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Dynamic OD matrix estimation exploiting bluetooth data in Urban networks

Time-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models. Microscopic and Mesoscopic traffic simulators are relevant examples of such models, traditionally used to assist in the design and evaluation of Traffic Management and Information Systems (ATMS/ATIS). Dynamic traffic models can also be used to support real-time traffic management decisions. 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 traffic 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 exploit traffic data available from Bluetooth sensors, and reports computational experiments for networks and corridors.