Comparison on PPCA, KPPCA and MPPCA Based Missing Data Imputing for Traffic Flow

Abstract

In recent studies, the Probabilistic Principal Component Analysis (PPCA) for imputing missing data was shown to be a good tool for traffic flow data processing. The PPCA method has two major benefits: it results in significantly smaller reconstruction errors and much less computation time, which make it outperform the conventional historical and regression imputing methods. In this paper, the possibility of applying more complex PPCA methods, e.g. Kernel Probabilistic Principal Component Analysis (KPPCA) or Mixed Probabilistic Principal Component Analysis (MPPCA), to impute missing data is explored. Considering reconstruction errors and computation time cost, test results show that the basic PPCA method is still our first choice in missing data imputing for traffic flow for online systems.