Towards explainable traffic flow prediction with large language models
Xusen Guo, Q Zhang, Junyue Jiang, Mingxing Peng, Meixin Zhu, Hao Yang
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 study contributes to advancing explainable traffic prediction models and lays a foundation for future exploration of LLM applications in transportation. • Multi-modality traffic forecasting dataset for the learning-based prediction tasks. • Traffic flow prediction with large language models, accountable and reliable prediction results. • Spatial-temporal alignment, zero-shot learning capability to other unseen traffic prediction tasks.