Improving traffic time-series predictability by imputing continuous non-random missing data

Abstract

Continuous non-random data missing can be a challenging task for model prediction in intelligent transport system (ITS). In ITS, many methods have been proposed to solve this problem. However, the imputation accuracy of them is far from accurate. Thus, the authors propose a novel cross-modality generative adversarial network, named as cross-modality GAN, to impute continuous non-random missing data from the cross-modality perspective. This model uses the cross-modality data fusion technique to fuse spatial and temporal modal data into vectorized features, and then imputes the target unseen missing data by a data generation pipeline. Different from the other existing models, this model overcomes the problem of zero observation data, and realizes long-term missing time series imputation. Many comparative experiments are conducted. The results verify that the cross-modality GAN achieves better imputation performances on Performance Measurement System (PeMS) dataset, a real public traffic dataset, compared to other baseline models. Furthermore, the results verify that the imputed data of cross-modality GAN can provide more traffic time-series predictability information, and improve prediction accuracy of prediction models effectively.