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

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.