The emergency evacuation route planning of cruise ships directly affects the safety of all crew members and passengers during emergencies. Research on the planning of emergency evacuation routes for cruise ships is a frontier subject of maritime safety. This study proposes an improved ant colony system (IACS) to solve the evacuation route planning of crowds on cruise ships. The IACS, which is different from common single-path ant colony system (ACS) evacuation algorithms, is used to solve the multipath planning problem of crowd evacuation from cruise ships by considering crowd density and speed in the model. An increasing flow method is introduced into the IACS to improve the efficiency of the proposed algorithm. Numerical experiments show that this method meets the requirements of evacuation analysis guidelines for new and existing passenger ships (MSC.1/Circ.1533)and can effectively and efficiently plan the emergency evacuation path for cruise ship crowd.

Sea wind speed forecast is important for meteorological navigation system to keep ships in safe areas. The high volatility and uncertainty of wind make it difficult to accurately forecast multistep wind speed. This paper proposes a new decomposition-based model to forecast hourly sea wind speeds. Because mode mixing affects the accuracy of the empirical mode decomposition- (EMD-) based models, this model uses the variational mode decomposition (VMD) to alleviate this problem. To improve the accuracy of predicting subseries with high nonlinearity, this model uses stacked gate recurrent units (GRU) networks. To alleviate the degradation effect of stacked GRU, this model modifies them by adding residual connections to the deep layers. This model decomposes the nonlinear wind speed data into four subseries with different frequencies adaptively. Each stacked GRU predictor has four layers and the residual connections are added to the last two layers. The predictors have 24 inputs and 3 outputs, and the forecast is an ensemble of five predictors’ outputs. The proposed model can predict wind speed in the next 3 hours according to the past 24 hours’ wind speed data. The experiment results on three different sea areas show that the performance of this model surpasses those of a state-of-the-art model, several benchmarks, and decomposition-based models.

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