Beyond Routines: Adaptive Mobility Prediction via Sequential-Relational Fusion

Predicting human mobility remains a fundamental challenge, especially when individuals deviate from routine patterns due to exploration, disruptions, or rare events. While sequential models like Transformers excel at capturing regular movement patterns, their performance often degrades under nonroutine scenarios involving rare or unfamiliar transitions. To address this, we propose ROAM (Routine-Oriented Adaptive Mobility Predictor), a novel framework that jointly models human mobility from both sequential and relational perspectives, enabling adaptive handling of both routine and nonroutine behaviors during prediction. ROAM combines a sequential encoder that captures historically frequent transitions with a complementary graph-based relational reasoning module that encodes both user-specific and group-level mobility structures. To dynamically integrate these views, we introduce a hierarchical confidence-aware gating mechanism that adaptively balances sequential and relational predictions based on their internal reliability. Extensive experiments on real-world mobility datasets show that ROAM consistently outperforms state-of-the-art baselines in next location prediction. Further analysis reveals that the combination of sequential and relational reasoning substantially improves robustness, particularly under out-of-routine scenarios.