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Mining railway delay dependencies in large-scale real-world delay data

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Abstract

The propagation of delays between trains has a considerable impact on railway operations. Ideally, planners would like to create timetables that avoid such propagation as much as possible. To improve existing timetables, tools for automatic detection of systematic dependencies of delays among trains would be of great aid. We present efficient algorithms to detect two of the most important types of dependencies, namely dependencies due to resource conflicts and due to maintained connections. We give experimental results on real-world data that demonstrate the practical applicability of our algorithms. © 2009 Springer-Verlag Berlin Heidelberg.

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APA

Flier, H., Gelashvili, R., Graffagnino, T., & Nunkesser, M. (2009). Mining railway delay dependencies in large-scale real-world delay data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5868 LNCS, pp. 354–368). https://doi.org/10.1007/978-3-642-05465-5_15

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