# Analysis of the Railway Accident-Related Damages in South Korea
## 5 Conclusions  
Although railroad accident evaluation is a very critical issue, not many studies related to accident evaluation have been conducted. In this study, we propose appropriate statistical models that handle the non-negative nature of accident data with respect to the damages from railway accident. As for the damage data of railroad accidents, there are many cases where the damage did not occur even though the railroad accident occurred, so a statistical approach that reflects this is necessary. To do this, we employed the two-part regression models for evaluating the damages caused by railway accidents such as the train delay time, the number of trains delayed and the costs incurred in handling accidents. For the data set, we extracted all the recorded accidents data with respect to the railway types (urban, general and high-speed railway), organization types (metro and KORAIL) and accident factors (human-related and non-human related). Further, we analyzed the statistical results to identify the variables that are highly correlated to the accident types and the magnitude of accidents reflecting the train delay and the costs. Overall, we found that the railway accidents in South Korea continued to decrease with the passage of time in terms of the number of accidents occurred and the amount of costs incurred during the period 2008 to 2016. During this analysis period, new rolling stocks replaced the old ones at the general railway system and PSDs were installed at the urban railway system in order to reduce suicide attempts. It seems to have played a positive role in reducing the railway accidents. From the statistical analyses, we found that the number of trains delayed tends to increase during the period of our interest to the railway accidents but KORAIL had smaller number of trains delayed than the other organizations. The KTX caused the least delay in trains, followed by the general trains and urban railway resulted in the most delay in trains. Human-related accidents resulted in more delays in trains compared to the non-human related accidents. It was also found that with increase in derailed trains more trains got delayed. Considering the amount of delay time due to railway accidents, KORAIL results in less delay time than the other organizations. Both the KTX and urban trains tend to decrease the amount of delay time compared to that of the general trains. Human-related accidents reduced the probability of having delay time rather than non-human related accidents. What is also important here is that, as the number of casualties increased, it took longer time for train operations to normalize. We included the number of delayed trains as the dependent variable and the amount of time delayed as potential independent variable in the TPMs, to comprehensively evaluate the amount of costs from railway accidents. Some potential interactions between independent variables were also included in the TPMS. With the passage of time, the probability of having railway accidents with no costs generally increased. Especially, KORAIL tends to reduce the probability of causing railway accidents with costs compared with metro. Railway accidents caused by human-related factors are more likely to increase the amount of costs. Urban railways have the least chance of getting involved in railway accidents with costs and KTX results in more costs than the general railways. It is interesting that, as the number of delayed trains increases, both the probability of incurring costs and its amount of costs increase. This is also the case for amount of delay time. The number of casualties has a positive impact on the amount of costs. When an accident was involved in urban railway and more subsequent trains are delayed, the costs are likely to increase compared to an accident involving general railway. If an accident is human-related and has occurred in KTX, then the costs also escalate.