Enhanced flight delay data for ASQP carriers

Delay Prediction of Aircrafts Based on Health Monitoring Data

International Journal of Business Analytics and Intelligence ◽

10.21863/ijbai/2016.4.1.015 ◽

2016 ◽

Vol 4 (1) ◽

Cited By ~ 1

Author(s):

B. A. Dattaram ◽

N. Madhusudanan

Keyword(s):

Machine Learning ◽

Health Monitoring ◽

Monitoring Data ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Flight Delay ◽

Delay Prediction ◽

The Relationship

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

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Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

Aerospace ◽

10.3390/aerospace8060152 ◽

2021 ◽

Vol 8 (6) ◽

pp. 152

Author(s):

Micha Zoutendijk ◽

Mihaela Mitici

Keyword(s):

Assignment Problem ◽

Absolute Error ◽

Aviation Industry ◽

Probabilistic Forecasting ◽

Flight Delays ◽

Mixture Density ◽

Assignment Model ◽

Gate Assignment ◽

Flight Delay ◽

Delay Prediction

The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.

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On the relevance of data science for flight delay research: a systematic review

Transport Reviews ◽

10.1080/01441647.2020.1861123 ◽

2020 ◽

pp. 1-30

Author(s):

Leonardo Carvalho ◽

Alice Sternberg ◽

Leandro Maia Gonçalves ◽

Ana Beatriz Cruz ◽

Jorge A. Soares ◽

...

Keyword(s):

Systematic Review ◽

Data Science ◽

Flight Delay

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Multiobjective Optimization Model for Collaborative En-Route and Slot Allocation

Mathematical Problems in Engineering ◽

10.1155/2018/5686281 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7

Author(s):

Shangwen Yang ◽

Jingting Zhang ◽

Ping Chen ◽

Yongjie Yan

Keyword(s):

Decision Making ◽

Traffic Control ◽

Programming Model ◽

Numerical Test ◽

Simulated Data ◽

Slot Allocation ◽

Collaborative Decision Making ◽

Average Delay ◽

Flight Delay ◽

Collaborative Decision

To allocate the en-routes and slots resource to the flights with collaborative decision-making, a multiobjective 0-1 integer programming model was proposed. According to different demands from air traffic control departments, airlines, and passengers, efficiency, equity, and effectiveness principles of collaborative decision-making were considered. With the aim to minimize the total flight delay costs, the total number of turning points, and average delay time of passengers, the effectiveness constraints were achieved. The algorithm was designed to solve the model on the basis of the objective method, and Lingo11 and MatlabR2007b were applied in numerical tests. To test how well the model works in real world, a numerical test was performed based on the simulated data of a civil en-route. Test results show that, compared with the traditional strategy of first come first served, the model gains better effect. The superiority of the model was verified.

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