Purpose

The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.

Design/methodology/approach

The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).

Findings

Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.

Originality/value

These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

中文翻译：

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智能算法应用于航空货运延误预测

目的

本文的目的是评估来自发货人、物流服务提供商 (LSP) 和收货人的数据是否有助于在机器学习应用程序中预测航空运输延误。

设计/方法论/途径

该研究数据库包含发往拉丁美洲 4 个汽车生产厂的 2,244 份洲际空运货物。在数据库知识发现（KDD）过程中测试了不同的算法类别：支持向量机（SVM）、随机森林（RF）、人工神经网络（ANN）和k近邻（KNN）。

发现

在组合类别平衡程序后，在交叉验证场景中，通过 RF 算法，发货人、收货人和 LSP 数据属性选择的准确率达到 86%。

原创性/价值

这些发现扩展了当前关于应用于航空货运延误管理的机器学习的文献，这些文献主要集中于天气、机场结构、航班时刻表、地面延误和拥堵作为解释属性。