Predicting flight delay based on multiple linear regression

Ding, Yi

doi:10.1088/1755-1315/81/1/012198

Cited by 37 publications

(16 citation statements)

References 6 publications

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“…LR is an approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables) [21]. A linear regression line has an equation of the form 𝑌𝑌 = 𝑎𝑎 + 𝑏𝑏𝑏𝑏, where 𝑏𝑏 are the independent variables and 𝑌𝑌 is the dependent variable.…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

Real-Time Passenger Train Delay Prediction Using Machine Learning: A Case Study With Amtrak Passenger Train Routes

Lapamonpinyo

Derrible

Corman³

2022

IEEE Open J. Intell. Transp. Syst.

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Passenger train delay significantly influences riders' decision to choose rail transport as their mode choice. This article proposes real-time passenger train delay prediction (PTDP) models using the following machine learning techniques: random forest (RF), gradient boosting machine (GBM), and multi-layer perceptron (MLP). In this article, the impact on the PTPD models using Real-time based Data-frame Structure (RT-DFS) and Real-time with Historical based Data-frame Structure (RWH-DFS) is investigated. The results show that PTDP models using MLP with RWH-DFS outperformed all other models. The influence of the external variables such as historical delay profiles at the destination (HDPD), ridership, population, day of the week, geography, and weather information on the real-time PTPD models are also further analyzed and discussed.

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Section: Linear Regression (Lr)mentioning

confidence: 99%

Real-Time Passenger Train Delay Prediction Using Machine Learning: A Case Study With Amtrak Passenger Train Routes

Lapamonpinyo

Derrible

Corman³

2022

IEEE Open J. Intell. Transp. Syst.

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“…In a study by Ding, flight delay prediction was considered as a regression problem, and a solution based on multiple linear regression was proposed and compared with Naïve Bayes and C4.5 [3].…”

Section: Related Workmentioning

confidence: 99%

Supervised Learning Approaches to Flight Delay Prediction

Atlıoğlu

Bolat

Şahin

et al. 2020

Sakarya University Journal of Science

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Delays in flights and other airline operations have significant consequences in quality of service, operational costs, and customer satisfaction. Therefore, it is important to predict the occurrence of delays and take necessary actions accordingly. In this study, we addressed the flight delay prediction problem from a supervised machine learning perspective. Using a realworld airline operations dataset provided by a leading airline company, we identified optimum dataset features for optimum prediction accuracy. In addition, we trained and tested 11 machine learning models on the datasets that we created from the original dataset via feature selection and transformation. CART and KNN showed consistently good performance in almost all cases achieving 0.816 and 0.807 F-Scores respectively. Similarly, GBM, XGB, and LGBM showed very good performance in most of the cases, achieving F-Scores around 0.810.

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“…At this moment, the dataset is not big but constantly updated; so in the future, the obtained results should be more accurate. The analysis is based on supervised machine learning model, where common algorithms have been used [ 9 , 10 ] and results are compared, such as a probabilistic neural network, multilayer perceptron, decision trees, random forest, tree ensemble, gradient boosted trees, and support vector machines [ 11 , 12 ]. To find the best parameters for each algorithm, the grid search is used.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model

Stefanovič

Štrimaitis

Kurasova

2020

Computational Intelligence and Neuroscience

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In the paper, the flight time deviation of Lithuania airports has been analyzed. The supervised machine learning model has been implemented to predict the interval of time delay deviation of new flights. The analysis has been made using seven algorithms: probabilistic neural network, multilayer perceptron, decision trees, random forest, tree ensemble, gradient boosted trees, and support vector machines. To find the best parameters which give the highest accuracy for each algorithm, the grid search has been used. To evaluate the quality of each algorithm, the five measures have been calculated: sensitivity/recall, precision, specificity, F-measure, and accuracy. All experimental investigation has been made using the newly collected dataset from Lithuania airports and weather information on departure/landing time. The departure flights and arrival flights have been investigated separately. To balance the dataset, the SMOTE technique is used. The research results showed that the highest accuracy is obtained using the tree model classifiers and the best algorithm of this type to predict is gradient boosted trees.

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Predicting flight delay based on multiple linear regression

Cited by 37 publications

References 6 publications

Real-Time Passenger Train Delay Prediction Using Machine Learning: A Case Study With Amtrak Passenger Train Routes

Real-Time Passenger Train Delay Prediction Using Machine Learning: A Case Study With Amtrak Passenger Train Routes

Supervised Learning Approaches to Flight Delay Prediction

Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model

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