Towards Explainable Traffic Flow Prediction with Large Language Models

TL;DR

This paper introduces xTP-LLM, a novel traffic flow prediction model leveraging large language models to provide accurate and explainable traffic forecasts by translating traffic data into natural language.

Contribution

It is the first to utilize large language models for explainable traffic flow prediction, combining multi-modal data translation with fine-tuning for interpretability.

Findings

xTP-LLM achieves competitive accuracy with deep learning models

Provides intuitive explanations for traffic predictions

Lays groundwork for LLM applications in transportation

Abstract

Traffic forecasting is crucial for intelligent transportation systems. It has experienced significant advancements thanks to the power of deep learning in capturing latent patterns of traffic data. However, recent deep-learning architectures require intricate model designs and lack an intuitive understanding of the mapping from input data to predicted results. Achieving both accuracy and explainability in traffic prediction models remains a challenge due to the complexity of traffic data and the inherent opacity of deep learning models. To tackle these challenges, we propose a Traffic flow Prediction model based on Large Language Models (LLMs) to generate explainable traffic predictions, named xTP-LLM. By transferring multi-modal traffic data into natural language descriptions, xTP-LLM captures complex time-series patterns and external factors from comprehensive traffic data. The LLM framework is fine-tuned using language-based instructions to align with spatial-temporal traffic flow data. Empirically, xTP-LLM shows competitive accuracy compared with deep learning baselines, while providing an intuitive and reliable explanation for predictions. This paper contributes to advancing explainable traffic prediction models and lays a foundation for future exploration of LLM applications in transportation. To the best of our knowledge, this is the first study to use LLM for explainable prediction of traffic flows.