Applying Machine Learning to Aviation Big Data for Flight Delay Prediction

Flight delay has been a serious and widespread problem that needs to be solved. One promising solution is the flight delay prediction. Although big data analytics and machine learning have been applied successfully in many domains, their applications in aviation are limited. This paper presents a comprehensive study of flight delay spanning data pre-processing, data visualization and data mining, in which we develop several machine learning models to predict flight arrival delays. Two data sets were used, namely Airline On-Time Performance (AOTP) Data and Quality Controlled Local Climatological Data (QCLCD). This paper aims to recognize useful patterns of the flight delay from aviation data and perform accurate delay prediction. The best result for flight delay prediction (five classes) using machine learning models is 89.07% (Multilayer Perceptron). A Convolution neural network model is also built which is enlightened by the idea of pattern recognition and success of neural network method, showing a slightly better result with 89.32% prediction accuracy.

[1]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Navoneel Chakrabarty,et al.  A Data Mining Approach to Flight Arrival Delay Prediction for American Airlines , 2019, 2019 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON).

[3]  Young Jin Kim,et al.  A deep learning approach to flight delay prediction , 2016, 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC).

[4]  Jian Li,et al.  Big data‐driven machine learning‐enabled traffic flow prediction , 2018, Trans. Emerg. Telecommun. Technol..

[5]  Rugved V Deolekar,et al.  Analyzing Factors Influencing Flight Delay Prediction , 2019, 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom).

[6]  Houbing Song,et al.  Internet of Things and Big Data Analytics for Smart and Connected Communities , 2016, IEEE Access.

[7]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[8]  Vineeth Vijayaraghavan,et al.  A machine learning approach for prediction of on-time performance of flights , 2017, 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC).

[9]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Zhihan Lv,et al.  Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics , 2017, IEEE Transactions on Industrial Informatics.

[11]  Alan Marsden,et al.  Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions , 2020 .

[12]  Shubham Sinha,et al.  A Novel Approach: Airline Delay Prediction Using Machine Learning , 2018, 2018 International Conference on Computational Science and Computational Intelligence (CSCI).

[13]  Jie Yang,et al.  Flight Delay Prediction Based on Aviation Big Data and Machine Learning , 2020, IEEE Transactions on Vehicular Technology.

[14]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[15]  Chuan Li,et al.  Impact on Flight Delay Predictions Based on Route Network of Multi-Airport under Joint Operations in Multi Airport , 2017, ICMA 2017.

[16]  Alexey Pozdnukhov,et al.  Using machine learning to analyze air traffic management actions: Ground delay program case study , 2019, Transportation Research Part E: Logistics and Transportation Review.