Hierarchical Delay Attribution Classification using Unstructured Text in Train Management Systems
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909095568605184 |
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| author | Borg, Anton Lingvall, Per Svensson, Martin |
| author_facet | Borg, Anton Lingvall, Per Svensson, Martin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04108 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hierarchical Delay Attribution Classification using Unstructured Text in Train Management Systems Borg, Anton Lingvall, Per Svensson, Martin Machine Learning Artificial Intelligence 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. |
| title | Hierarchical Delay Attribution Classification using Unstructured Text in Train Management Systems |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.04108 |