Beyond Regularity: Modeling Chaotic Mobility Patterns for Next Location Prediction

Abstract--Next location prediction is a key task in human mobility analysis, crucial for applications like smart city resource allocation and personalized navigation services. However, existing methods face two significant challenges: first, they fail to address the dynamic imbalance between periodic and chaotic mobile patterns, leading to inadequate adaptation over sparse trajectories; second, they underutilize contextual cues, such as temporal regularities in arrival times, which persist even in chaotic patterns and offer stronger predictability than spatial forecasts due to reduced search spaces. T o tackle these challenges, we propose CANOE, a C hA otic N eural O scillator nE twork for next location prediction, which introduces a biologically inspired Chaotic Neural Oscillatory Attention mechanism to inject adaptive variability into traditional attention, enabling balanced representation of evolving mobility behaviors, and employs a T ri-Pair Interaction Encoder along with a Cross Context Attentive Decoder to fuse multimodal "who-when-where" contexts in a joint framework for enhanced prediction performance. Extensive experiments on two real-world datasets demonstrate that CANOE consistently and significantly outperforms a sizeable collection of state-of-the-art baselines, yielding 3.17%-13.11% In particular, CANOE can make robust predictions over mobility trajectories of different mobility chaotic levels. A series of ablation studies also supports our key design choices. Next location prediction is a critical yet challenging task in human mobility modeling, serving as a fundamental building block for various location-based services [1]-[11]. Its core objective is to predict the specific location a user is most likely to visit next, based on the user's historical trajectory data.