Federated trajectory clustering based on multi-feature similarity calculation

Trajectory clustering plays an important role in numerous real-world applications, such as urban transportation planning and tourist route recommendation. Existing trajectory clustering approaches primarily focus on the spatial and temporal features of trajectories but neglect the velocity feature. Therefore, it is difficult for them to distinguish trajectories sharing spatial and temporal features but diverging velocities. Furthermore, in the context of distributed trajectory clustering among multiple participants, individuals’ privacy, such as the travel routes or habits of a person, should never be violated, which necessitates the equipment of trajectory clustering with privacy-preserving techniques. In this paper, we propose a Federated and Multi-Feature-based Trajectory Clustering (FMFTC) algorithm to address the above issues. First, we develop a Multi-Feature-based Trajectory Clustering (MFTC) algorithm with a new multi-feature to vector encoder (MF2Vec) to capture spatial, temporal and velocity features during trajectory embedding generation. Second, we adapt MFTC to the federated learning paradigm to construct FMFTC for privacy-preserving distributed trajectory clustering. The experiments on real-world datasets demonstrate that FMFTC achieves up to \(\varvec{24.4\%}\) higher accuracy than existing trajectory clustering algorithms and performs identically as MFTC with no accuracy loss.