Tucker factorization-based tensor completion for robust traffic data imputation

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY Transportation Research Part C-Emerging Technologies Pub Date : 2024-02-13 DOI:10.1016/j.trc.2024.104502
Cheng Lyu , Qing-Long Lu , Xinhua Wu , Constantinos Antoniou
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Abstract

Missing values are prevalent in spatio-temporal traffic data, undermining the quality of data-driven analysis. While prior works have demonstrated the promise of tensor completion methods for imputation, their performance remains limited for complicated composite missing patterns. This paper proposes a novel imputation framework combining tensor factorization and rank minimization, which is effective in capturing key traffic dynamics and eliminates the need for exhaustive rank tuning. The framework is further supplemented with time series decomposition to account for trends, spatio-temporal correlations, and outliers, with the intention of improving the robustness of imputation results. A Bregman ADMM algorithm is designed to solve the resulting multi-block nonconvex optimization efficiently. Experiments on four real-world traffic state datasets suggest that the proposed framework outperforms state-of-the-art imputation methods, including the context of complex missing patterns with high missing rates, while maintaining reasonable computation efficiency. Furthermore, the robustness of our model in extreme missing data scenarios, as well as under perturbation in hyperparameters, has been validated. These results also underscore the potential benefits of incorporating temporal modeling for more reliable imputation.

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基于塔克因式分解的张量补全,实现稳健的交通数据估算
时空交通数据中普遍存在缺失值,影响了数据驱动分析的质量。虽然之前的工作已经证明了张量补全方法在估算方面的前景,但对于复杂的复合缺失模式,其性能仍然有限。本文提出了一种结合了张量因式分解和秩最小化的新型归因框架,它能有效捕捉关键的交通动态,并且无需进行详尽的秩调整。该框架还辅以时间序列分解,以考虑趋势、时空相关性和异常值,从而提高估算结果的稳健性。设计了一种 Bregman ADMM 算法,以高效解决由此产生的多块非凸优化问题。在四个真实世界交通状态数据集上进行的实验表明,所提出的框架优于最先进的估算方法,包括高缺失率的复杂缺失模式,同时保持了合理的计算效率。此外,我们的模型在极端缺失数据情况下以及超参数扰动下的鲁棒性也得到了验证。这些结果还强调了将时间建模纳入更可靠的估算的潜在好处。
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来源期刊
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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