摘要
为了改善城市路网中短时交通流预测效果,提高预测精度,设计了一种基于改进的K近邻非参数回归和小波神经网络加权组合的短时交通流预测方法。针对K近邻非参数回归预测方法搜索量大、相似性差等问题,采用基于交叉口相关系数加权的欧氏距离选择K近邻值。小波变换与神经网络有机结合形成的前馈型网络,对非平稳的输入信号能够呈现出良好的时频特性和变焦能力,对短时交通流预测效果有着明显的提升。通过算例分析,说明所设计的预测方法能够获得比较精确的短时交通流预测结果。
In order to improve the effect and the prediction accuracy of short-term traffic flow prediction in urban road network, a short-term traffic flow prediction method based on a weighted combination method of improved K neighbor nonparametric regression and wavelet neural network（WNN） is designed. In order to solve the problems about K neighbor nonparametric regression prediction method, such as large amount of search and poor similarity, the Euclidean distance based on intersected weighted correlation coefficient was used to choose the K neighbor values. The feedforward network formed by the combination of wavelet transform and neural network can present a good time-frequency characteristic and zoom capability for non-stationary input signals, and has a obvious promotion for short-term traffic flow prediction. The example shows that the designed prediction method can obtain accurate results of short-term traffic flow prediction.
作者
雷斌
温乐
耿浩
李建明
LEI Bin;WEN Le;GENG Uao;LI Jian-ming(Mechatronics T＆R Institute, Lanzhou Jiaotong University, Lanzhou 730070, China;Engineering Technology Center for Informatization of Logistics ＆ Transport Equipment, Lanzhou 730070, China;Research Institute of Logistics and Information Technology, Lanzhou 730070, China;Xi＇an Schaltbau Electric Co., Ltd., Xi＇ an 710048, China)
出处
《测控技术》
CSCD
2018年第5期37-41,共5页
Measurement & Control Technology
基金
国家科技支撑计划项目(2012BAH20F05)
甘肃省高等学校科学研究项目(2016A-23)
兰州交通大学优秀科研平台(团队)资助计划(201604)
关键词
非参数回归
小波神经网络
短时交通流预测
组合预测
nonparametric regression
wavelet neural network
short-term traffic flow prediction
combination prediction
