Air Passenger Demand Research Articles

A Fuzzy Inference System for Predicting Air Traffic Demand based on Socioeconomic Drivers

The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a Graphical User Interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed Air Passenger Demand Prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

A hybrid forecasting architecture for air passenger demand considering search engine data and spatial effect

As the global process of digitalization accelerates and breakthroughs in internet technology emerge, governments worldwide are advocating for data-driven decision-making, aiming to enhance public service efficiency and stimulate economic growth. Against this backdrop, this study focuses on utilizing search engine data (SED) to improve air passenger demand forecasting, responding to national policies aimed at enhancing data analysis capabilities and promoting the development of intelligent transportation systems; however, the existing research is confined to the exploration of the temporal dependency between SED and air passenger demand variables with ignoring the spatial dependency. In order to eliminate this blind spot and catch from various parts of tourist attention, this study proposes a novel SED-driven hybrid forecasting architecture inspired by the theory of spatial effect between adjacent tourist destinations. The architecture includes three main steps: (1) construction of spatial-temporal SED variables, based on two-stage data preprocessing method; (2) variable decomposition and reconstruction, based on TVF-EMD algorithm; (3) prediction of different components of air passenger demand, employed ARIMA model and IHGS-KELM based multi-model fusion strategy respectively, where the IHGS algorithm integrates the circle chaos initialization strategy and the nonlinear convergence factor strategy. To confirm the practical applicability of this hybrid architecture, five comparative experiments based on the actual dataset are designed. The principal results are concluded as follows: (1) spatial-temporal SED is conducive to a fairly accurate air passenger demand forecasting; (2) the multi-model fusion strategy can integrate the fortes of various types of prediction models to obtain better prediction accuracy; (3) the adaptive ensemble method based on IHGS-KELM can contribute to the upgradation of prediction performance of air passenger demand.

The Impact of Vaccination, Infection and Fatality Rate on ASEAN Air Transport Industry in the Pandemic Period

This study aims to determine the impact of COVID-19 on air connectivity in the Association of Southeast Asian Nations (ASEAN) region, specifically focusing on infection, vaccination and fatality rates. To examine the impact of COVID-19 on the ASEAN air transport industry, a panel data series was created. The panel covered data from 57 airlines with different business models in the 10 ASEAN member states from January 2019 to July 2022 (31 months). The results of the study have shown that there is a significant inverse linear relationship between full-service and low-cost international flight movements, passenger demand and vaccination rate, infection rate and fatality rate. At the country level, Brunei experienced the lowest drop in air passenger demand, followed by the Philippines, Thailand, Malaysia and Cambodia. Other countries (Indonesia, Lao PDR, Myanmar, Singapore and Vietnam) were not significant predictors of passenger demand. The study’s results have indicated that an increase in infection rate has a greater impact on the international air travel market compared to the domestic market. The reduction rate of the domestic market is only half that of the international market, while the fatality rate demonstrated no differences. This study represents a valuable contribution to the existing knowledge of the pandemic’s impact on the air transport industry, focusing on Southeast Asian countries. It provides evidence to help stakeholders in the aviation industry prepare for future related crises. The study concludes that fostering greater cooperation and coordination among ASEAN member states in managing health crises and facilitating the aviation industry can promote economic growth, enhance resilience and strengthen regional integration in the long run. JEL Classification: F02, I30

Building a baseline to better integrate air passenger and air freight transport into a global Computable General Equilibrium (CGE) model

The Covid-19 pandemic has had opposite effects on demand for air passenger and air cargo transport and on the way these two sectors interact with other sectors of the economy. However, macroeconomic models generally represent aviation as a single sector, limiting the ability to assess those interactions. In this study, we split the air transport sector into two sectors (passenger and freight) in GTAP’s Multiregional Input–Output table. We assess the sectors’ response to a carbon tax by performing a sensitivity analysis. Our results highlight the environmental benefits from the Covid-19 pandemic will only be transitory unless policies and consumer demand rein in traffic growth in the aviation industry. Particular attention should be paid on leisure travel, due to its large share in the aviation industry and the potential steep rise in demand (87%) in the EU27 projected between 2015 and 2030.

Forecasting air passenger traffic and market share using deep neural networks with multiple inputs and outputs.

