ABSTRACT
The main goal of this research was to identify personal and professional factors of train drivers which imply in the occurrence of accidents (denoted in this study as situations to non-compliance with procedures of the train driver consisting of unsafe acts that could cause major accidents) of a Brazilian logistics company related to accidentes in the period from 2014 to 2016. The research involved 348 drivers and some independent variables associated with each worker such as distances of removal (railway sections), driver’s age, working time (time working in the company), marital status, number of trips (number of train trips as a driver); months of work (months of work in the company) and total hours driving trains. Responses of interest are accident rates per work month and accident counts. Under a Bayesian approach, Beta and Poisson regression models were assumed in the presence of excess zeros. For the accident rate/months of work response, the covariates number of trips, posting distances and age showed some evidence of significant effects. For the response accident count, the covariate hours driving trains shows some evidence that it is a significant covariate. These results may be of great interest to rail logistics company managers to improve rail safety using some Bayesian modeling approaches in the presence of excess zeros.
Keywords:
railroad accidents; beta regression in the presence of excess of zeros; bayesian analysis
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