# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 4 Related Work  
(1) Use the clustering algorithm to mine hotspot areas. [PERSON] et al. employed taxi trajectory data points and utilized kernel density clustering analysis to group the trajectory data into hotspots. They further categorized these clustering areas into different levels of hotspots and generated spatial distribution maps representing these hotspots [29]. In a related study, [PERSON] et al. enhanced the traditional K-means algorithm by proposing a clustering model that incorporates self-organizing mapping and K-means techniques. This modification reduced the computation time required for the hotspot clustering model and ultimately yielded stable travel hotspot regions [30]. Such research endeavors aim to provide consistent clustering of residents' travel locations. However, it is worth noting that the boundaries of clustered trajectory points may become less distinct when dealing with varying amounts of data. (2) Improve the clustering algorithm to improve the clustering accuracy of trajectory data. [PERSON] et al. introduced fundamental time threshold control parameters into the density clustering approach. They segmented the data based on time granularity and performed trajectory clustering according to specific time periods [31]. Meanwhile, [PERSON] et al. presented a framework centered on division and grouping for trajectory clustering. This approach enhanced the precision of local trajectory point clustering within a specific geographical range [32]. These research efforts enable more precise control and analysis of clustering in both time and space dimensions. However, it is important to note that the clustering results can still be influenced by the configuration of clustering parameters. (3) Analyze residents' travel mechanisms by mining travel hotspots  
[PERSON] et al. employed Markov decision-making techniques to learn from hourly taxi data. They conducted relevant parameter calculations to determine the optimal strategies for taxi movements that would maximize revenue [33]. In a related study, [PERSON] et al. took into account various factors, including cost and distance, to identify recommended peak travel times. They suggested that taxi drivers should focus on searching for passengers during these peak hours [34]. [PERSON] et al. conducted statistical analysis using taxi trajectory data in conjunction with precipitation data. They found that on rainy days, during the morning peak period, the demand for taxis significantly exceeded the available supply [35]. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Hotspot**} & \multicolumn{3}{c}{**Tianfu Square**} & \multicolumn{3}{c}{**Yindu Building**} & \multicolumn{3}{c}{**Taurus Park**} \\ \hline \multicolumn{1}{c}{**Time Period**} & **M** & **N** & **E** & **M** & **N** & **E** & **M** & **N** & **E** \\ \hline Shudu Avenue & 982 & 683 & 1249 & 0 & 0 & 0 & 0 & 0 & 0 \\ Xiyu Street & 844 & 631 & 986 & 0 & 0 & 0 & 0 & 0 & 0 \\ Xinhua Avenue & 0 & 0 & 0 & 893 & 702 & 1093 & 0 & 0 & 0 \\ Renmin Middle Road & 0 & 0 & 0 & 947 & 816 & 1276 & 0 & 0 & 0 \\ Jinfu Road & 0 & 0 & 0 & 0 & 0 & 0 & 1216 & 935 & 1427 \\ Seedling Road & 0 & 0 & 0 & 0 & 0 & 0 & 893 & 537 & 1046 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cross tabulation table example. Similarly, [PERSON] and [PERSON] analyzed the relationship between weather conditions and taxi travel demand. They discovered that rain and snow weather led to an increase in taxi travel demand, mainly driven by residents opting for shorter-distance trips [36]. This kind of research provides valuable insights into the primary travel patterns and dynamic changes in a city's residents. It aids in the development of more effective transportation planning and taxi management policies. However, it is important to acknowledge that research results can be influenced by the natural environment and human factors affecting the collection of trajectory data. (4) Solve the unbalanced supply and demand of transportation resources through travel characteristics. [PERSON] et al.