Beyond Regularity: Modeling Chaotic Mobility Patterns for Next Location Prediction

• URL: http://arxiv.org/abs/2509.11713v1
• Date: Mon, 15 Sep 2025 09:10:48 GMT
• Title: Beyond Regularity: Modeling Chaotic Mobility Patterns for Next Location Prediction
• Authors: Yuqian Wu, Yuhong Peng, Jiapeng Yu, Xiangyu Liu, Zeting Yan, Kang Lin, Weifeng Su, Bingqing Qu, Raymond Lee, Dingqi Yang,
• Abstract summary: Next location prediction is a key task in human mobility analysis, crucial for applications like smart city resource allocation and personalized navigation services.<n>Existing methods fail to address the dynamic imbalance between periodic and chaotic mobile patterns, leading to inadequate adaptation over trajectories.<n>We introduce a biologically inspired Chaotic Neuraly Attention mechanism to inject adaptive variability into traditional attention, enabling balanced representation of evolving mobility behaviors.
• Score: 21.810140501429803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. To tackle these challenges, we propose \textbf{\method}, a \underline{\textbf{C}}h\underline{\textbf{A}}otic \underline{\textbf{N}}eural \underline{\textbf{O}}scillator n\underline{\textbf{E}}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 Tri-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\% improvement over the best-performing baselines across different cases. 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. Our code is available at: https://github.com/yuqian2003/CANOE.

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