Trains are a popular mode of transportation in Indonesia. People from higher and lower socioeconomic brackets are increasing interest in the industry as it gets more well-known. Some safety equipment is available, but mishaps can still occur. People attempting to determine where a problem originated often use such as the Ishikawa model for assistance. The Poisson model is well-suited for crash simulation, and this work provides a valuable method for doing so. A root cause analysis allows for identifying and eliminating the accident’s most significant contributing factors. From 1999 to 2014, data was gathered through a variety of intermediaries. According to Ishikawa’s research, ten primary factors influence the frequency of accidents. There are issues with the route, the train, the signals, the upkeep, the communication, the processes, the personnel, the climate, and the machinery. Next, we use the Dispersion and Vuong tests to see which of the regression models provides the most accurate forecasts. Using the Vuong test, the Zero-inflated model has the best predictive power for accidents and events, with p-values of 0.19695481, 0.1301056, and 0.0689108. Train derailments, collisions, and SPAD are the most common causes of accidents.