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A Bayesian Regression Model to Estimate the Deterioration Rate of Track Irregularities

궤도틀림 진전율 추정을 위한 베이지안 회귀분석 모형 연구

  • Park, Bum Hwan (Department of Railroad Management and Logistics, Korea National University of Transportation)
  • Received : 2016.07.13
  • Accepted : 2016.08.05
  • Published : 2016.08.31

Abstract

This study considered how to estimate the deterioration rate of the track quality index, which represents track geometric irregularity. Most existing studies have used a simple linear regression and regarded the slope of the regression equation as the progress rate. In this paper, we present a Bayesian approach to estimate the track irregularity progress. This Bayesian approach has many advantages, among which the biggest is that it can formally include the prior distribution of parameters which can be derived from historic data or from expert experiences; then, the rate can be expressed as a probability distribution. We investigated the possibility of applying the Bayesian method to the estimation of the deterioration rate by comparing our bayesian approach to the conventional linear regression approach.

본 연구는 궤도 틀림을 관리하기 위한 궤도 품질 지수(TQI)의 진전율 추정에 관한 것이다. 이와 관련한 기존 연구 대부분은 시간에 따른 TQI 값의 선형 회귀분석을 통해 구해진 기울기를 기준으로 상수 진전율을 제시하는 데 그치고 있다. 본 연구는 과거 데이터 혹은 전문가의 식견으로부터 도출되는 파라미터의 사전 분포를 효과적으로 반영할 수 있으며, 파라미터값의 확률 분포를 유도해 낼 수 있는 베이지안 방법론에 기초한 진전율 추정 모델을 제안하고, 기존의 전통적인 회귀분석 모형과의 비교 연구를 통해, 베이지안 방법론의 활용 가능성을 검토해 보았다.

Keywords

References

  1. J. Lee, Y.B. Choi (2009) Application of track recording data for track maintenance, Proceedings of the Korean Railway Association Spring Conference, Gyeongju, pp. 3057-3063.
  2. A.R. Andrade and P. F. Teixeira (2012) A Bayesian model to assess rail track geometry degradation through its life-cycle, Research in Transportation Economics, 36(1), pp. 1-8. https://doi.org/10.1016/j.retrec.2012.03.011
  3. N. Kim, S. Lee, Y. Won, et al. (2009) Introduction of track quality index (TQI) methods using track induction data, Proceedings of the Korean Railway Association Fall Conference, Jeju, pp. 66-72.
  4. M. El-Sibaie and Y. Zhang (2004) Objective track quality indices, Transportation Research Record : Journal of the Transportation Research Board, 1863, pp. 81-87.
  5. Y. Zhang, M. El-Sibaie, Sung Lee (2004) FRA track quality indices and distribution characteristics, Proceedings of The American Railway Engineering and Maintenance-of-way Association 2004 Annual Conference, Nashville, pp. 1-26.
  6. M.C. Jeong, J.H. Kim, J. Lee, et al. (2012) Study for progress rate of standard deviation of irregularity based on track properties for the railway track maintenance Cycle Analysis, Journal of the Korea Institute for Structural Maintenance and Inspection, 15(3), pp. 31-40.
  7. G.C. Shin (2013) A Study on the progress of track irregularity by track structure in urban railway system, Mater thesis, Seoul National University of Science and Technology.
  8. H. Park, S.Y. Jang, S. Park (2014) Correlation analysis between track irregularity and maintenance of high-speed railway, Proceedings of the Korean Railway Association Fall Conference, Jeju, pp. 1130-1133.
  9. J.-H. Ko, M.-C. Kim, J.-H. Lee, J.-G. Cho, and Y.G. Park (2011) An analysis of the Track Irregularity Progress on the Various Track System in Urban Transit, Proceedings of the Korean Railway Association Fall Conference, Jeju, pp. 311-319.
  10. D.-Y. Kim et al. (2008) Track Deterioration Prediction and Scheduling for Preventive Maintenance of Railroad, Proceedings of the Korean Railway Association Fall Conference, Gwangju, pp. 1346-1357.
  11. M.S. Oh (2013) Bayesian Statistical Inference with R Monte Carlo, Freedom Academy.
  12. http://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/ (Accessed 15 May 2016).
  13. J. Zhao et al. (2006) Optimizing policies of railway ballast tamping and renewal, Transportation Research Record Journal of the Transportation Research Board, 1943, pp. 50-56.