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It has been revealed that single-pass airborne light detection and ranging (LiDAR) system (ALS) data could provide not only the spatial but also the dynamical information of a scanned scene due to the so-called motion artifact effect. A common strategy for extracting dynamical information from ALS data is established based on analyzing shape deformations of vehicles which have to be extracted in advance. Therefore, vehicle extraction results are directly related to the performance of motion analysis. In this letter, two vehicle extraction methods, namely, grid-cell- and 3-D point-cloud-analysis-based methods, which represent two main streams in LiDAR data processing, are to be evaluated and compared toward influences on the performance of motion analysis. Motion estimation based on the two methods is respectively applied to real ALS data sets. The results show that the 3-D data-based method can yield more accurate and robust dynamical traffic information such as motion state and velocity of vehicles, while the grid-cell-based method can provide more complete information by extracting more stationary vehicles.