Forecasting traffic volumes under adverse weather in advance contributes to allocating traffic resources for traffic managers and formulating optimal travel strategies for travelers, which assists in preventing and offsetting the impact of adverse weather on traffic. Consequently, the accurate prediction of traffic volume is vital. This paper proposes an adverse weather traffic volume prediction model combining convolution neural networks, bidirectional long short-term memory (BiLSTM), and the attention mechanism. Convolutional neural networks extract the spatial features of the traffic volume data and learn the connection between the traffic volume data and the data of each adverse weather impact factor; BiLSTM extracts the temporal features of the traffic volume data; and the attention mechanism captures the inhomogeneity of spatial-temporal features so that the model can pay more attention to the key features during the training process. The 5-min highway traffic volume data from December 1, 2021, to March 13, 2022, in Minnesota, United States, and the weather data in the same period provided by MesoWest were used as the experimental data. The proposed model was compared with three single prediction models, two validated hybrid models, and the model itself without integrating adverse weather impact factors. The experiments show that the prediction accuracy of the proposed model is higher than other comparison models.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Forecasting Freeway Traffic Volumes with Adverse Weather via a CNN-BiLSTM-Attention Model


    Additional title:

    J. Transp. Eng., Part A: Systems


    Contributors:
    Ci, Yusheng (author) / Gao, Xueyi (author) / Li, Haowen (author) / Yuen, Kum Fai (author) / Wu, Lina (author)


    Publication date :

    2025-02-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    BiLSTM- and GNN-Based Spatiotemporal Traffic Flow Forecasting with Correlated Weather Data

    Abdullah Alourani / Farzeen Ashfaq / N. Z. Jhanjhi et al. | DOAJ | 2023

    Free access

    Generalized Visibility of Freeway in Adverse Weather Conditions

    Huang, S. / Zhang, H. / Wen, S. et al. | British Library Conference Proceedings | 2010


    Impact of Adverse Weather on Freeway Bottleneck Performance

    Seeherman, Joshua / Skabardonis, Alexander | ASCE | 2020


    Generalized Visibility of Freeway in Adverse Weather Conditions

    Huang, Shougang / Zhang, Huili / Wen, Shaofang et al. | ASCE | 2010