktitle =	{21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021)},
  pages =	{3:1--3:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-213-6},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{96},
  editor =	{M\"{u}ller-Hannemann, Matthias and Perea, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/14872},
  URN =		{urn:nbn:de:0030-drops-148721},
  doi =		{10.4230/OASIcs.ATMOS.2021.3},
  annote =	{Keywords: Public Transportation, Timetabling, Machine Learning, Robustness}
}

Keywords: Public Transportation, Timetabling, Machine Learning, Robustness
Collection: 21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021)
Issue Date: 2021
Date of publication: 27.09.2021


DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI