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: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/14872/
Müller-Hannemann, Matthias ;
Rückert, Ralf ;
Schiewe, Alexander ;
Schöbel, Anita
Towards Improved Robustness of Public Transport by a Machine-Learned Oracle
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: |
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Public Transportation, Timetabling, Machine Learning, Robustness |
Collection: |
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21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021) |
Issue Date: |
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2021 |
Date of publication: |
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27.09.2021 |