Urban hotspot area detection is an important issue that needs to be explored for urban planning and traffic management. It is of great significance to mine hotspots from taxi trajectory data, which reflect residents’ travel characteristics and the operational status of urban traffic. The existing clustering methods mainly concentrate on the number of objects contained in an area within a specified size, neglecting the impact of the local density and the tightness between objects. Hence, a novel algorithm is proposed for detecting urban hotspots from taxi trajectory data based on nearest neighborhood-related quality clustering techniques. The proposed spatial clustering algorithm not only considers the maximum clustering in a limited range but also considers the relationship between each cluster center and its nearest neighborhood, effectively addressing the clustering issue of unevenly distributed datasets. As a result, the proposed algorithm obtains high-quality clustering results. The visual representation and simulated experimental results on a real-life cab trajectory dataset show that the proposed algorithm is suitable for inferring urban hotspot areas, and that it obtains better accuracy than traditional density-based methods.

中文翻译：

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使用最近邻相关质量聚类对出租车轨迹数据进行城市热点区域检测

城市热点区域检测是城市规划和交通管理需要探索的重要问题。从出租车轨迹数据中挖掘热点，反映居民出行特征和城市交通运行状况，具有重要意义。现有的聚类方法主要集中在指定大小的区域内包含的对象数量，而忽略了局部密度和对象之间的紧密度的影响。因此，提出了一种基于最近邻域相关质量聚类技术从出租车轨迹数据中检测城市热点的新算法。所提出的空间聚类算法不仅考虑了有限范围内的最大聚类，而且还考虑了每个聚类中心与其最近邻域之间的关系，有效解决不均匀分布数据集的聚类问题。结果，所提出的算法获得了高质量的聚类结果。在真实驾驶室轨迹数据集上的可视化表示和模拟实验结果表明，该算法适用于推断城市热点区域，并且比传统的基于密度的方法获得了更好的精度。