Integrating Large Language Models into Traffic Systems: Integration Levels, Capability Boundaries, and an Information-Theoretic Perspective

TL;DR

This paper explores how large language models can be integrated into traffic systems, highlighting their strengths and limitations through an information-theoretic perspective.

Contribution

The paper introduces a novel information-theoretic framework to analyze LLM integration in traffic systems and proposes hybrid architectures for future research.

Findings

LLMs excel in managing high semantic entropy tasks like contextual understanding and knowledge integration.

Classical models are better suited for real-time control and safety verification due to their low entropy requirements.

A hybrid intelligence approach is needed to bridge semantic and physical modeling in traffic systems.

Abstract

Large language models (LLMs) are fundamentally transforming intelligent traffic systems by enabling semantic abstraction, probabilistic reasoning, and multimodal information fusion across heterogeneous data. This review examines existing research on LLM integration, ranging from data representation to autonomous agents, through an information-theoretic lens, conceptualizing LLMs as entropy-minimizing probabilistic systems that shape their capabilities in uncertainty modeling and semantic compression. We identify core integration patterns and analyze fundamental limitations arising from the inherent mismatch between discrete, entropy-driven LLM reasoning and the continuous, causal, and safety-critical nature of physical traffic environments. This reflects a deep structural tension rather than mere technical gaps. We delineate clear boundaries: LLMs are indispensable for managing high semantic entropy in tasks like contextual understanding and knowledge integration, whereas classical physics-based and optimization models remain essential in domains requiring ultra-low physical, temporal, and causal/normative entropy, such as real-time control and safety verification. Finally, we propose a forward-looking research agenda centered on hybrid intelligence architectures that bridge semantic information processing with physical system modeling for next-generation traffic systems.

Figures