The last decade has witnessed a wealth of studies on characterizing human mobility patterns using movement datasets. Such efforts have highlighted a few salient dimensions of individual travel behavior relevant to urban planning and policy analysis. Despite the fruitful research outcomes, most of the findings are drawn upon urban residents. The behavioral characteristics of other population groups, such as tourists, remain underexplored. In this study, we introduce an analytical framework to gain insights into tourist mobility patterns. By analyzing mobile phone trajectories of international travelers to three different cities in South Korea, we introduce nine mobility indicators to capture different facets of tourist travel behavior (e.g., duration of stay in a city, spatial extent of activities, location visited and trips conducted, and mobility diversity), and examine their statistical properties across cities. An eigendecomposition approach is then introduced to better understand the interdependency of these mobility indicators and inherent variations among individual travelers. Based on the eigendecomposition results, we further employ a dimension reduction technique to describe the key characteristics of each traveler. Since the mobile phone dataset captures the nationality of tourists, we use such information to quantify the behavioral heterogeneity of travelers across countries and regions. Finally, we select a few traveler groups with distinctive mobility patterns in each city and examine the spatial patterns of their activities. Substantial differences are observed among traveler groups in their spatial preferences. The implications for location recommendation and deployment of tourism services (e.g., transportation) are discussed. We hope the study brings a synergy between classic human mobility analysis and the emerging field of tourism big data. The framework can be applied or extended to compatible datasets to understand travel behavior of tourists, residents, and special population groups in cities.

在过去的十年中，见证了许多有关使用运动数据集表征人类活动模式的研究。这些努力突出了与城市规划和政策分析有关的个人出行行为的几个重要方面。尽管取得了丰硕的研究成果，但大多数发现还是取材于城市居民。其他人群（如游客）的行为特征仍未得到充分挖掘。在这项研究中，我们介绍了一个分析框架，以深入了解游客的出行方式。通过分析前往韩国三个不同城市的国际旅行者的手机轨迹，我们引入了九种移动性指标来捕获游客旅行行为的不同方面（例如，在城市停留的时间，活动的空间范围，访问的地点和进行的旅行，和移动性多样性），并检查其在整个城市的统计属性。然后引入特征分解方法，以更好地理解这些移动性指标的相互依赖性以及各个旅行者之间的固有差异。基于特征分解结果，我们进一步采用降维技术来描述每个旅行者的关键特征。由于移动电话数据集捕获了游客的国籍，因此我们使用这些信息来量化各个国家和地区旅行者的行为异质性。最后，我们在每个城市中选择了几个具有独特出行方式的旅行者群体，并研究了他们活动的空间格局。旅行者群体之间在空间偏好方面存在显着差异。讨论了位置推荐和旅游服务（例如运输）的部署的含义。我们希望这项研究能够在经典的人员流动性分析和新兴的旅游大数据领域之间发挥协同作用。该框架可以应用于或扩展到兼容的数据集，以了解游客，居民和城市特殊人群的出行行为。