Rapid Prediction of Human Evacuation From Passenger Ships based on Machine Learning Methods

Compared to land-based evacuation scenarios, research on human evacuation from passenger ships presents unique challenges due to factors such as the complex geometric layout of ships, passengers' lower familiarity with the environment, and the impact of sea conditions.Rapidly predicting evacuation time is therefore important and crucial for safety of passenger ships at sea.This study aims to address the challenge of rapidly and accurately predicting human evacuation time from passenger ships using methods such as simulation modelling and predictive analysis.Firstly, the key risk factors affecting human evacuation from passenger ships are identified through literature reviews and accident report analysis, and a set of evacuation risk factors is established based on different combinations of these risk factors.Secondly, a simulation model for human evacuation from passenger ships is developed, and its reliability is verified by comparing the simulation results with actual evacuation drill outcomes.Based on this model, different evacuation scenarios are simulated using various combinations of risk factors, and the impact of key factors-such as guiding behaviour, personnel attributes and initial distribution, day/night environment, stair availability, and ship inclination-on evacuation efficiency is systematically analysed.Finally, several well-established machine learning models, including Random Forest, Support Vector Regression, and Neural Networks, are used to rapidly predict human evacuation time in different scenarios.The model with the shortest prediction time and highest accuracy is chosen.The results show that the simulation data closely align with the actual drill data.Among all the predictive models, Support Vector Regression performs the best, providing rapid and accurate predictions of human evacuation time from passenger ships.The findings make significant contributions to improve evacuation safety of passenger ships and crowd management.