## 4 Experimental Validation and Result Analysis
### Determination of Clustering Parameters and Evaluation of Clustering Results  
The experiment utilizes taxi trajectory data from 31 May 2020, as a sample to ascertain the approximate range of the \(Eps\) value. Road-network-based distances bypassing buildings are employed as similar distances between data points, and the D-minimum distribution is utilized to ascertain the value of \(Eps\). Provided a dataset \(X(x_{1},x_{2},\ldots,x_{k},\ldots,x_{n})\), the distribution of similar distances among the points is sequentially computed. We represent the point with the closest distance to each point as \(d_{min}\). Subsequently, all the \(d_{min}\) values of the points are arranged to obtain \(D(d_{min-1},d_{min-2},\ldots,d_{min-k},\ldots,d_{min-n})\). The graphical representation of the distribution of curves in D is displayed in Figure 5. The similar distances within the dataset exhibit substantial variation, ranging from [0.0016, 0.0035]. Furthermore, approximately 99.06% of trajectory points fall below the threshold of 0.0035.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frame Name** & **Configuration** \\ \hline Hadoop & 2.7.6 \\ JDK & JDK1.8 \\ HBase & 2.1.9 \\ Zookeeper & 3.4.14 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Environment configuration of the experiment.  
Figure 4: Heat map visualization complete process.  
Consequently, the clustering _Eps_ value for this experiment is defined within the range of [0.0016, 0.0035].  
Within the range of the specified Eps value, the taxi trajectory data for 31 May 2020 undergoes testing to select the optimal Minimum Inclusion Points, MinPts, using the silhouette coefficient as the evaluation criterion. Initially, the latitude and longitude of the test data are matched with the road network vector data. Next, the parameter value range for the neighborhood radius Eps is set to [0.0016, 0.0035]. The radius values are input at intervals of 0.0001. An integer within the scope of [50; 70] is chosen as the minimum inclusion point MinPts incoming parameter, and the silhouette coefficient serves as the criterion for assessing clustering quality. The clustering comparison outcomes of selected data are presented in Table 7 following numerous experiments.  
As observed in the table, the silhouette coefficient of the improved hybrid clustering algorithm is significantly superior to that of the conventional algorithm. Hence, the density division clustering algorithm proposed in this paper surpasses the traditional DBSCAN clustering algorithm based on Euclidean distance in terms of clustering accuracy. By testing the Minpts values within the Eps range, the optimal Minpts value corresponding to each Eps is selected using the silhouette coefficient. From Table 8, it can be deduced that, with Eps assuming the value of 0.0029 and Minpts adopting the value of 69, the silhouette coefficient attains its zenith, thus signifying the pinnacle of clustering efficacy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Eps**} & \multirow{2}{*}{**Minpts**} & **Traditional** & **Improved Hybrid** \\  & & **DBSCAN Algorithm** & **Clustering Algorithm** \\ \hline
0.0029 & 50 & 0.624551212 & 0.815335636 \\
0.0029 & 51 & 0.641254035 & 0.824145512 \\
0.0029 & 52 & 0.691455214 & 0.822155423 \\
0.0029 & 53 & 0.742684135 & 0.832145142 \\
0.0029 & 54 & 0.71244351 & 0.841223244 \\
0.0029 & 55 & 0.713623125 & 0.845512341 \\
0.0029 & 56 & 0.754215215 & 0.845215214 \\
0.0029 & 57 & 0.792145221 & 0.861542341 \\
0.0029 & 59 & 0.792532131 & 0.854136422 \\.... &.... &.... &.... \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of silhouette coefficients of different clustering methods.  
Figure 5: D-minimum distribution.