The primary data input used in principal traffic models comes from Origin-Destination (OD) trip matrices, which describe the patterns of commuters across the network. In this way, OD matrices become a critical requirement in Advanced Transport Control and Management and/or Information Systems that are supported by Dynamic Traffic Assignment models (DTA models). Dynamic Transit Assignment models are a research topic, but once a dynamic transit assignment be available to practitioners, the problem of estimating the time-dependent number of trips between transportation zones shall be a critical aspect for real applications. However, OD matrices are not directly observable, neither for private nor public transport, and the current practice consists on adjusting an initial or seed matrix from link/segment counts which are provided by counting stations or data gathering in the field (detection layout). The emerging Information and Communication Technologies, especially those based on the detection of the electronic signature of on-board devices provide a rich source of data that can be used in space-state models for dynamic matrix estimation. We present a linear Kalman filter approach that makes use of counts of passengers and travel times provided by Bluetooth devices to simplify an underlying space-state model. The formulation for dynamic passenger OD matrix estimation proposed was originally developed for auto trip matrices, but in this paper, we explore the possibility of adapting the approach to the estimation of OD matrices in public transport networks.