基于IOWGA的航空物流需求预测研究  

Research on Air Logistics Demand Forecasting Based on Induced Ordered Weighted Geometric Averaging Operator

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作  者:程小慷[1] 陈蔚璇 CHENG Xiaokang;CHEN Weixuan(Civil Aviation Flight University of China,Guanghan 618307,Sichuan,China)

机构地区:[1]中国民用航空飞行学院,四川广汉618703

出  处:《民航学报》2024年第6期6-10,59,共6页Journal of Civil Aviation

摘  要:广州白云机场作为重要的航空货运枢纽,航空物流需求呈现快速增长的趋势.为了准确预测广州白云机场未来的航空物流需求,本研究应用了三次指数平滑法和灰色GM(1,1)模型作为预测工具,并构建了基于诱导有序几何加权平均算子(induced ordered weighted geometric averaging,IOWGA)的组合预测模型.通过对2009~2023年广州白云机场货运量数据进行实证分析,结果显示组合预测模型在预测精度和误差方面均优于单一模型.预测结果显示,广州白云机场航空货运量将持续增长,预计2028年达到230.28万吨.研究表明,组合预测模型能够更准确地反映广州白云机场航空物流需求的发展趋势,提供了一种有效的建模和预测方法,对航空物流需求预测具有重要的参考价值.Guangzhou Baiyun International Airport,as an important air cargo hub,has witnessed rapid growth in air logistics demand.To accurately forecast the future air logistics demand of Guangzhou Baiyun International Airport,this study applies the Triple Exponential Smoothing method and the Grey GM(1,1)model as forecasting tools and constructs a combination forecasting model based on the Induced Ordered Weighted Geometric Averaging(IOWGA)operator.An empirical analysis of the cargo volume data of Guangzhou Baiyun International Airport during 2009 to 2023 reveals that the combination forecasting model outperforms single models in terms of forecasting accuracy and error.The forecasting results indicate that theair cargo volume of Guangzhou Baiyun International Airport will continue to grow,reaching 2302800 tons by 2028.The study demonstrates that the combination forecasting model can more accurately reflect the development trend of air logistics demand of Guangzhou Baiyun International Airport,and provides an effective modeling and forecasting approach and important reference for air logistics demand forecasting.

关 键 词:组合预测 航空物流 灰色GM(1 1)模型 三次指数平滑法 IOWGA算子 

分 类 号:U8[交通运输工程]

 

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