A Review on Flight Delay Prediction

Sternberg, Alice; Soares, Jorge; Carvalho, Diego; Ogasawara, Eduardo

doi:10.48550/arxiv.1703.06118

Cited by 6 publications

(7 citation statements)

References 99 publications

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“…We performed around 100 experiments and chose the hyper-parameter set with the best validation RMSE. The best hyper-parameters can be found in table 4. We then use this hyper-parameter set to train the final model on both the training and validation dataset.…”

Section: Trajcnnmentioning

confidence: 99%

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Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map

Shao,

Prabowo,

Zhao

et al. 2021

Preprint

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To model and forecast flight delays accurately, it is crucial to harness various vehicle trajectory and contextual sensor data on airport tarmac areas. These heterogeneous sensor data, if modelled correctly, can be used to generate a situational awareness map. Existing techniques apply traditional supervised learning methods onto historical data, contextual features and route information among different airports to predict flight delay are inaccurate and only predict arrival delay but not departure delay, which is essential to airlines. In this paper, we propose a vision-based solution to achieve a high forecasting accuracy, applicable to the airport. Our solution leverages a snapshot of the airport situational awareness map, which contains various trajectories of aircraft and contextual features such as weather and airline schedules. We propose an endto-end deep learning architecture, TrajCNN, which captures both the spatial and temporal information from the situational awareness map. Additionally, we reveal that the situational awareness map of the airport has a vital impact on estimating flight departure delay. Our proposed framework obtained a good result (around 18 minutes error) for predicting flight departure delay at Los Angeles International Airport.

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Section: Trajcnnmentioning

confidence: 99%

“…), attributes of flight data (day of a week, season, month, etc.) and flight delay time [3] [4]. There are also recent works in relevant Computer Science fields that have explored probabilistic models [5,6], network representation [7,6], and machine learning models [8,9].…”

Section: Introductionmentioning

confidence: 99%

Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map

Shao,

Prabowo,

Zhao

et al. 2021

Preprint

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“…and weather conditions (wind direction, wind speed, etc.) [10] [15]. Most computer scientists explore different models such as probabilistic models [3][16], network representation [1][2], and machine learning models [13][9] to predict flight delay and regard it as a regression or classification problem.…”

Section: Start Gathering Featuresmentioning

confidence: 99%

Flight Delay Prediction using Airport Situational Awareness Map

Shao

Prabowo

Zhao

et al. 2019

Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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The prediction of flight delays plays a significantly important role for airlines and travellers because flight delays cause not only tremendous economic loss but also potential security risks. In this work, we aim to integrate multiple data sources to predict the departure delay of a scheduled flight. Different from previous work, we are the first group, to our best knowledge, to take advantage of airport situational awareness map, which is defined as airport traffic complexity (ATC), and combine the proposed ATC factors with weather conditions and flight information. Features engineering methods and most state-of-the-art machine learning algorithms are applied to a large real-world data sources. We reveal a couple of factors at the airport which has a significant impact on flight departure delay time. The prediction results show that the proposed factors are the main reasons behind the flight delays. Using our proposed framework, an improvement in accuracy for flight departure delay prediction is obtained.

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“…Of particular interest is predicting flight delays using machine learning techniques. A recent study [15] presents a review of flight delay prediction works. The commonly investigated methods include decision tree, Bayesian learning, neural networks, support vector machines, and random forest [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Distribution Prediction of Strategic Flight Delays via Machine Learning Methods

Wang

Liao²,

Xu³

et al. 2022

Sustainability

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Predicting flight delays has been a major research topic in the past few decades. Various machine learning algorithms have been used to predict flight delays in short-range horizons (e.g., a few hours or days prior to operation). Airlines have to develop flight schedules several months in advance; thus, predicting flight delays at the strategic stage is critical for airport slot allocation and airlines’ operation. However, less work has been dedicated to predicting flight delays at the strategic phase. This paper proposes machine learning methods to predict the distributions of delays. Three metrics are developed to evaluate the performance of the algorithms. Empirical data from Guangzhou Baiyun International Airport are used to validate the methods. Computational results show that the prediction accuracy of departure delay at the 0.65 confidence level and the arrival delay at the 0.50 confidence level can reach 0.80 without the input of ATFM delay. Our work provides an alternative tool for airports and airlines managers for estimating flight delays at the strategic phase.

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A Review on Flight Delay Prediction

Cited by 6 publications

References 99 publications

Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map

Predicting Flight Delay with Spatio-Temporal Trajectory Convolutional Network and Airport Situational Awareness Map

Flight Delay Prediction using Airport Situational Awareness Map

Distribution Prediction of Strategic Flight Delays via Machine Learning Methods

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