Tourism is one of the largest growing industries worldwide. As the number of tourists is rapidly increasing, so too are tourist safety concerns. The increasing frequency of natural disasters along with the growth of urban areas makes it even more complex to address the resilience of tourists during such events. This article proposes a framework for collecting information about tourist locations and flows within urban areas and how to use this information for more efficient and safe evacuation routing. We define population behavior models that can be obtained from gathering empirical data and categorize them into three groups. We review the different evacuation scenarios (divided into sudden and predictable scenarios) and the types of information needed in each case. Further, we discuss the complexity of monitoring and forecasting tourists’ movements in the long term and for short-term predictions including the available data sources for doing so. The data gathering and tourist behavior are explained with examples from Kyoto, Japan, a major tourist attraction and a location that is prone to disasters. Finally, technological solutions for better guidance during the evacuation process of the population are discussed, including low-tech ones and advanced options such as websites, apps and Bluetooth Low Energy sensors, where the last one is demonstrated by a navigation experiment in a 3D environment.

旅游业是全球增长最快的产业之一。随着游客数量的迅速增加，游客安全问题也随之增加。随着自然灾害的增加以及城市地区的增长，应对此类事件中游客的适应力变得更加复杂。本文提出了一个框架，用于收集有关城市区域内游客位置和流量的信息，以及如何将这些信息用于更有效，更安全的疏散路线。我们定义了可以从收集经验数据中获得的人口行为模型，并将其分为三类。我们审查了不同的疏散方案（分为突发情况和可预测情况）以及每种情况下所需的信息类型。进一步，我们讨论了长期监控和预测游客活动的复杂性以及短期预测（包括这样做的可用数据源）的复杂性。数据收集和游客行为以日本京都为例进行了说明，日本京都是一个主要的旅游景点和容易发生灾害的地区。最后，讨论了在人员疏散过程中提供更好指导的技术解决方案，包括低技术含量的解决方案和高级选项，例如网站，应用程序和蓝牙低能耗传感器，其中最后一个方案是在3D环境中进行的导航实验演示的。一个主要的旅游景点和一个容易发生灾难的地方。最后，讨论了在人员疏散过程中提供更好指导的技术解决方案，包括低技术含量的解决方案和高级选项，例如网站，应用程序和蓝牙低能耗传感器，其中最后一个方案是在3D环境中进行的导航实验演示的。一个主要的旅游景点和一个容易发生灾难的地方。最后，讨论了在人员疏散过程中提供更好指导的技术解决方案，包括低技术含量的解决方案和高级选项，例如网站，应用程序和蓝牙低能耗传感器，其中最后一个方案是在3D环境中进行的导航实验演示的。