Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-25T06:39:33.403Z Has data issue: false hasContentIssue false
  • English
  • Français

Analysis of flight delays in aviation system using different classification algorithms and feature selection methods

Published online by Cambridge University Press:  05 August 2019

A. B. A. Anderson ,
A. J. Sanjeev Kumar  and
A. B. Arockia Christopher Open the ORCID record for A. B. Arockia Christopher [Opens in a new window]
A. B. A. Anderson
Affiliation:
Department of CSE Veerammal Engineering CollegeDindigul, TN, India
A. J. Sanjeev Kumar
Affiliation:
Department of MCA Thiagarajar College of Engineering, Madurai, TN, India
A. B. Arockia Christopher*
Affiliation:
Department of IT, Dr. Mahalingam College of Engineering & TechnologyPollachi, TN, India
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Data mining is a process of finding correlations and collecting and analysing a huge amount of data in a database to discover patterns or relationships. Flight delay creates significant problems in the present aviation system. Data mining techniques are desired for analysing the performance in which micro-level causes propagate to make system-level patterns of delay. Analysing flight delays is very difficult – both when looking from a historical view as well as when estimating delays with forecast demand. This paper proposes using Decision Tree (DT), Support Vector Machine (SVM), Naive Bayesian (NB), K-nearest neighbour (KNN) and Artificial Neural Network (ANN) to study and analyse delays among aircrafts. The performance of different data mining methods is found in the different regions of the updated datasets on these classifiers. Finally, the result shows a significant variation in the performance of different data mining methods and feature selection for this problem. This paper aims to deal with how data mining techniques can be used to understand difficult aircraft system delays in aviation. Our aim is to develop a classification model for studying and reducing delay using different data mining methods and, in this manner, to show that DT has a greater classification accuracy. The different feature selectors are used in this study in order to reduce the number of initial attributes. Our results clearly demonstrate the value of DT for analysing and visualising how system-level effects happen from subsystem-level causes.

Keywords

Data miningFlight delayClassification techniquesFeature selectionAviation
Type
Research Article
Information
The Aeronautical Journal , Volume 123 , Issue 1267 , September 2019 , pp. 1415 - 1436
Copyright
© Royal Aeronautical Society 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Wu, C. Inherent delays and operational reliability of airline schedules. Journal of Air Transport Management, 2005, 11, (4), pp 273–282.CrossRefGoogle Scholar
Eric, R. and Chatterji, B. Analysis of aircraft arrival and departure delay characteristics. NASA Ames Research Center, Moffett Field, CA 94035-1000, Integration, and Operations (ATIO) Technical, Los Angeles, California, 2002, pp 1–3.Google Scholar
Aisling, J. and Reynolds-Feighan, A.J. An Assessment of the Capacity and Congestion Levels at European Airports. 39th Congress of European Regional Science Association, University College Dublin, Ireland. 24–27 August 1999.Google Scholar
Eurocontrol. Delays to Air Transport in Europe, 2001, Annual Report.Google Scholar
Phillips, D., and Steele, P. A Graphical Tool to Visualize Predicted Minimum Delay Flights. Journal of Computational and Graphical Statistics, 2009, 20, (2), pp 294–297.Google Scholar
Wang, Y., Klein, A., and Jehlen, R. Eighth USA/Europe Air Traffic Management Research and Development Seminar (ATM) Modeling Flight Delays and Cancellations at the National, Regional and Airport Levels in the United States Banavar Sridhar, 2009.Google Scholar
Smith, A. and Sherry, L. Decision Support Tool for Predicting Aircraft Arrival Rates, Ground Delay Programs, and Airport Delays from Weather Forecasts. Center for Air Transportation, Systems Research George Mason University, Fairfax, VA, 2008, pp 1–4.Google Scholar
Christopher, A.B.A. and Balamurugan, S.A. Prediction of warning level in aircraft accidents using data mining techniques. The Aeronautical Journal, 2014, 118, (1206), pp 935–952.CrossRefGoogle Scholar
Aircraft accident database dataset retrieved from http://www.planecrashinfo.com/database.htm.Google Scholar