# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 5 Conclusions  
This research was built upon extensive taxi trajectory data and employed various analytical methods, including mathematical statistics, the HDBSCAN clustering algorithm, kernel density analysis, and hidden Markov model road matching. Starting with an examination of both time and space, the research scrutinized residents' behavior across varying time periods and geographical regions. This analysis included travel patterns and extracted key hotspot areas and paths preferred by residents within the study area. Additionally, it confirmed the connections between these hotspots, with the primary findings summarized as follows:  
1. Mathematical statistical analyses, individual assessments, and comparative examinations of taxi pick-up and drop-off point traffic volumes during distinct time intervals on weekdays and weekends were conducted. The investigation reveals that on weekdays, residents' travel times range from 19 to 40 min, while on weekends, the average travel time falls within the range of 16 to 35 min. Furthermore, it is observed that the morning and afternoon peak hours for residents' travel on weekdays occur earlier compared with those on weekends. 2. A fusion algorithm was used to cluster OD points on both weekdays and weekends, resulting in spatially continuous clusters. Kernel density analysis provided an accurate heat map of pick-up and drop-off points after eliminating the noise influence, identifying travel hotspots. Different functional area travel characteristics were observed: During the morning peak (7:00-9:00), taxis are frequently used in residential areas and transportation hubs, with drop-offs common at schools, commercial build ings, and government agencies. In the afternoon peak (11:00-13:00), travel shifts from schools, businesses, and government locations to dining and entertainment areas. In the evening peak (19:00-21:00), pick-up and drop-off points are widely distributed, moving from schools, commercial zones, and residential areas to dining streets, shopping plazas, and entertainment venues. 3. Utilizing the hidden Markov model approach, trajectory points were matched with the road network. Hotspot detection was subsequently conducted based on these hotspot areas, resulting in the identification of hotspot paths. Through the application of the chi-square test method, it was determined that the \(p\)-value from the chi-square test is 0.023, which is less than the conventional significance threshold of 0.05. This statistical analysis takes into account spatial changes in hotspot areas and the count of hotspot paths across different time periods. The result provides strong evidence to support the assertion that hotspot areas and hotspot paths exhibit a significant relationship. Future studies aim to utilize low-frequency taxi trajectory data to develop clustering methods with enhanced levels and accuracy. By integrating urban points of interest (POI) data, the research will further refine the identification of hotspot areas and paths for residents' travel. This will deepen the understanding of the correlation between hotspot areas and paths, offering more effective decision support for urban transportation planning and management. Conceptualization, J.D., C.M. and X.L. ; methodology, J.D. ; software, C.M. ; validation, J.D. and C.M. ; formal analysis, J.D. and X.L. ; investigation, C.M. and X.L. ; resources, J.D. ; data curation, J.D. ; writing--original draft preparation, C.M. ; writing--review and editing, J.D. and X.L. ; visualization, J.D. ; supervision, J.D. ; project administration, J.D. ; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (grant number 41801318) and the Key Scientific and Technological Project of Henan Province (grant numbers 232102321004 and 212102310436). Not applicable. The data presented in this study are available on request from the corresponding author.