发表机构
McGill University（麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出三智能体工作流，结合对话式调查、传统建模与多模态LLM预测，基于学生通勤数据实现天气敏感出行需求预测，视觉配置模型准确率达71.5%
AI 中文摘要
出行行为研究日益将数字数据收集与预测建模相结合，但这些阶段常被单独开发和评估。本研究提出一种三智能体工作流，整合对话式数据收集、结构化数据处理与行为预测。通过聊天机器人管理、图像增强的陈述偏好调查，收集了5个预设天气场景下学生通勤者的出行方式选择，共得到454个受访者-场景观测值。采用多项logit模型分析天气相关关联，同时以逻辑回归和随机森林作为机器学习基准。对9个本地部署的大语言模型（LLM）（参数规模从20亿到350亿不等），在4种零样本提示与上下文条件下进行评估，并通过角色设定、少样本及视觉配置进行扩展。随机森林取得69.6%的五分类准确率，最佳纯文本零样本LLM在无任务特定拟合时达到69.9%。日常出行信息带来最稳定的性能提升，专家框架通常优于角色扮演，且在无日常出行信息时角色设定信息最为有用。少样本提示提升了多个模型的预测性能，在少量示例后增益趋于稳定。利用与受访者相同的天气图像，最佳视觉配置达到71.5%的五分类准确率，表明视觉上下文可为部分模型提供额外预测信息。总体而言，本研究展示了如何在可审计的多智能体工作流中协调对话式调查、结构化数据处理、传统行为建模、机器学习及多模态LLM预测。
英文摘要
Travel behavior research increasingly combines digital data collection with predictive modeling, yet these stages are often developed and evaluated separately. This study proposes a three-agent workflow integrating conversational data collection, structured data processing, and behavioral prediction. A chatbot-administered, image-augmented stated-preference survey collected mode choices from student commuters across five predefined weather scenarios, yielding 454 respondent-scenario observations. Weather-related associations were analyzed using a multinomial logit model, while logistic regression and random forest provided machine-learning benchmarks. Nine locally deployed large language models (LLMs), ranging from 2 to 35 billion parameters, were evaluated across four zero-shot prompt-and-context conditions and extended through persona, few-shot, and vision-based configurations. Random forest achieved 69.6% five-class accuracy, while the best text-only zero-shot LLM reached 69.9% without task-specific fitting. Habitual travel information produced the most consistent gains, Expert framing generally outperformed Role-Play, and persona information was most useful when habitual travel information was unavailable. Few-shot prompting improved prediction for several models, with gains stabilizing after a small number of examples. Using the same weather images shown to respondents, the best vision-based configuration reached 71.5% five-class accuracy, indicating that visual context may provide additional predictive information for selected models. Overall, the study shows how conversational surveys, structured data processing, conventional behavioral modeling, machine learning, and multimodal LLM prediction can be coordinated within an auditable multi-agent workflow.
