Sequence-to-Sequence Traffic Missing Data Imputation via Self-Supervised Contrastive Learning

Missing traffic data is a common phenomenon in practice, which has serious implications for the development of downstream data-driven traffic research. Although numerous methods have been proposed to impute missing values, two limitations still exist: lack of complete ground truth and reliance on carefully constructed model structures. In this paper, we propose a novel deep neural imputation network framework named STIseq2seq, based on self-supervised contrastive learning. It follows a sequence-to-sequence architecture with attention mechanism to address the traffic data imputation problem. Specifically, the model consists of two key components: encoder and decoder, both composed of ST-block. Initially, we utilize dynamic graph convolution (DGC) and gated recurrent unit (GRU) to construct the fundamental unit, ST-block, for learning spatiotemporal dependencies. Then, a bidirectional ST-block is employed to model incomplete input data in the encoder; while a forward ST-block combined with an attention mechanism is used to accurately impute missing data in the decoder. Lastly, we devise a training method based on self-supervised contrastive learning, achieved through randomly shuffling the masking matrix, to enhance training effectiveness. Extensive experiments conducted on two available real-world traffic datasets demonstrate that our proposed STIseq2seq has superior and stable performance compared to multiple baseline models under different missing data scenarios with missing rates ranging from 20% to 80%. Visualization analysis of the imputation results further provides insights into the advantages of STIseq2seq.