This study analyses and compares the economic impact of Belgium's five commercial airports on their region and country. The airports represent different types, including Belgium's main airport, Brussels Airport and four regional airports: a low-cost regional airport (Brussels South Charleroi Airport), a specialised cargo regional airport (Liège Airport), and two small regional airports (Antwerp Airport and Ostend-Bruges Airport). The economic impact is measured through input-output analysis, which assesses added value and employment on a direct, indirect, and induced level. To improve accuracy, we employ a bottom-up approach that links company-level employment and added value data to the input-output framework via NACE classifications. Additionally, a Monte Carlo sensitivity analysis is introduced to strengthen the robustness of our findings.
Our results demonstrate significant differences in the airports' economic contributions based on airport size and operational focus, with Liège Airport's cargo specialisation generating a particularly strong regional impact. These findings lead to a broader discussion on airport subsidies based on the economic impact. Beyond the Belgian context, our bottom-up approach provides a replicable framework for more precise airport impact assessments.
{"title":"Reconsidering airport economic impact assessments: A bottom-up comparative analysis of Belgian airports","authors":"Jolien Pauwels , Sven Buyle , Wouter Dewulf , Bart Jourquin","doi":"10.1016/j.jairtraman.2025.102854","DOIUrl":"10.1016/j.jairtraman.2025.102854","url":null,"abstract":"<div><div>This study analyses and compares the economic impact of Belgium's five commercial airports on their region and country. The airports represent different types, including Belgium's main airport, Brussels Airport and four regional airports: a low-cost regional airport (Brussels South Charleroi Airport), a specialised cargo regional airport (Liège Airport), and two small regional airports (Antwerp Airport and Ostend-Bruges Airport). The economic impact is measured through input-output analysis, which assesses added value and employment on a direct, indirect, and induced level. To improve accuracy, we employ a bottom-up approach that links company-level employment and added value data to the input-output framework via NACE classifications. Additionally, a Monte Carlo sensitivity analysis is introduced to strengthen the robustness of our findings.</div><div>Our results demonstrate significant differences in the airports' economic contributions based on airport size and operational focus, with Liège Airport's cargo specialisation generating a particularly strong regional impact. These findings lead to a broader discussion on airport subsidies based on the economic impact. Beyond the Belgian context, our bottom-up approach provides a replicable framework for more precise airport impact assessments.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"128 ","pages":"Article 102854"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-06-29DOI: 10.1016/j.jairtraman.2025.102843
Chuhao Deng, Hong-Cheol Choi, Hyunsang Park, Inseok Hwang
Research in developing data-driven models for Air Traffic Management (ATM) has gained tremendous interest in recent years. However, data-driven models are known to have long training time and require large datasets to achieve good performance, and the majority of proposed data-driven models ignores ATM system’s multi-agent characteristic. To fill the research gaps, this paper proposes a Multi-Agent Bidirectional Encoder Representations from Transformers (MA-BERT) model, which fully considers the multi-agent characteristic of the ATM system and outputs results based on all agents in the airspace. Additionally, compared to most data-driven models that are designed for a single application, the proposed MA-BERT’s encoder architecture enables it to be pre-trained through a self-supervised method and fine-tuned for a variety of data-driven ATM applications, saving a substantial amount of training time and data usage. The proposed MA-BERT is tested and compared with other widely used models using the Automatic Dependent Surveillance-Broadcast (ADS-B) data recorded in three airports in South Korea in 2019. The results show that MA-BERT can achieve much better performance than the comparison models, and by pre-training MA-BERT on a large dataset from a major airport and then fine-tuning it to other airports and applications, a significant amount of the training time can be saved. For newly adopted procedures and constructed airports where no historical data is available, the results show that the pre-trained MA-BERT can achieve high performance by updating regularly with small amount of data.
