# 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  
Hotspots are intricately connected to hotspot paths. Hotspot areas serve to pinpoint regions with high flow and density of residents' travel activities, while hotspot paths offer a detailed description of the paths characterized by high flow and density in residents' travel. The synergy of these two aspects enables a comprehensive exploration of residents' travel hotspots. Notably, hotspot path analysis was conducted on the foundation of hotspot areas, and the primary steps involved are as follows:  
1. Road network matching: Trajectory data points are spatially matched to the nearest road network segment using the hidden Markov model method. This step associates each trajectory point with road segments, providing related information like road name and vehicle passenger status. 2. Hotspot detection: The geographical coordinates in the trajectory data are mapped to a discrete spatial grid for space discretization. Kernel density analysis is then applied to calculate the density of pick-up and drop-off points in each grid, identifying high-density areas and streets. 3. Road extraction: This involves determining the geographical coordinates of the start and end points of hotspot paths, followed by path searching using the Dijkstra algorithm based on passenger volume, and finally recording and exporting the generated paths. 4. Chi-square test: A crosstalk is created based on hotspot areas and paths, and chi-square statistics and \(p\)-values are calculated under the null hypothesis. A \(p\)-value below 0.05 suggests a significant relationship between hotspot areas and paths. Based on the analysis of hotspot paths during the specified time periods (7:00-9:00, 11:00-13:00, and 19:00-21:00), the visualization and analysis yield the following insights: (1) During the 7:00-9:00 time period, as shown in Figure 16. The hotspot paths for pick-up and drop-off points are primarily located within the identified hotspot areas, with their distribution closely mirroring these hotspots. Key pick-up locations include streets like Dongyu Street (near Tianfu Plaza and Chengdu Garden Hotel) and Jinli Middle Road (near Jinjiang Apartment and Jinjiang Times Garden). These areas are rich in residential buildings, hotels, and key transportation paths. Popular drop-off spots are located on streets such as Xinguanghua Street (near Sichuan Provincial Department of Education and Shishi United Middle School), Jiaodong Road (near Middle School Affiliated to Southwest Jiaotong University and Jinniu Civic Center), and Furong Avenue (near Wenjiang Campus of West China Hospital and Chengdu Food Inspection Institute). They are all proximate to educational institutions, government offices, and medical facilities. In general, during the 7:00-9:00 time period, the hotspots for pick-up tend to be associated with residential areas and transportation hubs, while the hotspots for drop-off are linked to schools, commercial buildings, and government offices. Thisdistribution of hotspots during this time period closely aligns with the peak travel patterns of residents. 2. The visualization of hotspots for pick-up and drop-off points during the 11:00-13:00 time period is displayed in Figure 17.  
The hotspots for pick-up during this time frame are primarily concentrated along streets such as West Street (near Chengdu Third People's Hospital and Second Middle School Hospital), Shuhan Street (near the People's Government of Wuhou District and Wuhou Campus of Southwest University for Nationalities), and  
Figure 16: Hotspot path map of pick-up and drop-off points during 7:00–9:00: (**a**) hotspot path for pick-up points; (**b**) hotspot path for drop-off points. Figure 17: Hotspot path map of pick-up and drop-off points during 11:00–13:00: (**a**) hotspot path for pick-up points; (**b**) hotspot path for drop-off points. Chengdu Jinnantian Street and Jinyan Building).