# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 3 Experiments, Results, and Analysis
### Mining Hotspots in Residents' Travel Space  
#### 3.3.1 Hotspot Area Identification  
The spatial characteristics of residents' travel are as significant as the temporal ones, primarily represented through hotspot areas and paths. Identifying and analyzing these hotspots are crucial to understanding residents' travel behaviors. Given that residents' travel activities on weekdays are more flexible and less geographically bound, making it challenging to detect travel hotspots, the analysis focused on weekdays. Representative time periods for this analysis were 7:00-9:00, 11:00-13:00, and 19:00-21:00. The OD point data for these intervals were extracted and subjected to the HDBSCAN algorithm for clustering. This process identified areas with a high concentration of pick-up and drop-off points. Subsequently, kernel density analysis on these high-density OD points was performed to visualize the hotspots of residents' travel. The heat map for the 7:00-9:00 time period, as shown in Figure 13.  
Figure 13 reveals that pick-up points are predominantly located in residential areas, transportation hubs, and commercial hotels, such as Yipintianxia Community, Tianfu Plaza, and Fengqiao Xiaoyue Elevator Apartment. In contrast, drop-off points are more common in areas associated with work, like schools, businesses, and government institutions, including Chengdu Jinxi Middle School, Mingyang Building, and Sichuan Orthopedic Hospital. When comparing the two heat maps, we can observe that during the morning peak, pick-up points are somewhat dispersed, resulting in more prominent hotspots for boarding. In contrast, drop-off points exhibit a more concentrated distribution, with fewer and more tightly concentrated hotspots. This disparity can primarily be attributed to the scattered nature of residential areas and transportation hubs, whereas schools, businesses, and government institutions tend to be clustered. This distribution pattern of hotspots aligns with the travel patterns of residents. The heat map for the 11:00-13:00 time period, presented in Figure 14.  
Figure 13: Heat map of pick-up and drop-off points from 7:00 to 9:00: (**a**) heat map of pick-up points; (**b**) heat map of drop-off points. Figure 14 illustrates numerous hotspots for both taxi pick-up and drop-off points, each notable in its magnitude. Pick-up points are mainly clustered around schools, business centers, government agencies, and transportation hubs. An example is the vicinity of West Street, adjacent to Chengdu Third People's Hospital, Chengdu Shude Experimental Middle School, and Qingyang District People's Government. The presence of these medical, educational, and governmental institutions contributes to the large and intensely colored hotspots in the West Street area. The distribution of drop-off points is similar, though slightly fewer in number compared with pick-up points. Considering residents' tendency to spend short periods near their workplaces or schools during weekday peak hours, the distribution of pick-up and drop-off points in these core areas is relatively similar. During the evening peak hours of 19:00-21:00, the heat map, as illustrated in Figure 15.  
Figure 14: Heat map of pick-up and drop-off points from 11:00 to 13:00: (**a**) heat map of pick-up points; (**b**) heat map of drop-off points. Figure 15 displays a widespread distribution of taxi pick-up and drop-off points across various points of interest like entertainment venues, dining areas, residential areas, offices, and business centers. This period witnesses significant taxi demand, with residents enjoying a high degree of travel freedom. Although many hotspots are formed, only a few exhibit exceptionally high density. Examples include business centers like Zhongkai Building and Jinke Shuangnan Tiandu, government agencies like the Sichuan Provincial Water Conservancy Planning Institute, and schools such as Wuhan Experimental Primary School.