这篇论文提出了一种结合多种数据收集和预测方法的创新工作流程，对于研究出行行为和天气敏感需求预测非常有用，特别是对于想要了解如何将不同技术整合在一起的人来说。
本研究提出了一种三智能体工作流程，结合对话数据收集、结构化数据处理和行为预测。通过聊天机器人管理的图像增强陈述偏好调查收集了学生通勤者在五种预定义天气场景下的出行方式选择，共获得454个受访者-场景观察。使用多项logit模型分析了与天气相关的关联，而逻辑回归和随机森林提供了机器学习基准。评估了九个本地部署的大型语言模型（LLMs），参数量从2亿到35亿不等，在四种零样本提示和上下文条件下进行评估，并通过角色、少量样本和基于视觉的配置进行扩展。随机森林实现了69.6%的五分类准确率，而最佳纯文本零样本LLM达到了69.9%的准确率，无需特定任务调整。习惯性出行信息产生了最一致的收益，专家框架通常优于角色扮演，而当习惯性出行信息不可用时，角色信息最有用。少量样本提示提高了几个模型的预测能力，在少量示例后收益稳定。使用向受访者展示的相同天气图像，最佳基于视觉的配置达到了71.5%的五分类准确率，表明视觉上下文可能为某些模型提供额外的预测信息。总体而言，该研究展示了如何在可审计的多智能体工作流程中协调对话调查、结构化数据处理、传统行为建模、机器学习和多模态LLM预测。
An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction
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.