计算机工程与科学2025，Vol.47Issue(3)：561-570,10.DOI:10.3969/j.issn.1007-130X.2025.03.017
基于多源异构融合与时空图卷积网络的集卡到港量预测模型
A container truck prediction model for ports based on multi-source heterogeneous fusion and spatiotemporal graph convolutional network
摘要
Abstract
Timely and accurate container truck prediction algorithms are crucial to the scheduling op-timization 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 con-tainer truck prediction model based on multi-source heterogeneous fusion and spatiotemporal graph con-volutional 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.关键词
智慧港口/交通流量预测/多源异构数据融合/时空图卷积网络Key words
smart port/traffic flow prediction/multi-source heterogeneous fusion/spatiotemporal graph convolutional network分类
计算机与自动化引用本文复制引用
薛桂香,陈宇昂,刘瑜,郑倩,宋建材..基于多源异构融合与时空图卷积网络的集卡到港量预测模型[J].计算机工程与科学,2025,47(3):561-570,10.基金项目
天津市科技计划项目(23ZGCXQY00030) （23ZGCXQY00030）
