Traffic data imputation is critical for both research and applications of intelligent transportation systems. To develop traffic data imputation models with high accuracy, traffic data must be large and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. In this paper, we propose a novel approach using parallel data and generative adversarial networks (GANs) to enhance traffic data imputation. Parallel data is a recently proposed method of using synthetic and real data for data mining and data-driven process, in which we apply GANs to generate synthetic traffic data. As it is difficult for the standard GAN algorithm to generate time-dependent traffic flow data, we made twofold modifications: 1) using the real data or the corrupted ones instead of random vectors as latent codes to generator within GANs and 2) introducing a representation loss to measure discrepancy between the synthetic data and the real data. The experimental results on a real traffic dataset demonstrate that our method can significantly improve the performance of traffic data imputation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Traffic Flow Imputation Using Parallel Data and Generative Adversarial Networks


    Contributors:


    Publication date :

    2020-04-01


    Size :

    2488832 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images

    Tongge Huang / Pranamesh Chakraborty / Anuj Sharma | DOAJ | 2023

    Free access

    A Gated Generative Adversarial Imputation Approach for Signalized Road Networks

    Zhang, Tong / Wang, Jianlong / Liu, Jie | IEEE | 2022


    Predicting Commercial Vehicle Parking Duration using Generative Adversarial Multiple Imputation Networks

    Low, Raymond / Tekler, Zeynep Duygu / Cheah, Lynette | Transportation Research Record | 2020

    Free access