The digitization of traffic sensing infrastructure has significantly accumulated an extensive traffic data warehouse, which presents unprecedented challenges for transportation analytics. The complexities associated with querying large-scale multi-table databases require specialized programming expertise and labor-intensive development. Additionally, traditional analysis methods have focused mainly on numerical data, often neglecting the semantic aspects that could enhance interpretability and understanding. Furthermore, real-time traffic data access is typically limited due to privacy concerns. To bridge this gap, the integration of Large Language Models (LLMs) into the domain of traffic management presents a transformative approach to addressing the complexities and challenges inherent in modern transportation systems. This paper proposes an intelligent online chatbot, TP-GPT, for efficient customized transportation surveillance and management empowered by a large real-time traffic database. The innovative framework leverages contextual and generative intelligence of language models to generate accurate SQL queries and natural language interpretations by employing transportation-specialized prompts, Chain-of-Thought prompting, few-shot learning, multi-agent collaboration strategy, and chat memory. Experimental study demonstrates that our approach outperforms state-of-the-art baselines such as GPT-4 and PaLM 2 on a challenging traffic-analysis benchmark TransQuery. TP-GPT would aid researchers and practitioners in real-time transportation surveillance and management in a privacy-preserving, equitable, and customizable manner.

翻译：交通感知基础设施的数字化显著积累了大量交通数据仓库，这给交通分析带来了前所未有的挑战。查询大规模多表数据库的复杂性需要专门的编程技能和繁琐的开发工作。此外，传统分析方法主要关注数值数据，往往忽略了能够增强可解释性和理解力的语义层面。同时，由于隐私问题，实时交通数据的访问通常受到限制。为弥补这一差距，将大型语言模型（LLMs）整合到交通管理领域，为解决现代交通系统固有的复杂性和挑战提供了一种变革性的方法。本文提出了一种智能在线聊天机器人TP-GPT，基于大型实时交通数据库实现对高效定制化交通监控与管理的支持。该创新框架利用语言模型的上下文与生成智能，通过采用交通专用提示、思维链提示、少样本学习、多智能体协作策略及聊天记忆，生成准确的SQL查询和自然语言解释。实验研究表明，我们的方法在具有挑战性的交通分析基准TransQuery上优于GPT-4和PaLM 2等当前最先进基线模型。TP-GPT能够以隐私保护、公平且可定制的方式，支持研究人员和实践者进行实时交通监控与管理。