Study on Delay Propagation Relations Among Airports Based on Transfer Entropy

Xiao, Yinhong; Zhao, Yaoshuai; Wu, Ge; Jing, Yizhen

doi:10.1109/access.2020.2996301

Cited by 15 publications

(5 citation statements)

References 28 publications

(35 reference statements)

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“…Operations research techniques have also played a role, with dynamic optimization models for flight operations [18], optimization of aircraft routes and departure times using mixed-integer programming [19], and multi-objective optimization leading to a 12-31% improvement in flight delays [20]. Network-based models, like the clustered airport modeling approach of optimizing flight operations [21], the minimum-cost flow problem for system design [22], and the investigation of delay propagation among airports with Granger causality [23,24] are also used.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods

Hatıpoğlu,

Tosun

2024

Applied Sciences

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Flight delays represent a significant challenge in the global aviation industry, resulting in substantial costs and a decline in passenger satisfaction. This study addresses the critical issue of predicting flight delays exceeding 15 min using machine learning techniques. The arrival delays at a Turkish airport are analyzed utilizing a novel dataset derived from airport operations. This research examines a range of machine learning models, including Logistic Regression, Naïve Bayes, Neural Networks, Random Forest, XGBoost, CatBoost, and LightGBM. To address the issue of imbalanced data, additional experiments are conducted using the Synthetic Minority Over-Sampling Technique (SMOTE), in conjunction with the incorporation of meteorological data. This multi-faceted approach ensures robust forecast performance under varying conditions. The SHAP (SHapley Additive exPlanations) method is employed to interpret the relative importance of features within the models. The study is based on a three-year period of flight data obtained from a Turkish airport. The dataset is sufficiently extensive and robust to provide a reliable foundation for analysis. The results indicate that XGBoost is the most proficient model for the dataset, demonstrating its potential to deliver highly accurate predictions with an accuracy of 80%. The impact of weather factors on the predictions is found to be insignificant in comparison to scenarios without weather data in this dataset.

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Section: Literature Reviewmentioning

confidence: 99%

Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods

Hatıpoğlu,

Tosun

2024

Applied Sciences

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“…Besides the Granger causality test, Xiao et al [32] proposed low-dimensional approximation of condition mutual information for transfer entropy test to mine causality among nonlinear flight delays. en, they designed a simulation experiment based on artificial nonlinear time series.…”

Section: Complexitymentioning

confidence: 99%

“…Belkoura and Zanin [29] and Mazzarisi et al [34] compared accuracy of proposed methods with the traditional method. Xiao et al [32] evaluated accuracy of the proposed scheme through flight delay propagation simulation experiment. No one has been testified, validated, or evaluated by real flight delay data.…”

Section: Complexitymentioning

confidence: 99%

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Recent Progress about Flight Delay under Complex Network

2021

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Flight delay is one of the most challenging threats to operation of air transportation network system. Complex network was introduced into research studies on flight delays due to its low complexity, high flexibility in model building, and accurate explanation about real world. We surveyed recent progress about flight delay which makes extensive use of complex network theory in this paper. We scanned analyses on static network and temporal evolution, together with identification about topologically important nodes/edges. And, we made a clarification about relations among robustness, vulnerability, and resilience in air transportation networks. Then, we investigated studies on causal relations, propagation modellings, and best spreaders identifications in flight delay. Ultimately, future improvements are summarized in fourfold. (1) Under Complex Network, flight operation relevant subsystems or sublayers are discarded by the majority of available network models. Hierarchical modelling approaches may be able to improve this and provide more capable network models for flight delay. (2) Traffic information is the key to narrow the gap between topology and functionality in current situations. Flight schedule and flight plan could be employed to detect flight delay causalities and model flight delay propagations more accurately. Real flight data may be utilized to validate and revise the detection and prediction models. (3) It is of great importance to explore how to predict flight delay propagations and identify best spreaders at a low cost of calculation complexity. This may be achieved by analyzing flight delay in frequency domain instead of time domain. (4) Summation of most critical nodes/edges may not be the most crucial group to network resilience or flight delay propagations. Effective algorithm for most influential sequence is to be developed.

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“…This interesting work finds patterns in realvalued variables related with flights, while in our problem, we consider passenger features, many of them of ordinal or categorical type. To understand the mechanism of delay propagation from the perspective of multiple airports, [21] proposed a low-dimensional approximation of conditional mutual information for transfer entropy. Chen et al [22] designed a visual analysis system for studying the delay propagation trends in one region and representing the relationship of delays occurring in multiple airports.…”

Section: A Related Workmentioning

confidence: 99%

Decision Support Models for Predicting and Explaining Airport Passenger Connectivity from Data

Guimarães¹,

Soares²,

Ventura³

2021

Preprint

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Predicting if passengers in a connecting flight will lose their connection is paramount for airline profitability. We present novel machine learning-based decision support models for the different stages of connection flight management, namely for strategic, pre-tactical, tactical and post-operations. We predict missed flight connections in an airline's hub airport using historical data on flights and passengers, and analyse the factors that contribute additively to the predicted outcome for each decision horizon. Our data is high-dimensional, heterogeneous, imbalanced and noisy, and does not inform about passenger arrival/departure transit time. We employ probabilistic encoding of categorical classes, data balancing with Gaussian Mixture Models, and boosting. For all planning horizons, our models attain an area under the curve (AUC) of the receiver operating characteristic (ROC) higher than 0.93. SHAP value explanations of our models indicate that scheduled/perceived connection times contribute the most to the prediction, followed by passenger age and whether border controls are required.

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Study on Delay Propagation Relations Among Airports Based on Transfer Entropy

Cited by 15 publications

References 28 publications

Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods

Predictive Modeling of Flight Delays at an Airport Using Machine Learning Methods

Recent Progress about Flight Delay under Complex Network

Decision Support Models for Predicting and Explaining Airport Passenger Connectivity from Data

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