Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning

Jardines, Aniel; Soler, Manuel; García-Heras, Javier

doi:10.1016/j.jairtraman.2021.102109

Cited by 18 publications

(8 citation statements)

References 14 publications

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“…They first quantified the impact of convective weather on airport operations, and then used delay features and deep learning methods to predict airport delays. Xie et al [22][23][24] first proposed an image representation of sector operation scenarios in 2021. Combined with deep learning technology, they proposed a Deep Convolutional Neural Network (DCNN)-based sector computational complexity (SOC) evaluation method.…”

Section: Related Workmentioning

confidence: 99%

Terminal Traffic Situation Prediction Model under the Influence of Weather Based on Deep Learning Approaches

et al. 2022

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In order to quantify the degree of influence of weather on traffic situations in real time, this paper proposes a terminal traffic situation prediction model under the influence of weather (TSPM-W) based on deep learning approaches. First, a feature set for predicting traffic situations is constructed based on data such as weather, traffic demand, delay conditions, and flow control strategies. When constructing weather data, a terminal area weather quantification method (TAWQM) is proposed to quantify various weather feature values. When constructing the traffic situation label, fuzzy C-means clustering (FCM) is used to perform cluster analysis on the traffic situation, and the traffic situation is marked as bad, average, or good. Accordingly, the multi-source data is fused as the input vector, based on the combined prediction model of convolutional neural network (CNN) and gated recurrent unit (GRU), TSPM-W is constructed. Finally, based on the historical operation data of the Guangzhou Baiyun International Airport terminal area, the proposed data set is used to predict the traffic situation time series at intervals of 1 h, 3 h, and 6 h. The comparative experimental results show that the proposed time series prediction model has higher prediction accuracy than other existing prediction methods. The proposed dataset is able to more accurately predict the traffic situation in the terminal area.

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Section: Related Workmentioning

confidence: 99%

Terminal Traffic Situation Prediction Model under the Influence of Weather Based on Deep Learning Approaches

et al. 2022

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“…Many research works have been carried out related to the WIs processing and analysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Some of the works are described as follows.…”

Section: Related Workmentioning

confidence: 99%

“…Weather information [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] is an important concern throughout the globe. It has the capability of lowering the weather related losses and enhancing societal benefits such as the protection of life, health, property, etc., if the weather information is shared among the entire human society at the right time properly.…”

Section: Introductionmentioning

confidence: 99%

Classification and Analysis of Weather Images Using Machine Intelligent Based Approach

Jena

2022

IJAEML

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Purpose: Weather information plays a crucial role in the human society. It helps to lower the weather related losses and enhance the societal benefits such as the protection of life, health, property, etc., It is very much essential for the proper classification of weather images (WIs) into several categories such as dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, snow, etc. so that appropriate information can be provided to the people as well as organizations for further analysis. Approach: In this work, a machine intelligent (MI) based approach is proposed for the classification of WIs into the dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, and snow types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Result: The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 1604 WIs having 149, 141, 146, 150, 144, 146, 142, 147, 149, 147, 143 numbers of dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, and snow types respectively are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD. Originality: In this work, a MI based approach is proposed by focusing on the stacking of LRG, SVMN, RFS and NNT methods to carry out the classification of WIs into several types such as dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, and snow type. The proposed approach performs better in terms of CA, F1, PR and RC as compared to LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGDC methods. Paper Type: Conceptual Research.

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“…It has been proved that, at the sector-level and during the pre-tactical phase, it is possible to accurately anticipate regulations due to weather using ML models [7], [8]. Similarly, [9] showed that ATFM capacity regulations can be predicted using supervised models.…”

Section: State-of-the-artmentioning

confidence: 99%

Pre-tactical prediction of ATFM delay for individual flights

Mas-Pujol

Falco

Salamí

et al. 2022

2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC)

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Airlines develop an operation plan, during the day prior to operations (D-1), to identify potential network issues and prepare potential pre-tactical preventing measures such as aircraft tail swapping or crew reassignment to be applied on D0. Flights might experience discrepancies between their plan and execution due to many different factors, and in particular demand-capacity imbalances in the network leading to Air Traffic Flow Management (ATFM) regulations. Dispatcher3, a Clean Sky 2 innovation action, focuses on the use of machine learning techniques to support the airlines processes prior departure: dispatching, understood as the broad flight planning from the day prior to operations to the flight plan definition and selection, and advisories to pilot. This paper focuses on the estimation of ATFM delay for individual flights during the pre-tactical phase (D-1), which could help airspace users apply mitigation actions. Four machine learning models are developed to produce individual independent estimations with different level of granularity. The first two are binary classifier models that provide information on the probability of a given flight being affected by ATFM delay, and the reason for this delay (airport or airspace congestion). These models reported an accuracy between 75% and 88%. The later two models estimate the impact of the delay (amount of delay assigned to the flight if regulated), with a Mean Absolute Error close to 9.35 minutes.

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Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning

Cited by 18 publications

References 14 publications

Terminal Traffic Situation Prediction Model under the Influence of Weather Based on Deep Learning Approaches

Terminal Traffic Situation Prediction Model under the Influence of Weather Based on Deep Learning Approaches

Classification and Analysis of Weather Images Using Machine Intelligent Based Approach

Pre-tactical prediction of ATFM delay for individual flights

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