Short-term Ship Traffic Flow Prediction in Vulnerable Segments Based on VMD-BP-GA Model
[Objective]Against the challenge of ship traffic flows being susceptible to external environmental disturbances in busy searoutes,a predictive model for identifying the vulnerability of ship traffic flows is proposed,aiming to determine the weakest segments through vulnerability identification.[Method]Firstly,the Variational Mode Decomposition(VMD)model was employed to decompose the parameters of ship traffic flows into multiple modal components.Then,combining Back-Propagation Network(BP)and Genetic Algorithm(GA),a constrained model was constructed to continuously update the center and bandwidth of each component,achieving the prediction of individual components.Through the application of VMD-BP-GA model,ship traffic flows were accurately predicted,and the rationality and effectiveness of the model were validated.[Result]Based on the VMD-BP-GA model,this study proposes a method for accurately predicting the vulnerability of ship traffic flow.In busy shipping routes,this method performs better in terms of error metrics compared to traditional models,with the lowest mean absolute error(MAE)reaching 2.095%,root mean square error(RMSE)reaching 2.610%,and mean percentage error(MAPE)reaching 2.114%for the ship traffic flow prediction model in the routes.In terms of segment density prediction,MAE,RMSE and MAPE of this method reach the lowest of 0.129%,0.162%and 2.112%respectively.Moreover,it achieves predictions in both spatial and temporal dimensions.[Conclusion]The model successfully identifies the vulnerability of ship traffic flow and determines the most vulnerable shipping route.It demonstrates efficient predictive performance,enabling accurate and rapid prediction of ship traffic flow,thus providing theoretical and practical guidance for ensuring maritime safety.
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