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Multi-stage deep residual collaboration learning framework for complex spatial-temporal traffic data imputation
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DOI：10.1016/j.asoc.2023.110814.png)
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Performing accurate and efficient traffic data repair has become an essential task before proceeding with other applications of intelligent transportation systems. However, existing repair models often impose assumptions on the underlying data generation process or fail to sufficiently capture the complex spatial-temporal dependencies between several nodes at different intervals. To address these issues, we propose a novel multi-stage deep residual collaboration learning framework (named Multi-DRCF) to tackle the complex task of repairing missing traffic data. In particular, a spatiotemporal fusion network (i.e., ST-imputator) incorporating the graph-based convolutions with recurrent structures is introduced for modeling the spatial topology and dynamic temporal coherences of the traffic graph network in turn. Moreover, to further achieve both higher accuracy and efficiency, we exploit a stackable bi-directional residual optimization (i.e., Bi-RO) structure to enhance the role of ST-imputator in the utilization of the spatiotemporal properties of the residuals. Indeed, this combination of ST-imputator and Bi-RO structure can serve as an iterable unit for Multi-DRCF, and the whole computation process is highly modular and flexible in operation. We evaluate Multi-DRCF using two real-world large-scale traffic speed datasets, i.e., Seattle-Loop and METR-LA. Experimental results demonstrate that Multi-DRCF can effectively extract the spatial and temporal properties from traffic data, resulting in an excellent repair performance with an acceptable computational cost. Compared with the state-of-the-art baseline models, Multi-DRCF performs more competitively for three types of missing patterns, and also provides steadier repair results with missing rates ranging from 10% to 90%. Finally, the ablation experiments and visualization analysis also present well insights for better understanding the superiority of the Multi-DRCF for complex spatial-temporal traffic data imputation.(c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Traffic data imputation
Graph-based convolutions
Recurrent structures
Residual optimization
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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