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.

翻译：我们从单路探测器收集的多式联运数据中估算车辆交通状况,同时保护为数据提供材料的个别车辆的隐私。为此,我们提议采用一种新的混合差异隐私(DP)方法,利用相关的交通状态动态和DP敏感性概念,利用最小随机化来保护隐私。我们通过理论分析和对现实世界数据进行实验,表明拟议方法在准确性和隐私保护方面大大超过相关的非私人和私人基线方法。