摘要
考虑到交通流的随机性和非线性特征导致预测精度低的问题,本文提出一种基于二次分解和融合多特征的组合预测模型。利用时序分解方法将提取交通流量中的趋势性和周期性特征,通过优化后的变分模态分解对残差分量进行二次分解,并对所得分量进行重构;使用相关系数法选取交通流的外部特征,建立3个相异模型对融合外部特征后的分量进行预测;利用强化学习优化各模型的权重,加权求和得到最终的预测结果。利用长沙市区的交通流量进行仿真分析,结果表明:与长短时记忆神经网络模型、卷积神经网络和门控循环单元的组合模型、二次分解后的BP和二次分解后的轻量级梯度提升机相比,本文建立的模型对城市道路交通流的预测效果更好,平均绝对误差为2.622,均方根误差为3.479,均优于对比模型的预测误差,验证了模型的有效性。
Considering the random and nonlinear characteristics of traffic flow that result in low prediction accuracy,a combined prediction model based on quadratic decomposition and fusion of multiple features is proposed,where the trend and periodic features in traffic flow are extracted by using the time series decomposition method,the residual components are quadratically decomposed by the optimized variational mode decomposition,the scored amount is reconstructed,the external features of the traffic flow are selected by the correlation coefficient method,and three different models are established to predict the components after fusing the external features.Reinforcement learning is used to optimize the weights of each model,and the final prediction result is obtained by weighted sum.Using the simulation analysis of traffic flow in Changsha urban area,experimental results show that compared with the long short-term memory neural network model.Combined model of convolutional neural networks and gated cyclic units,BP after quadratic decomposition and lightweight gradient lifter after quadratic decomposition,the model established in this paper has a better prediction effect on urban road traffic flow,with an average absolute error of 2.622 and a root mean square error of 3.479.The prediction errors are better than the existing models,which verifies the effectiveness of the proposed model.
作者
陈昆
曲大义
王少杰
王其坤
CHEN Kun;QU Dayi;WANG Shaojie;WANG Qikun(College of Mechanical and Automotive Engineering,Qingdao University of Technology,Qingdao Shandong 266520,China)
出处
《广西师范大学学报（自然科学版）》
CAS
北大核心
2023年第4期33-46,共14页
Journal of Guangxi Normal University:Natural Science Edition
基金
国家自然科学基金(51678320)。
