The problem of the acoustic sources identification has been widely treated in static or quasi-static source contexts, more recently for moving sources, in a wide range of applications. In the context of source identification from a passing-by vehicle, the beamforming method is a reference that has well-known limitations. Deconvolution methods are used to overcome them. Mostly developed in static contexts at the beginning, extensions to moving sources have been proposed in the transportation field, mainly in air transportation and underwater acoustics. In the present investigation, the method proposed by Fleury and Bulté is numerically tested with parameters fitted to the road vehicle context. Its performance is assessed at different speeds, source-to-array distances and with various additional noise levels, as well as its robustness regarding an error on some parameter inputs. Beamforming -- as the reference method -- and this moving source deconvolution method (here called MSA-PSF) are compared using performance indicators. The simulations showed better localization, quantification and separation results with MSA-PSF compared to beamforming in this road transportation context. An experiment involving an academic pendulum setup is presented and confirms the improvement brought by MSA-PSF.