用于交通调查和访谈的模块化人工智能代理:提高参与度、透明度和成本效率

IF 12.5 Q1 TRANSPORTATION
Jiangbo Yu , Jinhua Zhao , Luis Miranda-Moreno , Matthew Korp
{"title":"用于交通调查和访谈的模块化人工智能代理:提高参与度、透明度和成本效率","authors":"Jiangbo Yu ,&nbsp;Jinhua Zhao ,&nbsp;Luis Miranda-Moreno ,&nbsp;Matthew Korp","doi":"10.1016/j.commtr.2025.100172","DOIUrl":null,"url":null,"abstract":"<div><div>Surveys and interviews—structured, semi-structured, or unstructured—are widely used for collecting insights on emerging or hypothetical scenarios. Traditional human-led methods often face challenges related to cost, scalability, and consistency. For example, distributed questionnaires lack the ability to provide real-time guidance and request immediate clarifications. 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 socioeconomic and environmental consequences, surveys and interviews face unique challenges in integrating AI agents. This issue underscors the need for a rigorous, explainable, and resource-efficient approach that enhances participant engagement and ensures privacy. This paper bridges this gap by introducing a modular approach accompanied by a parameterized process for designing and deploying AI agents for surveys and interviews, thereby supporting decision-makings in high-stakes contexts. 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 consultations 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. We believe this work lays the foundation for next-generation surveys and interviews in transportation research.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"5 ","pages":"Article 100172"},"PeriodicalIF":12.5000,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modular AI agents for transportation surveys and interviews: Advancing engagement, transparency, and cost efficiency\",\"authors\":\"Jiangbo Yu ,&nbsp;Jinhua Zhao ,&nbsp;Luis Miranda-Moreno ,&nbsp;Matthew Korp\",\"doi\":\"10.1016/j.commtr.2025.100172\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Surveys and interviews—structured, semi-structured, or unstructured—are widely used for collecting insights on emerging or hypothetical scenarios. Traditional human-led methods often face challenges related to cost, scalability, and consistency. For example, distributed questionnaires lack the ability to provide real-time guidance and request immediate clarifications. 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 socioeconomic and environmental consequences, surveys and interviews face unique challenges in integrating AI agents. This issue underscors the need for a rigorous, explainable, and resource-efficient approach that enhances participant engagement and ensures privacy. This paper bridges this gap by introducing a modular approach accompanied by a parameterized process for designing and deploying AI agents for surveys and interviews, thereby supporting decision-makings in high-stakes contexts. 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 consultations 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. We believe this work lays the foundation for next-generation surveys and interviews in transportation research.</div></div>\",\"PeriodicalId\":100292,\"journal\":{\"name\":\"Communications in Transportation Research\",\"volume\":\"5 \",\"pages\":\"Article 100172\"},\"PeriodicalIF\":12.5000,\"publicationDate\":\"2025-03-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications in Transportation Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772424725000125\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications in Transportation Research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772424725000125","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0

摘要

调查和访谈——结构化的、半结构化的或非结构化的——被广泛用于收集对新兴或假设场景的见解。传统的以人为主导的方法经常面临与成本、可伸缩性和一致性相关的挑战。例如,分发的问卷缺乏提供实时指导和要求立即澄清的能力。最近,各个领域已经开始探索由生成式人工智能(AI)技术驱动的会话代理(聊天机器人)的使用。然而,考虑到交通投资和政策决策往往会带来重大的社会经济和环境后果,调查和访谈在整合人工智能代理方面面临着独特的挑战。这个问题强调需要一种严格的、可解释的和资源高效的方法,以增强参与者的参与并确保隐私。本文通过引入模块化方法和参数化过程来设计和部署用于调查和访谈的人工智能代理,从而支持高风险环境中的决策制定,从而弥合了这一差距。我们详细介绍了系统架构,集成了工程提示、专门的知识库和可定制的、面向目标的会话逻辑。我们通过三个实证研究证明了模块化方法的适应性、普遍性和有效性:(1)旅行偏好调查,强调条件逻辑和多模式(语音、文本和图像生成)能力;(2)对新建的新型基础设施项目进行民意调查,展示问题定制和多语种(英语和法语)能力;(3)就技术对未来交通系统的影响进行专家咨询,强调对开放式问题的实时性、澄清请求能力、处理不稳定输入的弹性以及高效的记录后处理。结果表明,人工智能代理提高了完成率和响应质量。此外,模块化方法展示了可控性、灵活性和鲁棒性,同时解决了关键的道德、隐私、安全和代币消费问题。我们相信这项工作为下一代交通研究中的调查和访谈奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modular AI agents for transportation surveys and interviews: Advancing engagement, transparency, and cost efficiency
Surveys and interviews—structured, semi-structured, or unstructured—are widely used for collecting insights on emerging or hypothetical scenarios. Traditional human-led methods often face challenges related to cost, scalability, and consistency. For example, distributed questionnaires lack the ability to provide real-time guidance and request immediate clarifications. 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 socioeconomic and environmental consequences, surveys and interviews face unique challenges in integrating AI agents. This issue underscors the need for a rigorous, explainable, and resource-efficient approach that enhances participant engagement and ensures privacy. This paper bridges this gap by introducing a modular approach accompanied by a parameterized process for designing and deploying AI agents for surveys and interviews, thereby supporting decision-makings in high-stakes contexts. 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 consultations 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. We believe this work lays the foundation for next-generation surveys and interviews in transportation research.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
15.20
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信