1 citations · 1 across the 4 of their papers we have counts for
4 papers
Pre-trained Transformer Uncovers Meaningful Patterns in Human Mobility Data
Alameen Najjar
We empirically demonstrate that a transformer pre-trained on country-scale unlabeled human mobility data learns embeddings capable, through fine-tuning, of developing a deep unders…
Towards A Foundation Model For Trajectory Intelligence
Alameen Najjar
We present the results of training a large trajectory model using real-world user check-in data. Our approach follows a pre-train and fine-tune paradigm, where a base model is pre-…
Where You Are Is What You Do: On Inferring Offline Activities From Location Data
Alameen Najjar, Kyle Mede
In this paper we investigate the ability of modern machine learning algorithms in inferring basic offline activities,~e.g., shopping and dining, from location data. Using anonymize…
Trajectory-User Linking Is Easier Than You Think
Alameen Najjar, Kyle Mede
Trajectory-User Linking (TUL) is a relatively new mobility classification task in which anonymous trajectories are linked to the users who generated them. With applications ranging…