A Methodology for Predicting Aggregate Flight Departure Delays in Airports Based on Supervised Learning

Ye, Bojia; Liu, Bo; Tian, Yong; Wan, Lili

doi:10.3390/su12072749

Cited by 27 publications

(8 citation statements)

References 21 publications

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“…The steps to determine the optimal segmentation point are: first traverse all bins. Then, using the current bin as the dividing point, accumulate the gradient from the bins on the left to the current bin, SL and the number of samples n L , and use the histogram to make the difference, and obtain the gradient on the right by SR=SP-SL; n R =n P -n L And the number of samples, the gain is calculated by the following formula6 [16]. Finally, the maximum gain is obtained in the traversal process, and the feature and bin feature value at this time are used as the feature and value of the split node.…”

Section: Lightgbm Algorithmmentioning

confidence: 99%

Body Shape Recognition and Prototype Construction Based on LightGBM Algorithm

Wang

Wang²,

Liu

2022

Advances in Transdisciplinary Engineering

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The regional characteristics of middle-aged women in Xinjiang are obvious, and the phenomenon of incompatible clothing is prominent. In order to avoid the information loss of principal component analysis or factor analysis during body type classification, this study uses the LightGBM algorithm to establish a body type recognition model based on the results of K-Means clustering, and compares it with random forest and linear regression recognition models. It is found that the LightGBM model has the best body type recognition effect. it is good. Through correlation analysis and regression analysis, regression equations between variables are obtained. Based on the new cultural prototype, Liu Ruipu women’s clothing prototype, and Donghua prototype, a clothing prototype suitable for middle-aged women in Xinjiang is established. Through virtual fitting evaluation and actual fitting, the experimental prototypes established based on the Donghua prototype and the Liu Ruipu prototype can meet the needs of middle-aged women for the comfort and aesthetics of clothing. The experimental prototype established by the Donghua prototype has the best effect.

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Section: Lightgbm Algorithmmentioning

confidence: 99%

Body Shape Recognition and Prototype Construction Based on LightGBM Algorithm

Wang

Wang²,

Liu

2022

Advances in Transdisciplinary Engineering

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show abstract

“…The results and advantages of these algorithms are mainly focus on a specific performance metric (i.e. accuracy, RMSE) [55] [56]. Although the challenge addressed is normally mentioned in the introduction of many papers, such as improving operational efficiency, experts and end users feedback emphasize the need for a more thorough description of the purpose and advantages of these algorithms within current practices.…”

Section: A Purpose Of the Ai Solutionmentioning

confidence: 99%

An Explainable Artificial Intelligence (xAI) Framework for Improving Trust in Automated ATM Tools

Hernandez

Ayo

Panagiotakopoulos

2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

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With the increased use of intelligent Decision Support Tools in Air Traffic Management (ATM) and inclusion of non-traditional entities, regulators and end users need assurance that new technologies such as Artificial Intelligence (AI) and Machine Learning (ML) are trustworthy and safe. Although there is a wide amount of research on the technologies themselves, there seem to be a gap between research projects and practical implementation due to different regulatory and practical challenges including the need for transparency and explainability of solutions. In order to help address these challenges, a novel framework to enable trust on AI-based automated solutions is presented based on current guidelines and end user feedback. Finally, recommendations are provided to bridge the gap between research and implementation of AI and ML-based solutions using our framework as a mechanism to aid advances of AI technology within ATM.

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“…One of the newest studies in the area of machine learning method has been presented by a model which applicate supervised learning methods to aggregate flight departure delays in china airports [128]. The expected departure delays in airports was selected as the prediction target while four popular supervised learning methods: multiple linear regression, support vector machine, extremely randomized trees and LightGBM were investigated to improve the predictability and accuracy of the model.…”

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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A Methodology for Predicting Aggregate Flight Departure Delays in Airports Based on Supervised Learning

Cited by 27 publications

References 21 publications

Body Shape Recognition and Prototype Construction Based on LightGBM Algorithm

Body Shape Recognition and Prototype Construction Based on LightGBM Algorithm

An Explainable Artificial Intelligence (xAI) Framework for Improving Trust in Automated ATM Tools

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

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