Understanding the features of travel activities is important in elaborating travel behaviors and segmenting travelers based on the similarity of activity patterns. This research applying mobile big data analytics suggests a novel method to classify travelers by considering the sequences of travel activity with individuals' trajectories. The result revealed five distinct travel types visiting city destinations and demonstrated dynamic travel flow among different mobility types. Recognizing that different types of travel patterns present important information in understanding destinations’ roles (or functions), this study attempts to characterize the functionality dynamics of city destinations based on travel activity types. As a result, the findings of this research provide insights into the demand-driven construct (or flow-based) of destination planning, which is the foundation of smart destination design. In addition, important methodological and practical implications that could be useful for city destination planners/designers are suggested.

了解旅行活动的特征对于详细阐述旅行行为和根据活动模式的相似性对旅行者进行细分非常重要。这项应用移动大数据分析的研究提出了一种通过考虑旅行活动的顺序和个人轨迹来对旅行者进行分类的新方法。结果揭示了访问城市目的地的五种不同旅行类型，并展示了不同移动类型之间的动态旅行流。认识到不同类型的旅行模式在理解目的地的角色（或功能）方面提供了重要信息，本研究试图根据旅行活动类型来描述城市目的地的功能动态。因此，这项研究的结果提供了对目的地规划的需求驱动结构（或基于流量）的见解，这是智能目的地设计的基础。此外，还提出了可能对城市目的地规划者/设计师有用的重要方法论和实践意义。