An Econometric Dynamic Model to estimate passenger demand for air transport industry

Benítez, Rafael; Nieto, María Rosa; Miranda, Danya

doi:10.1016/j.trpro.2017.05.191

Cited by 20 publications

(8 citation statements)

References 35 publications

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“…Carmona-Benítez et al [ 21 ] proposed an econometric dynamic to better investigate the dynamic nature of economic factors to understand the economic determinants of passenger demand better. This study was conducted in the Mexican air transportation industry.…”

Section: Techniquesmentioning

confidence: 99%

“… Data Format Method Hardware/Software Market 2007 [ 16 ] Time series Data Holt-Winters Public Domain Software- R Limited Memory-BFGS quasi-Newton UK 2017 [ 99 ] Time Series + Panel Time series: linear regression. Panel: PLS + FE + RE STATA software 2 GB RAM Multicore CPU Indonesia 2017 [ 15 ] Panel Econometric Model: APDM Statistical Software RAM-2GB CPU Nepal 2017 [ 21 ] Panel Econometric Dynamic Model Ram- 2GB Multicore CPU Mexico 2019 [ 97 ] Time series Data MIV + NVARNN Open-source Beijing, China 2019 [ 95 ] Time series Data Logistic Regression Open-source statistical software US commercial airports 2019 [ 116 ] Time-series Data SARIMA-SVR R- with forecast function China 2019 [ 62 ] Time-series Data ARIMA-REGRESSION MATLAB’s optimization toolbox China 2020 [ 69 ] Time series Data ARIMA + BSTS Public Domain Software- R India 2020 [ 23 ] Time series Data K-Means + DT Public Domain Software- R China (Sanja to Beijing) 2020 [ 82 …”

Section: Techniquesmentioning

confidence: 99%

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Systematic review of passenger demand forecasting in aviation industry

Zachariah¹,

Sharma

Kumar

2023

Multimed Tools Appl

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Forecasting aviation demand is a significant challenge in the airline industry. The design of commercial aviation networks heavily relies on reliable travel demand predictions. It enables the aviation industry to plan ahead of time, evaluate whether an existing strategy needs to be revised, and prepare for new demands and challenges. This study examines recently published aviation demand studies and evaluates them in terms of the various forecasting techniques used, as well as the advantages and disadvantages of each. This study investigates numerous forecasting techniques for passenger demand, emphasizing the multiple factors that influence aviation demand. It examined the benefits and drawbacks of various models ranging from econometric to statistical, machine learning to deep neural networks, and the most recent hybrid models. This paper discusses multiple application areas where passenger demand forecasting is used effectively. In addition to the benefits, the challenges and potential future scope of passenger demand forecasting were discussed. This study will be helpful to future aviation researchers while also inspiring young researchers to pursue careers in this industry.

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Section: Techniquesmentioning

confidence: 99%

Section: Techniquesmentioning

confidence: 99%

Systematic review of passenger demand forecasting in aviation industry

Zachariah¹,

Sharma

Kumar

2023

Multimed Tools Appl

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“…GDP, tourism and employment are the main factors causing this growth in air transport and an increase in these factors drives the demand for air transport (ATAG, 2016), (Küçükönal &Sedefoğlu, 2017), and(Dimitrios &Maria, 2018). Already, the evolution of new airline business models has increased passenger demand and the need for more airport infrastructure (Carmona-benítez et al, 2017). However, among the biggest barriers to air cargo trade are customs regulations and procedures that do not keep up with the rapid development of the industry (Zhang, 2002).…”

Section: Impact Of Airport Infrastructurementioning

confidence: 99%

Impact of airport infrastructure investment on the growth of the Angolan economy: An Auto-Regressive Distributed Lag analysis

Campos¹

2023

J. airl. airpt. manag.

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Purpose: The airport infrastructure plays capital importance in the development of national trade. It facilitates the flow of people, goods, and services more quickly. Thus, given its importance, this study seeks to analyze the impact of the Angolan investment in airport infrastructure between 2000 – 2020.Design/methodology/approach: this article presents the results of quantitative and qualitative research, based on narrative review and output of the Auto-Regressive Distributed Lag (ARDL) technique, which aimed to analyze the impact of investment in airport infrastructure on the growth of Angola GDP per capita.Findings: The results of the stationarity tests performed shows mixed integration in both I(0) and I(1), which justifies the used ARDL. Similarly, the bounds test showed that there is a very strong relationship between airport infrastructure investment and GDP per capita growth in the short and long run. However, in the Angola case study, the impact that exists is negative, meaning that airport infrastructure negatively impacts per capita GDP growth in both the short run and in the long run.Research limitations/implications: this research has been among the first to analyze the impact of investment in airport infrastructure on the growth of Angola GDP per capita. Besides the contribution of this research, some limitations are the difficulty to find data of investment and the literature about transportation infrastructure in Angola.Originality/value: the analyze of the impact of investment in airport infrastructure in the growth of Angola GDP per capita is a fundamental step that can help public and private entities in making decisions that aim to improve the management of the Angola transport infrastructure.

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“…Kim and Shin (2016) used basic regression analysis to consider causal relationships between air passenger demand and other variables. Carmona-Benitez et al (2017) proposed a forecasting approach using an econometric dynamic model (EDM) to estimate passenger demand in the Mexican air transport industry. They applied the panel data Arellano-Bover method to calibrate the EDM, which was validated by the Sargan test and the Arellano-Bond autocorrelation test.…”

Section: Background On Air Passenger Demand Forecastingmentioning

confidence: 99%

The chaos on U.S. domestic airline passenger demand forecasting caused by COVID-19

Jafari¹

2022

IJBFMI

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Commercial aviation is a major contributor to the US economy, directly or indirectly generating approximately US$680 billion, or 4% of GDP, and supporting millions of jobs. Approximately 965 million passengers flew to US destinations in 2017 (https://rosap.ntl.bts.gov/view/dot/37861). Given the importance of the industry, accurate forecasting of air passenger demand is valuable, and the most sophisticated forecasting technologies can be applied to this endeavour. The ongoing COVID-19 crisis has had an unprecedented impact on air traffic. Effective forecast of passenger demand would benefit airlines to develop adequate recovery plans and prevent (or minimise) any catastrophe in handling passengers during and post pandemic. The purpose of this study is to investigate COVID-19's impact on the US domestic air passengers demand, identify the most influential features on air passenger demand, and design more accurate forecast models. In addition, we address a computational challenge in developing forecasting models due to the volatility of the recent data as a result of the COVID-19 crisis. We use both traditional and artificial intelligence methods and discuss their capabilities to handle the challenge.

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An Econometric Dynamic Model to estimate passenger demand for air transport industry

Cited by 20 publications

References 35 publications

Systematic review of passenger demand forecasting in aviation industry

Systematic review of passenger demand forecasting in aviation industry

Impact of airport infrastructure investment on the growth of the Angolan economy: An Auto-Regressive Distributed Lag analysis

The chaos on U.S. domestic airline passenger demand forecasting caused by COVID-19

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