A Flow Volumes Data Compression Approach for Traffic Network Based on Principal Component Analysis

With the rapid development of detecting technology, the amount and scale of detected traffic data are increasing in an unbelievable speed. This paper proposed an approach for compression of traffic network flow volume data based on principal component analysis (PCA). After pre-processing by mean filter method, all the 230,400 data points are compressed together and the PCs matrix has much smaller dimensions compared to the original data. The data are recovered with the compression ratio of 6.2 and the recovery error of 13%. In addition, this compression and recovery approach is proved to be robust to the abnormal data points such as the congestion data.