Surveys and interviews are widely used for collecting insights on emerging or hypothetical scenarios. Traditional human-led methods often face challenges related to cost, scalability, and consistency. Recently, various domains have begun to explore the use of conversational agents (chatbots) powered by generative artificial intelligence (AI) technologies. However, considering decisions in transportation investments and policies often carry significant public and environmental stakes, surveys and interviews face unique challenges in integrating AI agents, underscoring the need for a rigorous, resource-efficient approach that enhances participant engagement and ensures privacy. This paper addresses this gap by introducing a modular approach and its resulting parameterized process for designing AI agents. We detail the system architecture, integrating engineered prompts, specialized knowledge bases, and customizable, goal-oriented conversational logic. We demonstrate the adaptability, generalizability, and efficacy of our modular approach through three empirical studies: (1) travel preference surveys, highlighting conditional logic and multimodal (voice, text, and image generation) capabilities; (2) public opinion elicitation on a newly constructed, novel infrastructure project, showcasing question customization and multilingual (English and French) capabilities; and (3) expert consultation about the impact of technologies on future transportation systems, highlighting real-time, clarification request capabilities for open-ended questions, resilience in handling erratic inputs, and efficient transcript postprocessing. The results suggest that the AI agent increases completion rates and response quality. Furthermore, the modular approach demonstrates controllability, flexibility, and robustness while addressing key ethical, privacy, security, and token consumption concerns.

翻译：调查与访谈被广泛用于收集新兴或假设性场景的见解。传统的人工主导方法常面临成本、可扩展性和一致性方面的挑战。近年来，各领域开始探索利用生成式人工智能技术驱动的对话代理（聊天机器人）。然而，考虑到交通投资与政策决策往往涉及重大的公共与环境利益，调查与访谈在整合AI代理时面临独特挑战，这凸显了对一种严谨、资源高效且能提升参与者参与度并确保隐私的方法的需求。本文通过引入一种模块化方法及其衍生的参数化AI代理设计流程来填补这一空白。我们详细阐述了系统架构，该架构整合了工程化提示、专用知识库以及可定制的目标导向对话逻辑。我们通过三项实证研究展示了模块化方法的适应性、泛化性与有效性：（1）出行偏好调查，突显了条件逻辑与多模态（语音、文本及图像生成）能力；（2）针对新建创新基础设施项目的公众意见征集，展示了问题定制与多语言（英语和法语）能力；（3）关于技术对未来交通系统影响的专家咨询，突显了针对开放式问题的实时澄清请求能力、处理异常输入的鲁棒性以及高效的转录后处理能力。结果表明，AI代理提高了完成率与回答质量。此外，该模块化方法在应对关键伦理、隐私、安全及令牌消耗问题的同时，展现了可控性、灵活性与鲁棒性。