# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
###### Abstract
By comparing the POS model's analysis and predictions of residents' travel activities with actual behaviors, they found that the POS model's results were generally consistent with real-world observations [11]. Taxis have increasingly become a preferred mode of transport for many due to their immediate availability, ease of use, and adaptable scheduling. Their 24/7 availability and direct point-to-point service further enhance their appeal [12; 13]. Consequently, analyzing taxi trajectory data to understand urban residents' travel patterns has emerged as a significant research method [14; 15; 16]. Residents' travel behavior covers both temporal aspects, reflecting travel duration and frequency, and spatial aspects, indicating movement patterns and dynamic trajectories (manifested as hotspot areas and paths). These dual characteristics unveil the intricate travel patterns of residents and identify urban travel hotspots [17]. Traditional methods for analyzing travel characteristics and hotspots from trajectory data involve data normalization using taxi GPS data, extraction of travel features, and clustering of trajectory points using algorithms like K-means or DBSCAN. In the end, the clustering results are examined to identify patterns in the behavior of residents [18]. However, these methods face challenges such as dependence on data quality, which directly impacts analysis accuracy [19]; low clustering accuracy due to simplistic data clustering [20]; and limited processing capabilities when dealing with large-scale, high-dimensional data [21; 22; 23]. This paper delves into the utilization of taxi trajectory data to address issues related to the quality of trajectory data and clustering accuracy. It introduces a novel density clustering algorithm that integrates the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) clustering algorithm and kernel density analysis. This integration effectively identifies hotspot areas for passenger pick-up and drop-off. The study also explores the connections between these hotspot areas. The algorithm's methodology comprises three stages: First, it incorporates time threshold limitations into HDBSCAN to form clusters of trajectory points across various time periods and levels. Second, it employs kernel density analysis to conduct a detailed examination of these clusters. Finally, it visualizes the processed trajectory points on a heat map, facilitating a deeper analysis of residents' travel patterns. After verification using experimental data, the fusion algorithm achieved the following results:  
1. The HDBSCAN algorithm, with high efficiency in processing noise points, accurately identifies trajectory point clusters and noise points in taxi trajectory data on both weekdays and weekends. It generates three distinct peak periods in the morning, middle of the day, and evening based on different time intervals for trajectory clustering. 2. Building upon the clustering results, it eliminates noise points outside the trajectory data clusters and conducts kernel density analysis. This leads to the creation of a more accurate heat map compared with single kernel density analysis. It also identifies hotspot areas where residents frequently travel. 3. Using the fusion algorithm, hidden Markov model road matching and hotspot detection were performed to determine hotspot paths. The chi-square test was employed to calculate the \(p\)-value for residents' travel hotspot areas and hotspot paths during various peak hours on weekdays, resulting in a value of 0.023, which is less than 0.05. This demonstrates a significant correlation between the two. The paper is structured into several sections: Section 2 details the experimental setup and methodologies, Section 3 analyzes residents' travel time patterns and explores travel hotspots and paths, Section 4 discusses related research, and Section 5 concludes with a summary of the experimental process and findings.