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Vehicle reidentification is the process of reidentifying or tracking vehicles from one point on the roadway to the next. By performing vehicle reidentification, important traffic parameters including travel time, section density and partial dynamic origin/destination demands can be obtained. This provides for anonymous tracking of vehicles from site-to-site and has the potential for improving intelligent transportation systems (ITS) by providing more accurate data. The paper presents a fusion based vehicle reidentification algorithm that uses four different features to achieve high accuracy: (1) the wavelet transform of the inductive signature vector acquired from loop detectors; (2) vehicle velocity; (3) traversal time; (4) color information (based on images acquired from video cameras). A nearest neighbor approach classifies the features and linear feature fusion is shown to improve performance. With the fusion of four features, more than a 92% accuracy is obtained on real data collected from a parkway in California. Also, it is found that the wavelet transform improves performance and reduces the dimension of the feature vector when compared to the raw vehicle signatures.