The invention discloses a road monthly traffic volume prediction method based on an SARIMA-NAR combination model, and the method comprises the steps: collecting monthly traffic volume data over the years, and building a road monthly traffic volume time sequence; performing linear prediction on the monthly traffic volume time sequence by using an SARIMA model to obtain a linear result; calculating a residual error according to a linear result obtained by the SARIMA model and an original sequence, and extracting a residual error sequence; constructing an NAR model by using the residual sequence, and performing nonlinear prediction to obtain a nonlinear result; superposing the linear result and the nonlinear result to obtain a final monthly traffic volume prediction result; and finally, evaluating the prediction result of the combined model through a plurality of indexes including a mean absolute percentage error (MAPE), a mean absolute error (MAE) and a root-mean-square error (RMSE), wherein the prediction result has high effectiveness and reliability, and the prediction process is more convenient and efficient.

    本发明公开了一种基于SARIMA‑NAR组合模型的道路月度交通量预测方法,首先收集历年的月度交通量数据,建立道路月度交通量时间序列;然后将月度交通量时间序列运用SARIMA模型进行线性预测,得到线性结果;由SARIMA模型所得线性结果和原始序列计算残差,提取残差序列;后利用残差序列构建NAR模型,进行非线性预测,获取非线性结果;再叠加线性结果和非线性结果,获得最终月度交通量的预测结果;最后,通过平均绝对百分比误差(MAPE)、平均绝对误差(MAE)、均方根误差(RMSE)多个指标对组合模型预测结果进行评价,预测结果具有较高的有效性和可靠度,预测过程也更加便捷、高效。


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    Title :

    Road monthly traffic volume prediction method based on SARIMA-NAR combined model


    Additional title:

    基于SARIMA-NAR组合模型的道路月度交通量预测方法


    Contributors:
    WANG YOU (author) / JIA RUXUE (author) / YE YUNXIA (author) / ZHAO XIAOWEI (author)

    Publication date :

    2021-10-19


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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