Flight delay is a significant problem that negatively impacts the aviation industry and costs billion of dollars each year. Most existing studies investigated this issue using various methods based on historical data. However, due to the highly dynamic environments of the aviation industry, relying only on historical datasets of flight delays may not be sufficient and applicable to forecast the future of flights. The purpose of this research is to study the flight delays from a new angle by utilising data generated from the emerging Internet of Things (IoT) paradigm. Our primary goal is to improve the understanding of the roots and signs of flight delays as well as discovering related factors. In this paper, we present a framework that aims at improving the flight delay problem. We consider the IoT data generated from distributed sensors that have not been considered in existing works in the analysis of flight delays, and for that purpose, an automatic tool is developed to collect IoT data from various data sources including flight, weather, and air quality index. Based on the heterogeneous data, an algorithm is developed to merge different features from diverse data sources. We adopt predictive modelling to study the factors that contribute to flight delays and to predict the flight delays in the future. The results of our work show a high correlation among the developed features. In particular, the results clearly demonstrate the association between the flight delays and the air quality index factor. In particular, our current prediction model achieves 85.74% in accuracy.

中文翻译：

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基于物联网数据有效预测航班延误的系统

航班延误是一个重大问题，对航空业产生负面影响，每年造成数十亿美元的损失。大多数现有研究使用基于历史数据的各种方法调查了这个问题。然而，由于航空业的高度动态环境，仅依靠航班延误的历史数据集可能不足以预测航班的未来。本研究的目的是利用新兴的物联网 (IoT) 范式生成的数据，从新的角度研究航班延误。我们的主要目标是提高对航班延误的根源和迹象的理解以及发现相关因素。在本文中，我们提出了一个旨在改善航班延误问题的框架。我们在航班延误分析中考虑了现有工作中未考虑的分布式传感器生成的物联网数据，为此，开发了一种自动工具来从包括航班、天气和空气质量在内的各种数据源收集物联网数据指数。基于异构数据，开发了一种算法来合并来自不同数据源的不同特征。我们采用预测模型来研究导致航班延误的因素并预测未来的航班延误。我们的工作结果表明，开发的特征之间存在高度相关性。特别是，结果清楚地证明了航班延误与空气质量指数因素之间的关联。特别是，我们当前的预测模型的准确率达到了 85.74%。