# Analysis of the Railway Accident-Related Damages in South Korea
## 4 Real Application
The covariates related with the accidents are year, organization, train type, accident factor, accident type, the number of fatalities (casualties) and the number of derailed trains. These variables were given from the KOTSA dataset. For example, the accident factor was already defined as human and non-human factors. The human factors are the accidents caused by passenger or driver. The non-human factors are defined as the accidents are caused by all factors other than human such as weather, traffic, nal failure, rolling-stock malfunctioned and so forth. Table 2 presents the list of variables used in the analysis. Figure 2: Map of the number of railway accidents. 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. As the accident factor, the human-related accidents and non-human-related accidents were 2606 (51.6%) and 2445 (48.4%), respectively. The railroad type, which was categorized into high-speed railway, urban railway and general railway were 740 (14.7%), 1587 (31.4%) and 2718 (53.8%), respectively. The accident type is divided into four groups: traffic accident, safety-related accident, rolling-stock related accident and miscellaneous accident. The number of traffic accidents were 1557 (30.8%), which can be classified into collisions between, vehicle(s) and trains at a railway-crossing, passenger(s) and trains and road worker(s) and trains. The number of safety related accidents were 697 (13.8%), which occurred at railway facilities around the station, the platform and the train. The number of rolling-stock related accidents were 2357 (46.7%) that happened due to fire in the train, train collision and derailment. The number of miscellaneous accidents were 440 (8.7%) which are not included in the previous three categories. We considered the number of fatalities, casualties and derailed trains for modeling and evaluating the impact of railway accidents. For the real application of the railway accident data, we used the SAS software (Version 9.4, SAS Institute Inc., Cary, NC, USA.) for modeling TPMs and the R software (Version 3.6.3, R Foundation for Statistical Computing, Vienna, Austria) for visualizing the data. The continuous dependent variables in this study are delay time (Figure 3) and the amount of costs incurred (Figure 4) due to railway accidents. As can be seen in Figures 3 and 4, 22.5% (1139) and 79.8% (4029) of the sample size were recorded as no time delay and no cost, respectively. The histograms in the figures show that the positive measurements for each variable are extremely right-skewed and, thus we considered the gamma distribution and the log-normal distribution as good fits. The five-number summary (minimum, 25 th percentile, median, 75 th percentile, maximum) for delay time is (0, 10, 22, 37 and 1432). For the positive measurements (20.2%) of the cost amount, the five-number summary is expressed as (0.001, 0.434, 1.724, 6.6 and 13,933). No probability distribution of positive real-value