Data-driven optimization of railway maintenance for track geometry. (May 2018)

• Record Type: Journal Article
• Authors: Sharma, Siddhartha; Cui, Yu; He, Qing; Mohammadi, Reza; Li, Zhiguo
• Is Part Of: Transportation research. Volume 90(2018)
• Page Start: 34
• Page End: 58
• Publication Date: 2018-05
• Subjects: Railway track inspection and maintenance -- Track geometry defects -- Condition-based maintenance -- Markov decision process; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2018.02.019
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Examine 33-month foot-by-foot track geometry inspection data. Predict the occurrence of geo-defects with Track Quality Index. Develop a Markov Decision Process to derive the optimal maintenance policy. Consider costs from both preventive maintenance and corrective maintenance. Abstract: Railway big data technologies are transforming the existing track inspection and maintenance policy deployed for railroads in North America. This paper develops a data-driven condition-based policy for the inspection and maintenance of track geometry. Both preventive maintenance and spot corrective maintenance are taken into account in the investigation of a 33-month inspection dataset that contains a variety of geometry measurements for every foot of track. First, this study separates the data based on the time interval of the inspection run, calculates the aggregate track quality index (TQI) for each track section, and predicts the track spot geo-defect occurrence probability using random forests. Then, a Markov chain is built to model aggregated track deterioration, and the spot geo-defects are modeled by a Bernoulli process. Finally, a Markov decision process (MDP) is developed for track maintenance decision making, and it is optimized by using a value iteration algorithm. Compared with the existing maintenance policy using Markov chain Monte Carlo (MCMC) simulation, the maintenance policy developed in this paper results in an approximately 10% savings in the total maintenance costs for every 1 mile of track.