为了提高短时交通流量预测的精度，构建了基于相空间重构和正则化极端学习机的短时交通流量预测模型． 首先采用C-C 算法求解交通流量时间序列的最佳时间延迟和嵌入维数，进行相空间重构; 然后选用G-P 算法计算序列关联维数，判断出短时交通流量序列具有混沌特性． 在此基础上，将重构数据作为正则化极端学习机的输入和输出来训练模型，并采用网格搜索法优化模型参数． 最后以实测数据为基础，对模型的预测效果进行对比分析． 结果表明，新构建模型的预测效果良好，能够有效提高短时交通流量预测精度．

In order to increase the accuracy of short-time traffic flow prediction,a flow prediction model based on the phase space reconstruction and the regularized extreme learning machine is put forward.In this method，the CC method is used to calculate the best time delay and embedding dimension of traffic flow time series for phase space reconstruction,and the G-P algorithm is used to calculate the correlative dimension of the seriesthat is an important judgment index ofthe chaotic characteristics of traffic flow series,Then,the reconstructed phase point data are taken as the inputs and outputsto trainthe regularized extreme learning machine model,and the main parameters of the model are determined by means of grid searching.Finally,a comparative analysis is carried out based on the actual measured traffic flow data.The results show that the proposed model possesses high performance and is effective in improving the accuracy of short-time traffic flow prediction.