Traffic Flow Detection at Road Intersections Based on K-Means and NURBS Trajectory Clustering

In view of the variety and occlusion of vehicle target motion on the urban intersection, it is difficult to accurately detect the traffic flow parameters in all directions and categories of the intersection, so an improved k-means trajectory clustering method based on NURBS curve fitting is designed...

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Bibliographic Details
Published in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-6
Main Authors: Song, Jun-fang, Zhao, Hai-li, Wang, Shu-yu
Format: Article
Language:English
Subjects:
Algorithms
Cluster analysis
Clustering
Curve fitting
Engineering
Interpolation
Neural networks
Occlusion
Occupancy
Parameters
Pattern recognition systems
Roads & highways
Time setting
Traffic congestion
Traffic flow
Traffic intersections
Trajectories
Vector quantization
Vehicles
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Summary:In view of the variety and occlusion of vehicle target motion on the urban intersection, it is difficult to accurately detect the traffic flow parameters in all directions and categories of the intersection, so an improved k-means trajectory clustering method based on NURBS curve fitting is designed to obtain the traffic flow parameters. Firstly, the B-spline quadratic interpolation function is used to fit the smooth NURBS curve of vehicle trajectory; secondly, K-means clustering is used to measure the minimum distance, and the location of the first and last end points of the vehicle trajectory is used to realize the automatic division of the intersection area; finally, according to the intersection area where the start and end points of vehicle trajectory belong, respectively, the moving mode of a vehicle is determined, and the traffic flow parameters are classified and counted. Experiments show that the method has high accuracy and simple algorithm, which can meet the application requirements of intelligent transportation. It can provide effective data for traffic congestion analysis and lane occupancy estimation, and it is an important parameter for dynamic time setting of intersection information lights.
ISSN:1024-123X
1563-5147
DOI:10.1155/2020/1383198