基于機(jī)器學(xué)習(xí)的天河機(jī)場(chǎng)物流預(yù)測(cè)研究

打開文本圖片集

摘  要：全球經(jīng)濟(jì)快速增長的形勢(shì)下，八大區(qū)域性樞紐之一的武漢天河機(jī)場(chǎng)的物流需求也在攀升。文章針對(duì)天河機(jī)場(chǎng)的貨郵吞吐量，運(yùn)用機(jī)器學(xué)習(xí)中的線性回歸模型通過Python對(duì)其進(jìn)行需求預(yù)測(cè)，并用二次指數(shù)平滑法與之對(duì)比，在平均絕對(duì)百分誤差比較下得出機(jī)器學(xué)習(xí)對(duì)預(yù)測(cè)具有更好精準(zhǔn)度。

關(guān)鍵詞：物流預(yù)測(cè)；機(jī)器學(xué)習(xí)；線性回歸；航空物流

 中圖分類號(hào)：F560    文獻(xiàn)標(biāo)志碼：A    DOI：10.13714/j.cnki.1002-3100.2023.05.023

Abstract： 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.

Key words： logistics forecast; machine learning; linear regression; aviation logistics

0  引  言

 武漢是九省通衢的湖北省省會(huì)，是華中地區(qū)的對(duì)外貿(mào)易港口，其航空更是長江領(lǐng)域的發(fā)展中心，武漢的航空樞紐網(wǎng)絡(luò)以及航空物流運(yùn)輸系統(tǒng)一直備受當(dāng)?shù)卣推髽I(yè)關(guān)注。（剩余9540字）