Probability density analysis of dual-frequency component fusion of intelligent transportation big data
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.
probability density analysisintelligent transportationdual frequency componentbig datawavelet transform
NETL
NSTL
万方数据
