评估基于视频的对话式聊天机器人调查在测量感知骑行安全性的可行性：纽约市试点研究
Assessing the Feasibility of a Video-Based Conversational Chatbot Survey for Measuring Perceived Cycling Safety: A Pilot Study in New York City
AI总结：
本文提出利用大语言模型结合视频调查和对话式AI聊天机器人，评估骑行安全感知的可行性，并通过纽约市九个街区的试点调查验证方法的有效性。
摘要：
自行车安全对骑行能力和交通效率至关重要。然而，传统调查在捕捉人们实际对骑行环境的感知方面存在不足，因为它们依赖于被调查者的回忆而非即时体验。通过利用大型语言模型（LLMs），本文提出了一种结合视频调查与对话式AI聊天机器人的新方法，以收集人们对骑行安全的感知及其原因。本文开发了AI聊天机器人，采用模块化LLM架构，整合提示工程、状态管理和基于规则的控制，以支持人机交互的结构。本文通过纽约市九个街区的试点调查，对所提出的基于视频的对话式聊天机器人进行了可行性评估，使用七点量表评估用户体验（即易用性、支持性、效率）和五点量表评估聊天机器人可用性（即个性、机械性、友好性），得到积极结果，平均得分为5.00分（标准差=1.6）和3.47分（标准差=0.43）。数据可行性通过多种技术评估：（1）自然语言处理（NLP），如KeyBERT，用于整体安全和特征分析以提取建成环境属性；（2）K-means聚类用于语义分析以识别原因和建议；（3）回归用于估计建成环境和人口统计变量对感知安全结果的影响。结果表明，AI聊天机器人作为收集人类感知、行为和未来交通规划愿景数据的新方法具有潜力。
英文摘要：
Bicycle safety is important for bikeability and transportation efficiency. However, conventional surveys often fall short in capturing how people actually perceive cycling environments because they rely heavily on respondents' recall rather than in-the-moment experience. By leveraging large language models (LLMs), this study proposes a new method of combining video-based surveys with a conversational AI chatbot to collect human perceptions of cycling safety and the reasons behind these perceptions. The paper developed the AI chatbot using a modular LLM architecture, integrating prompt engineering, state management, and rule-based control to support the structure of human-AI interaction. This paper evaluates the feasibility of the proposed video-based conversational chatbot using complete responses from sixteen participants to the pilot survey across nine street segments in New York City. The method feasibility was assessed using a seven-point scale rating for user experience (i.e., ease of use, supportiveness, efficiency) and a five-point scale for chatbot usability (i.e., personality, roboticness, friendliness), yielding positive results with mean scores of 5.00 out of 7 (standard deviation = 1.6) and 3.47 out of 5 (standard deviation = 0.43), respectively. The data feasibility was assessed using multiple techniques: (1) Natural language processing (NLP), such as KeyBERT, for overall safety and feature analysis to extract built-environment attributes; (2) K-means clustering for semantic analysis to identify reasons and suggestions; and (3) regression to estimate the effects of built-environment and demographic variables on perceived safety outcomes. The results show the potential of AI chatbots as a novel approach to collecting data on human perception, behavior, and future visions for transport planning.
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。
