基于PCA和ICA的交通流量数据压缩方法比较研究

针对交通流量数据的特点,分别采用基于主成份分析(PCA)和独立成份分析(ICA)的方法对其进行数据压缩研究和比较。首先,对城市道路和高速公路的交通流统计特性进行分析:发现采样时间的大小不会影响研究结果,而流量离差的统计分布为接近高斯分布的超高斯分布。然后分别采用基于PCA和ICA的方法进行交通流数据的压缩与重构,并对结果进行全面比较。试验结果表明:由于城市道路和高速公路的交通流离差数据均接近高斯分布,因此PCA在数据压缩中的效果较好;而高速公路的交通流数据压缩结果优于城市道路,因为其更加规律和稳定。这一结果反映了交通流波动的随机特征,对于进一步的交通流分析有着重要的意义。

Comparative Study of PCA and ICA Based Traffic Flow Compression

Data compression methods of traffic flow volumes based on principle component analysis(PCA) and independence component analysis(ICA) were provided and compared.First,the statistical properties of freeway and urban road traffic flow were examined.It was found that the sample rate doesn't affect the analysis result and the distributions of deviation for traffic flow of different sample rates are similar.All of them are super-gaussian distribution but near gaussian distribution.Second,the compressions and reconfiguration based on PCA and ICA were tested and compared for freeway and urban road traffic flow respectively.The result shows that(1)PCA is a better choice for both traffic flows,since they yield less non-gaussian distribution;(2) the compression for freeway is better than for urban road,since the freeway flow is more stable and regular.This result also gives some insight of the intrinsic nature of traffic flow and it is helpful for further studies.