Passenger Ship Evacuation Time Prediction Based on Sobol Sequence Sampling and the Bayesian-Optimized Random Forest Method

With the rapid growth of global waterborne tourism, the passenger capacity of ships continues to increase, which means passenger evacuation safety is becoming a critical concern. Existing passenger ship evacuation prediction methods mainly rely on computationally intensive evacuation simulations, which suffer from high computational cost. This limitation hinders rapid evacuation assessment and emergency decision-making for large passenger ships. The objective of this study is to develop a rapid and interpretable surrogate prediction model for passenger ship evacuation time. To achieve this objective, Sobol sequence sampling is employed to efficiently construct representative evacuation scenarios, while Bayesian optimization is used to improve the prediction performance of the Random Forest model. The results show that the Sobol sequence sampling method efficiently designs multi-scenario evacuation cases for the passenger ship. The optimized model achieves high prediction accuracy with an R2 of 0.901, effectively capturing the nonlinear relationship between evacuation factors and total evacuation time. Feature importance analysis demonstrates that stairways near the embarkation stations and passengers positioned farther away from them significantly affect total evacuation time, highlighting the spatial disparity in evacuation efficiency. This study provides a reliable data-driven approach and technical support for emergency decision-making and safety management in passenger ships.