融合PCA和ESN的交通流周期预测模型

Traffic flow cycle prediction based on the PCA-ESN model

针对传统交通流多步预测精度低的问题,提出了一种交通流周期预测模型。该模型结合交通流的周期性特征重构时间序列,并引入主成分分析降维思想,利用回声状态网络模型进行交通流时间序列预测,同时采用自适应扰动粒子群算法优化模型中的重要参数。将该模型应用到实际交通流时间序列中进行有效性验证,其预测结果的平均绝对百分比误差为9.8%,比传统回声状态网络多步预测模型降低了12.7%。实验结果表明,该模型可有效地避免预测结果延迟问题并大幅提高多步预测的精度。

Aiming at the problem of low precision of multi-step traffic flow prediction,a cycle prediction model for traffic flow forecasting is presented.First,the time series is reconstructed by considering the periodicity of traffic flow in our model,and Principal Component Analysis(PCA)is explored as a dimensionality reduction method.Then the Echo State Network(ESN)model is used to predict the traffic flow time series.Meanwhile,an adaptive disturbance particle swarm optimization algorithm is used to optimize the parameters of the model.The availability of the proposed model is proved by predicting the time series of real traffic flow.The Mean Absolute Percentage Error(MAPE)of the prediction results is 9.8%,which is 12.7%lower than that of the traditional ESN multi-step prediction model.Experiments demonstrate that the proposed model can effectively prevent the delay of prediction results and greatly improve the precision of multi-step prediction.