Amsterdam to Dublin Eventually Delayed? LSTM and Transfer Learning for Predicting Delays of Low Cost Airlines

McCarthy, Nicholas; Karzand, Mina; Lécué, Freddy

doi:10.1609/aaai.v33i01.33019541

Cited by 11 publications

(9 citation statements)

References 10 publications

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“…Kim et al placed delays at different levels for prediction, using a recurrent neural network (RNN) to consider time and other direct factors such as weather and visibility [10]. Based on a single airport, McCarthy et al studied the delays of multiple airports with a long shortterm memory (LSTM) algorithm, using the time series of the past 24 h to predict delays [11]. The method was shown to be accurate and robust for low-cost airlines in Europe.…”

Section: Literature Reviewmentioning

confidence: 99%

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Flight delay forecasting and analysis of direct and indirect factors

Wang

Xie

et al. 2022

IET Intelligent Trans Sys

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The accurate prediction of flight delays is of great significance to airports, airlines and passengers. This paper presents a causal flight delay prediction model developed for a single airport. A long short-term memory network of delay prediction with an attention mechanism (LSTM-AM) is established to predict flight delays and analyse their primary causes. In this model, the direct and indirect factors related to delays are comprehensively considered. LSTM-AM can focus on input data combined with the attention vector to capture the critical time points, which can make the prediction more accurate. The model's performance is verified by actual operational data of Beijing International Airport, one of the busiest airports in the world. Experimental results show that LSTM-AM has better prediction accuracy than baseline algorithms such as some machine learning methods and deep learning methods. The mean absolute error of LSTM-AM is about 8.15 min on the test dataset. The study found that using the predicted results of this paper to release delayed information in advance can effectively alleviate the nervousness of passengers. The critical time point captured by LSTM-AM combined with runway and apron flow control can reduce or eliminate delays of one flight.

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Section: Literature Reviewmentioning

confidence: 99%

“…Combining Equations (9)(10)(11)(12), the stress of the passenger is related to the time of the notification and the accuracy of the expected time. In this paper, Equation ( 9) is used to measure the impact of delay information released by the forecast results in Section 3.1 on passenger stress.…”

Section: Passenger Waiting Stressmentioning

confidence: 99%

Flight delay forecasting and analysis of direct and indirect factors

Wang

Xie

et al. 2022

IET Intelligent Trans Sys

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“…LSTM models are trained with historical data on sequences of flights for the specific airport and particular aircraft. Then, a transfer learning approach is used to predict flight delays for other companies and other airports (N. McCarthy et al, 2019). There are already existing XAI approaches that can provide explainability of models based on transfer learning (J.…”

Section: Inventory and Logisticsmentioning

confidence: 99%

AI-enabled Enterprise Information Systems for Manufacturing

Zdravković

Panetto

Weichhart

2021

Enterprise Information Systems

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Research work presented in this paper considers the contemporary Enterprise Information Systems (EIS) functional architecture and carries out the literature review of AI applications integrated in Customer Relationship Management, Supply Chain Management, Inventory and logistics, Production Planning and Scheduling, Finance and accounting, Product Lifecycle Management and Human Resources, with special attention to the manufacturing enterprises. As a review outcome, the enhanced capabilities are identified in each of those processes and proposed as AI services. AI-enablement is related to the capabilities of the enterprise to implement improved decision making or automation by using (a) Machine Learning (ML) models or (b) logicbased systems. It is a process of the enterprise transformation leading to the convergence of the 4 major disruptive technologies, namely Industrial Internet of Things (or Cyber-Physical Systems), Agent-based Distributed Systems, Cloud Computing and Artificial Intelligence (non-symbolic AI like Machine Learning, Neural Networks; symbolic AI like Reasoning and Logic/Model-based AI).

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“…With the rapid development of data science, researchers started to predict the flight delays by using machine learning models [30,31]. The models widely used for flight delay prediction include neural networks, k-NN and random forests.…”

Section: Literature Reviewmentioning

confidence: 99%

Generation and prediction of flight delays in air transport

Jing

2021

IET Intelligent Trans Sys

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This paper presents models for flight delay prediction by considering both the local effects and network effects for the individual airport. Following a complex network approach, the authors analyse the local and network effects separately. Results indicate that the longterm flight delays are mainly caused by network effects, while the short-term flight delays are strongly associated with local delays. Therefore, the existing factors such as temporal variables, weather condition and seasonal effects are replaced with specific novel factors (e.g. crowdedness degree of airport and air traffic system, demand-capacity imbalance) for flight delay prediction. More specifically, this paper shows that the model prediction performance for both classification (predict whether the flight is delayed) and regression (predict the delay values) achieves higher accuracy when using the novel factors. Random Forest algorithms were trained and tested on the U.S. domestic flights in July 2018, and the results show that for classification model, the accuracy, precision and recall score reach 96.48%, 94.39% and 90.26% when classifying delays are within 15 min. Similarly, for regression model, 93.92% of the test errors are within 15 min.

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Amsterdam to Dublin Eventually Delayed? LSTM and Transfer Learning for Predicting Delays of Low Cost Airlines

Cited by 11 publications

References 10 publications

Flight delay forecasting and analysis of direct and indirect factors

Flight delay forecasting and analysis of direct and indirect factors

AI-enabled Enterprise Information Systems for Manufacturing

Generation and prediction of flight delays in air transport

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