In this study, we address the challenge of accurate time series forecasting of air passenger demand using historical market demand data from the U.S. commercial aviation industry in the 21st century. Commercial aviation is a major contributor to the U.S. economy, directly or indirectly generating ~US$1.37 trillion annually, or 5% of annual GDP, and supporting more than 10 million jobs (Airlines for America, 2024). Over 1 billion passengers flew through U.S. airports in 2023 (Bureau of Transportation Statistics, 2024a). Using multiple correlated time series inputs predicts future values of multiple interrelated time series and leverages their mutual dependencies to enhance accuracy. In this study, we introduce a two-stage algorithm employing a deep neural network for correlated time series forecasting, addressing scenarios where multiple input variables are interrelated. This approach is designed to capture the influence that one time series can exert on another, thereby enhancing prediction accuracy by leveraging these interdependencies. In the first stage, we fit four Recurrent Neural Network (RNN) models to generate accurate univariate forecasts, each functioning as a single input-output model to predict aggregated market demand. The Gated Recurrent Unit (GRU) model was the top performer for our dataset overall. In the second stage, we apply the best fitted model (GRU Model) from Stage 1 to each individual competitor (disaggregated from the market) and then merge all input tensors using the Concatenate function. We hope to contribute to the relevant body of knowledge with a deep neural network framework for forecasting market share among competitors in the U.S. commercial aviation industry, as no similar approach has been documented in the literature. Given the importance of the industry, there is potentially great value in applying sophisticated forecasting techniques to achieve accurate predictions of air passenger demand. Moreover, these techniques may have wider applications and can potentially be employed in other contexts.

An artificial neural network for predicting air traffic demand based on socio-economic parameters

Over the past five years, emerging economies have consistently progressed toward achieving greater economic independence. The aviation industry has played a significant role in driving this progress. Accurately forecasting the future demand for air travel is crucial for making long-term commitments to these economies. The findings of this study provide valuable insights into the provision of low-cost passenger carrier demand. They can assist low-cost carriers, airports, airport consultants, and government agencies in their strategic planning and development processes. The primary objective of this study is to identify and evaluate artificial intelligence-based solutions, specifically, Artificial Neural Networks (ANN), to determine the most effective model for projecting domestic demand for low-cost carriers. The study analyzes nine variables that impact the accuracy of predicting domestic air passenger demand in emerging countries using a Multilayer Feed-Forward Backpropagation Neural (MFFBPN) network. The performance of the developed ANN model for predicting air passenger demand is assessed through training, validation, and testing using 240 data sets. The evaluation metrics employed, including mean square error (MSE), root mean square error (RMSE), accuracy, precision, sensitivity, and specificity, indicate the effectiveness of the developed ANN model, with results of 0.0019 for MSE, 0.0440 for RMSE, 0.92 for accuracy, 0.92 for precision, 0.93 for sensitivity, and 0.95 for specificity. We show that modeling based on artificial intelligence approaches and ANN is more effective than conventional linear regression models.

Comparison of Artificial Intelligence Techniques for The UK Air Passenger Short-Term Demand Forecasting: A Destination Insight Study

Web search queries become essential drivers to forecast air passenger demand for operational benefits. Scholars and marketing experts. Forecasting passenger demand is one of the most important marketing problems that experts frequently encounter, but there are very few studies in the literature using search queries. The main novelty of this study is to show that Destination Insight (DI) can be useful as an air passenger demand proxy in the UK. To prove this primary objective, this work uses several machine and deep learning multi-layer perceptron (MLP) methods based on a big-data framework. The findings indicate that DI is a crucial predictor of the UK air passenger demand. Besides, popular error metrics (RMSE, MAPE, MAD and AIC) were compared to find the best model in this study. Specifically, results indicate that MLP following feed forward neural networks works better for the UK air passenger market.

Reproducible air passenger demand estimation

The availability of passenger demand estimates for air traffic routes is crucial to a plethora of application and research problems ranging from, e.g., optimization of airline fleet utilization to complex simulations of whole air transport systems. However, somewhat surprisingly, such demand estimates appear hard to come by directly or even to generate by means of published models. This is in large parts due to the widespread use of expensive proprietary data (such as airline-specific ticket prices for certain flight connections) which is typically employed both to calibrate demand estimation models as well as to evaluate such models in order to obtain demand estimates for given origin–destination airport pairs. With this work, we propose building a data set for the European air transport system from given base data and automatically extracted and processed data from external sources, all of which are (made) freely available in the public domain and thus enable reproducibility and facilitate comparability of research involving air passenger transportation. Moreover, we challenge the long-standing tradition of calibrating so-called gravity models for demand estimation by standard linear regression. For the European air transport system, using the aforementioned publicly available data, we demonstrate that machine learning models and techniques like neural networks or the “kernel trick” can significantly improve the estimation quality with respect to ordinary least-squares. In fact, our results—the best of which were obtained using our feed-forward neural network model (four hidden layers with tanh and ReLU activations)—achieve a performance at least comparable to what has been reported in earlier works that utilized non-public data. Computer code to generate air passenger demand estimates is made publicly available online along with our base data and implementation to collect and curate external data.

