Abnormal recognition of the ETC gantry license plate data based on an improved DBSCAN

Abstract

Electronic non-stop toll collection system(ETC)is the most popular highway toll collection system at home and abroad. Based on ETC gantry license plate data, this paper systematically describes the highway toll collection process and points out that abnormal data in the license plate data will significantly reduce the accuracy of subsequent data fusion and toll collection. And summarizes the abnormal situation of ETC gantry license plate data. Aiming at the spatial-temporal characteristics of such data, the ETC gantry license plate data abnormality recognition method based on an improved DBSCAN is proposed to realize the adaptive clustering separation of data, which could extract the average travel speed feature set and traffic volume feature set to recognize the abnormal data. The results show that the recognition rates of average speed and traffic volume reaches 96.47% and 88.47%, and the false recognize rate decreases to 0.71% and 0.91%, which could consider data spatial-temporal correlation for abnormal data recognition and solve the problems existing on the current application of ETC gantry data.