Abstract: Data quality control is one of the key technologies for intelligent transportation systems.Radio Frequency Identification (RFID)data generally contain redundancy.According to the different characteristics,they can be broadly divided into two types:duplicate data and similar data.Redundancy detection is based on an analysis of the adjacent time for one vehicle.To identify the redundant RFID data,the curve of redundancy rate and time points of redundant data are extracted.Due to different characteristics of the redundancy types,their redundancy rates are computed separately.A de-tection algorithm is proposed,and applied to analyze the redundancy rate of RFID data in two aspects:RFID stations and shapes of redundancy curves.Moreover,a cleansing method for redundant data is also proposed.As a case study,raw RFID data are collected from 21 RFID stations on the main road in the City of Nanjing.The results show that the average rate of duplicate data is 0.006 2%;which of similar data is 0.92%.Moreover,in each RFID station,the amount of similar data is much larger than that of duplicate data.From the shape-of-redundancy-curve point of view,it is observed that the leveling off or tail rising curves are related to the stations with high redundancy rates;while the straight up curves imply low redundancy rates.Based on the analysis,several measures are proposed to control redundant RFID data.