Towards Explainable Traffic Flow Prediction with Large Language Models
<|reference_start|>Towards Explainable Traffic Flow Prediction with Large Language Models: 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.<|reference_end|>
@article{guo2024towards,
    title={Towards Explainable Traffic Flow Prediction with Large Language Models},
    author={Xusen Guo, Qiming Zhang, Junyue Jiang, Mingxing Peng, Meixin Zhu and
  Hao (Frank) Yang},
    journal={arXiv preprint arXiv:2404.02937},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02937},
    primaryClass={cs.LG cs.AI}
}