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

TL;DR

This paper explores the use of large language models for predicting traffic incident impacts, demonstrating they can match traditional models' accuracy without extensive training data, thus offering a practical alternative.

Contribution

It introduces a fully LLM-based approach for traffic impact forecasting, highlighting effective example selection for in-context learning and validating LLMs' viability in this domain.

Findings

Best LLM matches accuracy of top machine learning models

LLMs do not require large training datasets for this task

LLMs effectively utilize free-text incident logs

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.