Flight delay prediction for commercial air transport: A deep learning approach

Yu, Bin; Guo, Zeng-Yuan; Arisian, Sobhan; Wang, Huaizhu; Chen, Gang

doi:10.1016/j.tre.2019.03.013

Cited by 148 publications

(68 citation statements)

References 40 publications

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“…In the methodology section describes the flow of work for the algorithms that were applied .The main objective is to build a model to predict the delay of the flights that meets the state of art. neural network gave an amazing performance in terms of flight delay prediction [16], [18], [24], [25], [26], [27], [11], especially RNN,LTSM [17] and DBN [29].…”

Section: Methodsmentioning

confidence: 99%

AHybrid Artificial Intelligence Approach to Predict Flight Delay

Alharbi¹,

Prince²

2020

International Journal of Engineering Research and Technology

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Commercial airlines and the passengers suffer from flight delay. Flight delay causes huge loss for the airlines and unsatisfied passengers. The researchers attempt to solve this problem through prediction extensively by machine learning approach and data mining tools. Accurate and robust performance is still to get through existing models. Our proposed hybrid approach is intended to use the power of machine learning as data mining tool and to predict the delay using classification algorithm of deep learning. An extensive evaluation of the proposed method is carried out by comparing the performance by using two data sets: one is local and the other is benchmark from Kaggle to obtain the best performing classifier. Three predictive models were applied on the datasets: logistic regression, decision tree and the proposed approach. The result shows that the proposed method performed well as comparing to the existing state-ofthe art.

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Section: Methodsmentioning

confidence: 99%

AHybrid Artificial Intelligence Approach to Predict Flight Delay

Alharbi¹,

Prince²

2020

International Journal of Engineering Research and Technology

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“…By converting their data matrices into images, they were able to use a CNN model to extract image features and predict the network-wide traffic speed. Yu et al (2019) improved this approach later by adding a temporal gated convolution layer to extract temporal features.…”

Section: Traffic Characteristics Predictionmentioning

confidence: 99%

Applications of Deep Learning in Intelligent Transportation Systems

Haghighat

Ravichandra-Mouli

Chakraborty

et al. 2020

J. Big Data Anal. Transp.

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In recent years, Intelligent Transportation Systems (ITS) have seen efficient and faster development by implementing deep learning techniques in problem domains which were previously addressed using analytical or statistical solutions and also in some areas that were untouched. These improvements have facilitated traffic management and traffic planning, increased safety and security in transit roads, decreased costs of maintenance, optimized public transportation and ride-sharing company's performance, and advanced driver-less vehicle development to a new stage. This papers primary objective was to provide a review and comprehensive insight into the applications of deep learning models on intelligent transportation systems accompanied by presenting the progress of ITS research due to deep learning. First, different techniques of deep learning and their state-of-the-art are discussed, followed by an in-depth analysis and explanation of the current applications of these techniques in transportation systems. This enumeration of deep learning on ITS highlights its significance in the domain. The applications are furthermore categorized based on the gap they are trying to address. Finally, different embedded systems for deployment of these techniques are investigated and their advantages and weaknesses over each other are discussed. Based on this systematic review, credible benefits of deep learning models on ITS are demonstrated and directions for future research are discussed.

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“…Some Research [152] combined a deep belief network with a support vector machine to create a prediction model (DBN-SVR), in which DBN extracted the main factors with tangible impacts on flight delays, reduced the dimension of inputs, and eliminated redundant information. The output of DBN was then used as the input of the SVR model to capture the key influential factors (leading to flight delays) and generated the prediction value of delays.…”

Section: Deep Neural Networkmentioning

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 for commercial air transport: A deep learning approach

Cited by 148 publications

References 40 publications

AHybrid Artificial Intelligence Approach to Predict Flight Delay

AHybrid Artificial Intelligence Approach to Predict Flight Delay

Applications of Deep Learning in Intelligent Transportation Systems

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

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