With only 24 in-context examples, GPT-4.1 and Claude 3.7 Sonnet match random forest and XGBoost accuracy (macro-F1 about 0.59 at 15 minutes) for classifying traffic incident impact as mild, moderate, or severe.
Text analysis in incident duration prediction,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.AI 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents
With only 24 in-context examples, GPT-4.1 and Claude 3.7 Sonnet match random forest and XGBoost accuracy (macro-F1 about 0.59 at 15 minutes) for classifying traffic incident impact as mild, moderate, or severe.