Real-time privacy-preserving model-based estimation of traffic flows

Road traffic information systems rely on data streams provided by various sensors, e.g., loop detectors, cameras, or GPS, containing potentially sensitive location information about private users. This paper presents an approach to enhance real-time traffic state estimators using fixed sensors with a privacy-preserving scheme providing formal guarantees to the individuals traveling on the road network. Namely, our system implements differential privacy, a strong notion of privacy that protects users against adversaries with arbitrary side information. In contrast to previous privacy-preserving schemes for trajectory data and location-based services, our procedure relies heavily on a macroscopic hydrodynamic model of the aggregated traffic in order to limit the impact on estimation performance of the privacy-preserving mechanism. The practicality of the approach is illustrated with a differentially private reconstruction of a day of traffic on a section of I-880 North in California from raw single-loop detector data.