Research Progress and Future Prospects of Large Language Models in the Field of Traffic Flow Prediction

Abstract

With the enhancement of computer performance, the predictive capabilities of large language models have also improved, and these models have been applied in many fields. Currently, the application of large language models in the field of traffic flow prediction has entered a rapid development stage. Thanks to its powerful feature learning and generalization capabilities, it has provided new ideas and methods for traffic flow prediction, and a series of achievements have been achieved. This article reviews the research progress of mainstream large language models in the field of traffic flow prediction. This introduces the background and significance of applying large language models to traffic flow prediction, as well as the current mainstream large language models, and elaborates on the relevant model architectures and methods in detail. Analyzed its advantages in enhancing predictive accuracy and interpretability, as well as the challenges it faces. At the same time, an outlook on the future development directions of large language models in this field has been provided, aiming to offer a reference for further research and application of traffic flow prediction in intelligent transportation systems.