  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 responsibility 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 Responsible and
Reliable Traffic flow forecasting model with Large Language Models (R2T-LLM),
which leverages large language models (LLMs) to generate responsible traffic
predictions. By transferring multi-modal traffic data into natural language
descriptions, R2T-LLM captures complex spatial-temporal 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, R2T-LLM shows competitive accuracy compared with deep learning
baselines, while providing an intuitive and reliable explanation for
predictions. We discuss the spatial-temporal and input dependencies for
conditional future flow forecasting, showcasing R2T-LLM's potential for diverse
city prediction tasks. This paper contributes to advancing accountable 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 accountable and reliable prediction of traffic flows.
