AI 中文总结
本研究针对众包有序拥挤数据，调查不同不确定性可视化方法的有效性，经在线研究发现聚类可视化可降低认知负荷并提升用户信任，气泡树图能提高拥挤程度判断准确性。
AI 中文摘要
通勤者在铁路系统中常遇到拥挤情况，尤其是乘客密度随一天时间变化的队列，这使得拥挤程度存在不确定性，让人们难以预判状况并有效规划行程。众包是收集本地化用户数据的重要方法，但众包的不可预测性及信息不确定性给决策带来新挑战，然而目前人们对如何有效可视化拥挤程度的不确定性以支持明智的通勤决策（尤其是使用众包有序数据时）知之甚少。本研究调查了不同的不确定性可视化方法及其在表示众包拥挤数据的变异性和可靠性方面的有效性，通过在线研究对这些方法进行评估，结果发现，聚类可视化最适合降低认知负荷，同时最大化用户的信心和信任；而参与者使用气泡树图时，判断拥挤程度的准确性更高。
英文摘要
Commuters often encounter crowding in railway systems, particularly in queues where passenger density varies throughout the day. This introduces uncertainty in crowdedness, making it difficult for individuals to anticipate conditions and plan their trips effectively. Crowdsourcing has been a valuable method for collecting localized user data. But the unpredictability of crowds and the uncertainty of crowdsourced information pose new challenges for decision-making. However, we know little about how to effectively visualize uncertainty in crowdedness to support informed commuting decisions, particularly when using crowdsourced ordinal data. Here, we investigated different uncertainty visualizations and their effectiveness in representing the variability and reliability of crowdsourced crowding data. They were evaluated through an online study, and we found that cluster visualization is best suited to reduce cognitive load while maximizing user confidence and trust. On the other hand, participants showed higher accuracy in determining crowd levels when using bubble treemaps.
