CHATATC：基于大语言模型驱动的对话代理，用于支持战略性空中交通流量管理
CHATATC: Large Language Model-Driven Conversational Agents for Supporting Strategic Air Traffic Flow Management
AI总结：
本研究基于2000-2023年超80,000次地面延误计划数据训练了LLM模型CHATATC，并测试其查询响应能力及设计交互界面，以支持非安全关键的策略性空中交通流量管理。
摘要：
生成式人工智能（AI）和大语言模型（LLMs）通过ChatGPT等公开可用工具迅速普及。人机交互的自然性以及强大的摘要和文本生成能力，推动了LLMs在个人和专业领域的应用。鉴于此类生成式AI工具的广泛使用，本研究探讨如何将其部署于非安全关键的策略性交通流量管理场景中。具体而言，我们基于2000至2023年、包含超过80,000次地面延误计划（GDP）实施、修订和取消的历史大型数据集，训练了名为CHATATC的LLM。我们测试了CHATATC的查询与响应能力，记录了其成功之处（例如提供正确的GDP速率、持续时间和原因）及不足之处（例如最高级问题）。此外，我们还详细设计了图形用户界面，以供未来用户与CHATATC对话代理进行交互与协作。
英文摘要：
Generative artificial intelligence (AI) and large language models (LLMs) have gained rapid popularity through publicly available tools such as ChatGPT. The adoption of LLMs for personal and professional use is fueled by the natural interactions between human users and computer applications such as ChatGPT, along with powerful summarization and text generation capabilities. Given the widespread use of such generative AI tools, in this work we investigate how these tools can be deployed in a non-safety critical, strategic traffic flow management setting. Specifically, we train an LLM, CHATATC, based on a large historical data set of Ground Delay Program (GDP) issuances, spanning 2000-2023 and consisting of over 80,000 GDP implementations, revisions, and cancellations. We test the query and response capabilities of CHATATC, documenting successes (e.g., providing correct GDP rates, durations, and reason) and shortcomings (e.g,. superlative questions). We also detail the design of a graphical user interface for future users to interact and collaborate with the CHATATC conversational agent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
