Traffic Speed Imputation with Spatio-Temporal Attentions and Cycle-Perceptual Training

The phenomena of data missing are common in the field of traffic, yet existing solutions for data imputation are not sufficient due to challenges of data sparsity, complex traffic situations and the lack of complete ground truths. In this paper, we propose a novel solution called STCPA for the speed imputation problem. STCPA captures complex traffic correlations among the spatial and temporal dimensions via the attention mechanism, which helps mitigate the data sparsity issue. In addition, STCPA adopts an imputation cycle consistency constraint for providing reliable supervisions on unobserved entries, which improves the training. Furthermore, it incorporates an extra Road-aware Perceptual Loss, which helps encourage to preserve more meaningful semantics for imputation. Extensive experiments are conducted on two real-world datasets, namely, Chengdu and New York, to demonstrate the effectiveness of STCPA, e.g., it outperforms the best baseline by 7.64% and 5.00% on Chengdu and New York datasets, respectively. The code is available at https://github.com/Sam1224/STCPA.