Abstract: Timely and accurate container truck prediction algorithms are crucial to the scheduling optimization and resource allocation of port logistics systems. Because the arrival volume of container trucks is affected by many complex factors, such as the traffic condition of the adjacent road, weather, and port operation plan, it shows highly nonlinear and complex characteristics. Traditional traffic flow prediction methods are complicated by effectively integrating the influence of internal and external factors and accurately extracting their spatial and temporal correlations. Regarding this matter, a hybrid container truck prediction model based on multi-source heterogeneous fusion and spatiotemporal graph convolutional network (MHF-STGCN) is proposed, which adopts the attention mechanism to adaptively extract the critical information from multi-source heterogeneous historical data of port traffic flow and mine its dynamic spatiotemporal evolution characteristics. Multi-source data fusion decreases the models MAE by 34.99% and RMSE by 31.10% compared to single traffic data. Detailed comparative experimental results show that the model significantly outperforms the baseline model in terms of MAE, RMSE, and R-Square.

Key words: smart port, traffic flow prediction, multi-source heterogeneous fusion, spatiotemporal graph convolutional network