ChatPlanner: 一个用于个性化公共交通路线规划的大型语言模型框架
ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing
AI总结：
提出ChatPlanner框架，利用大型语言模型和检索增强生成技术从自然语言查询中提取用户偏好并融入路线规划算法，实验证明其能生成更符合个性化需求的可行路线方案。
摘要：
在公共交通系统中，由于难以捕捉和整合多样化的用户偏好到路线规划算法中，个性化路线规划仍然具有挑战性。本文提出了ChatPlanner，一个新颖的框架，利用大型语言模型（LLMs）实现偏好感知的公共交通路线规划。我们的方法采用微调后的LLMs结合检索增强生成（RAG），从自然语言查询中提取路线参数并解释细微的用户偏好，随后将这些偏好整合到公共交通路线规划算法的目标函数中。本研究设计了包含八种角色和五种上下文的偏好感知数据集，为微调和RAG建立评分标准。本文进行了三项实验，以验证解决方案的可行性、路线信息和偏好的提取、以及解决方案集的质量和完整性。结果表明，ChatPlanner能够可靠地生成可行方案。微调强制了所需的输出结构并学习了通用的偏好模式，而RAG提供了查询特定的上下文以解决不精确或会话式的表达，并校准连续分数。两者的结合在路线信息提取和用户偏好解释方面达到了最高准确性。基于选定案例研究的结果表明，通过捕捉用户偏好，ChatPlanner在现有路线规划器忽略的不同维度上识别出了有价值的解决方案，生成了更有价值的路线备选方案。本研究为将自然语言理解整合到交通优化中建立了一个新范式。
英文摘要：
Personalized public transit routing in public transit systems remains challenging due to the difficulty of capturing and integrating diverse user preferences into routing algorithms. This paper presents ChatPlanner, a novel framework that leverages Large Language Models (LLMs) to enable preference-aware public transit routing. Our approach employs fine-tuned LLMs with Retrieval-Augmented Generation (RAG) to extract routing parameters and interpret conversationally expressed preferences from natural language queries as preference scores, subsequently integrating these preferences into the objective function of a public transit routing algorithm. This study designs preference-aware datasets incorporating eight personas and five contexts to establish scoring standards for both fine-tuning and RAG. This work conducted four experiments to validate the solutions' feasibility, extraction of routing information and preferences, solution set quality and completeness, and latency and computational tractability. Results demonstrate that ChatPlanner generates feasible solutions reliably. Fine-tuning enforces the required output structure and learns general preference patterns, while RAG provides query-specific context to resolve imprecise or conversational expressions and calibrate continuous scores. The combination of both achieves the highest accuracy in routing information extraction and rubric-consistent user preference interpretation. Results based on selected case studies show that by capturing user conversationally expressed preferences, ChatPlanner identifies preference-relevant solutions across different dimensions that existing route planners overlook, generating more route alternatives. The latency evaluation confirms that the framework is computationally tractable. This research establishes a new paradigm for integrating natural language understanding into transportation optimization.
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。