Deep Learning Models for Airport Demand Forecasting With Google Trends

Managers gain new insights into how operational benefits can be achieved. Forecasting problems for passenger flow in airports are gaining interest among marketing researchers, but comparison of stochastic optimisation methods via deep learning forecasts with search query data is not yet available in the aviation field. To fill this gap, the current study predicts the demand of Madrid airport demand with Google search query data using H2O deep learning method. The findings indicate that there is a long-term relationship between search queries and actual passenger demand. Besides, search queries “fly to madrid,” and “flights to madrid spain” were found to be the cause of the actual domestic air passenger demand in Madrid. Also, to determine the best forecasting accuracy, stochastic gradient descent (SGD) optimisers were used. Specifically, findings indicate that Adam is a better optimiser increasing forecasting accuracy for Madrid airports.

Macroeconomic Determinants of Air passenger Demand in Egypt

Macroeconomic Determinants of Air passenger Demand in Egypt

시스템 다이내믹스를 이용한 남북한 항공수요 예측에 관한 연구

This study aims to forecast of Air Passenger Demand between South Korea and North Korea using the system dynamics analysis methodology that is based on the system thinking. System dynamics is not only a tool that makes the systematic thought to a model but also a computer program-based analysis methodology that mathematically models the system varying according to time variation. This study analyzed the causal relationship based on the interrelation among variables and structured them by considering various variables that affect aviation cooperation from the perspective of Air passenger demand forecasting. In addition, based on the causal relationship between variables, this study also completed the causal loop diagram that forms a feedback loop, constructed the stock-flow diagram of Inter-Korean model using Vensim program. In this study, Air passenger demand was using by the simulation variable value into System Dynamics. This study was difficult to reflect the various variables constituting the North Korea environment, and there is a limit to the occurrence of events in North Korea.

A Study towards Passenger Satisfaction Level of Sardar Vallabhbhai Patel International Airport, Gujarat, India

This significant expansion is expected to be underpinned by a trebling in the proportion of middle-class households and further increases in time-saving options for air passengers. This highlights the important role aviation can play in connecting the country – both internally and with the rest of the world. This strong growth outlook for air passenger demand will see India overtake Germany, Japan, Spain, and the UK within the next 10 years to become the world’s third largest air passenger market. These are exciting times for the air transport industry in India. The course, the future will not be without challenges – for those in the industry and policy-makers and regulators alike. These challenges will include making sure that the right type of infrastructure is put into place, at the right time and in the right location to ensure that the demand can be met, as well as ensuring that the regulatory environment is one which successfully fosters a competitive and healthy airline transport sector that will continue to make a major contribution to the Indian economy in the years to come. A robust and financially sound industry is critical to delivering the benefits that aviation can bring – creating jobs, bringing families together, facilitating business, and supporting trade, investment and economic growth.

Do Internet Search Data Help Forecast Air Passenger Demand? Evidence From China's Airports.

Before making travel plans, people often use the Internet to collect relevant information to help themselves make better decisions. Among the numerous information search channels, Internet search engine is used by the vast number of travelers because of its low cost and high efficiency. To a large extent, Internet search behavior is the external manifestation of users’ psychological activities, reflecting their concerns, needs and preferences. Therefore, Internet search data can reflect the air passenger demand information to a certain extent. In this manuscript, a novel decomposition ensemble model is proposed to discuss the role of Internet search data in air passenger demand forecasting. In the empirical study, the relevant data of Shanghai Pudong International Airport and Beijing Capital International Airport are taken as samples. The results show that the proposed forecasting model can integrate the advantages of decomposition-ensemble strategy and deep learning algorithm, and achieve more accurate and reliable prediction results than all benchmark models. This further indicates that adding Internet search data into the forecasting model can effectively improve the prediction performance of air passenger demand, and can provide scientific and reliable decision support for air transport management.

