Trajectory-User Linking Using Higher-Order Mobility Flow Representations

Trajectory-user linking (TUL) is a problem in trajectory classification that links anonymous trajectories to the users who generated them. TUL has various uses such as identity verification, personalized recommendation, epidemiological monitoring, and threat assessments. A major challenge in TUL modeling is sparse data. Previous TUL research heavily relies on recurrent neural networks models such as RNNs and LSTMs, with trajectory segmentation to combat sparsity, but segmentation does not sufficiently address the issue and existing models often ignore data skewness, resulting in poor precision and performance. To address these problems, we present TULHOR, a TUL model inspired by BERT, a popular language representation model. One of TULHOR's innovations is the use of higher-order mobility flow data representations enabled by geographic area tessellation. This allows the model to alleviate the sparsity problem and also to generalize better. TULHOR consists of a spatial embedding layer, a spatial-temporal embedding layer and an encoder layer, which encodes properties and learns a rich trajectory representation. It is trained in two steps, first using a masked language modeling task to learn general embeddings, then fine-tuned using a balanced cross-entropy loss to make predictions while handling imbalanced data. Experiments on real-life mobility data show TULHOR's effectiveness as compared to current state-of-the-art models.