短期高速公路交通流量預(yù)測方法研究
發(fā)布時(shí)間：2019-02-28 21:30
【摘要】：短時(shí)交通流量預(yù)測能夠推算高速公路未來短時(shí)刻內(nèi)的交通流量的發(fā)展動向,指導(dǎo)即時(shí)的高速公路交通運(yùn)營管理并改善交通運(yùn)行狀況。傳統(tǒng)短期預(yù)測模型只反映交通流量部分信息,受高速公路流量數(shù)據(jù)的流量成分復(fù)雜性和非線性影響較大,預(yù)測精確度較低。為了提高高速公路短期交通流量的預(yù)測精度,結(jié)合交通流量數(shù)據(jù)中的周期性特征,提出一種改進(jìn)的流量預(yù)測方法。首先提取流量數(shù)據(jù)的周期分量,然后用自回歸滑動平均預(yù)測模型對去除周期分量后的殘余分量進(jìn)行預(yù)測,最后將得到的殘余分量預(yù)測值與周期分量進(jìn)行累加,得到最終的預(yù)測值。并進(jìn)行若干組對比實(shí)驗(yàn)研究周期分量比例不同對預(yù)測的影響。當(dāng)殘余分量出現(xiàn)負(fù)值時(shí),通過增減偏移量的方法對周期分量進(jìn)行修正。實(shí)驗(yàn)表明,修正了周期分量后,提取了周期分量的數(shù)據(jù)再進(jìn)行預(yù)測,精度能得到提高;周期分量的能量比例越大,精度提升越明顯。
[Abstract]:The prediction of short-term traffic flow can predict the development trend of expressway traffic flow in the short time in the future, guide the real-time traffic operation and management of expressway and improve the traffic condition. The traditional short-term forecasting model only reflects some information of traffic flow, which is greatly influenced by the complexity and nonlinearity of traffic components and non-linearity of highway flow data, and the prediction accuracy is low. In order to improve the prediction accuracy of highway short-term traffic flow, an improved traffic forecasting method is proposed based on the periodic characteristics of traffic flow data. First, the periodic component of the flow data is extracted, and then the residual component after removing the periodic component is predicted by using the autoregressive moving average prediction model. Finally, the residual component prediction is accumulated with the periodic component. The final prediction is obtained. Several groups of comparative experiments were carried out to study the effect of different proportion of periodic components on the prediction. When the residual component is negative, the periodic component is modified by the method of increasing and decreasing the offset. The experimental results show that after the periodic component is modified, the accuracy can be improved by extracting the periodic component data, and the higher the energy ratio of the periodic component is, the more obvious the accuracy improvement will be.
【作者單位】： 四川大學(xué)電氣信息學(xué)院;
【基金】：四川省交通科技項(xiàng)目(2013c7-1)
【分類號】：U491.14
[Abstract]:The prediction of short-term traffic flow can predict the development trend of expressway traffic flow in the short time in the future, guide the real-time traffic operation and management of expressway and improve the traffic condition. The traditional short-term forecasting model only reflects some information of traffic flow, which is greatly influenced by the complexity and nonlinearity of traffic components and non-linearity of highway flow data, and the prediction accuracy is low. In order to improve the prediction accuracy of highway short-term traffic flow, an improved traffic forecasting method is proposed based on the periodic characteristics of traffic flow data. First, the periodic component of the flow data is extracted, and then the residual component after removing the periodic component is predicted by using the autoregressive moving average prediction model. Finally, the residual component prediction is accumulated with the periodic component. The final prediction is obtained. Several groups of comparative experiments were carried out to study the effect of different proportion of periodic components on the prediction. When the residual component is negative, the periodic component is modified by the method of increasing and decreasing the offset. The experimental results show that after the periodic component is modified, the accuracy can be improved by extracting the periodic component data, and the higher the energy ratio of the periodic component is, the more obvious the accuracy improvement will be.
【作者單位】： 四川大學(xué)電氣信息學(xué)院;
【基金】：四川省交通科技項(xiàng)目(2013c7-1)
【分類號】：U491.14
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