This paper focuses on GNSS trajectory aggregation, building upon the work of [4], a modular and iterative aggregation algorithm. The last allows to compute aggregated trajectories with high geometric accuracy for a set of trajectories having the same origin/destinations and following the same path. To analyze the performances of the algorithm, we use a metrological perspective, i.e. its ability to reconstruct the accurate aggregated trajectory, from a minimum number of input GNSS trajectory samples to determine an optimal parameterization of the algorithm. We validate our modular framework on two types of data: synthetic and experimental GNSS trajectories collected with multiple sensors under varying canopy conditions.