Using technologies such as Wi-Fi and Bluetooth allows to gather passive mobility data, useful for ensuring the sustainable development of transport infrastructures. The challenge of passive data collection is to be able to identify relevant data. Our research presents interesting solutions for sorting the transmitted signals and reconstructing quality Origin-Destination matrices. Its originality consists not only in comparing the results with those of other data sources, but also in proposing a methodology that can be reproduced. Thanks to a partitioning algorithm, it is possible to automatically distinguish passengers from non-passengers to get transit ridership flow and O-D matrices. The findings show that this algorithm provides concrete and replicable solutions to transport operators for understanding travel demand.