2026-07-23T01:41:48Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:23837 2025-11-12T13:20:22Z ddc:004 open_access

BERT4Traj: Transformer-Based Trajectory Reconstruction for Sparse Mobility Data Yang, Hao Yao, Angela Whalen, Christopher C. Mai, Gengchen Human Mobility Trajectory Reconstruction Deep Learning CDR GPS Understanding human mobility is essential for applications in public health, transportation, and urban planning. However, mobility data often suffers from sparsity due to limitations in data collection methods, such as infrequent GPS sampling or call detail record (CDR) data that only capture locations during communication events. To address this challenge, we propose BERT4Traj, a transformer-based model that reconstructs complete mobility trajectories by predicting hidden visits in sparse movement sequences. Inspired by BERT’s masked language modeling objective and self-attention mechanisms, BERT4Traj leverages spatial embeddings, temporal embeddings, and contextual background features such as demographics and anchor points. We evaluate BERT4Traj on real-world CDR and GPS datasets collected in Kampala, Uganda, demonstrating that our approach significantly outperforms traditional models such as Markov Chains, KNN, RNNs, and LSTMs. Our results show that BERT4Traj effectively reconstructs detailed and continuous mobility trajectories, enhancing insights into human movement patterns. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Hao Yang and Angela Yao and Christopher C. Whalen and Gengchen Mai 2025 Is Part Of LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GIScience.2025.8 urn:nbn:de:0030-drops-238373 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.8 eng https://creativecommons.org/licenses/by/4.0/legalcode