  This research showcases the innovative integration of Large Language Models
into machine learning workflows for traffic incident management, focusing on
the classification of incident severity using accident reports. By leveraging
features generated by modern language models alongside conventional data
extracted from incident reports, our research demonstrates improvements in the
accuracy of severity classification across several machine learning algorithms.
Our contributions are threefold. First, we present an extensive comparison of
various machine learning models paired with multiple large language models for
feature extraction, aiming to identify the optimal combinations for accurate
incident severity classification. Second, we contrast traditional feature
engineering pipelines with those enhanced by language models, showcasing the
superiority of language-based feature engineering in processing unstructured
text. Third, our study illustrates how merging baseline features from accident
reports with language-based features can improve the severity classification
accuracy. This comprehensive approach not only advances the field of incident
management but also highlights the cross-domain application potential of our
methodology, particularly in contexts requiring the prediction of event
outcomes from unstructured textual data or features translated into textual
representation. Specifically, our novel methodology was applied to three
distinct datasets originating from the United States, the United Kingdom, and
Queensland, Australia. This cross-continental application underlines the
robustness of our approach, suggesting its potential for widespread adoption in
improving incident management processes globally.
