Abstract

Predicting human mobility across multiple cities is essential for urban applications but remains challenging due to the complex and diverse spatiotemporal dynamics in human trajectories. While recent work often leverages language modeling by treating trajectories as sequences for next-location prediction, these approaches typically rely on raw movement data, process long trajectories without distinguishing between individual trips, and use a single model for all users within a city. To address these limitations, this paper presents MoVER, a transformer encoder-decoder that enriches trajectory representations with location profiles and explicit trip separators. Furthermore, we introduce a clustering-based finetuning strategy to handle user heterogeneity by tailoring models to user groups of similar travel patterns. MoVER outperforms baselines on a validation set and achieves a top-7 ranking among over 50 …

Date

November 3, 2025

Authors

Yijun Lin, Fandel Lin, Jina Kim, Yao-Yi Chiang

Book

Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems

Pages

1234-1237