Short-term Forecasting for Airline Industry: The Case of Indian Air Passenger and Air Cargo

Madhavan, Meena; Sharafuddin, Mohammed Ali; Piboonrungroj, Pairach; Yang, Ching-Chiao

doi:10.1177/0972150920923316

Cited by 13 publications

(8 citation statements)

References 34 publications

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“…Madhavan et al [69] attempted to forecast aviation demand throughput in the Indian aviation industry. The bayesian structural time series (BSTS) approach is used.…”

Section: Tablementioning

confidence: 99%

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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“…Madhavan et al [69] attempted to forecast aviation demand throughput in the Indian aviation industry. The bayesian structural time series (BSTS) approach is used.…”

Section: Tablementioning

confidence: 99%

Systematic review of passenger demand forecasting in aviation industry

Zachariah¹,

Sharma

Kumar

2023

Multimed Tools Appl

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“…This method was implemented to air travel passenger demand and it was shown that the proposed method outperforms other common methods in terms of forecasting passenger traffic. Madhavan et al 23 used ARIMA and Bayesian Structural Time Series (BSTS) models to forecast the air passenger and cargo demand of the Indian aviation industry. According to the findings, it was concluded that the BSTS approach, together with ARIMA, is also suitable for short‐term forecasting of international passenger, domestic passenger, international air cargo and domestic air cargo.…”

Section: Literature Reviewmentioning

confidence: 99%

The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

Korkmaz

Akgüngör

2021

Concurrency and Computation

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The imbalance between modes of transport in our country appears as the most important problem. Therefore, in air transportation, which has a significant increasing trend, estimating the passenger demand with directly related parameters and novel algorithms is important for Turkey. In this study, different prediction models were developed applying for the first time with five different meta-heuristic algorithms which are Flower Pollination Algorithm (FPA), Artificial Bee Colony Algorithm (ABC), Crow Search Algorithm (CSA), Krill Herd Algorithm (KH), and the Butterfly Optimization Algorithm (BOA) to estimate Turkey's air transport demand. While developing the models, Fuel Price, Gross Domestic Product per Capita, Seat Capacity, and Annual Fuel Consumption were selected as the model parameters. Although each model developed using different approaches is applicable, quadratic and power models developed using CSA showed the highest performance. For this reason, future projections were based on these models. Air transport passenger demand was examined using two scenarios in a process until 2035. In the first scenario, according to model forms, Turkey's future air transport passenger demand will reach about 460 and 490 million passengers, respectively. In the second scenario, the number of passengers will reach approximately 375 and 660 million for quadratic and power models, respectively. The results of this study will contribute to the evaluation of the current investment plans and the development of strategic plans that will meet the demands. Additionally, they will help take necessary measures and introduce some necessary regulations to ensure the income and expense balance so that the efficiency of airline companies can be improved. K E Y W O R D Sair transport passenger demand, artificial bee colony algorithm, butterfly optimization algorithm, crow search algorithm, flower pollination algorithm, krill herd algorithm INTRODUCTIONAir transport is one of the most preferred modes of transport by passengers in the world and in Turkey because it provides important benefits such as safety, speed, and comfort. Air transport, which has a very important place in terms of trade and tourism development, also contributes to the social development and economic growth of countries.Air transport has shown a rapid development worldwide after the Second World War. The most important reasons for this progress are the development of aircraft production technology in the war years, the desire of the pilots to transform their skills to a commercial profit, and the desire of countries to undertake development ventures by rapidly erasing the negative traces of the war. For all these reasons, passenger and freight

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“…There are several approaches for predicting data. To maintain a large variation in prediction and forecasting, the normalization method is required to make them closer [21]. Standard deviation can still be used with normalised data because both translation and linear scaling have no effect on it.…”

Section: Normalization 431 Normalize Datamentioning

confidence: 99%

Assessing mangrove deforestation using pixel-based image: a machine learning approach

Dawod

Sharafuddin

2021

Bulletin EEI

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Mangrove is one of the most productive global forest ecosystems and unique in linking terrestrial and marine environment. This study aims to clarify and understand artificial intelligence (AI) adoption in remote sensing mangrove forests. The performance of machine learning algorithms such as random forest (RF), support vector machine (SVM), decision tree (DT), and object-based nearest neighbors (NN) algorithms were used in this study to automatically classify mangrove forests using orthophotography and applying an object-based approach to examine three features (tree cover loss, above-ground carbon dioxide (CO2) emissions, and above-ground biomass loss). SVM with a radial basis function was used to classify the remainder of the images, resulting in an overall accuracy of 96.83%. Precision and recall reached 93.33 and 96%, respectively. RF performed better than other algorithms where there is no orthophotography.

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Short-term Forecasting for Airline Industry: The Case of Indian Air Passenger and Air Cargo

Cited by 13 publications

References 34 publications

Systematic review of passenger demand forecasting in aviation industry

Systematic review of passenger demand forecasting in aviation industry

The forecasting of air transport passenger demands in Turkey by using novel meta‐heuristic algorithms

Assessing mangrove deforestation using pixel-based image: a machine learning approach

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