Air passenger demand forecast through the use of Artificial Neural Network algorithms

Airport planning depends to a large extent on the levels of activity that are anticipated. To plan the facilities and infrastructures of an airport system and to be able to satisfy future needs, it is essential to predict the level and distribution of demand. This document presents a short- and medium-term forecast of the demand for air passengers carried out through a specific case study (Colombia), in which the impact of the pandemic period due to COVID-19 on air traffic was taken into account. To make the forecast, an algorithm that implements techniques based on Artificial Neural Networks (ANN) (Machine Learning (ML)) was developed. In particular, for the analysis of the available time series, techniques of encoder-decoder networks of the type ConvLSTM2D have been applied. These architectures are a hybrid between Convolutional Neural Networks (CNN), very useful for the extraction of invariant patterns in their spatial position, and Recurrent Neural Networks (RNN), appropriate for the extraction of patterns within their temporal context (time series). The most relevant result of the present research is that the recovery in demand (volume and trend) to the levels reported before the pandemic is forecast for the period between the end of 2022 and the beginning of 2024 (depending on the type of traffic and scenario considered). Finally, the application of the forecasting model based on Machine Learning/Deep Learning (DL) presents, as a metric performance, a Mean Absolute Percentage Error (MAPE) value from 3% to 9% (depending on the scenario), which enables predictions of relative precision and introduces a new alternative technical approach to develop reliable air traffic forecasts, at least in the short and medium term

Forecasting monthly airline passenger numbers with small datasets using feature engineering and a modified principal component analysis

In this study, a machine learning approach based on time series models, different feature engineering, feature extraction, and feature derivation is proposed to improve air passenger forecasting. Different types of datasets were created to extract new features from the core data. An experiment was undertaken with artificial neural networks to test the performance of neurons in the hidden layer, to optimise the dimensions of all layers and to obtain an optimal choice of connection weights – thus the nonlinear optimisation problem could be solved directly. A method of tuning deep learning models using H2O (which is a feature-rich, open source machine learning platform known for its R and Spark integration and its ease of use) is also proposed, where the trained network model is built from samples of selected features from the dataset in order to ensure diversity of the samples and to improve training. A successful application of deep learning requires setting numerous parameters in order to achieve greater model accuracy. The number of hidden layers and the number of neurons, are key parameters in each layer of such a network. Hyper-parameter, grid search, and random hyper-parameter approaches aid in setting these important parameters. Moreover, a new ensemble strategy is suggested that shows potential to optimise parameter settings and hence save more computational resources throughout the tuning process of the models. The main objective, besides improving the performance metric, is to obtain a distribution on some hold-out datasets that resemble the original distribution of the training data. Particular attention is focused on creating a modified version of Principal Component Analysis (PCA) using a different correlation matrix – obtained by a different correlation coefficient based on kinetic energy to derive new features. The data were collected from several airline datasets to build a deep prediction model for forecasting airline passenger numbers. Preliminary experiments show that fine-tuning provides an efficient approach for tuning the ultimate number of hidden layers and the number of neurons in each layer when compared with the grid search method. Similarly, the results show that the modified version of PCA is more effective in data dimension reduction, classes reparability, and classification accuracy than using traditional PCA.</div

Identification of weather influences on flight punctuality using machine learning approach

One of the top long-term threats to airport resilience is extreme climate-induced conditions, which negatively affect the airport and flight operations. Recent examples, including hurricanes, storms, extreme temperatures (cold/hot), and heavy rains, have damaged airport facilities, interrupted air traffic, and caused higher operational costs. With the development of civil aviation and the pre-COVID-19 surging demand for flights, the passengers’ complaints of flight delay increased, according to FoxBusiness. This study aims to discover the weather factors affecting flight punctuality and determine a high-dimensional scale of consequences stemming from weather conditions and flight operational aspects. Machine learning has been developed in correlation with the weather and statistical data for operations at Birmingham Airport as a case study. The cross-correlated datasets have been kindly provided by Birmingham Airport and the Meteorological Office. The scope and emphasis of this study is placed on the machine learning application to practical flight punctuality prediction in relation to climate conditions. Random forest, artificial neural network, support vector machine, and linear regression are used to develop predictive models. Grid-search and cross-validation are used to select the best parameters. The model can grasp the trend of flight punctuality rates well where R2 is 0.80 and the root mean square error (RMSE) is less than 15% using the model developed by random forest technique. The insights derived from this study will help Airport Authorities and the Insurance industry in predicting the scale of consequences in order to promptly enact and enable adaptative airport climate resilience plans, including air traffic rescheduling, financial resilience to climate variances and extreme weather conditions

Air passenger demand forecast through the use of Artificial Neural Network algorithms

