Analysis of Lifeboat Embarkation Efficiency for Cruise Passengers under Multiple Scenarios

Abstract

Lifeboats are important equipment to ensure the safety of passengers when a serious accident occurs in the ship. Higher efficiency of lifeboat embarkation is beneficial to improving passenger survival. This study divides lifeboat embarkation into queuing and seat selection stages and studies the embarkation process of cruise passengers. According to the characteristics of two stages of the embarkation process, transfer rules of queue and activity rules of the passenger are proposed. Combined with the rules, 16 types of embarkation simulation scenarios are established. The simulation results show that the embarkation efficiency of the group passenger is lower than that of the individual passenger. When passengers select seats from outboard row to inboard row, the speed of embarkation is faster than that of random seating selection. Compared with only considering the queue length, the embarkation is more efficient if passengers also consider the seat availability of lifeboats when they transfer between queues. The analysis of the results proves that the embarkation efficiency can be improved through proper guidance on the behavior of passengers in queuing and seat selection.

Developing Pedestrian Fatality Prediction Models Using Historical Crash Data: Application of Binary Logistic Regression and Boosted Tree Mechanism

“…Similarly, few researchers [12][13] have also utilized decision trees, like CART, Random Forest, Boosted Trees, or ANN, to model the safety of vehicles and pedestrians on road. Hu and Cai (2022) [14] reported that the logistic model is first applied for relationship explanation, and then the machine learning classifiers like ANN or Fuzzy are applied for prediction modeling. Tamakloe et al (2022) [15] opined that logistic regression helps in identifying the individual effect of factors affecting crash severity.…”