Independent Mobility GPT (IDM-GPT): A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Models

2025-02-25

AI Summary

The proliferation of urban traffic sensors poses significant challenges, including massive data volumes, high domain expertise requirements, and prominent privacy risks. To address these, we propose IDM-GPT—the first self-supervised, multi-agent large language model (LLM) framework tailored for traffic intelligence. It integrates LLMs, multi-agent systems, self-supervised learning, prompt engineering, and ML model orchestration. Through intelligent agent collaboration, IDM-GPT enables user intent understanding, dynamic prompt optimization, automatic model selection, and closed-loop performance enhancement—allowing non-expert users to obtain real-time, high-accuracy traffic analytics and actionable management recommendations via natural language queries. Experiments demonstrate that IDM-GPT achieves strong real-time performance, accuracy, and interpretability across diverse traffic tasks, substantially reducing dependence on domain expertise and minimizing raw data exposure. This framework establishes a novel paradigm for low-barrier, privacy-preserving intelligent traffic governance.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Language Grounding & Multi-modal NLP

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

With the urbanization process, an increasing number of sensors are being deployed in transportation systems, leading to an explosion of big data. To harness the power of this vast transportation data, various machine learning (ML) and artificial intelligence (AI) methods have been introduced to address numerous transportation challenges. However, these methods often require significant investment in data collection, processing, storage, and the employment of professionals with expertise in transportation and ML. Additionally, privacy issues are a major concern when processing data for real-world traffic control and management. To address these challenges, the research team proposes an innovative Multi-agent framework named Independent Mobility GPT (IDM-GPT) based on large language models (LLMs) for customized traffic analysis, management suggestions, and privacy preservation. IDM-GPT efficiently connects users, transportation databases, and ML models economically. IDM-GPT trains, customizes, and applies various LLM-based AI agents for multiple functions, including user query comprehension, prompts optimization, data analysis, model selection, and performance evaluation and enhancement. With IDM-GPT, users without any background in transportation or ML can efficiently and intuitively obtain data analysis and customized suggestions in near real-time based on their questions. Experimental results demonstrate that IDM-GPT delivers satisfactory performance across multiple traffic-related tasks, providing comprehensive and actionable insights that support effective traffic management and urban mobility improvement.

Problem

• Customized traffic mobility analysis
• Privacy preservation in data processing
• User-friendly traffic management suggestions

Innovation

• Utilizes large language models
• Multi-agent framework for traffic
• Self-supervised learning approach