Human activities generate diverse and sophisticated functional areas and may impact the existing planning of functional areas. Understanding the relationship between human activities and functional areas is key to identifying the real-time urban functional areas based on trajectories. Few previous studies have analyzed the interactive information on humans and regions for functional area identification. The relationship between human activities and residential areas is the most representative for urban functional areas because residential areas cover a wide range and are closely connected with human life. The aim of this paper is to propose the CARA (Commuting Activity and Residential Area) model to quantify the correlation between human activities and urban residential areas. In this model, human activities are represented by hot spots extracted by the Gaussian Mixture Model algorithm while residential areas are represented by POI (point of interest) data. The model shows that human activities and residential areas present a logarithmic relationship. The CARA model is further assessed by retrieving urban residential areas in Tengzhou City from shared e-bike trajectories. Compared with the actual map, the accuracy reaches 83.3%, thus demonstrating the model’s reliability and feasibility. This study provides a new method for functional areas identification based on trajectory data, which is helpful for formulating the urban people-oriented policies.

中文翻译：

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基于共享电动自行车轨迹数据探索居住区对人类活动的吸引力

人类活动产生了各种复杂的功能区域，并且可能会影响功能区域的现有计划。了解人类活动与功能区域之间的关系是基于轨迹识别实时城市功能区域的关键。很少有先前的研究分析有关人和区域的交互信息以进行功能区域识别。人类活动与居住区之间的关系是城市功能区中最具代表性的关系，因为居住区覆盖范围广且与人类生活紧密相关。本文的目的是提出一种CARA（通勤活动和居住区）模型，以量化人类活动与城市居住区之间的相关性。在这个模型中 人类活动由高斯混合模型算法提取的热点表示，而居住区由POI（兴趣点）数据表示。该模型显示，人类活动和居住区呈对数关系。通过从共享电动自行车轨迹中检索滕州市的城市居民区，进一步评估了CARA模型。与实际地图相比，精度达到了83.3％，证明了该模型的可靠性和可行性。该研究为基于轨迹数据的功能区识别提供了一种新方法，有助于制定以人为本的城市政策。该模型显示，人类活动和居住区呈对数关系。通过从共享电动自行车轨迹中检索滕州市的城市居民区，进一步评估了CARA模型。与实际地图相比，精度达到了83.3％，证明了该模型的可靠性和可行性。该研究为基于轨迹数据的功能区识别提供了一种新方法，有助于制定以人为本的城市政策。该模型显示，人类活动和居住区呈对数关系。通过从共享电动自行车轨迹中检索滕州市的城市居民区，进一步评估了CARA模型。与实际地图相比，精度达到了83.3％，证明了该模型的可靠性和可行性。该研究为基于轨迹数据的功能区识别提供了一种新方法，有助于制定以人为本的城市政策。