The issue of acoustic source identification has been widely explored in static or quasi-static source contexts, more recently for moving sources, in a wide range of applications. In the case of a passing-by vehicle, the beamforming method is a reference having well-known limitations that deconvolution methods try to overcome. Mostly developed in static source situations at the beginning, some recent deconvolution extensions to moving sources have been proposed in the transportation field, mainly in air transportation and underwater acoustics. In the present investigation, a new approach - called CLEANT for CLEAN-Time domain - is proposed and numerically tested with parameters fitted to the road vehicle context. Inspired by the CLEAN algorithm used to solve deconvolution problems, it is performed exclusively in the time domain and takes advantage of the source signal reconstruction available from the beamforming procedure. Its performance is assessed at several different speeds, source-to-array distances and with various additional noise levels, as well as its robustness regarding an uncertainty on some input parameters. The proposed approach is compared to a reference method using performance indicators. The simulations show better localization and quantification with CLEANT as compared to the other method tested in this road transportation context. Laboratory measurements in scaled pass-by conditions have been conducted and the results are presented and confirm the improvement brought by CLEANT.