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S. Krezo¹, O. Mirza¹, Y. He¹ and S. Kaewunruen²

¹School of Computing, Engineering and Mathematics, Western Sydney University, Australia
²School of Civil Engineering, Birmingham Centre for Railway Research and Education, The University of Birmingham, Edgbaston, United Kingdom

doi:10.4203/ccp.110.300

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S. Krezo, O. Mirza, Y. He, S. Kaewunruen, "Bootstrap Statistical Analysis of GHG Emission from Railway Maintenance and Renewal Projects", in J. Pombo, (Editor), "Proceedings of the Third International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 300, 2016. doi:10.4203/ccp.110.300

Keywords: ballasted track bed, random, uncertainty, bootstrap analysis, greenhouse gas, emissions.

Summary

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.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 110 PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 300 Bootstrap Statistical Analysis of GHG Emission from Railway Maintenance and Renewal Projects S. Krezo¹, O. Mirza¹, Y. He¹ and S. Kaewunruen² ¹School of Computing, Engineering and Mathematics, Western Sydney University, Australia ²School of Civil Engineering, Birmingham Centre for Railway Research and Education, The University of Birmingham, Edgbaston, United Kingdom doi:10.4203/ccp.110.300 purchase the full-text of this paper Full Bibliographic Reference for this paper S. Krezo, O. Mirza, Y. He, S. Kaewunruen, "Bootstrap Statistical Analysis of GHG Emission from Railway Maintenance and Renewal Projects", in J. Pombo, (Editor), "Proceedings of the Third International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Stirlingshire, UK, Paper 300, 2016. doi:10.4203/ccp.110.300 Keywords: ballasted track bed, random, uncertainty, bootstrap analysis, greenhouse gas, emissions. Summary 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. purchase the full-text of this paper (price £22) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £85 +P&P)
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