Abstract
The importance of robust flight delay prediction has recently increased in the air transportation industry. This industry seeks alternative methods and technologies for more robust flight delay prediction because of its significance for all stakeholders. The most affected are airlines that suffer from monetary and passenger loyalty losses. Several studies have attempted to analysed and solve flight delay prediction problems using machine learning methods. This research proposes a novel alternative method, namely social ski driver conditional autoregressive-based (SSDCA-based) deep learning. Our proposed method combines the Social Ski Driver algorithm with Conditional Autoregressive Value at Risk by Regression Quantiles. We consider the most relevant instances from the training dataset, which are the delayed flights. We applied data transformation to stabilise the data variance using Yeo-Johnson. We then perform the training and testing of our data using deep recurrent neural network (DRNN) and SSDCA-based algorithms. The SSDCA-based optimisation algorithm helped us choose the right network architecture with better accuracy and less error than the existing literature. The results of our proposed SSDCA-based method and existing benchmark methods were compared. The efficiency and computational time of our proposed method are compared against the existing benchmark methods. The SSDCA-based DRNN provides a more accurate flight delay prediction with 0.9361 and 0.9252 accuracy rates on both dataset-1 and dataset-2, respectively. To show the reliability of our method, we compared it with other meta-heuristic approaches. The result is that the SSDCA-based DRNN outperformed all existing benchmark methods tested in our experiment.
Highlights
The civil aviation sector is a distributed network of large interconnected elements designed to meet the common aim of on-time air transportation and passengers expectations [1, 2]
This section describes the results of the proposed SSDCAbased Deep long short-term memory (LSTM) based on some benchmark metrics and compares our method with a set of methods from the literature
The dataset considered for the experimentation is the flight delay prediction dataset with United States (US) flight data downloaded from the US Government Bureau of transportation statistics [71, 72]
Summary
The civil aviation sector is a distributed network of large interconnected elements designed to meet the common aim of on-time air transportation and passengers expectations [1, 2]. A few years ago, the research methodologies utilised for predicting delay propagation were from statistical, network theory, machine learning and agent-enabled methods [4, 36]. We introduce a feature fusion method that utilises the complete flight information on different routes and combines them to improve the performance. Our paper aims to propose a novel flight delay prediction strategy that utilises social ski driver conditional autoregressive-based (SSDCA-based) deep long short-term memory (LSTM). We train the Deep RNN by the developed SSDCA, which improves the model learning process. We perform flight delay prediction using the Deep LSTM. The contribution of the paper is: 1.1 Proposed SSDCA enabled Deep LSTM for flight delay prediction.
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