mlsanityData to 7 Oct 2026

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

arXiv 2501.04437 ↗First posted 8 Jan 2025eess.SY cs.AI cs.ETcited by 23

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Abstract (arXiv)

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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 147 of its 303 references that are in the map. First posted on arXiv 8 Jan 2025.

Builds on

147 of 303 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 16; 2026: 7.

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  1. 2025A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectivescited by 27
  2. 2025Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmapcited by 26
  3. 2025V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridorscited by 13
  4. 2025Next-Generation LLM for UAV: From Natural Language to Autonomous Flightcited by 10
  5. 2025Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Reviewcited by 8
  6. 2025CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysiscited by 6
  7. 2025Leveraging Multimodal-LLMs Assisted by Instance Segmentation for Intelligent Traffic Monitoringcited by 5
  8. 2025AgentSUMO: An Agentic Framework for Interactive Simulation Scenario Generation in SUMO via Large Language Modelscited by 3
  9. 2025LIFT: Interpretable truck driving risk prediction with literature-informed fine-tuned LLMscited by 2
  10. 2025Multimodal Large Language Models for Enhanced Traffic Safety: A Comprehensive Review and Future Trendscited by 1
  11. 2025Towards Locally Deployable Fine-Tuned Causal Large Language Models for Mode Choice Behaviourcited by 1
  12. 2025A Survey of the State-of-the-Art in Conversational Question Answering Systemscited by 1
11 more
  1. 2025Jailbreaking Large Vision Language Models in Intelligent Transportation Systemscited by 1
  2. 2026C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Drivingcited by 1
  3. 2025Automated Traffic Incident Response Plans using Generative Artificial Intelligence: Part 1 -- Building the Incident Response Benchmarkcited by 0
  4. 2025Application and Evaluation of Large Language Models for Forecasting the Impact of Traffic Incidentscited by 0
  5. 2025CLIPTime: Time-Aware Multimodal Representation Learning from Images and Textcited by 0
  6. 2026Spatiotemporal Semantic V2X Framework for Cooperative Collision Predictioncited by 0
  7. 2026Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehiclescited by 0
  8. 2026Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Supportcited by 0
  9. 2026Large Multimodal Agents for Intelligent Transportation Systems: Architectures, Evidence, and Deployment Challengescited by 0
  10. 2026On the security and privacy of LLMs in Mobilitycited by 0
  11. 2026Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logisticscited by 0