Short term traffic flow prediction provides effective information and decision-making through the prediction of future traffic conditions, so as to improve people's travel efficiency and alleviate urban traffic pressure. Improving the accuracy of short-term traffic flow prediction has become a hot issue in current research. By introducing random factors, this paper improves the movement strategy of the colony of the imperial competition algorithm. In addition to the assimilation of colonies by the Empire, colonies have a certain probability of reform and inheritance. By constructing a Back Propagation (BP) neural network model for short-term traffic flow prediction, the improved algorithm is applied to solve the weight and threshold of the model. According to the simulation results, the Mean Absolute Percent Error (MAPE) of RICA-BP is only 4.15%, which achieves a good prediction accuracy.


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    Titel :

    Short-term traffic flow prediction based on improved imperial competition algorithm


    Beteiligte:
    Yin, Fengping (Autor:in) / Zhang, Danhong (Autor:in) / Luo, Wenhui (Autor:in) / Gao, Bin (Autor:in)

    Kongress:

    International Conference on Mechanisms and Robotics (ICMAR 2022) ; 2022 ; Zhuhai,China


    Erschienen in:

    Proc. SPIE ; 12331


    Erscheinungsdatum :

    10.11.2022





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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