Human mobility patterns refer to the regularities and trends in the way people move, travel, or navigate through different geographical locations over time. Detecting human mobility patterns is essential for a variety of applications, including smart cities, transportation management, and disaster response. The accuracy of current mobility prediction models is less than 25%. The low accuracy is mainly due to the fluid nature of human movement. Typically, humans do not adhere to rigid patterns in their daily activities, making it difficult to identify hidden regularities in their data. To address this issue, we proposed a web platform to visualize human mobility patterns by abstracting the locations into a set of places to detect more realistic patterns. However, the platform was initially designed to detect individual mobility patterns, making it unsuitable for representing the crowd in a smart city scale. Therefore, we extend the platform to visualize the mobility of multiple users from a city-scale perspective. Our platform allows users to visualize a graph of visited places based on their historical records using a modified PrefixSpan approach. Additionally, the platform synchronizes, aggregates, and displays crowd mobility patterns across various time intervals within a smart city. We showcase our platform using a real dataset.

翻译：人类移动模式是指人们在不同地理位置上随时间移动、出行或导航的方式所呈现的规律性与趋势。检测人类移动模式对于智慧城市、交通管理和灾害响应等多种应用至关重要。当前移动预测模型的准确率不足25%，其主要原因在于人类运动的流动性本质。通常情况下，人类的日常活动并不遵循固定的模式，这使得难以从其数据中识别出隐藏的规律性。为解决这一问题，我们提出一个通过将位置抽象为一组场所来检测更真实模式的可视化人类移动模式的网络平台。然而，该平台最初设计用于检测个体移动模式，不适合在智慧城市规模上代表人群行为。因此，我们扩展该平台以从城市尺度视角可视化多个用户的移动情况。该平台允许用户基于历史记录，利用改进的PrefixSpan方法可视化访问场所的图。此外，平台同步、聚合并展示智慧城市中不同时间间隔内的人群移动模式。我们采用真实数据集对该平台进行演示。