近年来，空中交通管理（ATM）数据驱动模型的研究引起了人们极大的兴趣。然而，众所周知，数据驱动模型的训练时间长，需要大量的数据集才能达到良好的性能，并且大多数提出的数据驱动模型都忽略了ATM系统的多智能体特性。为了填补研究空白，本文提出了一种多智能体双向编码器表示（Multi-Agent Bidirectional Encoder Representations from Transformers, MA-BERT）模型，该模型充分考虑了ATM系统的多智能体特性，并基于空域中所有智能体输出结果。此外，与大多数为单一应用设计的数据驱动模型相比，所提出的MA-BERT编码器架构使其能够通过自监督方法进行预训练，并针对各种数据驱动的ATM应用进行微调，从而节省了大量的训练时间和数据使用。利用2019年在韩国三个机场记录的自动相关监视广播（ADS-B）数据，对拟议的MA-BERT进行了测试，并与其他广泛使用的模型进行了比较。结果表明，与比较模型相比，MA-BERT可以获得更好的性能，并且通过在主要机场的大型数据集上预训练MA-BERT，然后对其进行微调，可以节省大量的训练时间。对于新采用的程序和没有历史数据的新建机场，结果表明，预训练的MA-BERT可以通过少量数据定期更新来获得高性能。
{"title":"Multi-agent learning for data-driven air traffic management applications","authors":"Chuhao Deng, Hong-Cheol Choi, Hyunsang Park, Inseok Hwang","doi":"10.1016/j.jairtraman.2025.102843","DOIUrl":"10.1016/j.jairtraman.2025.102843","url":null,"abstract":"<div><div>Research in developing data-driven models for Air Traffic Management (ATM) has gained tremendous interest in recent years. However, data-driven models are known to have long training time and require large datasets to achieve good performance, and the majority of proposed data-driven models ignores ATM system’s multi-agent characteristic. To fill the research gaps, this paper proposes a Multi-Agent Bidirectional Encoder Representations from Transformers (MA-BERT) model, which fully considers the multi-agent characteristic of the ATM system and outputs results based on all agents in the airspace. Additionally, compared to most data-driven models that are designed for a single application, the proposed MA-BERT’s encoder architecture enables it to be pre-trained through a self-supervised method and fine-tuned for a variety of data-driven ATM applications, saving a substantial amount of training time and data usage. The proposed MA-BERT is tested and compared with other widely used models using the Automatic Dependent Surveillance-Broadcast (ADS-B) data recorded in three airports in South Korea in 2019. The results show that MA-BERT can achieve much better performance than the comparison models, and by pre-training MA-BERT on a large dataset from a major airport and then fine-tuning it to other airports and applications, a significant amount of the training time can be saved. For newly adopted procedures and constructed airports where no historical data is available, the results show that the pre-trained MA-BERT can achieve high performance by updating regularly with small amount of data.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"128 ","pages":"Article 102843"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-06-30DOI: 10.1016/j.jairtraman.2025.102849
Manel Ouni , Rafaa Mraihi
The inclusive growth paradigm has gained significant attention, with several organizations emphasizing its importance. However, while the concept of inclusive growth remains a topic of ongoing debate, the specific role of air transportation in promoting inclusive growth remains underexplored, particularly in its capacity to enhance accessibility, promote regional development, and integrate marginalized areas into national and global economies. This study investigates the relationship between air transportation and inclusive growth in Tunisia using annual data from 1965 to 2021. This study utilized the Autoregressive Distributed Lag (ARDL) model to analyze both short- and long-term relationships between the variables. At the same time, the wavelet coherence approach is used to examine how these relationships evolve over time and across different frequencies. The results from the ARDL model indicate that air transport, foreign direct investment, and social globalization are key determinants of inclusive growth in Tunisia. Moreover, the wavelet coherence analysis reveals that these factors positively influence inclusive growth, while the wavelet causality identifies a bidirectional causality between inclusive growth and the regressors, with variations in the timing and frequency of causality. This study contributes to the growing literature on transport infrastructure and inclusive growth by providing robust methodological insights and practical policy recommendations. These findings show the critical role of air transportation as a catalyst for sustainable and inclusive growth, emphasizing the importance of targeted investments and strategic policy interventions in Tunisia.
{"title":"Air transportation and inclusive growth in Tunisia: Evidence from Autoregressive Distributed Lag and wavelet coherence approach","authors":"Manel Ouni , Rafaa Mraihi","doi":"10.1016/j.jairtraman.2025.102849","DOIUrl":"10.1016/j.jairtraman.2025.102849","url":null,"abstract":"<div><div>The inclusive growth paradigm has gained significant attention, with several organizations emphasizing its importance. However, while the concept of inclusive growth remains a topic of ongoing debate, the specific role of air transportation in promoting inclusive growth remains underexplored, particularly in its capacity to enhance accessibility, promote regional development, and integrate marginalized areas into national and global economies. This study investigates the relationship between air transportation and inclusive growth in Tunisia using annual data from 1965 to 2021. This study utilized the Autoregressive Distributed Lag (ARDL) model to analyze both short- and long-term relationships between the variables. At the same time, the wavelet coherence approach is used to examine how these relationships evolve over time and across different frequencies. The results from the ARDL model indicate that air transport, foreign direct investment, and social globalization are key determinants of inclusive growth in Tunisia. Moreover, the wavelet coherence analysis reveals that these factors positively influence inclusive growth, while the wavelet causality identifies a bidirectional causality between inclusive growth and the regressors, with variations in the timing and frequency of causality. This study contributes to the growing literature on transport infrastructure and inclusive growth by providing robust methodological insights and practical policy recommendations. These findings show the critical role of air transportation as a catalyst for sustainable and inclusive growth, emphasizing the importance of targeted investments and strategic policy interventions in Tunisia.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"128 ","pages":"Article 102849"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-07-04DOI: 10.1016/j.jairtraman.2025.102844
Haonan Lin, Jian Luo, Guofang Nan
Air cargo subsidy policies are being implemented in several Chinese provinces to promote industrial development and stimulate regional economic growth. This study explores the impact of government quantity subsidies to the airline or shippers on the air cargo market. We consider the impact of these subsidy policies from the perspectives of all-cargo airline and economies of scale. Our findings show that the subsidy provided to the airline or shippers have similar effects on air cargo quantity, airline’s profits, and social welfare. However, these subsidies influence airline’s pricing decisions differently due to different payment streams. Thus, if the government focuses on air cargo volume or airline’s profit, both the airline and shipper can benefit from the subsidy policy, although social welfare may be compromised. This analysis provides managerial insights for the government to formulate air cargo policies accordingly.
