用于时空交通数据的自关注图卷积推算网络

IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL
Xiulan Wei;Yong Zhang;Shaofan Wang;Xia Zhao;Yongli Hu;Baocai Yin
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引用次数: 0

摘要

时间序列中的缺失数据是一个普遍存在的问题,是后续交通数据分析的障碍。因此,人们对交通缺失数据估算任务进行了广泛的研究。最先进的交通数据估算模型大多基于递归神经网络。然而,这些方法属于自回归模型,极易受到误差传播的影响。基于注意力的方法属于非自回归模型,可以避免复合误差,有助于获得更好的估算质量。此外,基于注意力的方法目前已被广泛应用,并取得了显著的效果,但其在交通数据归因上的应用还很有限。因此,本文提出了针对时空交通数据的自注意力图卷积归约网络(SAGCIN)。为确保数据估算的准确性,有必要充分捕捉交通数据的时空背景信息来估算缺失值。为此,SAGCIN 模型将自我关注机制与扩散图卷积网络相结合。SAGCIN 模型由两个时空块组成,其中包括一个时空编码器和一个估算解码器。编码器学习专门用于交通数据估算任务的时空表示。基于学习到的表示,解码器对缺失数据执行两个阶段的估算操作。SAGCIN 引入了估算和重构的联合优化训练方法,以执行交通数据的缺失值估算。实证结果表明,SAGCIN 模型在相关真实世界基准的估算任务中表现优于最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-Attention Graph Convolution Imputation Network for Spatio-Temporal Traffic Data
Missing data in time series is a pervasive problem that serves as obstacles for subsequent traffic data analysis. Consequently, extensive research works have been conducted on traffic missing data imputation tasks. The state-of-the-art traffic data imputation models are mostly based on recurrent neural networks. However, these methods belong to autoregressive models which are highly susceptible to error propagation. The attention-based methods are non-autoregressive models that can avoid compounding errors and help achieve better imputation quality. Moreover, the attention-based methods in now widely applied and have achieved remarkable results, whereas their application on traffic data imputation is still limited. Thus, this paper proposes Self-Attention Graph Convolution Imputation Network (SAGCIN) for spatio-temporal traffic data. To ensure the accuracy of data imputation, it is necessary to fully capture the spatio-temporal contextual information of traffic data to impute missing values. To this end, the SAGCIN model incorporates self-attention mechanism with diffusion graph convolution network. The SAGCIN model consists of two spatio-temporal blocks with a spatio-temporal encoder and an imputation decoder. The encoder learns spatio-temporal representations specialized for traffic data imputation tasks. Based on the learned representation, the decoder performs two stages of imputation operator for missing data. A joint-optimization training approach of imputation and reconstruction is introduced for SAGCIN to perform missing value imputation for traffic data. Empirical results demonstrate that SAGCIN model outperforms state-of-the-art methods in imputation tasks on relevant real-world benchmarks.
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来源期刊
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems 工程技术-工程:电子与电气
CiteScore
14.80
自引率
12.90%
发文量
1872
审稿时长
7.5 months
期刊介绍: The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.
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