Capturing complexity over space and time via deep learning: an application to real-time delay prediction in railways

DOI10.1016/j.ejor.2023.03.040OpenAlexW4362452066MaRDI QIDQ6168572

Veerle Hennebel, Marijn Verschelde, Bart Roets, Léon Sobrie

Publication date: 11 July 2023

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2023.03.040

zbMATH Keywords

complexitydelaysrailway transportationanalyticsdeep learning

Mathematics Subject Classification ID

Operations research and management science (90Bxx)

Cites Work

• Reassessing the scope of OR practice: the influences of problem structuring methods and the analytics movement
• Multi-output efficiency and operational safety: an analysis of railway traffic control centre performance
• Using a general-purpose mixed-integer linear programming solver for the practical solution of real-time train rescheduling
• A multi-objective optimization-simulation approach for real time rescheduling in dense railway systems
• Operational research from Taylorism to terabytes: a research agenda for the analytics age
• The Transitive Reduction of a Directed Graph

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