###### Abstract  
Automatic intersection identification and extraction are an important foundation for urban road network updates and traffic network analysis and modeling. Existing intersection extraction methods based on steering angles and stopping points suffer from inadequate sampling amounts and threshold settings. To address this problem, we propose a road network intersection automatic extraction method based on vehicle trajectory intersection clustering. First, the continuous trajectory segments are extracted from trajectory data based on the sampling interval. Second, the maximum reconstruction error method is developed to extract straight-line trajectory segments from continuous trajectory segments. The overlapped straight-line trajectory segments belonging to the same direction are merged to reduce the number of segments and enhance road network patterns. To further improve the calculation efficiency of the intersection points of straight-line segments, bounding box filtering and orthogonal filtering are used to filter the straight-line trajectory segments that do not have an intersection relationship. Finally, the obtained straight-line segment intersection points are clustered using a density peak clustering algorithm. The road intersections are automatically extracted using the clustering center. The experimental results on real vehicle trajectories in Lianyungang City show that the proposed method performs well on intersection recognition and calculation efficiency. intelligent transportation; intersection extraction; vehicle trajectory; reconstruction error; density peak clustering +
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**Citation:** [PERSON]; [PERSON]; [PERSON]. the common low-quality trajectory data. The authors used a point-to-segment matching algorithm to acquire line segment road network structure but did not take advantage of trajectory information. [PERSON] et al. [6] used low-sample frequency data to reconstruct vehicle trajectories through road intersections. The trajectory was adjusted microscopically using a probability method; these studies all focus on intersections as the core area. The location extraction and structure recognition of road network intersections based on trajectory data is the first part or kernel problem of such studies, and it is increasingly becoming a current research hotspot. By using trajectory data, the intersection extraction is based on the characteristics of trajectories within the intersection area. There is much literature on intersection extraction by clustering the characteristics of each sampling point in a single trajectory. The widely used characteristic is the steering angle, which is used by [PERSON] et al. [7] and [PERSON] et al. [8]. Some studies try to use different clustering algorithms to improve performance. [PERSON] et al. [9] used a mean-shift algorithm for the clustering steering angle. [PERSON] et al. [10] detected the traffic intersection using floating car data; they used the angle of the direction difference for detecting the traffic intersection, and a density clustering algorithm (DBSCAN) for identifying traffic intersections. [PERSON] et al. [11] improved the density peak clustering algorithm. Some studies enhance the steering point feature by integrating multiple features. [PERSON] et al. [12] detected road intersections from large-scale vehicle trajectories by clustering turning points and then removing the false detections using direction discrepancy and turning discrepancy. [PERSON] et al. [13] detected road intersections by proposing a novel turn-point position compensation in order to improve the concentration of selected turn-points under low sampling rates, and indeed there are also studies using other single-trajectory characteristics. [PERSON] et al. [14] determined the location and area of the intersection using density-based clustering on vehicle stop points. [PERSON] et al. [15] proposed a geospatial method to extract functionally critical network location (FCNL) as road intersections from trajectories.