Traffic Flow Prediction Based on Large Language Models and Future Development Directions

Abstract

As the application of deep learning in intelligent transportation systems becomes increasingly widespread, the accuracy and reliability of traffic flow prediction have become crucial. However, existing deep learning methods are often complex in model design and lack intuitiveness, making it challenging to provide responsible explanations for traffic predictions. This paper references a responsible and reliable traffic flow prediction model (R2T-LLM) based on large language models (LLMs). This model captures complex spatiotemporal patterns and external factors by converting multimodal traffic data into natural language descriptions. By leveraging LLMs’ advanced understanding capabilities, R2T-LLM provides more transparent and interpretable predictions. It bridges the gap between technical performance and real-world application needs. While maintaining accuracy comparable to deep learning baselines, R2T-LLM offers intuitive and reliable prediction explanations. This paper also explores the spatiotemporal and input dependencies of conditional future traffic predictions and compares the model with other different approaches and types. Furthermore, it evaluates the potential of R2T-LLM in addressing challenges in large-scale urban traffic systems, highlighting its developmental and application prospects.