Abstract

Railway accidents are critical issues characterized by a large number of injuries and fatalities per accident due to massive public transport systems. This study proposes a new approach for evaluating the damages resulting from railway accidents using the two-part models (TPMs) such as the zero-inflated Poisson regression model (ZIP model) and the zero-inflated negative-binomial regression model (ZINB model) for the non-negative count measurements and the zero-inflated gamma regression model (ZIG model) and the zero-inflated log-normal regression model (ZILN model) for the semi-continuous measurements. The models are employed for the evaluation of the railway accidents on Korea Railroad, considering the accident damages, such as the train delay time, the number of trains delayed and the cost of considering the accident count responses, for the period 2008 to 2016. From the results obtained, we found that the human-related factors, the high-speed railway system or the Korea Train Express (KTX) and the number of casualties, are the main cost-escalating factors. The number of trains delayed and the amount of delay time tend to increase both the probability of incurring costs and the amount of cost. For better evaluation, the railway accident data should contain accurate information with less recurrence of zeros.

Highlights

  • The evaluation factors of the railway accidents are represented by the severity of injuries, the fatalities, the damage of rolling stock and the associated infrastructure, and the environmental cost.the evaluation of railway safety is mainly related to the most common factors that have an impact on accidents and their significance to the severity of the injury caused [1]

  • From the descriptive statistics of the railway accident data, it is found that about 88.8% of railway accidents occur in the railways operated by KORAIL, which dominate the railroad service network

  • We propose appropriate statistical models that handle the non-negative nature of accident data with respect to the damages from railway accident

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Summary

Introduction

The evaluation factors of the railway accidents are represented by the severity of injuries, the fatalities, the damage of rolling stock and the associated infrastructure, and the environmental cost. The analysis of accidents occurring at railroad-crossing related to facilities research was dominant [7,8,9] These studies have explored factors which have an impact on accidents occurring between vehicles and trains, such as the number of train tracks, the number of highway lanes, train and traffic volumes, train and vehicle speeds, site and surface characteristics, road/rail-side appurtenances and so forth. Accident evaluation models have to consider the non-negative and zero-inflated nature of accident damage data as dependent measures like the train delay time, the number of trains delayed and the cost of considering the accident count responses. We propose reliable statistical models and implement them to the train delay time, the number of trains delayed and the cost by using railway accident data observed in Korea. The appropriate accident evaluation models can accurately assess the damages caused by railroad accidents and the railroad operator can reasonably establish a plan for necessary actions to reduce the accident cost in the future

State of Art Statistical Models
Two-Part Model
TPMs for Non-Negative Count Measurements
TPMs for Semi-Continuous Measurements
Real Application
Distribution of Delay
Contingency
Findings
Conclusions

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