Bootstrap Statistical Analysis of GHG Emission from Railway Maintenance and Renewal Projects

Greenhouse gas (GHG) emission data associated with the maintenance activities of railway tracks is scarce in open literature and practically difficult to obtain from the field due logistic difficulties. This paper attempts to improve the statistical description of the GHG emission intensity from the maintenance work of plain-line ballasted track by applying a bootstrapping statistical analysis to the limited raw data obtained from a field study. Bootstrapping resamples of various sizes were subjected to statistical analysis to obtain the mean, standard deviation, bias, skewness and confidence levels of the GHG emission intensity due to rail maintenance. The bootstrap analysis showed that there is a very small bias when compared with the field data. The standard deviation and standard error were less than those of the field study. The frequency distribution analysis showed that the GHG emission intensity could be approximately described by a Gaussian distribution. A 95% percentile confidence interval was implemented in the bootstrapping analysis and revealed that GHG emission intensity in rail maintenance activity is highly likely to fall between 12.66 kg/m and 41.35 kg/m for ballast maintenance activities.