Road link speed is often employed as an essential measure of traffic state in the operation of an urban traffic network. Not only real-time traffic demand but also signal timings and other local planning factors are major influential factors. This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation. The proposed method applies Wasserstein Generative Adversarial Nets (WGAN) for robust data-driven traffic modeling using a combination of generative neural network and discriminative neural network. The generative neural network models the road link features of the adjacent intersections and the control parameters of intersections using a hybrid graph block. In addition, the spatial-temporal relations are captured by stacking a graph convolutional network (GCN), a recurrent neural network (RNN), and an attention mechanism. A comprehensive computational experiment was carried out including comparing model prediction and computational performances with several state-of-the-art deep learning models. The proposed approach has been implemented and applied for predicting short-term link traffic speed in a large-scale urban road network in Hangzhou, China. The results suggest that it provides a scalable and effective traffic prediction solution for urban road networks.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework


    Beteiligte:
    Jin, Junchen (Autor:in) / Rong, Dingding (Autor:in) / Zhang, Tong (Autor:in) / Ji, Qingyuan (Autor:in) / Guo, Haifeng (Autor:in) / Lv, Yisheng (Autor:in) / Ma, Xiaoliang (Autor:in) / Wang, Fei-Yue (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-09-01


    Format / Umfang :

    3134763 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    The Urban Road Short-Term Traffic Flow Prediction Research

    Qin, Zhen Hai | Trans Tech Publications | 2013


    Urban Road Traffic Flow Prediction with Attention-Based Convolutional Bidirectional Long Short-Term Memory Networks

    Liu, Zhiquan / Hu, Yao / Ding, Xiangying | Transportation Research Record | 2023


    Urban road network short-term traffic operation state estimation and prediction method

    REN GANG / SONG JIANHUA / CAO QI et al. | Europäisches Patentamt | 2020

    Freier Zugriff

    Short-term traffic flow prediction of road network based on deep learning

    Han, Lei / Huang, Yi-Shao | IET | 2020

    Freier Zugriff

    Short‐term traffic flow prediction of road network based on deep learning

    Han, Lei / Huang, Yi‐Shao | Wiley | 2020

    Freier Zugriff