Selection of Best Machine Learning Model to Predict Delay in Passenger Airlines

Article Computer Science, Artificial Intelligence

Flight delay prediction from spatial and temporal perspective

Qiang Li et al.

Summary: This paper proposes a novel prediction framework (ST-Random Forest) for flight delay prediction from temporal and spatial perspective. The framework combines complex network theory, LSTM units, and Random Forest classifier to extract spatial and temporal features and predict flight delays. The proposed model achieves an accuracy of 92.39% and provides valuable insights for aviation authorities and airport regulators to develop efficient air traffic control strategies.

EXPERT SYSTEMS WITH APPLICATIONS (2022)

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Article Economics

Timescales of delay propagation in airport networks

Yanjun Wang et al.

Summary: This article introduces an algorithm to estimate time lags between airport delays and analyzes the US airport delays in 2017. The research results have potential implications for delay prediction models and airline schedule design.

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW (2022)

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Article Engineering, Civil

A Deep Learning Approach for Flight Delay Prediction Through Time-Evolving Graphs

Kaiquan Cai et al.

Summary: This paper investigates the problem of flight delay prediction from a network perspective, proposing an approach based on graph convolutional neural network (GCN) that considers time-evolving and periodic graph-structured information. By using temporal convolutional block and adaptive graph convolutional block, the proposed method mines and analyzes the time-varying patterns of flight delays and exposes spatial interactions hidden in airport networks. Through extensive experiments, it is shown that the deep learning approach based on graph-structured inputs has great potentials in improving accuracy of flight delay prediction.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

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Proceedings Paper Computer Science, Artificial Intelligence

Multi-Task Local-Global Graph Network for Flight Delay Prediction

Tianyi Wang et al.

Summary: This paper proposes a deep learning framework based on graph convolutional networks and multi-task learning for flight delay prediction. The experimental results demonstrate that the model outperforms all baseline methods in predicting short to medium-term flight delays.

2022 IEEE 23RD INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION FOR DATA SCIENCE (IRI 2022) (2022)

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Article Computer Science, Information Systems

Machine Learning and Prediction of Masked Motors With Different Materials Based on Noise Analysis

Po-Jiun Wen et al.

Summary: The effect of noise on the human body has received increasing research attention, particularly in relation to motor noise pollution in factories. To reduce the impact, masks made of different materials are used. This study aimed to predict the acoustic sound of masked motors using machine learning. The results showed that the Gradient Boost Model (GBM) had higher prediction accuracy compared to other traditional methods.

IEEE ACCESS (2022)

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Article Engineering, Civil

Flight Delay Classification Prediction Based on Stacking Algorithm

Jia Yi et al.

Summary: This study explores the advantages of the Stacking algorithm in predicting airport flight delays, using a combination of machine learning technologies, feature selection, and methods for processing imbalanced datasets to successfully improve prediction accuracy and stability.

JOURNAL OF ADVANCED TRANSPORTATION (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Analysis of Flight Delay and Cancellation Prediction Based on Machine Learning Models

Zhan Shu

Summary: Flight delays and cancellations are inevitable and can cause concern for passengers as well as profit loss for airlines and airports. This paper proposes machine learning models on 2018 flight data to improve customer satisfaction and increase airline income. The models are compared using ROC curves and learning curves to determine the best fit for the data.

2021 3RD INTERNATIONAL CONFERENCE ON MACHINE LEARNING, BIG DATA AND BUSINESS INTELLIGENCE (MLBDBI 2021) (2021)

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Article Computer Science, Theory & Methods