Origin-Destination (OD) trip matrices are the primary data input used in principal traffic and transit models, which describe the patterns of trips/passengers across the area of study. In this way, OD matrices become a critical requirement in Advanced Transport Management and/or Information Systems that are supported by Dynamic Assignment models. In the future, once combined dynamic traffic and transit assignment tools will be available to practitioners, the problem of estimating the time-dependent number of trips/passengers between transportation zones would be a critical aspect for real applications. However, because OD matrices are not directly observable, the current practice consists of adjusting an initial or seed matrix from link/segment counts which are provided by an existing layout of traffic counting stations or data gathering in the field (detection layout) for non-dynamic models. The typical approaches to time-dependent OD estimation have been based either on Kalman-Filtering or on bi-level mathematical programming approaches that can be considered in most cases as ad hoc heuristics. The advent of the new Information and Communication Technologies (ICT) makes available new types of real-time traffic and passenger data with higher quality and accuracy, allowing new modeling hypotheses which lead to more computationally efficient algorithms. This paper presents a Kalman Filtering approach that explicitly exploits data available from Bluetooth sensors to simplify an underlying space-state model, and describes the validation of the proposal through a set of simulation experiments, either on networks or corridors. Those involve car data provided by the detection of the electronic signature of on-board devices. Finally, an extension of the framework to the estimation of passenger matrices is addressed when data from passenger’s electronic signature devices are available.