It is an important content of smart city research to study the activity track of urban residents, dig out the hot spot areas and spatial interaction patterns of different residents’ activities, and clearly understand the travel rules of urban residents' activities. This study used community detection to analyze taxi passengers’ travel hot spots based on taxi pick-up and drop-off data, combined with multisource information such as land use, in the main urban area of Nanjing. The study revealed that, for the purpose of travel, the modularity and anisotropy rate of the community where the passengers were picked up and dropped off were positively correlated during the morning and evening peak hours and negatively correlated at other times. Depending on the community structure, pick-up and drop-off points reached significant aggregation within the community, and interactions among the communities were also revealed. Based on the type of land use, as passengers' travel activity increased, travel hot spots formed clusters in urban spaces. After comparative verification, the results of this study were found to be accurate and reliable and can provide a reference for urban planning and traffic management.

中文翻译：

---

基于社区检测的出租车乘客出行热点分析

研究城市居民的活动轨迹，挖掘出不同居民活动的热点区域和空间相互作用模式，清楚地了解城市居民活动的出行规律，是智慧城市研究的重要内容。这项研究使用社区检测方法，基于出租车的起降数据，并结合南京主要城市地区的土地利用等多源信息，对出租车乘客的旅行热点进行了分析。该研究表明，出于旅行的目的，在早上和晚上的高峰时段，接送乘客的社区的模块性和各向异性率呈正相关，而在其他时间则呈负相关。根据社区结构，接送点在社区内达到了显着的聚集，并且社区之间的互动也得到了揭示。根据土地用途的类型，随着旅客旅行活动的增加，旅行热点在城市空间中形成了集群。经过比较验证，本研究结果准确可靠，可为城市规划和交通管理提供参考。