发表机构
School of Mathematics and Statistics, Central China Normal University; School of Mathematics and Statistics, Beijing Institute of Technology; State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences; School of Mathematical Sciences, University of Chinese Academy of Sciences（华中师范大学数学与统计学学院; 北京理工大学数学与统计学学院; 中国科学院数学与系统科学研究院数学科学国家重点实验室; 中国科学院大学数学科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对不完善多视图观测的在线交通预测问题，提出MVCTD模型，通过耦合张量分解与组稀疏正则化提升预测精度，轻量级设计适配流部署，真实数据集实验验证其有效性。
AI 中文摘要
准确的在线交通预测对智能交通系统至关重要，该系统需在感知条件不完善的情况下持续进行预测。缺失观测值和异常干扰使该任务颇具挑战性，尤其是当预测依赖单一交通视图时。本文提出一种多视图耦合张量分解（MVCTD）模型，用于从不完善的多视图观测（如速度、流量和占有率）中进行在线交通预测。该模型采用耦合张量分解构建结构化潜在预测空间，联合建模跨交通视图的共享空间结构与视图特有的时间动态。进一步引入组稀疏正则化以捕捉真实交通异常引发的相关异常响应，从而降低其对预测的影响。针对流部署场景，MVCTD仅对当前潜在张量进行迭代优化，其余模型变量则基于汇总的历史信息通过轻量级闭式步骤更新，避免对完整历史序列重复优化。在真实交通数据集上的实验表明，MVCTD在严重缺失情况下仍能实现准确预测且运行时间优异，证实其适用于在线交通预测。
英文摘要
Accurate online traffic prediction is essential for intelligent transportation systems, where forecasting must be performed continuously under imperfect sensing conditions. Missing observations and anomalous disturbances make this task challenging, particularly when prediction relies on a single traffic view. This paper proposes a Multi-View Coupled Tensor Decomposition (MVCTD) model for online traffic prediction from imperfect multi-view observations, such as speed, flow, and occupancy. The proposed model uses coupled tensor decomposition to build a structured latent forecasting space, in which shared spatial structures across traffic views and view-specific temporal dynamics are jointly modeled. A group sparse regularization is further introduced to capture correlated abnormal responses induced by real traffic anomalies and thus reduce their influence on forecasts. For streaming deployment, MVCTD performs iterative refinement only on the current latent tensor, while the remaining model variables are updated by lightweight closed-form steps based on summarized historical information, thereby avoiding repeated optimization over the full historical sequence. Experiments on real-world traffic datasets demonstrate that MVCTD achieves accurate forecasts with favorable runtime under severe missingness, confirming its suitability for online traffic prediction.