Airport planning depends to a large extent on the levels of activity that are anticipated. In order to plan facilities and infrastructures of an airport system and to be able to satisfy future needs, it is essential to predict the level and distribution of demand. This document presents a short- and medium-term forecast of the demand for air passengers carried out through a specific case study (Colombia), in which the impact of the pandemic period due to COVID-19 on air traffic was taken into account. To make the forecast, an algorithm that implements techniques based on Artificial Neural Networks (ANN) and Machine Learning (ML) was developed. In particular, for the analysis of the available time series, techniques of encoder-decoder networks of the type ConvLSTM2D have been applied. These architectures are a hybrid between Convolutional Neural Networks (CNN), very useful for the extraction of invariant patterns in their spatial position, and Recurrent Neural Networks (RNN), appropriate for the extraction of patterns within their temporal context (time series). The most relevant result of the present research is that the recovery in demand (volume and trend) to the levels reported before the pandemic is forecast for the period between the end of 2022 and the beginning of 2024 (depending on the type of traffic and scenario considered). Finally, the application of the forecasting model based on ML/Deep Learning (DL) presents, as a metric performance, a Mean Absolute Percentage Error (MAPE) values from 3% to 9% (depending on the scenario), which enables predictions of relative precision and introduces a new alternative technical approach to develop reliable air traffic forecasts, at least in the short and medium term

Application of Data Mining in Air Traffic Forecasting

The main goal of the study centers on developing a model for the purpose of air traffic forecasting by using off-the-shelf data mining and machine learning techniques. Although data driven modeling has been extensively applied in the aviation sector, little research has been done in the area of air traffic forecasting. This study is inspired by previous research focused on improving the Federal Aviation Administration (FAA) Terminal Area Forecasting (TAF) methodology, which historically assumed that the US air transportation system (ATS) network structure was static. Recent developments use data mining algorithms to predict the likelihood of previously un-connected airport-pairs being connected in the future, and the likelihood of connected airport-pairs becoming un-connected. Despite the innovation of this research, it does not focus on improving the FAA’s existing methodology for forecasting future air traffic levels on existing routes, which is based on relatively simple regression and growth models. We investigate different approaches for improving and developing new features within the existing data mining applications in air traffic forecasting. We focus particularly on predicting detailed traffic information for the US ATS. Initially, a 2-stage log-log model is applied to establish the significance of different inputs and to identify issues of endogeneity and multi-colinearity, while maintaining the simplicity of current models. Although the model shows high goodness of fit, it tested positive for both mentioned issues as well as presenting problems with causality. With the objective of solving these issues, a 3-stage model that is under development is introduced. This model employs logistic regression and discrete choice modelling. As part of future work, machine learning techniques such as clustering and neural networks will be applied to improve this model’s performance

A study on the prediction of flight delays of a private aviation airline

The delay is a crucial performance indicator of any transportation system, and flight delays cause financial and economic consequences to passengers and airlines. Hence, recognizing them through prediction may improve marketing decisions. The goal is to use machine learning techniques to predict an aviation challenge: flight delay above 15 minutes on departure of a private airline. Business and data understanding of this particular segment of aviation are revised against literature revision, and data preparation, modelling and evaluation are addressed to lead towards a model that may contribute as support for decision-making in a private aviation environment. The results show us which algorithms performed better and what variables contribute the most for the model, thereafter delay on departure.O atraso de voo é um indicador fulcral em toda a indútria de transporte aéreo e esses atrasos têm consequências económicas e financeiras para passageiros e companhias aéras. Reconhecê- los através de predição poderá melhorar decisões estratégicas e operacionais. O objectivo é utilizar técnicas de aprendizagem de máquina (machine learning) para prever um eterno desafio da aviação: atraso de voo à partida, utilizando dados de uma companhia aérea privada. O conhecimento do contexto do negócio e dos dados adquiridos, num segmento singular da aviação, são revistos à luz das literatura vigente e a preparação dos dados, a modelização e respectiva avaliação são conduzidos de modo a contribuir para uma ferramenta de apoio à decisão no contexto da aviação privada. Os resultados obtidos revelam quais dos algoritmos utilizados demonstra uma melhor performance e quais as variáveis dos dados obtidos que mais contribuem para o modelo e consequentemente para o atraso à partida

A forecasting Tool for Predicting Australia\u27s Domestic Airline Passenger Demand Using a Genetic Algorithm

This study has proposed and empirically tested for the first time genetic algorithm optimization models for modelling Australia’s domestic airline passenger demand, as measured by enplaned passengers (GAPAXDE model) and revenue passenger kilometres performed (GARPKSDE model). Data was divided into training and testing datasets; 74 training datasets were used to estimate the weighting factors of the genetic algorithm models and 13 out-of-sample datasets were used for testing the robustness of the genetic algorithm models. The genetic algorithm parameters used in this study comprised population size (n): 200; the generation number: 1,000; and mutation rate: 0.01. The modelling results have shown that both the quadratic GAPAXDE and GARPKSDE models are more accurate, reliable, and have greater predictive capability as compared to the linear models. The mean absolute percentage error in the out of sample testing dataset for the GAPAXDE and GARPKSDE quadratic models are 2.55 and 2.23%, respectively

