Browse > Article
http://dx.doi.org/10.7782/JKSR.2016.19.4.547

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

Park, Bum Hwan (Department of Railroad Management and Logistics, Korea National University of Transportation)
Publication Information
Journal of the Korean Society for Railway / v.19, no.4, 2016 , pp. 547-554 More about this Journal
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.
Keywords
Track irregularity; Track quality index; Linear regression; Bayesian method;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
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.   DOI
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.