Forecasting air passenger traffic volume : evaluating time series models in long-term forecasting of Kuwait air passenger data

Accurate estimation of air transport demand is vital for airlines, related aviation companies, and government agencies. For example, both short-term and long-term business plans of airlines require accurate forecasting of future air traffic flows. This study aims to forecast the volume of air passengers in Kuwait International Airport (KIA), which is in the state of Kuwait. Using monthly air traffic volume data between January 2012 and December 2018, this study focuses on the modelling and forecasting the number of air passengers in KIA. A wide range of time series forecasting models are considered in this research, including autoregressive-integrated-moving average model (ARIMA), exponential smoothing with errors term (ETS), Holt-Winters exponential smoothing, neural network autoregression (NNAR), hybrid and Bayesian structural time series (BSTS), and a hybrid model. The forecasting performance of these models are compared using multiple train-test splits where the models are fitted on the training sets and evaluated on the test sets. The mean absolute percentage error (MAPE) is used to compare the performance of various models. Empirical analysis suggests that the BSTS model compares favorably against the other time series models in its ability to forecast complex time series. The BSTS model may be applied to study other complex time series forecasting problems with irregularity

Airport Choice in Germany - New Empirical Evidence of the German Air Traveller Survey 2003

The paper deals with the quantitative relationship between the number of air travellers in any region and the airports chosen in Germany in 2003. The purpose of the paper is to present results of an analysis of airport choice behaviour of total air passenger demand in Germany, based on data of the German air traveller survey conducted at 17 international and 5 regional airports. About 210 000 passengers were interviewed about their trip origin, destination, choice of travel mode to the airport, purpose of their journey and further journey and person related attributes. As a result of the analysis so far, the distribution of airports chosen by all passengers coming from any region in Germany can be shown in relation to the journey purpose and destination. Based on these data, logit models have been calibrated for each market segment to forecast airport choice in relation to the accessibility and attractiveness of airports. As a further methodological step the outline of a combined neural and nested logit model of access mode and airport choice is given, which will be calibrated on the basis of the data of the German air traveller survey. Typically, the nearest airport will be chosen by most travellers, there are, however, on average eight airports serving one region (defined as a Spatial Planning Region, of which there are 97 in Germany). If there is an international airport in a region about two thirds of the demand coming from that region will choose that airport, and about one third will choose to depart from one of seven other airports. Vice versa, each airport attracts passengers coming from almost 40 regions. There is thus an intense interaction between an airport and a large influential area.Regional air travel demand; airport choice; air traveller survey; catchment areas of airports; travel route from origin via departing airport to destination area; logit model on airport choice; neural networks

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

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

Analysing and forecasting tourism demand in Vietnam with artificial neural networks

Mestrado APNORVietnam has experienced a tourism boom over the last decade with more than 18 million international tourists in 2019, compared to 1.5 million twenty-five years ago. Tourist spending has translated into rising employment and income for the tourism sector, making it the key driver to the socio-economic development of the country. Facing the COVID-19 pandemic, Vietnam´s tourism has suffered extreme economic losses. However, the number of international tourists is expected to reach the pre-pandemic levels in the next few years after the COVID-19 pandemic subsides. Forecasting tourism demand plays an essential role in predicting future economic development. Accurate predictions of tourism volume would facilitate decision-makers and managers to optimize resource allocation as well as to balance environmental and economic aspects. Various methods to predict tourism demand have been introduced over the years. One of the most prominent approaches is Artificial Neural Network (ANN) thanks to its capability to handle highly volatile and non-linear data. Given the significance of tourism to the economy, a precise forecast of tourism demand would help to foresee the potential economic growth of Vietnam. First, the research aims to analyse Vietnam´s tourism sector with a special focus on international tourists. Next, several ANN architectures are experimented with the datasets from 2008 to 2020, to predict the monthly number of international tourists traveling to Vietnam including COVID-19 lockdown periods. The results showed that with the correct selection of ANN architectures and data from the previous 12 months, the best ANN models can forecast the number of international tourists for next month with a MAPE between 7.9% and 9.2%. As the method proves its forecasting accuracy, it would serve as a valuable tool for Vietnam´s policymakers and firm managers to make better investment and strategic decisions to promote tourism after the COVID-19 situation.O Vietname conheceu um boom turístico na última década com mais de 18 milhões de turistas internacionais em 2019, em comparação com 1,5 milhões há vinte e cinco anos. As despesas turísticas traduziram-se num aumento do emprego e de receitas no sector do turismo, tornando-o no principal motor do desenvolvimento socioeconómico do país. Perante a pandemia da COVID-19, o turismo no Vietname sofreu perdas económicas extremas. Porém, espera-se que o número de turistas internacionais, pós pandemia da COVID-19, atinja os níveis pré-pandémicos nos próximos anos. A previsão da procura turística desempenha um papel essencial na previsão do desenvolvimento económico futuro. Previsões precisas facilitariam os decisores e gestores a otimizar a afetação de recursos, bem como o equilíbrio entre os aspetos ambientais e económicos. Vários métodos para prever a procura turística têm sido introduzidos ao longo dos anos. Uma das abordagens mais proeminentes assenta na metodologia das Redes Neuronais Artificiais (ANN) dada a sua capacidade de lidar com dados voláteis e não lineares. Dada a importância do turismo para a economia, uma previsão precisa da procura turística ajudaria a prever o crescimento económico potencial do Vietname. Em primeiro lugar, a investigação tem por objetivo analisar o sector turístico do Vietname com especial incidência nos turistas internacionais. Em seguida, várias arquiteturas de ANN são experimentadas com um conjunto de dados de 2008 a 2020, para prever o número mensal de turistas internacionais que se deslocam ao Vietname, incluindo os períodos de confinamento relacionados com a COVID-19. Os resultados mostraram, com a correta seleção de arquiteturas ANN e dados dos 12 meses anteriores, os melhores modelos ANN podem prever o número de turistas internacionais para o próximo mês com uma MAPE entre 7,9% e 9,2%. Como o método evidenciou a sua precisão de previsão, o mesmo pode servir como uma ferramenta valiosa para os decisores políticos e gestores de empresas do Vietname, pois irá permitir fazer melhores investimentos e tomarem decisões estratégicas para promover o turismo pós situação da COVID-19

