Integrating Large Language Models for Severity Classification in Traffic Incident Management: A Machine Learning Approach

arXiv:2403.13547v1 Announce Type: new
Abstract: 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 …

abstract accuracy arxiv classification cs.lg cs.sy eess.sy features generated impact incident language language models large language large language models machine machine learning management match modern predictions processes study traffic type