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.ii To Mom, Dad and my grandparents iii Praise be to Allah, the Most Gracious, the Most Merciful. Prayer and peace be upon the Prophet Muhammad. First of all, I am grateful to my supervisor, Manos Papagelis, for his unwavering guidance, remarkable patience, tireless support, and invaluable advice throughout my studies. His contributions played a significant role in the success of this work, and I would not have been able to complete it without his help. Thanks to Ameeta Agrawal for her help and guidance for the past two years. I would like to thank committee members Aijun An and Mehdi Nourinejad for taking the time to read my thesis and for providing feedback. I would like to express my deepest gratitude to my parents, grandparents, brothers, and sisters for their love and support throughout this journey. I would like to extend my sincere appreciation to my friends, whose company may have delayed my graduation and reduced my productivity in terms of published works. However, their friendship was invaluable and made the journey worthwhile. In addition, I would like to express my gratitude to my cats who provided me with emotional support during the course of my studies. Although they could be a distraction at times, their positive impact cannot be overstated.