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

Developing advanced methods to predict air traffic network growth

This dissertation describes a forecasting methodology that takes into account changes in the connectivity of an air transportation system and assesses the impact at other levels of the network, such as route demand and air traffic levels. To achieve this, the modelling framework looks at city pair demand generation, route demand assignment and air traffic estimation. While generating air traffic forecasts, the resulting model is also intended to highlight the most important factors driving air traffic network growth. This is achieved by considering a larger set of drivers than those considered in existing methodologies and research as well as exploring the use of alternative modelling techniques. Network evolution is incorporated in the method through an airport connectivity model which identifies how and when airport-pairs across the network change their connectivity status. The problem is split into two models: one identifying those airport-pairs that are added to the network; and another one identifying those airport-pairs that are removed from the network. The modelling approach explores the use of network theory metrics along with other input variables, such as passenger demand, to see whether existing models employing only network theory metrics could be improved. The impact of network evolution is assessed by the effect on air itinerary shares. Two itinerary choice models are developed using two different modelling approaches: multinomial logit and neural networks. While the multinomial logit formulation is the most common approach used to model itinerary shares, only few studies have looked at modelling itinerary shares at the network level. Neural networks have yet to be explored in this field. In this research, air itinerary choice models have been developed at the most aggregate level, using open-source booking data, for a large group of city-pairs within the US Air Transportation System. The output of the itinerary choice models, influenced by the consideration of network evolution, is then used to project air traffic levels and assess the impact of network structure changes in the number of operations in the US ATS. The results reflect the complexity behind network evolution, especially for cases when a mature system is considered (e.g. US ATS): comparisons between the case of a static network and the case when network evolution is considered indicate that the impact of network changes on overall system metrics is relatively minor in the US. However, they indicate that changes in fossil fuel prices may influence changes in the overall network characteristics, and consequently network evolution. The results also prove the feasibility of estimating a single itinerary choice model at the network level for an entire air transportation system. Although the multinomial logit model results have better accuracy, the potential of neural networks for this purpose is also demonstrated, the latter being more representative of the hub-and-spoke network strategy

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

Channels of Synthesis Forty Years On: Integrated Analysis of Spatial Economic Systems

Isard’s vision of integrated modeling that was laid out in the 1960s book Methods of Regional Science provided a road map for the development of more sophisticated analysis of spatial economic systems. Some forty years later, we look back at this vision and trace developments in a sample of three areas – demographic-econometric integrated modeling, spatial interaction modeling, and environmental-economic modeling. Attention will be focused on methodological advances and their motivation by new developments in theory as well as innovations in the applications of these models to address new policy challenges. Underlying the discussion will be an evaluation of the way in which spatial issues have been addressed, ranging from concerns with regionalization to issues of spillovers and spatial correlation.Spatial economic system, Integrated analysis,