{"title":"The effects of different subsidy policy modes on China’s air cargo market: The all-cargo airline and scale economies perspective","authors":"Haonan Lin, Jian Luo, Guofang Nan","doi":"10.1016/j.jairtraman.2025.102844","DOIUrl":"10.1016/j.jairtraman.2025.102844","url":null,"abstract":"<div><div>Air cargo subsidy policies are being implemented in several Chinese provinces to promote industrial development and stimulate regional economic growth. This study explores the impact of government quantity subsidies to the airline or shippers on the air cargo market. We consider the impact of these subsidy policies from the perspectives of all-cargo airline and economies of scale. Our findings show that the subsidy provided to the airline or shippers have similar effects on air cargo quantity, airline’s profits, and social welfare. However, these subsidies influence airline’s pricing decisions differently due to different payment streams. Thus, if the government focuses on air cargo volume or airline’s profit, both the airline and shipper can benefit from the subsidy policy, although social welfare may be compromised. This analysis provides managerial insights for the government to formulate air cargo policies accordingly.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"128 ","pages":"Article 102844"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-06-10DOI: 10.1016/j.jairtraman.2025.102832
Ahmed Abdelghany , Khaled Abdelghany , Vitaly S. Guzhva , Mary Kai
Accurate prediction of origin-destination (O-D) air travel passengers is critical for airline schedule profitability analysis, as it enables airlines to align capacity with demand, optimize fare structures, and minimize operational inefficiencies. Reliable forecasts also support strategic decision-making by identifying profitable routes, reducing overcapacity risks, and enhancing network connectivity. This study explores the role of seat capacity and fares in forecasting O-D passengers through the development of three experimental models: the baseline Seasonal Autoregressive Integrated Moving Average (SARIMA), Vector Autoregression (VAR) models with endogenous variables, and SARIMAX models with exogenous variables. The models are applied to two O-D pairs, LAX-JFK and IST-LHR, and extended to a larger sample of 2000 O-D pairs for more comprehensive analysis. Results reveal that treating seat capacity and fares as exogenous variables significantly improves forecasting accuracy of passengers, with the SARIMAX models outperforming the VAR models, which incorporate these variables as endogenous factors. The findings suggest that seat capacity is best modeled as an exogenous variable, consistent with airlines’ scheduling practices, where seat capacity may vary across different scheduling periods. This study contributes to the literature by providing insights into the complex relationships between seat capacity, fares, and passengers, while offering a scalable approach for forecasting across large airline networks.
