Florian H.W. Dahms
Vulpes AI GmbH, Frankfurt am Main, Germany
Anna-Lena Frank
neXt Lab, Timetable and Capacity Management, DB Netz AG, Franfurt am Main, Germany
Sebastian Kühn
neXt Lab, Timetable and Capacity Management, DB Netz AG, Franfurt am Main, Germany
Daniel Pöhle
neXt Lab, Timetable and Capacity Management, DB Netz AG, Franfurt am Main, Germany
Ladda ner artikel
Ingår i: RailNorrköping 2019. 8th International Conference on Railway Operations Modelling and Analysis (ICROMA), Norrköping, Sweden, June 17th – 20th, 2019
Linköping Electronic Conference Proceedings 69:19, s. 280-289
Publicerad: 2019-09-13
ISBN: 978-91-7929-992-7
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
In this article, we present a practical approach for the optimized creation of railway timetables. The algorithms are intended to be used by Deutsche Bahn, Germanys largest railway infrastructure provider. We show how our methods can be used, both for creating a timetable in advance and for answering ad-hoc requests coming in via a digital app. Numerical ex-periments are provided to show that our solution exceeds manual creation of timetables in terms of capacity usage, travel times and the time taken for creating the timetable.
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