## 4 Results
### Result Analysis of Road Intersection Detection  
Based on the density homogenization and coarse-to-fine trajectory segmentation strategy, we hierarchically extracted the trajectory points of road-intersection, the spatial coverages of different road intersections and the internal turning modes within road intersections. Figure 9a,b illustrates the results of road intersections detection with and without adaptive density homogenization. To evaluate the accuracy of road intersection detection, we referred to the national platform for common geospatial information services ([[https://map.tianditu.gov.cn/](https://map.tianditu.gov.cn/)]([https://map.tianditu.gov.cn/](https://map.tianditu.gov.cn/))) and checked the identified road intersections one by one. By manual inspection, the road intersections correctly identified by the proposed method are classified as true positive (red triangle), the road intersections wrongly identified by the proposed method are classified as false positive (green circle) and, meanwhile, the road intersection not identified by the proposed method are marked as false negative (blue rectangle).  
It can be seen from Figure 9 that more correctly-detected and fewer wrongly-identified road intersections are found in the result of road intersection detection with adaptive density homogenization. In particular, as shown in the two cyan ellipses B of Figure 9, through trajectory density homogenization, the proposed method identifies some road intersections with sparse trajectory density, indicating our method can efficiently overcome large, varied trajectory densities caused by uneven traffic flow. In addition, as depicted in the right of cyan rectangle A of Figure 9a, local movement indicators, such as speed change and direction change, may misidentify some trajectory points passing through parking lots and residential areas as the trajectory points at road intersection. We introduce the turning distance ratio to distinguish the trajectory points of road intersection and non-intersection, appropriately avoiding the road intersection misidentification based on local movement indicators.  
After manual inspection, three accuracy indicators of _Precision_, _Recall_ and F-Value are calculated to quantitatively evaluate the performance of road intersection detection, which is stated in Table 3. It was found that 132 road intersections were identified when  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Density Homogenization**} & \multicolumn{6}{c}{**Hierarchical Trajectory Segmentation**} & \multirow{2}{*}{**Road Structure**} \\ \cline{2-3} \cline{5-8}  & & \multicolumn{2}{c}{**Category-Level**} & & \multicolumn{2}{c}{**Object-Level**} & \multicolumn{2}{c}{**Turning-Modes**} & \multicolumn{2}{c}{**Delineation**} \\ \hline \(N_{grid}\) & \(N_{circle}\) & \(T_{v}\) & \(T_{a}\) & \(T_{dr}\) & \(Eps\) & _MinPts_ & _Ratio_ & \(\varepsilon\) \\ \hline
0.0012 & 0.98 & 5 km/h & 15\({}^{\circ}\) & 0.7 & 82 m & 4 & 0.03 & 0.1 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameters setting.  
Figure 9: Comparison of road intersections detection without/with density homogenization. (**a**) without density homogenization, (**b**) with density homogenization.  
directly using raw trajectory data without density homogenization, of which 109 were correctly identified and 23 were wrongly identified, as well as 50 road intersections which were not detected. Overall, _Precision_ was about 82.6%, _Recall_ was about 68.5%, and F-Value was about 74.9%. Through trajectory density homogenization, 166 road intersections were identified, of which 149 were correctly identified, 17 were incorrectly identified as well as only 15 missed road intersections. It can be seen that the accuracy indicators of road intersection detection by trajectory density homogenization were significantly improved, of which _Precision_ increased to 89.8%, _Recall_ increased to 90.8%, and F-Value reached 90.3%.