###### Abstract
The FCNL has multiple characterizations, such as a large number of activity trajectories, and is traversed by trajectories exhibiting diverse patterns; however, among the existing research, the intersection extraction method based on characteristics of the single trajectory is influenced by the trajectory sampling amount, which is leading to ignoring road intersections with low sampling amounts. There are also some studies based on the properties of multiple trajectories within the grid of a small area; [PERSON] et al. [16] extracted an intersection and stop bar from crowdsourced GPS trajectories. They gridded the target area and calculated the entropy of the moving direction within each grid to identify intersection areas. [PERSON] et al. [17] introduced a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments. In [PERSON] [18]'s study on generating routable road maps from vehicle GPS traces, the authors detected conflict points in the grid and clustered these trajectory conflict points to extract road intersection information. [PERSON] et al. [19] introduced a local shape descriptor to represent the distribution of the GPS trajectory in a circular area and constructed an intersection detector based on the local shape descriptor by introducing an intersection detector that uses a localized shape descriptor to represent the distribution of GPS traces around a point. Although the methods based on grids are less influenced by the sample amount, the grid size is difficult to determine when facing the uneven distribution of trajectory density in a large-scale road network. One study attempted to automatically set up grids. [PERSON] et al. [20] proposed an automatic calibration method for road intersection topology using trajectories and determined the location and coverage of road intersections by employing a top-down quad-tree-based cell division method; however, the geometric structure relationship between trajectories has not been effectively utilized. [PERSON] et al. [21] proposed a regularized mean-shift algorithm to refine GPS scattered points and road segments were extracted from these refined points to represent road network structure. In our approach, however, we extract straight-line segments from each single trajectory directly. We propose an intersection extraction method from GPS trajectory data. Compared to previous work in this field, our method directly extracts straight-line segments from each single trajectory. Then, we use the intersection points of all straight-line segments to identify road intersections. The algorithm for separating line segments from GPS trajectories is an important part of our approach. The intersection points of vehicle straight-line trajectories mostly occur in road intersection areas. We use the geometrical structure of the road network carried by the trajectory and propose an intersection extraction method based on clustering the trajectory straight-line segment intersection points. The workflow for extracting road intersections is shown in Figure 1.  
First, the original trajectory dataset is segmented according to sampling time interval to extract spatial-temporal continuous trajectories. Second, the maximum reconstruction error method is proposed to extract straight-line trajectory segments from spatial-temporal continuous trajectories. The overlapped straight-line segments belonging to the same direction are merged to reduce the number of segments and enhance the straight-line road pattern. The amount of calculation at the intersection of a straight-line segment is significantly reduced by bounding box overlapping filtering and orthogonal filtering. The intersection points are calculated for any two filtered straight-line segments. Finally, the intersection points are clustered using the density peaking algorithm [22] and the cluster centers are used as the location of the intersections.