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A system for effectively predicting flight delays based on IoT data

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Abstract

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.

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Notes

  1. https://en.wikipedia.org/wiki/Haversine_formula.

  2. https://www.flightradar24.com/data/flights.

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Authors and Affiliations

  1. Macquarie University, Sydney, NSW, 2109, Australia

    Abdulwahab Aljubairy, Adnan Mahmood & Quan Z. Sheng

  2. Umm Al Qura University, Mecca, Makkah Province, 21955, Saudi Arabia

    Abdulwahab Aljubairy

  3. The University of Adelaide, Adelaide, SA, 5005, Australia

    Wei Emma Zhang

  4. Complexica Pty Ltd, West Lake, SA, 5021, Australia

    Ali Shemshadi

Authors
  1. Abdulwahab Aljubairy
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  2. Wei Emma Zhang
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  3. Ali Shemshadi
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  4. Adnan Mahmood
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  5. Quan Z. Sheng
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Correspondence to Abdulwahab Aljubairy.

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Aljubairy, A., Zhang, W.E., Shemshadi, A. et al. A system for effectively predicting flight delays based on IoT data. Computing 102, 2025–2048 (2020). https://doi.org/10.1007/s00607-020-00794-w

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