Traffic flow prediction provides essential future views in the intelligent
transportation system. Explainable predictions offer valuable insights into the
factors influencing traffic patterns, which help urban planners, traffic
engineers, and policymakers make informed decisions about infrastructure
development, traffic management strategies, and public transportation planning.
Despite their widespread popularity and commendable accuracy, prediction
methods grounded in deep learning frequently disappoint in terms of
transparency and interpretability. Recently, the availability of large-scale
spatio-temporal data and the development of large language models (LLMs) have
opened up new opportunities for urban traffic prediction. With the popularity
of LLMs, people witnessed the potential reasoning and generating ability of
foundation models in various tasks. Considering text as input and output, LLMs
have advantages in generating more intuitive and interpretable predictions.
Hence, this work introduces TP-LLM, an explainable foundation-model-based
method for traffic prediction, aiming at more direct and reasonable
forecasting. TP-LLM presents a framework to unify multi-modality factors as
language-based inputs, TP-LLM avoids complex spatial-temporal data programming
and outperforms state-of-art baselines merely under fine-tuning foundation
models. Also, TP-LLM can generate input-dependency explanations for more
confident prediction and can be easily generalized to different city dynamics
for zero-shot prediction with a similar framework. These findings demonstrate
the potential of LLMs for explainable traffic prediction.

这项研究介绍了一种基于 TP-LLM 的可解释交通预测方法，通过将多模态因素作为语言输入统一起来，避免了复杂的时空数据编程，证明了大语言模型在交通预测方面的潜力。