  We estimate vehicular traffic states from multimodal data collected by
single-loop detectors while preserving the privacy of the individual vehicles
contributing to the data. To this end, we propose a novel hybrid differential
privacy (DP) approach that utilizes minimal randomization to preserve privacy
by taking advantage of the relevant traffic state dynamics and the concept of
DP sensitivity. Through theoretical analysis and experiments with real-world
data, we show that the proposed approach significantly outperforms the related
baseline non-private and private approaches in terms of accuracy and privacy
preservation.
