Integrating Large Language Models for Severity Classification in Traffic
  Incident Management: A Machine Learning Approach
Artur Grigorev Khaled Saleh Yuming Ou Adriana-Simona Mihaita
This study evaluates the impact of large language models on enhancing machine
learning processes for managing traffic incidents. It examines the extent to
which features generated by modern language models improve or match the
accuracy of predictions when classifying the severity of incidents using
accident reports. Multiple comparisons performed between combinations of
language models and machine learning algorithms, including Gradient Boosted
Decision Trees, Random Forests, and Extreme Gradient Boosting. Our research
uses both conventional and language model-derived features from texts and
incident reports, and their combinations to perform severity classification.
Incorporating features from language models with those directly obtained from
incident reports has shown to improve, or at least match, the performance of
machine learning techniques in assigning severity levels to incidents,
particularly when employing Random Forests and Extreme Gradient Boosting
methods. This comparison was quantified using the F1-score over uniformly
sampled data sets to obtain balanced severity classes. The primary contribution
of this research is in the demonstration of how Large Language Models can be
integrated into machine learning workflows for incident management, thereby
simplifying feature extraction from unstructured text and enhancing or matching
the precision of severity predictions using conventional machine learning
pipeline. The engineering application of this research is illustrated through
the effective use of these language processing models to refine the modelling
process for incident severity classification. This work provides significant
insights into the application of language processing capabilities in
combination with traditional data for improving machine learning pipelines in
the context of classifying incident severity.