To predict traffic flow under adverse weather, a hybrid deep learning model concerning adverse weather (DLW-Net) is formulated. The DLW-Net model consists of the target and global analysis parts. For the target analysis part, the spatio-temporal characteristics of traffic flow data are analyzed using the convolutional neural network (CNN), the long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks. For the global analysis part, the variation rules of traffic flow and weather data are extracted using the LSTM model. Additionally, the characteristics of traffic flow under normal and adverse weather are also discussed. The developed model is verified using three cases. The results show that traffic volume and speed would reduce under heavy rain compared to normal weather, however, drizzle has little impact on traffic flow patterns; the rules of traffic speed data are disturbed by strong wind; and the DLW-Net model performs best under all the conditions.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    DLW-Net model for traffic flow prediction under adverse weather


    Beteiligte:
    Yao, Ronghan (Autor:in) / Zhang, Wensong (Autor:in) / Long, Meng (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    31.12.2022


    Format / Umfang :

    26 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Traffic volume prediction method under adverse weather conditions

    CI YUSHENG / GAO XUEYI / LI HAOWEN | Europäisches Patentamt | 2024

    Freier Zugriff

    Saturation flow under Adverse Weather Conditions

    Asamer, Johannes / Van Zuylen, Henk J. | Transportation Research Record | 2011


    Traffic Signal Control Strategy under Adverse Weather Condition

    Zhang, Lun / Zhao, Wenwen / Zhang, Xiyu | ASCE | 2018


    Saturation Flow Under Adverse Weather Conditions

    Asamer, Johannes | Online Contents | 2011


    Spatiotemporal Data Model on Urban Road Traffic Network under Adverse Weather Conditions

    Zhang, X.-q. / An, S. / Miao, X. et al. | British Library Conference Proceedings | 2007