IDM-GPT introduces a novel multi-agent LLM framework that enables customized traffic mobility analysis without requiring extensive domain expertise or data processing infrastructure.

• Self-supervised architecture that connects transportation data with machine learning models
• Privacy-preserving approach to handling sensitive transportation data
• Democratizes access to sophisticated traffic analysis for wider range of stakeholders
• Reduces barriers to leveraging transportation big data through natural language interfaces

This research represents a significant advancement in transportation engineering by bridging the gap between complex mobility data and practical applications, potentially accelerating innovation in smart city development and traffic management systems.

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