Thanks to Wi-Fi technology, it is possible to gather passive mobility data over a long time and to analyze travel behaviors variability. However, the main challenge is to identify relevant data, in a way that is efficient whatever the chosen period of network. Our research evaluates the replicability of a method, based on a partitioning algorithm, that allows to identify signals emitted by passengers from those emitted by non-passengers automatically. The findings show that this algorithm is easily replicable and useful to study individual mobility from passive data. It is hence a good opportunity for understanding travel demand.