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

Steve Krezo, Olivia Mirza, Yaping He, Sakdirat Kaewunruen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Greenhouse gas (GHG) emission data associated with the maintenance activities of railway tracks is scarce in the 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 tracks 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 using a Gaussian distribution. A ninety-five percentile confidence interval was implemented in the bootstrapping analysis and revealed that the 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.
Original languageEnglish
Title of host publicationProceedings of the Third International Conference on Railway Technology: Research, Development and Maintenance
Subtitle of host publicationRailways 2016
Place of PublicationStirlingshire
PublisherCivil-Comp Press
Number of pages8
Publication statusPublished - 8 Apr 2016


  • ballasted track bed
  • random
  • uncertainty
  • bootstrap analysis


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