We estimate a vehicle’s speed, wheelbase length, and its tire track length by jointly estimating its acoustic wave-pattern using a single passive acoustic sensor that records the vehicle’s drive-by noise. The acoustic wave-pattern is determined using three envelope shape (ES) components, which approximate the shape variations of the received signal’s power envelope. We incorporate the parameters of the ES components along with estimates of the vehicle engine RPM and the number of cylinders, and the vehicle’s loudness and speed to form a vehicle profile vector. This vector provides a compressed statistics that can be used for vehicle identification and classification. We also provide possible reasons for why some of the existing methods are unable to provide unbiased vehicle speed estimates using the same framework. The approach is illustrated using vehicle speed estimation and classification results obtained with field data.