Driving Behavior Clustering and Abnormal Detection Method Based on Agglomerative Hierarchy
摘要
At present,the abnormal behavior detection based on video mainly focuses on the single vehicle restricted scene,which is difficult to monitor the whole transport process.Meanwhile,the track analysis of GPS is mainly based on the prior threshold judgment,which lacks the steps of in-depth data analysis and information mining.Aiming at these problems,this paper proposes an abnormal detection method of driving behavior based on GPS data.It uses the global and local features such as time,speed,acceleration,direction and rotation angle and their corresponding statistics,to construct the characteristic attributes of the driving behavior of the vehicle.It clusters and analyzes the existing commercial vehicle trajectory data to get the result of abnormal driving behavior detection,which is based on above features.Experimental results show that this method can accurately determine typical abnormal driving behaviors such as overspeed,rapid acceleration/deceleration and frequent lane change of the vehicle under test.