智能交通大数据双频分量融合概率密度分析
Probability density analysis of dual-frequency component fusion of intelligent transportation big data
陆兴 1王笑笑 1宋非 2孙利国1
作者信息
- 1. 睿至科技集团有限公司,北京 100005
- 2. 山河汇(北京)科技发展有限公司,北京 100005
- 折叠
摘要
为了提高智能交通大数据概率密度分析的精准度和数据覆盖率,缩短其分析过程中所耗费的时间,文中研究了智能交通大数据双频分量融合的概率密度分析方法.在进行双频融合前,对数据进行预处理,消除数据中存在的噪声,再融合智能交通大数据的低频分量和高频分量,对得到的双频分量提取交通大数据的语义关联特征,实现概率密度分析.实验结果表明:文中方法在智能交通大数据的概率密度分析方面精准度较高、数据覆盖率较大、分析过程耗时较短,有效实现了智能交通大数据的概率密度分析.
Abstract
In order to improve the accuracy and data coverage of probability density analysis of intelligent transportation big data and shorten the time spent in the analysis process,this paper studies the probability density analysis method of dual-frequency component fusion of intelligent transportation big data.Before du-al-frequency fusion,the data is preprocessed to eliminate the noise in the data,and then the low-frequency and high-frequency components of the intelligent transportation big data are fused.For the obtained dual-frequency components,the semantic association features of traffic big data are extracted to realize probabili-ty density analysis.The experiment results show that the proposed method has high accuracy,large data coverage and short analysis time in the probability density analysis of intelligent transportation big data,which effectively realizes the probability density analysis of intelligent transportation big data.
关键词
概率密度分析/智能交通/双频分量/大数据/小波变换Key words
probability density analysis/intelligent transportation/dual frequency component/big data/wavelet transform引用本文复制引用
出版年
2024
NETL
NSTL
万方数据