TY - GEN
T1 - Flight delay predictions and the study of its causal factors using machine learning algorithms
AU - Yiu, Cho Yin
AU - Ng, Kam K.H.
AU - Kwok, Kin Chung
AU - Lee, Wing Tung
AU - Mo, Ho Tung
N1 - Funding Information:
Our gratitude is extended to the Research Committee of the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong SAR for the support of the research project (UALL).
Publisher Copyright:
© 2021 IEEE
PY - 2021/10
Y1 - 2021/10
N2 - In recent years, the global civil aviation industry has been developing rapidly. Due to the rising demand for air transportation, airports are confronting saturation problems. Heavy traffic and long queues are expected for take-off and landing. Hence, the physical constraints have magnified the problem of having surging flight delays. Yet, the operational efficiency and the reputation of the airport will deteriorate if the delay propagates. Additional expenditures are also expected. Several machine learning approaches were adopted in this research to predict flight delay, including the decision tree, random forest, k-nearest neighbour, Naive Bayes, and artificial neural networks. The results show that all algorithms achieved more than 80% of accuracy and artificial neural networks perform the best among the alternatives. While Naive Bayes is the least accurate, k-nearest neighbour have the lowest Fj score.
AB - In recent years, the global civil aviation industry has been developing rapidly. Due to the rising demand for air transportation, airports are confronting saturation problems. Heavy traffic and long queues are expected for take-off and landing. Hence, the physical constraints have magnified the problem of having surging flight delays. Yet, the operational efficiency and the reputation of the airport will deteriorate if the delay propagates. Additional expenditures are also expected. Several machine learning approaches were adopted in this research to predict flight delay, including the decision tree, random forest, k-nearest neighbour, Naive Bayes, and artificial neural networks. The results show that all algorithms achieved more than 80% of accuracy and artificial neural networks perform the best among the alternatives. While Naive Bayes is the least accurate, k-nearest neighbour have the lowest Fj score.
KW - Flight delay
KW - Forecasting
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85123718021&partnerID=8YFLogxK
U2 - 10.1109/ICCASIT53235.2021.9633571
DO - 10.1109/ICCASIT53235.2021.9633571
M3 - Conference article published in proceeding or book
AN - SCOPUS:85123718021
T3 - Proceedings of 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021
SP - 179
EP - 183
BT - Proceedings of 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021
A2 - Sun, Huabo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021
Y2 - 20 October 2021 through 22 October 2021
ER -