Network Feature-Enriched Machine Learning Model for Predictive Analytics of Flight Departure Delays in Smart Aviation Southeast University

Format:

Book

Conference/Event

Author/Creator:

Chen, Linxian, author.

Contributor:

Chen, Jingxu

Liu, Xize

Shen, Xiuyu

Conference Name:

2024 International Conference on Smart Transportation Interdisciplinary Studies (2024-12-13 : Nanjing, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Due to the crucial impact on flight scheduling, airline planning, and airport operations, flight departure delay prediction has emerged as a severe and prominent issue within the realm of smart aviation systems. Accurately predicting flight departure delay durations constitutes a crucial aspect of smart aviation management. Such predictive capability empowers aviation authorities and airport regulators to implement optimized air traffic control strategies, mitigating delays and elevating airport operational efficiency, while enhancing the satisfaction of travelers. The methodology employed in flight delay prediction has undergone substantial evolution in recent years, progressing from rudimentary statistical models to more sophisticated and intricate machine learning models. In this study, we introduce a novel machine learning model enriched with network features and grid search-based parameter selection for advanced predictive analytics of flight departure delays. This model integrates air traffic network feature extraction, feature selection, and machine learning-based prediction. Specifically, we leverage complex network theory to extract both node-level and edge-level features from the air traffic network. Subsequently, the XGBoost algorithm is employed for feature selection and delay prediction, capitalizing on its flexibility and robust performance. A case study utilizing a high-dimensional flight dataset from the U.S. Bureau of Transportation Statistics (BTS) was conducted to assess the model's effectiveness. The experimental results and the visualization results demonstrate that the proposed framework surpasses several benchmark models, achieving an average delay prediction accuracy with a deviation of about 3.7 minutes. This framework exhibits strong potential for addressing high-dimensional, large-scale predictive challenges in flight delay management while maintaining superior accuracy

Notes:

Vendor supplied data

Publisher Number:

2025-01-7133

Access Restriction:

Restricted for use by site license