A Framework for Generating Network-Based Moving Objects
Geoinformatica
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Estimating Origin-Destination Flows Using Mobile Phone Location Data
IEEE Pervasive Computing
Vehicle Reidentification using multidetector fusion
IEEE Transactions on Intelligent Transportation Systems
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Origin-destination matrices are important for effective real time traffic management. These matrices contain the spatial and temporal distribution of traffic demand, which is a vital input for transportation planning processes. Trajectories represent the different traffic flow routes between the source destination pairs taken by the travelling vehicles. We present a privacy aware model to compute origin-destination (OD) matrix and the corresponding trajectories. Our main contribution is the use of partial vehicle information to compute the trajectories and OD matrix while maintaining accuracy and privacy. We propose local re-identification of vehicles to build local transition matrices at every node of the network. We introduce k-anonymous l-grouping for our trajectory estimation to provide a trade-off between accuracy and privacy. We present a Trajectory Estimation algorithm to determine trajectories and estimate the OD matrix.