Hierarchical Delay Attribution Classification using Unstructured Text in Train Management Systems
<|reference_start|>Hierarchical Delay Attribution Classification using Unstructured Text in Train Management Systems: 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.<|reference_end|>
@article{borg2024hierarchical,
    title={Hierarchical Delay Attribution Classification using Unstructured Text in
  Train Management Systems},
    author={Anton Borg, Per Lingvall, Martin Svensson},
    journal={arXiv preprint arXiv:2402.04108},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04108},
    primaryClass={cs.LG cs.AI}
}