Towards improved robustness of public transport by a machine-learned oracle
Müller-Hannemann M, Rückert R, Schiewe A, Schöbel A (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
Book Volume: 96
Conference Proceedings Title: OpenAccess Series in Informatics
Event location: Virtual, Lisbon, PRT
ISBN: 9783959772136
DOI: 10.4230/OASIcs.ATMOS.2021.3
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.
Involved external institutions
Martin-Luther-Universität Halle-Wittenberg (MLU)
Germany (DE)
Technische Universität Kaiserslautern
Germany (DE)
Germany (DE)
Technische Universität Kaiserslautern
Germany (DE)
How to cite
APA:
Müller-Hannemann, M., Rückert, R., Schiewe, A., & Schöbel, A. (2021). Towards improved robustness of public transport by a machine-learned oracle. In Matthias Muller-Hannemann, Federico Perea (Eds.), OpenAccess Series in Informatics. Virtual, Lisbon, PRT: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing.
MLA:
Müller-Hannemann, Matthias, et al. "Towards improved robustness of public transport by a machine-learned oracle." Proceedings of the 21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ATMOS 2021, Virtual, Lisbon, PRT Ed. Matthias Muller-Hannemann, Federico Perea, Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2021.
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