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Multivariate Analyses for Finding Significant Track Irregularities to Generate an Optimal Track Maintenance Schedule
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作者 Mami Matsumoto Masashi Miwa Tatsuo Oyama 《American Journal of Operations Research》 2022年第6期261-292,共32页
We first discuss the relationship between the optimal track maintenance scheduling model and an efficient detection method for abnormal track irregularities given by the longitudinal level irregularity displaceme... We first discuss the relationship between the optimal track maintenance scheduling 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 the degree 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 to characterize the abnormal LLIDs, whereas ordinal LRM was used to distinguish the degree of influence of factors that are considered to have a significant impact on LLIDs. 展开更多
关键词 Multivariate Analysis Track Maintenance Scheduling Track Irregularity Longitudinal Level Irregularity Displacement Cluster Analysis Principal Component Analysis Binomial logit regression model ordinal logit regression model
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