短时交通流预测模型

针对短时交通流变化周期性与随机性的特点,提出了新的混合预测模型,包含非参数回归模型与BP神经网络模型2种单项模型。非参数回归模型利用相关历史交通流数据,通过数据库匹配操作,确定预测结果,以充分体现交通流的周期稳定性。采用3层BP神经网络模型反映交通流的动态与非线性特点。采用模糊控制算法确定各单项模型的权重,并按不同权重有效组合成新的混合模型。采用西安市某路段30d的交通流量数据验证混合模型的预测效果。试验结果表明:该混合模型的平均相对误差为1.26%,最大相对误差为3.53%,其预测精度明显高于单项模型单独预测时的精度,能较准确地反映交通流真实情况。

Short-term traffic flow prediction model

A new hybrid prediction model including two single models of nonparametric regression model and BP neural network model was proposed according to the periodicity and randomness properties of short-term traffic flow.Relevant historical traffic flow data were used in nonparametric regression model to make the prediction result abtained from the databases matching proceeding fully illustrate the cyclical stability of traffic flow.Three-tier BP neural network model was used to reflect the dynamic and nonlinear characters of traffic flow.Fuzzy control algorithm was adopted to get the weight coefficient of each model.New mixed model was constituted by the two single models according to different weight coefficients.The prediction effect of hybrid prediction model was verified by the traffic flow data in 30 d from a certain section in Xi’an.Experimental result indicates that the average relative error of mixed model is 1.26%,and its maximum relative error is 3.53%,so the prediction accuracy of mixed model is obviously higher than two single models,and can accurately reflect the real situation of traffic flow.6 tabs,5 figs,16 refs.