Flight Delay Prediction using Airport Situational Awareness Map

Shao, Wei; Prabowo, Arian; Zhao, Sichen; Tan, Siyu; Koniusz, Piotr; Chan, Jeffrey; Hei, Xinhong; Feest, Bradley; Salim, Flora D.

doi:10.1145/3347146.3359079

Cited by 27 publications

(12 citation statements)

References 9 publications

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“…In order to extract appropriate information, features selection which consists of only considering the relevant features is applied. Collected data are generally noisy, incomplete, and redundant [41]. Since ours contain undesired information, cleaning and preprocessing techniques which involve correcting, transforming, replacing, and deleting data need to be performed at this stage.…”

Section: Description Of the Proposed Methodmentioning

confidence: 99%

A Multilayer Perceptron Neural Network with Selective-Data Training for Flight Arrival Delay Prediction

Alla

Moumoun

Balouki

2021

Scientific Programming

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Flight delay is the most common preoccupation of aviation stakeholders around the world. Airlines, which suffer from a monetary and customer loyalty loss, are the most affected. Various studies have attempted to analyze and solve flight delays using machine learning algorithms. This research aims to predict flights’ arrival delay using Artificial Neural Network (ANN). We applied a MultiLayer Perceptron (MLP) to train and test our data. Two approaches have been adopted in our work. In the first one, we used historical flight data extracted from Bureau of Transportation Statistics (BTS). The second approach improves the efficiency of the model by applying selective-data training. It consists of selecting only most relevant instances from the training dataset which are delayed flights. According to BTS, a flight whose difference between scheduled and actual arrival times is 15 minutes or greater is considered delayed. Departure delays and flight distance proved to be very contributive to flight delays. An adjusted and optimized hyperparameters using grid search technique helped us choose the right architecture of the network and have a better accuracy and less error than the existing literature. The results of both traditional and selective training were compared. The efficiency and time complexity of the second method are compared against those of the traditional training procedure. The neural network MLP was able to predict flight arrival delay with a coefficient of determination R 2 of 0.9048, and the selective procedure achieved a time saving and a better R 2 score of 0.9560. To enhance the reliability of the proposed method, the performance of the MLP was compared with that of Gradient Boosting (GB) and Decision Trees (DT). The result is that the MLP outperformed all existing benchmark methods.

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Section: Description Of the Proposed Methodmentioning

confidence: 99%

A Multilayer Perceptron Neural Network with Selective-Data Training for Flight Arrival Delay Prediction

Alla

Moumoun

Balouki

2021

Scientific Programming

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“…Constraint (14), which refers to a deterministic aircraft presence in the deterministic FGAP model, is replaced by [21]:…”

Section: Mathematical Formulation Of the Probabilistic Fgapmentioning

confidence: 99%

“…Here, the regression approach using Random Forests produced an RMSE of 8.7 min. Ayhan et al [13] and Shao et al [14] introduce features based on flight trajectory data. Ayhan et al [13] predict flight delays for domestic flights in Spain within an RMSE of 4 min.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

Zoutendijk¹,

Mitici²

2021

Aerospace

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The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.

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“…A group of Researchers [129] designed a framework to integrate multiple data sources to predict the departure delay of a scheduled flight and discuss the details of the data pipeline. They were the first group, to take advantage of airport situational awareness map, which was defined as airport traffic complexity (ATC), and combined the proposed ATC factors with weather conditions and flight information.…”

Section: Literature Reviewmentioning

confidence: 99%

Flight delay prediction based on deep learning and Levenberg-Marquart algorithm

et al. 2020

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Flight delay is inevitable and it plays an important role in both profits and loss of the airlines. An accurate estimation of flight delay is critical for airlines because the results can be applied to increase customer satisfaction and incomes of airline agencies. There have been many researches on modeling and predicting flight delays, where most of them have been trying to predict the delay through extracting important characteristics and most related features. However, most of the proposed methods are not accurate enough because of massive volume data, dependencies and extreme number of parameters. This paper proposes a model for predicting flight delay based on Deep Learning (DL). DL is one of the newest methods employed in solving problems with high level of complexity and massive amount of data. Moreover, DL is capable to automatically extract the important features from data. Furthermore, due to the fact that most of flight delay data are noisy, a technique based on stack denoising autoencoder is designed and added to the proposed model. Also, Levenberg-Marquart algorithm is applied to find weight and bias proper values, and finally the output has been optimized to produce high accurate results. In order to study effect of stack denoising autoencoder and LM algorithm on the model structure, two other structures are also designed. First structure is based on autoencoder and LM algorithm (SAE-LM), and the second structure is based on denoising autoencoder only (SDA). To investigate the three models, we apply the proposed model on U.S flight dataset that it is imbalanced dataset. In order to create balance dataset, undersampling method are used. We measured precision, accuracy, sensitivity, recall and F-measure of the three models on two cases. Accuracy of the proposed prediction model analyzed and compared to previous prediction method. results of three models on both imbalanced and balanced datasets shows that precision, accuracy, sensitivity, recall and F-measure of SDA-LM model with imbalanced and balanced dataset is improvement than SAE-LM and SDA models. The results also show that accuracy of the proposed model in forecasting flight delay on imbalanced and balanced dataset respectively has greater than previous model called RNN.

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Flight Delay Prediction using Airport Situational Awareness Map

Cited by 27 publications

References 9 publications

A Multilayer Perceptron Neural Network with Selective-Data Training for Flight Arrival Delay Prediction

A Multilayer Perceptron Neural Network with Selective-Data Training for Flight Arrival Delay Prediction

Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

Flight delay prediction based on deep learning and Levenberg-Marquart algorithm

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