用大模型打造可定制交通分析框架，让外行也能秒懂路况并提建议。
Independent Mobility GPT (IDM-GPT): A Self-Supervised Multi-Agent Large Language Model Framework for Customized Traffic Mobility Analysis Using Machine Learning Models
- 基于大语言模型构建多智能体系统，自动理解用户问题并调用算法
- 无需专业背景的用户也能实时获得交通分析与管理建议
- 兼顾隐私保护，降低数据处理成本，适合城市管理者快速部署
随着城市化进程加速，交通系统中部署的传感器越来越多，导致交通大数据爆发式增长。为挖掘这些数据价值，各类机器学习（ML）与人工智能（AI）方法被引入解决交通难题。但现有方法通常需大量数据采集、处理、存储投入，并依赖交通与机器学习领域的专业人才，同时存在隐私泄露风险。为此，研究团队提出一种基于大语言模型（LLM）的创新多智能体框架——独立出行大模型（IDM-GPT），用于定制化交通分析、管理建议生成及隐私保护。IDM-GPT高效连接用户、交通数据库与机器学习模型，通过训练和定制多种基于大模型的AI智能体，实现用户查询理解、提示优化、数据分析、模型选择及性能评估与提升。用户无需具备交通或机器学习背景，即可在近实时内通过自然语言提问，获得精准分析结果与个性化建议。实验表明，IDM-GPT在多项交通任务中表现优异，提供全面且可操作的洞察，有效支持交通管理与城市出行优化。
原文摘要 · Abstract (English)
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.
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