Ship Traffic Flow Analysis and Prediction in High-Traffic Areas Under Complex Environments

The inland canal environment is highly complex, and effective management of vessel traffic necessitates accurate forecasting. However, pronounced fluctuations in vessel traffic flow make reliable prediction particularly challenging in traffic-intensive areas, including ports and lock regions. Furthermore, strong nonlinearities in vessel traffic dynamics—exacerbated by factors such as lock operations and adverse weather conditions—further exacerbate the difficulty of accurate forecasting. To address these challenges, this paper proposes a WVMA-LSTM prediction framework that decomposes vessel traffic flow series prior to forecasting. The proposed model consists of three main components. First, vessel traffic data are decomposed using variational mode decomposition (VMD), while the parameters of VMD are simultaneously optimized via the whale optimization algorithm (WOA). Second, the Pearson correlation coefficient (PCC) is employed to select highly correlated components for input into the processing layer, thereby mitigating the impact of noise on prediction accuracy. Finally, the LSTM module combined with a multi-head attention mechanism is utilized to extract both trend information and local fluctuations from the sequences, after which a fully connected layer integrates the prediction outputs to obtain the final result. Experimental results demonstrate that the proposed model achieves an R2 exceeding 0.89 when predicting vessel traffic at locks and other complex environments, indicating high forecasting accuracy and robustness and offering valuable support for smart canal traffic management.