When getting on and off the taxis, people prefer to choose around landmarks, such as a shopping mall, the gate of a residential unit, or a road intersection. This preference leads to multiple local peaks in spatial density, existing in both highly popular and less popular regions. These multiple local peaks provide a natural form to describe small-scale hotspots where the pick-up and drop-off events actually happen. However, they have not been fully studied. In this paper, we propose the local maximum density (LMD) approach to identify the hotspots as a small area around a local maximum density of incidents. It is evaluated using a 6-month taxi dataset of Wuhan City, where 90 m × 90 m square is recommended as the size of a hotspot by LMD. Results show that LMD not only identifies multiple local hotspots in highly popular regions, but also detects potential hotspots in less popular regions. Moreover, a non-uniform spatial pattern is found between pick-up and drop-off local hotspots. Comparing with pick-up behaviors, drop-off behaviors diffuse more in less popular hotspots, but also concentrate more on highly popular hotspots. The driving factors of this phenomenon are further explored by analyzing the match mode of pick-up and drop-off local hotspots with different popularity.

人们上下出租车时，更喜欢选择地标附近，如商场、小区门口、路口等。这种偏好导致在高度流行和较不流行的区域中都存在空间密度的多个局部峰值。这些多个局部峰值提供了一种自然的形式来描述接送事件实际发生的小规模热点。然而，它们还没有得到充分研究。在本文中，我们提出了局部最大密度（LMD）方法来将热点识别为局部最大事件密度附近的小区域。它是使用武汉市为期6个月的出租车数据集进行评估的，LMD建议将90 m×90 m平方作为热点的大小。结果表明，LMD不仅可以识别热门地区中的多个本地热点，还能检测不太受欢迎的地区的潜在热点。此外，在接送局部热点之间发现了不均匀的空间模式。与上车行为相比，下车行为在不太受欢迎的热点中扩散得更多，但也更多地集中在非常受欢迎的热点上。通过分析不同热度的接送当地热点的匹配模式，进一步探讨了这一现象的驱动因素。