摘要
在短时间内准确、稳定地预测出交通流量,是实现智能交通控制系统的重要环节,对于交叉口信号控制方案的实时调整具有重要意义。鉴于此,提出一种WD-BNN小波降噪与贝叶斯神经网络(wavelet denoising-Bayesian neural network,WD-BNN)联合模型的预测方式,引入平均绝对百分比误差(mean absolute percentage error,MAPE)和标准误差(root mean square error,RMSE)作为模型评价指标,从精度和稳定性两个方面对模型进行评价。结果表明:在5、10、15 min不同时间预测尺度下,WD-BNN联合模型的MAPE和RMSE均小于小波网络、贝叶斯网络、列文伯格-马夸尔特(Levenberg-Marquardt,L-M)网络等方法,短时交通流量预测结果的精度和稳定性得到了不同程度的提高。
Accurate and stable prediction of traffic flow in a short period of time is an important link in the realization of intelligent traffic control system,which is of great significance for real-time adjustment of intersection signal control scheme.In view of this,a prediction method of wavelet denoising-Bayesian neural network(WD-BNN)joint model was proposed,and mean absolute percentage error(MAPE)and root mean square error(RMSE)were introduced as evaluation indexes to evaluate the model from two aspects of accuracy and stability.The results show that MAPE and RMSE of WD-BNN combined model are smaller than Wavelet network,Bayesian network,Levenberg-Marquardt(L-M)network and other methods under different time prediction scales of 5,10,15 min,etc.,while the accuracy and stability of results have been improved to varying degrees.
作者
牟振华
李克鹏
申栋夫
MU Zhen-hua;LI Ke-peng;SHEN Dong-fu(School of Transportation,Shandong Jianzhu University,Jinan 250101,China)
出处
《科学技术与工程》
北大核心
2020年第33期13881-13886,共6页
Science Technology and Engineering
基金
教育部人文社科基金(19YJC630124)
山东省研究生教育质量提升计划(SDYKC18081)。
关键词
小波降噪
贝叶斯神经网络
短时交通流预测
Wavelet denoising
Bayesian neural network
short-term traffic flow forecast
