The Applications and Prospects of Large Language Models in Traffic Flow Prediction

Predicting traffic flow is crucial for the functionality of intelligent transportation systems. It is of critical importance to relieve traffic pressure, reduce accident rates, and alleviate environmental pollution. It is an important part of the construction of modern intelligent road networks. With advancements in deep learning (DL), DL models have made notable strides in prediction. However, due to the complexity and non-transparency of DL models themselves, there are still problems of low accuracy and interpretability in traffic flow prediction (TFP). Leveraging large language models (LLM) helps to improve the negative conditions caused by other DL models in prediction. This paper first briefly summarizes the basic characteristics of LLM and their advantages in TFP; then conducts relevant research and analysis in the order of experimental design steps comparison and results and conclusions comparison; then analyzes and discusses the current problems and challenges faced by LLM; finally, it looks forward to future research directions and development trends, and summarizes this paper.