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.
@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/entities/document/10.4230/OASIcs.ATMOS.2021.3}, URN = {urn:nbn:de:0030-drops-148721}, doi = {10.4230/OASIcs.ATMOS.2021.3}, annote = {Keywords: Public Transportation, Timetabling, Machine Learning, Robustness} }
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