A Survey of Large Language Models for Traffic Forecasting: Methods and Applications

Large Language Models (LLMs) have achieved prominent success in various applications, driven by their foundational capabilities and strong generalization potential. While their impact on natural language processing is well-established, recent works highlight their significant promise in other applications as well. One such area is traffic forecasting, where LLMs demonstrated the ability to generate powerful analytical insights, offering new opportunities for advancing Intelligent Transportation Systems (ITS). This survey summarizes 160 related works, providing a comprehensive review of methods, applications, and challenges. Specifically, we summarize the efforts to bridge the gap between time series data and language models, exploring their design principles and applications in various traffic scenarios, including traffic forecasting, traffic recommendation, mobility forecasting, urban management, signal control, and safety analysis. For deeper insights, we discuss existing challenges and highlight future research directions. This survey identifies three key limitations of current works: 1) LLM-based traffic models face practical deployment hurdles; 2) there lacks a unified benchmark across various applications; and 3) resident privacy protection demands attention in their real-world applications. By providing a foundational understanding of LLMs for traffic forecasting, this survey aims to benefit not only the traffic mining community but also contribute to the broader advancement of Artificial General Intelligence (AGI).