Analysis of Railroad Accident Prediction using Zero-truncated Negative Binomial Regression and Artificial Neural Network Model: A Case Study of National Railroad in South Korea

Conventional statistical models, such as Poisson or negative binomial, have predefined underlying relationships between explanatory variables. However, artificial neural network (ANN), which overcomes the limitations of statistical prediction model, have gained popularity in practice and research for their ability to increase prediction accuracy. Thus, this study employs zero-truncated negative binomial (ZTNB) models and artificial neural network (ANN) models to analyze the distribution of railroad accident frequency and the corresponding number of casualties for 1995–2021 accident dataset of Korea’s national railroad. The study mainly focused on two most dominant accident types which are human-involved accidents (accounted for 89.2% of all accidents) and ground-level crossing accidents (9.6%) from the historic dataset. This is because not just data proportion, rather such accident types were received very little attention compared to fatal train accident in the accident prediction study. Further, these types of accident showed clearly tended to decrease over time, but time trend has been found very weak at the type of fatal train accident. The performance of the developed models was estimated by mean square error (MSE), the root mean square error (RMSE), and the coefficient of determination (R2). Results present that ANN models outperform ZTNB models in fitting and prediction, demonstrating once again ANN’s superiority over statistical models for predicting accident frequency and casualty count.