Application of Data Mining to forecast Air Traffic: A 3-Stage Model using Discrete Choice Modeling

The main goal of this study centers on developing an aggregate air itinerary share model estimated at the city-pair level within the US air transportation system. This route demand assignment model is part of a new modeling approach that has as its ultimate output the prediction of detailed traffic information for the US air transportation system. In this approach, city-pair demand generation, route demand assignment and air traffic levels estimations are completed in 3 different stages within a single framework. Aiming to fully develop the overall model, in this paper we focus on estimating the 2nd stage, the air itinerary choice model. In order to achieve this, the first approach taken applies a multinomial logit model and uses a combination of stated preferences (SP) and revealed preferences (RP) data to estimate the model. By using a mixed dataset, we attempt to improve the RP model results, which often perform poorly due to high demand inelasticity. Preliminary results show the potential of this approach, although further analysis is required to understand the results obtained. For the final paper, different approaches and further interactions among the model attributes will be applied to improve the model’s performance

A study of flight cancellation and delays in the UK

Flight delays and flight cancellations have always been a problem for the aviation industry. However, the different nature of both phenomena has made research focus almost solely on studying and predicting delays. This is due to the fact that, ultimately, it is the airline who decides whether a flight gets cancelled, whereas delays are an involuntary result of a vast array of different causes, many times due to bad management practices by airports and airlines. The literature has studied delays from a wide range of perspectives, taking into consideration several factors that influence them. Some studies have predicted delays from a machine learning perspective, while others have taken into consideration the importance of the time series component of the data. However, research shows that it is actually flight cancellations that is the most important determinant for consumer dissatisfaction and complaints, being detrimental for airlines' reputation and resulting in passengers switching carriers. Therefore, a more careful study and comprehension of what drives and affects flight cancellations is needed. Analyzing the research that has focused on understanding the underlying patterns of cancellations, what can mostly be found are theoretical and machine learning approaches. Some findings have been made in determining what further increases or helps reduce the number of cancellations, like the importance of a well-managed airport capacity to improve service quality in terms of cancellations \citep{mead2000flight}. As mentioned, there is also behavioral research on the consequences that cancellations have on airlines (Yanying et al., 2019), pointing towards an increased dissatisfaction and distrust from customers, resulting in serious damages for the airline's corporate reputation and passengers' loyalty. Nevertheless, there are components of the understanding of cancellations that remained unclear. On the one hand, a thorough time series analysis of cancellations needs to be done. In fact, as Lemke et al. (p. 85, 2009) point out, the diverse characteristics and underlying data generation processes of time series has resulted in the fact that "it seems as if no method has ever proven successful across various studies and time series". On the other hand, delays and cancellations are two phenomena that cannot be completely understood independently and, although there is a vast number of studies analyzing delay propagation, there are no conclusive results on the impact of delays on cancellations. Therefore, research must determine whether taking delays into account when analyzing cancellations improves the accuracy of cancellations forecasts and the relation among these parameters. Lastly, as they cannot only be studied alone, a more thorough study of the capacity factors that influence the number of cancellations also needs to be done. Moreover, the outbreak of the COVID-19 in the midst of the research process made the accuracy of the forecasts deviate. Delays and cancellations have evolved dramatically differently over the first months of 2020. Hence, there is a need for taking a new parameter into account that would help make sense of the abnormal cancellations in 2020 and improve forecasts accuracies. For this, the behavioral changes of the population have been taking into consideration, which has been done with Google Trends. Also, it opened a door for understanding the passengers' behavioral reaction towards air travel under these circumstances, taking into consideration both local and global factors. Therefore, this study is divided into three sections. The first one studies the relationship between delays and cancellations from a time series perspective, and it is found that taking delays into account as a parameter in the study of cancellations improves the accuracy of time series forecasts at different levels of aggregation. The second one focuses on studying the relevance of competition and network factors in the distribution of cancellations. Flights arriving or departing from a hub airport are found to be less likely to be cancelled, pointing towards the relevance of maintaining networks for airlines, thus strengthening passenger reliability and trust. However, it was found that route and airport competition, while confirming the nature of the impact, was not statistically significant in predicting flight cancellations. Finally, it was found that public concern in the context of a global pandemic varies according to local circumstances, and that shortly after the first and most shocking news, both concern and a positive consumer attitude decrease to a stabilized level, which indicating double-edged passive behavior, in which both concern and willingness to purchase flight or event tickets (i.e., requiring travel or social gatherings) are reduced to similarly low levels for at least one month after the initial mayhe