Lee, J.S., Hwang, S.H., Choi, I.Y. and Kim, I.K. (2018) Prediction of Track Deterioration Using Maintenance Data and Machine Learning Schemes. Journal of Transportation Engineering Part A Systems, 144, Article ID: 4018045.
https://doi.org/10.1061/JTEPBS.0000173

has been cited by the following article:

• TITLE: Multivariate Analyses for Finding Significant Track Irregularities to Generate an Optimal Track Maintenance Schedule

  AUTHORS: Mami Matsumoto, Masashi Miwa, Tatsuo Oyama

  KEYWORDS: Multivariate Analysis, Track Maintenance Scheduling, Track Irregularity, Longitudinal Level Irregularity Displacement, Cluster Analysis, Principal Component Analysis, Binomial Logit Regression Model, Ordinal Logit Regression Model

  JOURNAL NAME: American Journal of Operations Research, Vol.12 No.6, November 29, 2022

  ABSTRACT: We first discuss the relationship between the optimal track maintenancescheduling model and an efficient detection method for abnormal track irregularities given by the longitudinal level irregularity displacement (LLID). The results of applying the cluster analysis technique to the sampling data showed that maintenance operation is required for approximately 10% of the total lots, and these lots were further classified into three groups according to thedegree of maintenance need. To analyze the background factors for detecting abnormal LLID lots, a principal component analysis was performed; the results showed that the first principal component represents LLIDs from the viewpoints of the rail structure, equipment, and operating conditions. Binomial and ordinal logit regression models (LRMs) were used to quantitatively investigate the determinants of abnormal LLIDs. Binomial LRM was used tocharacterize the abnormal LLIDs, whereas ordinal LRM was used to distinguish the degree of influence of factors that are considered to have a significantimpact on LLIDs.