Automatic Recognition of Intersections Based on Vehicle Trajectory Turning Points Clustering

Gao, Lei; Lu, Wenzhe; Yang, Jian; Li, Jinhong

doi:10.1109/ccet52649.2021.9544259

Cited by 1 publication

(3 citation statements)

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“…The main conclusions obtained are as follows. To further validate the method in this paper, the density peak clustering method based on vehicle turning point features proposed by GAO [11] was compared with the global threshold (DPCA).…”

Section: Discussionmentioning

confidence: 99%

“…The square locations in the figure are the roundabouts and overpasses, respectively, which were misidentified as intersections, corresponding to the circular dashed boxed area in Figure 12. To further validate the method in this paper, the density peak clustering method based on vehicle turning point features proposed by GAO [11] was compared with the global threshold (DPCA).…”

Section: Methodsmentioning

confidence: 99%

“…Hu 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. Gao et al [11] improved the density peak clustering algorithm. Some studies enhance the steering point feature by integrating multiple features.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Intersection Extraction Method for Urban Road Networks Based on Trajectory Intersection Points

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Yang

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Applied Sciences

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%