# Analyzing the Factors Influencing Time Delays in Korean Railroad Accidents
## 2 Literature Review  
Railroad safety studies have seen the emergence of numerous predictive models designed to evaluate both the frequency and severity of accidents. A predominant approach in these models involves the analysis of mean count response data, establishing relationships between independent and dependent variables under specific statistical distribution assumptions. Notably, [PERSON] [2; 3; 4] conducted an in-depth examination of fatal train accidents on Britain's mainline railways, leveraging 31 years of historical data. His analysis encompassed the study of trends in accident frequencies and fatalities arising from 75 fatal collisions, derailments, and buffer stop overruns, totaling 273 fatalities among passengers, employees, and the public. In addressing annual accident frequencies, [PERSON] employed a Poisson distribution model to account for and estimate the mean number of accidents per billion train kilometers. Similarly, [PERSON] et al. [5] investigated train accidents within the Japanese railroad system. They adopted an exponential regression model to scrutinize 71 serious train accidents occurring between 1987 and 2003, employing the time interval between consecutive accidents as a predictor variable. Their findings indicated that the exponential distribution aptly characterized the number of days between successive accidents, the log-normal distribution fitted the train suspension time (using the factors of accident types, causes and years), and a Poisson distribution aligned with the number of casualties. When estimating the total annual accident count, the exponential function emerged as the preferred model for representing the relationship between annual accident frequency and the time period. In terms of predicting train delay time, [PERSON] et al. [6] revealed that the length of duration of delayed time due to railway accidents is dependent on the factors such as train types (express train vs. metro), causes (human-related vs. non-human-related), and magnitude of occurrence (i.e., number of casualties, number of derailed trains). In the realm of railway accident severity assessment, [PERSON] et al. [7] investigated cargo-train derailments, scrutinizing the relationship between the number of derailed cabins and severity. They employed a zero-truncated negative binomial (ZTNB) regression model along with a quantile regression model. Their analysis was grounded in a dataset comprising 458 cargo-train derailments attributed to brake-rail causes in the United States spanning the years 2001 to 2010. [PERSON] [8], in a distinct context, applied modeling techniques, including zero-truncated negative binomial (ZTNB) regression and Artificial Neural Networks (ANNs), to predict railway accidents on South Korea's National Railroad. This inquiry leveraged historical accident data to assess the effectiveness of the models in accurately forecasting accidents, thereby offering valuable insights for accident prevention. In the domain of train accident rate calculation, [PERSON] [9] determined the fatal train accident rate per billion train kilometers for Europe's mainline railways spanning the years 1990 to 2019. This estimation relied on the assumption of accidents following a Poisson distribution within specified time periods and train kilometers per year. Employing a similar methodology, [PERSON] et al. [10] analyzed the frequency of derailments and collisions in freight train accidents in the United States, attributed to human factors. Their model incorporated yearly train miles and the occurrence of train accidents as explanatory variables, deploying a Negative Binomial (NB) model. Numerous prior studies in the field of highway-rail crossing analysis have employed the Generalized Linear Model (GLM) approach and its extensions, which make assumptions about specific statistical distributions, as mentioned earlier.