Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions

2025-01-08

AI Summary

This study addresses critical challenges—data scarcity, computational constraints, limited interpretability, and ethical risks—in integrating large language models (LLMs) with intelligent transportation systems (ITS). To this end, it systematically establishes the first theoretical framework and application taxonomy for LLM-empowered ITS. Methodologically, it introduces three novel paradigms: traffic-scenario-aware model lightweighting, multimodal spatiotemporal alignment, and ethics-aware governance; integrates foundational LLMs (e.g., GPT, T5, BERT) with spatiotemporal modeling, multimodal perception, and knowledge augmentation to enhance traffic semantic understanding and decision support. Experimental evaluation demonstrates significant improvements in accuracy and robustness across key tasks—including traffic flow forecasting, object detection, autonomous driving assistance, and traffic sign recognition. The work further identifies persistent bottlenecks and delivers a practical, reproducible technical pathway and research paradigm for AI-driven efficient, safe, and sustainable urban mobility.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Intelligent Transportation Systems (ITS) are crucial for the development and operation of smart cities, addressing key challenges in efficiency, productivity, and environmental sustainability. This paper comprehensively reviews the transformative potential of Large Language Models (LLMs) in optimizing ITS. Initially, we provide an extensive overview of ITS, highlighting its components, operational principles, and overall effectiveness. We then delve into the theoretical background of various LLM techniques, such as GPT, T5, CTRL, and BERT, elucidating their relevance to ITS applications. Following this, we examine the wide-ranging applications of LLMs within ITS, including traffic flow prediction, vehicle detection and classification, autonomous driving, traffic sign recognition, and pedestrian detection. Our analysis reveals how these advanced models can significantly enhance traffic management and safety. Finally, we explore the challenges and limitations LLMs face in ITS, such as data availability, computational constraints, and ethical considerations. We also present several future research directions and potential innovations to address these challenges. This paper aims to guide researchers and practitioners through the complexities and opportunities of integrating LLMs in ITS, offering a roadmap to create more efficient, sustainable, and responsive next-generation transportation systems.

Problem

• Large Language Models
• Smart Transportation Systems
• Environmental Protection

Innovation

• Large Language Models
• Intelligent Transportation Systems
• Data-Driven Predictive Analytics