TY - JOUR
T1 - A novel parallel series data-driven model for IATA-coded flight delays prediction and features analysis
AU - Khan, Waqar Ahmed
AU - Chung, Sai Ho
AU - Eltoukhy, Abdelrahman E.E.
AU - Khurshid, Faisal
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2024/1
Y1 - 2024/1
N2 - Predicting and analysing flight delays is essential for successful air traffic management and control. We propose a novel parallel-series model and novel adaptive bidirectional extreme learning machine (AB-ELM) method for prediction and feature analysis to better understand the causes of flight delays as stated by the International Air Transport Association (IATA). The IATA-coded flight delays are rarely examined in the existing studies. The IATA-coded flight delay subcategories decision boundaries are improved by the proposed parallel-series model. In application areas, where multiclass-multilabel classification may produce erroneous performance, the parallel-series model can be regarded as an alternate strategy. To improve network generalization performance, the proposed AB-ELM optimizes the covariance objective function by altering the learning rate adaptively during gradient ascent as opposed to gradient descent. The historical data from one of Hong Kong's international airlines, which contains information about the airport, flight, aircraft, weather, and IATA flight delay subcategories is considered a case study. Using fourteen different sampling approaches, the influence of imbalanced and noisy data was reduced. The results showed that employing proper sampling approaches in conjunction with the parallel-series model and AB-ELM method is effective for uncovering hidden patterns in the complicated IATA-coded flight delay subcategories system. When compared to other data-driven approaches, AB-ELM attained a high accuracy of 80.66 percent. This study enables airlines to develop adequate contingency measures in advance based on potential flight delay reasons and duration.
AB - Predicting and analysing flight delays is essential for successful air traffic management and control. We propose a novel parallel-series model and novel adaptive bidirectional extreme learning machine (AB-ELM) method for prediction and feature analysis to better understand the causes of flight delays as stated by the International Air Transport Association (IATA). The IATA-coded flight delays are rarely examined in the existing studies. The IATA-coded flight delay subcategories decision boundaries are improved by the proposed parallel-series model. In application areas, where multiclass-multilabel classification may produce erroneous performance, the parallel-series model can be regarded as an alternate strategy. To improve network generalization performance, the proposed AB-ELM optimizes the covariance objective function by altering the learning rate adaptively during gradient ascent as opposed to gradient descent. The historical data from one of Hong Kong's international airlines, which contains information about the airport, flight, aircraft, weather, and IATA flight delay subcategories is considered a case study. Using fourteen different sampling approaches, the influence of imbalanced and noisy data was reduced. The results showed that employing proper sampling approaches in conjunction with the parallel-series model and AB-ELM method is effective for uncovering hidden patterns in the complicated IATA-coded flight delay subcategories system. When compared to other data-driven approaches, AB-ELM attained a high accuracy of 80.66 percent. This study enables airlines to develop adequate contingency measures in advance based on potential flight delay reasons and duration.
KW - Air traffic management
KW - Classification
KW - Extreme learning machine
KW - IATA flight delays
KW - Imbalance learning
UR - http://www.scopus.com/inward/record.url?scp=85172684725&partnerID=8YFLogxK
U2 - 10.1016/j.jairtraman.2023.102488
DO - 10.1016/j.jairtraman.2023.102488
M3 - Article
AN - SCOPUS:85172684725
SN - 0969-6997
VL - 114
JO - Journal of Air Transport Management
JF - Journal of Air Transport Management
M1 - 102488
ER -