Traffic state prediction plays an important role in intelligent transportation systems, but the complex spatial influence of traffic networks and the non-stationary temporal nature of traffic states make it a challenging task. In this study, a new traffic network state prediction model for freeways based on a generative adversarial framework is proposed. The generator based on the long short-term memory networks is adopted to generate future traffic states, and a discriminator with multiple fully connected layers is applied to simultaneously ensure the prediction accuracy. The results of experiments show that the proposed framework can effectively predict future traffic network states and is superior to the baselines.


    Zugriff

    Zugriff über TIB


    Exportieren, teilen und zitieren



    Titel :

    Road traffic network state prediction based on a generative adversarial network


    Beteiligte:
    Xu, Dongwei (Autor:in) / Peng, Peng (Autor:in) / Wei, Chenchen (Autor:in) / He, Defeng (Autor:in) / Xuan, Qi (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    30.07.2020


    Format / Umfang :

    9 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Road traffic network state prediction based on a generative adversarial network

    Xu, Dongwei / Peng, Peng / Wei, Chenchen et al. | Wiley | 2020

    Freier Zugriff

    Generative Adversarial Network-Based Regional Epitaxial Traffic Flow Prediction

    Kang, Yan / Li, Jinyuan / Lee, Shin-Jye et al. | Springer Verlag | 2019


    Traffic flow prediction method based on generative adversarial network

    XU DONGWEI / LIN ZHENQIAN / YANG HAO et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Urban traffic flow prediction method based on generative adversarial network

    ZHU NINGBO / PU BIN / FAN XINXIN et al. | Europäisches Patentamt | 2020

    Freier Zugriff

    TrafficGAN: Network-Scale Deep Traffic Prediction With Generative Adversarial Nets

    Zhang, Yuxuan / Wang, Senzhang / Chen, Bing et al. | IEEE | 2021