# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 3 Experiments, Results, and Analysis
### Mining Hotspots in Residents' Travel Space
#### 3.3.2 Hotspot Path Mining
Meanwhile, hotspots for drop-off are mainly found in areas like Ningxia Street (near New City Plaza and Golden Hawaii), Gaoshengqiao Road (near Jinli Ancient Street and Jincheng Impression Hotpot Restaurant), and Changshun Shangjie (near Kuanzhai Alley and Zuncheng International). Analyzing these hotspot paths in conjunction with the specific functions of these streets reveals that pick-up points are concentrated near commercial buildings, campuses, government offices, and so forth, while drop-off points are concentrated around restaurants, shopping streets, and other leisure and entertainment venues. This distribution aligns with the travel patterns of residents during the weekday afternoon peak period. In Figure 18, we can observe the visualization of hotspots for pick-up and drop-off points during the 19:00-21:00 time period. Notably, the distribution of hotspots during this time frame is more extensive compared with other time periods. The volume of travel is relatively evenly spread, with only a few specific hotspot paths carrying a significant number of passengers. Hotspots for drop-off are primarily located in areas like Luoguoxiang (near New Times Square and Chengdu Municipal People's Government Government Service Center), West First Ring Road (near Wuhou Campus of Chengdu Institute of Physical Education and Gaosheng Building), and Jiangxi Street (near Sichuan Provincial Committee of the Chinese People's Political Consultative Conference and Hongda International Plaza). On the other hand, drop-off hotspots are mainly found at Fangcao West Second Street (near Jinxingyuan and Golden Age), the Qingyang Avenue section of Middle Ring Road (near Xiaotaooyuan Restaurant and Chengdu Garden Plaza), and Shuangxin South Road (near Cuiliu Garden and Twenty First Century Garden). A comparison reveals that during the evening peak period, pick-up and drop-off points tend to shift away from workplaces and school districts toward dining venues, entertainment spots, and residential areas. This is because residents have more free travel time and flexibility during this part of the day, allowing them to make travel choices that align with their leisure and lifestyle preferences. This observation confirms the earlier analysis of the widespread distribution of hotspot areas during this particular time period in Section 3.3.1. Information regarding the geographical boundaries of hotspot areas and the paths commonly taken for pick-up and drop-off activities during the time intervals of  
Figure 18: Hotspot path map of pick-up and drop-off points during 19:00–21:00: (**a**) hotspot path for pick-up points; (**b**) hotspot route for drop-off points. Table 3 displays a subset of the cross tabulation data involving hotspot areas and paths. Given the extensive volume of data, importing it into Python for analysis is a practical approach. The calculated \(p\)-value for the chi-square test concerning hotspot areas and paths is 0.023. When comparing this \(p\)-value with the significance level (typically set at 0.05), it is evident that both values are less than 0.05. This suggests a significant correlation between hotspot areas and hotspot paths.