Application of an ANFIS to Estimate Kansai International Airport’s International Air Passenger Demand

This study presents an Adaptive Network Based Inference System (ANFIS) model to forecast international passenger demand at Osaka’s Kansai International Airport. The study covered the period 1994 to 2018. The study used nine determinants of air travel demand and three dummy variables as input variables. The results reveal that the model successfully forecasts Kansai International Airport’s international passenger demand. The coefficient of determination (R2) was high, being around 0.9776%. The overall MAPE of Kansai International Airport’s international air passenger demand model was 7.40%.

The impact of COVID-19 on air passenger demand and CO2 emissions in Brazil

This paper estimates the impact of the COVID-19 on air travel demand and emissions in Brazil, the largest aviation market in Latin America. Combining detailed flight data and data on combustion emission factors, we estimate the CO2 emissions of domestic flights. A Bayesian structural time-series model was used to estimate the impact of COVID-19 on daily trends of air travel demand and emissions. The COVID-19 caused a reduction of 68% in national passengers and 63% in total CO2 emissions compared to what would have occurred if the pandemic had not happened. Despite such a sharp drop, fuel efficiency decreased after the COVID-19 outbreak, and passenger demand recovered to 64.2% of pre-pandemic levels by the end of 2020. The fast recovery in domestic flights by December 2020 indicates that the emissions could soon return to pre-pandemic levels, demonstrating the challenges of reducing emissions in the aviation sector in the short term.

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.

How do changes in economic activity affect air passenger traffic? The use of state-dependent income elasticities to improve aviation forecasts

This paper explores the causal relationship between income and air travel demand to provide insights for the future growth of the aviation industry, with particular focus on the recovery period after a crisis. Using US data, we estimate the long-run and short-run income elasticities of air passenger demand and we estimate how they vary over the business cycle. We find that long-run income elasticities are lower than short-run elasticities and that the short-run elasticities are lower during the recovery period following an economic downturn. A reduction in income elasticity of demand during the recovery period is consistent with an increase in precautionary savings. We then apply our results to IMF economic forecasts, and our analysis suggests that a return of the air traffic to its pre-crisis levels would be slower that the return of economic activities to their levels. Forecasts using our estimates are likely to be more accurate than one based on constant income elasticities of demand and suggest that air traffic in the US would return to its 2019 level in 2025, which is three years after the recovery of real GDP in this scenario. The use of state dependent income elasticities – in conjunction with other techniques that can control for other likely changes in demand and supply in a post-COVID world – could play an important role in helping to generate better forecasts for the aviation industry.

Updating National Air Passenger Demand from Traffic Counts: The Case of a Secondary Airport in an Underdeveloped Region

Demand models allow to estimate the choices made by users on different alternatives. Demand models depend on the characteristic attributes of the users and transport networks, as well as on parameters. Their significance translates into the reliability of the model in reproducing users’ choices as demand values. Traffic counts are aggregated data that can be used to update demand values of O/D matrix and/or for re-calibrating parameters from sets of parameters obtained in different situations or at different times in the same scenario using a reverse assignment modal. This paper provides the use of passenger counts to update national air transport demand by calibrating a hierarchical logit model. The application focuses on estimating the demand values for a secondary airport of an underdeveloped European region with the calibration of the logsum parameter working between distribution and modal choice. The updated model can be used to test new conditions for the supply of a new service or to increase the frequency or to modify the ticket level by means of public service obligations. The results show that the introduction of public obligations in the secondary airport in an underdeveloped region is crucial for future sustainability. Considering the decline in the economic, social and environmental sustainability in the region, the airport is central to economic and social development at the same time as being important for environmental sustainability, as it limits the impacts on the territory related to the construction of large transport infrastructures.

Forecasting air passenger numbers with a GVAR model

This study employs a GVAR model on the passenger numbers of the top 20 busiest airports of the world and the Asia-Pacific and Latin America-Caribbean regions. With air passenger numbers representing a demand measure, country-level proxies for economic drivers are included as domestic and foreign variables. In terms of ex-ante forecast accuracy, the GVAR model performs best for several airports – yet not for the entirety of airports – compared to four benchmarks for horizons one and three quarters ahead. It also achieves several second and third ranks for these and two other horizons and when all horizons are evaluated jointly. Considering the connectivity of airports is worthwhile to achieve accurate and economically interpretable air passenger demand forecasts.

Air passenger demand forecasting for the Incheon airport using time series models

Air passenger demand forecasting for the Incheon airport using time series models