2026-07-20T15:08:05Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:14872 2024-03-06T10:31:34Z ddc:004 open_access

Towards Improved Robustness of Public Transport by a Machine-Learned Oracle Müller-Hannemann, Matthias Rückert, Ralf Schiewe, Alexander Schöbel, Anita Public Transportation Timetabling Machine Learning Robustness 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Matthias Müller-Hannemann and Ralf Rückert and Alexander Schiewe and Anita Schöbel 2021 Is Part Of OASIcs, Volume 96, 21st Symposium on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.ATMOS.2021.3 urn:nbn:de:0030-drops-148721 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ATMOS.2021.3 eng https://creativecommons.org/licenses/by/4.0/legalcode