Privacy aware trajectory determination in road traffic networks

  • Authors:
  • Preeti Goel;Lars Kulik;Ramamohanarao Kotagiri

  • Affiliations:
  • The University of Melbourne, Victoria, Australia;The University of Melbourne, Victoria, Australia;The University of Melbourne, Victoria, Australia

  • Venue:
  • Proceedings of the 20th International Conference on Advances in Geographic Information Systems
  • Year:
  • 2012

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Abstract

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.