用双范数融合提升交通速度缺失数据填补精度。
A Double-Norm Aggregated Tensor Latent Factorization Model for Temporal-Aware Traffic Speed Imputation
- 结合L2与平滑L1范数构建损失函数，增强鲁棒性。
- 在三个真实数据集上均优于现有方法，准确率显著提升。
- 适合需要高精度交通数据的智能交通系统应用。
在智能交通系统中，交通管理部门依赖传感器、摄像头和GPS设备采集实时交通数据。由于传感器故障、传输延迟或遮挡，某些路段的交通速度数据常出现缺失。现有基于张量分解的方法多采用L2-范数构建学习目标，导致算法鲁棒性不足。为此，本文提出时间感知交通速度填补模型TATSI，将L2-范数与平滑L1（SL1）-范数融合于损失函数中，兼顾高精度与强鲁棒性。TATSI采用单隐因子依赖、非负且乘法更新（SLF-NMU）策略，高效求解张量上的非负隐因子分析（LFA）。在三个真实世界时变交通速度数据集上的实证研究显示，相比当前最先进的预测方法，TATSI更精准捕捉时间模式，对缺失交通速度数据的填补效果最优。
原文摘要 · Abstract (English)
In intelligent transportation systems (ITS), traffic management departments rely on sensors, cameras, and GPS devices to collect real-time traffic data. Traffic speed data is often incomplete due to sensor failures, data transmission delays, or occlusions, resulting in missing speed data in certain road segments. Currently, tensor decomposition based methods are extensively utilized, they mostly rely on the $L_2$-norm to construct their learning objectives, which leads to reduced robustness in the algorithms. To address this, we propose Temporal-Aware Traffic Speed Imputation (TATSI), which combines the $L_2$-norm and smooth $L_1$ (${SL}_1$)-norm in its loss function, thereby achieving both high accuracy and robust performance in imputing missing time-varying traffic speed data. TATSI adopts a single latent factor-dependent, nonnegative, and multiplicative update (SLF-NMU) approach, which serves as an efficient solver for performing nonnegative latent factor analysis (LFA) on a tensor. Empirical studies on three real-world time-varying traffic speed datasets demonstrate that, compared with state-of-the-art traffic speed predictors, TATSI more precisely captures temporal patterns, thereby yielding the most accurate imputations for missing traffic speed data.
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