Automatic Recognition of Intersections Based on Vehicle Trajectory Turning Points Clustering

Urban road intersection recognition and feature extraction are crucial for road network modeling and traffic flow analysis. This paper proposes a novel method of intersection recognition and feature extraction based on vehicle trajectory data. First, valid spatial-temporal continuous trajectory segments of each vehicle are obtained by time differences analysis between adjacent trajectory points. Second, based on the basis that the turning behaviors of vehicles mostly occurring at the intersection area, the turning angles are calculated, and the valid turning points are extracted as the samples of clustering algorithm. Finally, the density peak clustering algorithm (DPCA) is used to recognize the intersections from road network which reconstructed from the vehicle trajectory data. The experiment on real-world vehicle navigation trajectories in the city of Lianyungang shows that the proposed method is able to recognize intersection accurately with scalability.