Gross Error Detectability and Identifiability Analysis in Track Control Network for High-Speed Railway Based on GEJE

Abstract

To ensure that the high-speed train with speeds from 200 to 350 km/h or even faster can run safely and smoothly, the ballastless or ballast track must have high riding comfort. As the precise control reference of railway track, the surveying control network called base-pile control points III (CPIII) in China must be precise, smooth, and reliable. Therefore, careful field surveying and rigorous internal data checking are both required. However, the reliability of such a significant surveying system is still ambiguous for us, which is obviously not conducive to the effective control of data quality. In this paper, in order to select a feasible method to properly and effectively analyze the gross error separability of a CPIII network, two current separability analysis methods, that is, methods using correlation coefficient and gross error judgment equation (GEJE), are first comparatively analyzed in detail. The results show that the two methods are not only equivalent but also that the GEJE method is more convenient to study gross error separability among multiple observations. Some general regularities of gross error detectability and identifiability that exist in single and multiple observations of the CPIII network are then mined using the GEJE method; moreover, those regular findings are demonstrated using Monte Carlo simulations. The research results will be beneficial for deep understanding of the reliability of the CPIII network and further gross error detection.