# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
###### Abstract  
This study utilizes taxi trajectory data to uncover urban residents' travel patterns, offering critical insights into the spatial and temporal dynamics of urban mobility. A fusion clustering algorithm is introduced, enhancing the clustering accuracy of trajectory data. This approach integrates the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm, modified to incorporate time factors, with kernel density analysis. The fusion algorithm demonstrates a higher noise point detection rate (15.85%) compared with the DBSCAN algorithm alone (7.31%), thus significantly reducing noise impact in kernel density analysis. Spatial correlation analysis between hotspot areas and paths uncovers distinct travel behaviors: During morning and afternoon peak hours on weekdays, travel times (19-40 min) exceed those on weekends (16-35 min). Morning peak hours see higher taxi utilization in residential and transportation hubs, with schools and commercial and government areas as primary destinations. Conversely, afternoon peaks show a trend towards dining and entertainment zones from the abovementioned places. In the evening rush, residents enjoy a vibrant nightlife, and there are numerous locations for picking up and dropping off people. A chi-square test on weekday travel data yields a _p_-value of 0.023, indicating a significant correlation between the distribution of travel hotspots and paths. track data; spatiotemporal feature analysis; hotspot areas; hotspot paths +
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- shed light on how these meteorological changes influence travel behaviors in different geographic areas [5]. 2. Behavioral pattern studies for travel forecasting: Focusing on the analysis and prediction of residents' travel characteristics, this type of research is crucial for enhancing public transit efficiency and offering customized travel services. [PERSON] et al. employed non-negative tensor decomposition (NTD) methods to visually represent the travel patterns of bus passengers in different areas of Davangere City over various time periods, including hours, days, and months. This approach made it easier to analyze how the mobility patterns of residents in the same areas changed across different seasons and allowed them to predict the spatial and temporal variations in resident movements in the near future [6]. Likewise, [PERSON] et al. leveraged advanced deep recurrent neural network techniques, which were trained on extensive resident travel data, to effectively capture the evolving and sequential characteristics of urban traffic flow. This effort resulted in the creation of a highly accurate urban traffic model, achieving an impressive accuracy rate of up to 95% [7]. 3. Trajectory data mining and personalized travel data collection: Traditionally, when mining trajectory data for transportation analysis, researchers relied on data from questionnaires and fixed-route buses, which failed to efficiently capture personalized travel information of residents. However, with the rapid advancement of network technology and the public transportation industry in recent years, it has become more convenient to collect personalized travel data from sources such as taxi global navigation satellite system (GNSS) trajectory data, residents' mobile signaling data, and social media data [8; 9]. In one study, [PERSON] et al. used shared bicycle travel data of residents and introduced the time series weighted regression (TSWR) model to address the challenge of sparse statistical data when making long-term predictions. They enhanced the model's accuracy by over 35% by incorporating the rule-based adjustment optimization (RAO) method to refine nonlinear components, considering various factors, compared with using the RAO method alone [10]. In another study, [PERSON] et al. developed the position opportunity selection (POS) model by taking into account both the population and quantity of points of interest (POI). They obtained residents' travel data in Harbin through the Unicom Smart Footprint platform.