## 4 Conclusions  
In this paper, the geometric structure of trajectories is used to achieve the automatic extraction of urban road intersections using extract straight-line segments and calculating their intersection points. The main conclusions obtained are as follows.  
1. Compared with research methods in intersection extraction that use the vehicle steering angle and stopping point as trajectory features, this paper uses trajectory line segment intersection points for intersection extraction, which are less affected by the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Extraction Total** & **Number of Misidentifications** \\ \hline Steering angle feature & 61 & 7 \\ This paper & 65 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of intersection extraction methods.  
Figure 12: Results of intersection extraction (65).  
Figure 13: Location of real intersections in the area of the dataset.  
distribution of sampling data amount, and which can effectively identify intersections in low-traffic or low-sampling areas.
2. A maximum reconstruction error method is proposed to extract straight-line segments, which can effectively avoid the problem that the threshold value of the steering angle is difficult to determine and can effectively segment the progressive steering trajectory, while at the same time eliminating the fluctuations in the trajectory caused by lane changing and drift.
3. By merging straight-line segments, the road network structure pattern represented by straight-line segment trajectories is effectively enhanced, the confidence of intersection calculation is improved and the number of straight-line segments to be calculated for intersections can be reduced by 90%. The use of orthogonal and bounding box filtering using the spatial relationship of straight-line segments reduced the intersection calculation by 96%.  
However, besides cross-type intersections, there are other types of intersections, such as T-intersection, Y-intersection and roundabouts and our approach only identifies intersections with angles formed of legs greater than 60\({}^{\circ}\); therefore, there is a need to further improve our method for identification of intersections of different types. There is another limitation of our approach. We assume that the minimum distance between intersections is about 60 m; therefore, when distances of multiple adjacent intersections are all less than 60 m, only one intersection can be identified. In our approach, the straight-line segments are directly extracted from each single trajectory; therefore, the greater the amount of valid trajectory, the more straight-line segments would be extracted; however, a greater number of straight-line segments would involve unexpected behavior leading to misidentification. Such limitations will be considered in future work.  
Conceptualization, J.L.; Data curation, J.Y.; Investigation, L.W.; Methodology, L.G. All authors have read and agreed to the published version of the manuscript. This research was funded by Beijing Social Science Foundation grant number 21 XCC013.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.