基于贝叶斯优化的武汉港务集团集装箱需求量预测研究  

Research on Container Demand Prediction of Wuhan Port Group Based on Bayesian Optimization

作　　者：黄旭 张洪[1] HUANG Xu;ZHANG Hong(School of Management,Wuhan University of Science and Technology,Wuhan 430065,China)

机构地区：[1]武汉科技大学管理学院,湖北武汉430065

出　　处：《物流工程与管理》2024年第7期98-100,共3页Logistics Engineering and Management

摘　　要：在我国,港口物流业展现出了巨大的增长潜力,同时也带来了诸多市场机遇。因此,精准预测港口企业的需求量已成为企业制定战略发展规划的关键。文中从经济发展水平、区域贸易水平、交通运输技术水平和港口运载能力四个维度构建评价指标体系,同时建立了基于贝叶斯优化的LSTM模型、BP神经网络模型和随机森林模型,对2011-2021年武汉港务集团集装箱吞吐量的相关数据进行预测。结果表明,LSTM模型的预测精度较高。该研究为武汉港务集团提供了有效的预测手段,可助力企业应对未来挑战,实现可持续发展。China s port logistics industry shows significant growth potential,which brought many market opportunities.Accurate demand forecasting has become crucial for strategic planning.This study constructs an evaluation index system from four dimensions of economic development,regional trade,transportation technology,and port capacity.Using Bayesian-optimized LSTM model,BP neural network,and random forest,the container throughput of Wuhan Port Group from 2011 to 2021 is predicted.Results show that the LSTM model achieves the highest accuracy.This study provides effective forecasting tools to help Wuhan Port Group meet future challenges and achieve sustainable growth.

关 键 词：港口物流需求 LSTM模型 武汉港 

分 类 号：F253[经济管理—国民经济]