Towards Explainable Traffic Flow Prediction with Large Language Models

• URL: http://arxiv.org/abs/2404.02937v5
• Date: Tue, 3 Sep 2024 11:32:50 GMT
• Title: Towards Explainable Traffic Flow Prediction with Large Language Models
• Authors: Xusen Guo, Qiming Zhang, Junyue Jiang, Mingxing Peng, Meixin Zhu, Hao, Yang,
• Abstract summary: We propose a Traffic flow Prediction model based on Large Language Models (LLMs) to generate explainable traffic predictions. By transferring multi-modal traffic data into natural language descriptions, xTP-LLM captures complex time-series patterns and external factors from comprehensive traffic data. Empirically, xTP-LLM shows competitive accuracy compared with deep learning baselines, while providing an intuitive and reliable explanation for predictions.
• Score: 36.86937188565623
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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