基于机器学习的天河机场物流预测研究  

Research on Logistics Prediction of Tianhe Airport Based on Machine Learning

作　　者：彭婷 邓旭东[1] PENG Ting;DENG Xudong(Wuhan University of Science and Technology,Wuhan 430065,China)

机构地区：[1]武汉科技大学恒大管理学院,湖北武汉430065

出　　处：《物流科技》2023年第5期97-100,108,共5页Logistics Sci-Tech

摘　　要：全球经济快速增长的形势下,八大区域性枢纽之一的武汉天河机场的物流需求也在攀升。文章针对天河机场的货邮吞吐量,运用机器学习中的线性回归模型通过Python对其进行需求预测,并用二次指数平滑法与之对比,在平均绝对百分误差比较下得出机器学习对预测具有更好精准度。With the rapid growth of global economy, logistics demand of Wuhan Tianhe Airport, one of the eight regional hubs, is also rising. Based on the cargo throughput of Tianhe Airport, this paper uses the linear regression model of machine learning to predict its demand through Python, and compares it with quadratic exponential smoothing method. Under the comparison of average absolute percentage error, it is found that machine learning has better accuracy for prediction.

关 键 词：物流预测 机器学习 线性回归 航空物流 

分 类 号：F560[经济管理—产业经济]