Jagadeesh, George Rosario (ST Engineering), Iyer, Srikrishna (ST Engineering), Polanowski, Michal (ST Engineering), Thia, Kai Xin (ST Engineering)

Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidents

Scheduled for presentation during the Invited Session "S22b-Emerging Trends in AV Research" (TH-EA-T22), Thursday, November 20, 2025, 14:50−15:30, Coolangata 1

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

This study examines the feasibility of applying large language models (LLMs) for forecasting the impact of traffic incidents on the traffic flow. The use of LLMs for this task has several advantages over existing machine learning-based solutions such as not requiring a large training dataset and the ability to utilize free-text incident logs. We propose a fully LLM-based solution that predicts the incident impact using a combination of traffic features and LLM-extracted incident features. A key ingredient of this solution is an effective method of selecting examples for the LLM’s in-context learning. We evaluate the performance of three advanced LLMs and two state-of-the-art machine learning models on a real traffic incident dataset. The results show that the best-performing LLM matches the accuracy of the most accurate machine learning model, despite the former not having been trained on this prediction task. The findings indicate that LLMs are a practically viable option for traffic incident impact prediction.