Hierarchical Delay Attribution Classification using Unstructured Text in
  Train Management Systems
Anton Borg Per Lingvall Martin Svensson
EU directives stipulate a systematic follow-up of train delays. In Sweden,
the Swedish Transport Administration registers and assigns an appropriate delay
attribution code. However, this delay attribution code is assigned manually,
which is a complex task. In this paper, a machine learning-based decision
support for assigning delay attribution codes based on event descriptions is
investigated. The text is transformed using TF-IDF, and two models, Random
Forest and Support Vector Machine, are evaluated against a random uniform
classifier and the classification performance of the Swedish Transport
Administration. Further, the problem is modeled as both a hierarchical and flat
approach. The results indicate that a hierarchical approach performs better
than a flat approach. Both approaches perform better than the random uniform
classifier but perform worse than the manual classification.