CRATE: Privacy-Preserving Travel Time Estimation

Travel Time Estimation (TTE) stands as a cornerstone of efficient transportation systems. However, the critical imperative of privacy preservation within the TTE context remains notably underexplored. This gap underscores the pressing necessity for innovative solutions that prioritize the safeguarding of users' geo-privacy, particularly in light of the expanding prevalence of data-driven TTE algorithms. In this paper, a novel privacy-preserving TTE framework, CRATE, is proposed to ensure comprehensive privacy preservation for TTE without compromising service quality. CRATE achieves this objective by identifying random routes within a transportation network that yield identical travel times to the actual, privacy-rich route. This is accomplished through exploiting the embedding representations for road segments and routes, followed by the development of a highly efficient heuristic for random route generation. Furthermore, a travel time aggregation and calibration model is devised to enhance estimation accuracy while upholding user privacy. Case studies conducted on three real-world vehicular trajectory datasets demonstrate that CRATE attains comparable estimation accuracy to state-of-the-art non-privacy-preserving TTE algorithms while maintaining strict privacy protection. Additionally, CRATE's efficiency is showcased through deployment on both high- and low-end mobile handsets spanning the past decade.