# Analysis of the Railway Accident-Related Damages in South Korea
## 1 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. Therefore, 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]. The cost of trains getting delayed, which is required for handling accidents, are commonly included in the transport network cost [2, 3]. In order to evaluate and quantify the costs of railway accidents, conventionally, the accident data is analyzed by using the aggregated cost of a long-term period, since railway accidents are not as frequent as road accidents.  
The studies for evaluating railroad accident-related damages have not been conducted as much as road accident studies. This is because road accidents happen more often and plenty of data is available to the public. Railway accidents rarely occur and access to the data is generally restricted by the railway operators. Due to the limited number of studies on railway accidents, some studies are related to the railway accident prediction models [4, 5, 6, 7, 8, 9, 10]. These studies analyzed accident factors and constructed a reliable accident prediction model for the prevention of the railway accidents.  
The factors for predicting accidents consist of human, rolling stock, facilities, and operational factors [4; 5; 6]. In particular, 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. Based on the accident factors, these studies introduced a logistic regression model for the occurrence/non-occurrence accident data and a Poisson regression model for the number of accident data.  
Unlike accident prediction models, 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. Even if an accident occurs, there are quite a lot of cases where casualties, train delays and accident costs do not occur. These cases are recorded as 'zero' in accident damage data. Therefore, statistical models for accident evaluation should be applied differently from existing accident prediction models.  
In order to introduce appropriate statistical models for railway accident evaluation, we review the literature related to the statistical models implemented on railway accident data and the models dealing with non-negative and the zero-inflated nature of data. Then, 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 accordingly, the railroad operator can reasonably establish a plan for necessary actions to reduce the accident cost in the future.