Large Language Models for Traffic and Transportation Research: Methodologies, State of the Art, and Future Opportunities

TL;DR

Large Language Models (LLMs) are increasingly applied to traffic and transportation research, addressing the challenge of unstructured text and complex data integration. The paper conducts a scoping review of methodologies, tools, and applications, highlighting zero-/few-shot learning, prompt engineering, fine-tuning, and retrieval-augmented approaches. It synthesizes applications across autonomous driving, travel behavior, traffic safety, emergency management, forecasting, signal control, and logistics, and identifies critical research gaps and future directions, including integration with OR, KG, and LVLMs. The findings point to substantial opportunities for smarter, more sustainable transportation systems, contingent on addressing safety, privacy, and benchmark standardization.

Abstract

The rapid rise of Large Language Models (LLMs) is transforming traffic and transportation research, with significant advancements emerging between the years 2023 and 2025 -- a period marked by the inception and swift growth of adopting and adapting LLMs for various traffic and transportation applications. However, despite these significant advancements, a systematic review and synthesis of the existing studies remain lacking. To address this gap, this paper provides a comprehensive review of the methodologies and applications of LLMs in traffic and transportation, highlighting their ability to process unstructured textual data to advance transportation research. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. Our analysis identifies critical research gaps. From the methodological perspective, many research gaps can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, we identify numerous opportunities for LLMs to tackle a variety of traffic and transportation challenges, building upon existing research. By synthesizing these findings, this review not only clarifies the current state of LLM adoption and adaptation in traffic and transportation but also proposes future research directions, paving the way for smarter and more sustainable transportation systems.

Figures (9)

• Figure 1: Key aspects of LLMs
• Figure 2: ORLM huang2024orlm
• Figure 3: Integration of LLMs with Reinforcement Learning cao2024survey
• Figure 4: Advancements in Autonomous Driving
• Figure 5: Autonomous Driving Framework yang2023llm4drive