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

作　　者：薛桂香 陈宇昂 刘瑜 郑倩 宋建材 XUE Guixiang;CHEN Yu'ang;LIU Yu;ZHENG Qian;SONG Jiancai(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401;School of Civil and Transportation Engineering,Hebei University of Technology,Tianjin300401;Intelligent Transportation Monitoring Center of Tianjin,Tianjin,300250;School of Information and Engineering,Tianjin University of Commerce,Tianjin 300134,China)

机构地区：[1]河北工业大学人工智能与数据科学学院,天津300401 [2]河北工业大学土木与交通学院,天津300401 [3]天津市智能交通运行监测中心,天津300250 [4]天津商业大学信息工程学院,天津300134

出　　处：《计算机工程与科学》2025年第3期561-570,共10页Computer Engineering & Science

基　　金：天津市科技计划项目(23ZGCXQY00030)。

摘　　要：及时准确的集卡到港量预测算法对于港口物流系统的调度优化和资源分配至关重要。由于集卡到港量受到临港路段交通情况、天气和港口作业计划等多种复杂因素影响而表现出高度非线性和复杂性特征,传统交通流量预测方法难以有效融合内、外部因素的影响并准确提取其时空相关性。针对这一问题,提出了一种基于多源异构数据融合和时空图卷积网络的混合集卡到港量预测模型MHF-STGCN,该模型采用注意力机制自适应提取港口交通流多源异构历史数据的关键信息并挖掘其动态时空演化特征。与单一交通数据相比,多源数据融合使模型MAE下降约34.99%,RMSE下降约31.10%,详细对比实验结果表明该模型在MAE、RMSE和R-Square等指标上显著优于基线模型。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.

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 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]