# Analyzing the Factors Influencing Time Delays in Korean Railroad Accidents
## 5 Analysis Results
### Human-Related Time Delays  
Table 4 represents the modeling outputs for the delayed time caused by human-related factors with some of the explanatory variables shown in Table 3. In general, the direction of estimates indicates a certain trend. For instance, in the outputs of the negative binomial part, a positive estimate suggests a higher likelihood of an increase in the delayed time, especially when associated with a \(p\)-value equal to or greater than 0.05. Similarly, in the logit part outputs, a positive estimate signifies a greater probability of occurrence of time delays. These trends remain consistent across various models, except for estimates in the type of event within the negative binomial part. Nevertheless, the likelihood of delayed time occurrence is notably higher when the type of event is linked to high-potential events (2.708 in Two-stage; 2.980 in ZINB) or ground-level crossing accidents (0.931 in Two-stage; 2.980 in ZINB), as opposed to events related to humans (\(-\)2.867 in Two-stage; \(-\)2.865 in ZINB). However, the duration of delayed time for human-involved (\(-\)0.357 in ZINB) or ground-level crossing accidents (\(-\)0.234 in ZINB) tends to be less than that for other types of events such as crashes, derailments, facility-involved accidents, and so on. The likelihood of a time delay occurring is not only higher during the fall season, but is also associated with a longer duration of delayed time, and this is statistically significant only at the negative binomial part of Two-stage model. When railroad accidents involve the public (2.215 and 1.513 in Two-stage, respectively) or passengers (2.382 and 1.645 in ZINB, respectively), the likelihood of a time delay is higher compared to other person types. However, during the duration of the delay, events related to 'passengers' are likely to experience a reduction. The categorization of train operators into three groups based on the number of operating routes did not yield statistically significant explanations for time delays. In cases involving conventional trains in railroad accidents, there is a diminished likelihood of time delays; however, the temporal duration of the delay shows a statistically significant likelihood of increase (0.189 in Two-stage; 0.243 in ZINB). Conventional trains, operated exclusively by 'KORAIL' across the country, generally have time headways at least exceeding 20-30 min--a notable contrast to express trains and metros, which operate with headways of 5 min or less. Constituting a mere 1.8% of the total passenger volume, compared to 95.5% for metros and 2.6% for express trains (as of Aug. 2023 in  
Figure 7: Flow of computational processes within an artificial neural network model. KOSIS), conventional trains are allocated a comparatively smaller share of resources by KORAIL. Consequently, accidents involving conventional trains result in inevitably longer delay times, given the relatively sluggish response attributed to their diminished strategic importance. The analysis indicates that when an incident occurs on the main line, between railroad stations, both models consistently show an increase in the probability of a delay occurring (positive signs in the logit part) as well as an extension in the length of the delay time (positive signs in the negative binomial part). Similarly, accidents related to suicide are associated with an elevated likelihood of delays and an extended duration of time. To configure the ANN model, the input layer is designed to incorporate the same independent variables as detailed in Table 4. The input layer comprises two variables, each accommodating 12 neurons, representing predictive factors related to the duration of time delays. During the development of the ANN model, the sigmoid activation function was