CAJ | 학술논문

根据交通流的历史周期性和空间相关性，文中综合SARIMA模型在历史周期性预测上的优势和RBF模型在空间相关性预测上的优势，提出了SARIMA-RBF模型。该模型采用SARIMA模型通过历史数据预测下一时刻的交通流，然后将预测值与该点上下游关联的交通流数据相结合，采用RBF神经网络模型得出输出值，并将该输出值作为SARIMA-RBF模型对下一时刻交通流的预测结果。实验结果表明，该模型因同时考虑了交通流的历史周期性和空间相关性，相比SARIMA模型和RBF模型具有更好的交通流预测效果。
근거교통류적역사주기성화공간상관성，문중종합SARIMA모형재역사주기성예측상적우세화RBF모형재공간상관성예측상적우세，제출료SARIMA-RBF모형。해모형채용SARIMA모형통과역사수거예측하일시각적교통류，연후장예측치여해점상하유관련적교통류수거상결합，채용RBF신경망락모형득출수출치，병장해수출치작위SARIMA-RBF모형대하일시각교통류적예측결과。실험결과표명，해모형인동시고필료교통류적역사주기성화공간상관성，상비SARIMA모형화RBF모형구유경호적교통류예측효과。
According to the historical cycles and spatial correlation of traffic flows , a model named as SARIMA-RBF is proposed to forecast the short-term traffic flow , which integrates the advantage of the SARIMA model in making use of the historical cyclic data with that of the RBF model in making use of the spatial correlation data .In the proposed model , the SARIMA model is adopted to forecast the traffic flow at the next time by using historical data, and then the RBF model is employed to obtain the output value by combining the SARIMA-based forecast data with the relevant traffic flow data of the upstream and downstream of test point .The output value of the RBF model is exactly the prediction result of the SARIMA-RBF model .Experimental results show that , in comparison with the SARIMA model and the RBF model , the SARIMA-RBF model achieves better results in forecasting the short-term traffic flow, because it considers both the historical cycles and the spatial correlation .