Expressway Data De-noised Approach Based on Wavelet Analysis

摘要

Compared with traditional collection methods using detectors or sensors,rnfloating-car data collection has advantages such as a broad coverage area, lowrncost installation and maintenance costs and little influence from external sources.rnTherefore, floating-car data is being broadly applied in traffic relative studies.rnTraffic original data usually contains some random components, which disturb therndistinction of traffic status. The original data must be de-noised in order to filterrnthese random components. This paper presents wavelet analysis to de-noise thernoriginal data from floating-car data collection. In this paper, the data from RTMSrn(Remote Traffic Microwave Sensor) collection is treated as true data. Trafficrnoriginal data, from various ring mainlines of Beijing on various days, arernprocessed through wavelet threshold de-noising. The experiment results show thatrnde-noised data is better than the original data in terms of evaluation indexes. Forrnexample, MSE of the de-noised data can drop as low as 6.36% and the correlationrncoefficient can be increased by more than 1.18%.