The prediction of traffic accident duration is the basis of highway emergency management. Timely and accurate prediction of traffic accident duration can provide a reliable basis for road guidance and rescue organization. This paper discusses the traffic accident duration prediction method of N-BEATS model in detail. Through the change of sliding window size and the continuous adjustment of the number of iterations, the appropriate parameters are found to produce a good prediction effect. The dataset used in this paper is US Accidents, a nation-wide dataset of traffic accidents covering 49 states in the US. The experimental results show that compared with the classical time series prediction models such as Bi-LSTM, SVM, RNN-GRU and AttnAR, prediction of traffic accident duration model based on N-BEATS proposed in this paper is optimal in the three evaluation indicators of RMSE, MAE and SD, which shows that the model has the highest prediction accuracy and good performance.


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    Titel :

    Prediction of traffic accident duration based on N-BEATS


    Beteiligte:
    Subramaniam, Kannimuthu (Herausgeber:in) / Palanisamy Muthuramalingam, Arunkumar (Herausgeber:in) / He, Yue (Autor:in) / Zhang, Senchang (Autor:in) / Zhong, Peiyao (Autor:in) / Li, Zhenliang (Autor:in)

    Kongress:

    Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023) ; 2023 ; Zhengzhou, China


    Erschienen in:

    Proc. SPIE ; 12635


    Erscheinungsdatum :

    08.05.2023





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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