{"title":"Unraveling endogeneity in seat capacity and Fares: Time series econometric models for airline origin-destination passengers forecasting","authors":"Ahmed Abdelghany , Khaled Abdelghany , Vitaly S. Guzhva , Mary Kai","doi":"10.1016/j.jairtraman.2025.102832","DOIUrl":"10.1016/j.jairtraman.2025.102832","url":null,"abstract":"<div><div>Accurate prediction of origin-destination (O-D) air travel passengers is critical for airline schedule profitability analysis, as it enables airlines to align capacity with demand, optimize fare structures, and minimize operational inefficiencies. Reliable forecasts also support strategic decision-making by identifying profitable routes, reducing overcapacity risks, and enhancing network connectivity. This study explores the role of seat capacity and fares in forecasting O-D passengers through the development of three experimental models: the baseline Seasonal Autoregressive Integrated Moving Average (SARIMA), Vector Autoregression (VAR) models with endogenous variables, and SARIMAX models with exogenous variables. The models are applied to two O-D pairs, LAX-JFK and IST-LHR, and extended to a larger sample of 2000 O-D pairs for more comprehensive analysis. Results reveal that treating seat capacity and fares as exogenous variables significantly improves forecasting accuracy of passengers, with the SARIMAX models outperforming the VAR models, which incorporate these variables as endogenous factors. The findings suggest that seat capacity is best modeled as an exogenous variable, consistent with airlines’ scheduling practices, where seat capacity may vary across different scheduling periods. This study contributes to the literature by providing insights into the complex relationships between seat capacity, fares, and passengers, while offering a scalable approach for forecasting across large airline networks.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"128 ","pages":"Article 102832"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-05-20DOI: 10.1016/j.jairtraman.2025.102799
Daiki Iwata , Yuki Nonaka , Eri Itoh
Growing air traffic demand in recent years means that the aviation industry is faced with challenges in rising CO2 emissions, associated fuel costs, congestion, noise and operational complexity. Approach and sequencing in terminal airspace is one such phase of flight, at which congestion has high cost in fuel and management of operational complexity. A novel solution for mitigating these negative impacts in a simple and cost-effective manner are welcome. The present study evaluated the efficacy of air traffic control operations combining fixed-flight path angle descent and speed control techniques, on fuel efficiency and pilot operability. We designed a series of flight scenarios for Kansai International Airport arrivals and ran them in a simulation environment. The fixed-flight path angle descent facilitates more precise and reliable prediction of arrival trajectory and reduction in air traffic control operation complexity. The fixed-flight path angle descent procedure has additional anticipated benefits, namely, reduction in fuel burn and the ability to control aircraft speed without compromising fuel efficiency. It may therefore be a viable contender for arrival sequencing and separation maintenance tactic, in place of today’s common vectoring technique. Furthermore, the fixed-flight path angle descent can be performed without modifications or additions to the current onboard electronic equipment of aircraft. This paper demonstrates that combination of fixed-flight path angle descent and speed control performed in a commercial aircraft has the same utility in the route extension by vectoring performed by air traffic controllers during congested air traffic and can be performed while achieving reduction in fuel consumption. Flight simulator tests of an aircraft approaching Kansai International Airport from a southwestern or western direction show that combination of fixed-flight path angle descent and speed control can reduce fuel consumption by up to approximately 260 pounds per flight, without causing noticeable problem in the aircraft operation. Furthermore, the relationship between the en-route sector, upstream of the terminal airspace, and the angle selected for the fixed-flight path angle descent, the operational issues for performing the proposed speed control method are discussed.
{"title":"Air traffic control method for more fuel efficient arrivals in terminal airspace","authors":"Daiki Iwata , Yuki Nonaka , Eri Itoh","doi":"10.1016/j.jairtraman.2025.102799","DOIUrl":"10.1016/j.jairtraman.2025.102799","url":null,"abstract":"<div><div>Growing air traffic demand in recent years means that the aviation industry is faced with challenges in rising CO<sub>2</sub> emissions, associated fuel costs, congestion, noise and operational complexity. Approach and sequencing in terminal airspace is one such phase of flight, at which congestion has high cost in fuel and management of operational complexity. A novel solution for mitigating these negative impacts in a simple and cost-effective manner are welcome. The present study evaluated the efficacy of air traffic control operations combining fixed-flight path angle descent and speed control techniques, on fuel efficiency and pilot operability. We designed a series of flight scenarios for Kansai International Airport arrivals and ran them in a simulation environment. The fixed-flight path angle descent facilitates more precise and reliable prediction of arrival trajectory and reduction in air traffic control operation complexity. The fixed-flight path angle descent procedure has additional anticipated benefits, namely, reduction in fuel burn and the ability to control aircraft speed without compromising fuel efficiency. It may therefore be a viable contender for arrival sequencing and separation maintenance tactic, in place of today’s common vectoring technique. Furthermore, the fixed-flight path angle descent can be performed without modifications or additions to the current onboard electronic equipment of aircraft. This paper demonstrates that combination of fixed-flight path angle descent and speed control performed in a commercial aircraft has the same utility in the route extension by vectoring performed by air traffic controllers during congested air traffic and can be performed while achieving reduction in fuel consumption. Flight simulator tests of an aircraft approaching Kansai International Airport from a southwestern or western direction show that combination of fixed-flight path angle descent and speed control can reduce fuel consumption by up to approximately 260 pounds per flight, without causing noticeable problem in the aircraft operation. Furthermore, the relationship between the en-route sector, upstream of the terminal airspace, and the angle selected for the fixed-flight path angle descent, the operational issues for performing the proposed speed control method are discussed.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"128 ","pages":"Article 102799"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-07-10DOI: 10.1016/j.jairtraman.2025.102850
Hasnain Ali, Xuan Tao Hoo, Van-Phat Thai, Duc-Thinh Pham, Sameer Alam
Airport airside congestion, driven by the growing imbalance between air traffic demand and constrained capacity, presents significant operational challenges that affect efficiency, safety, and environmental impact. Effectively addressing this requires models that capture the complex interactions within the airside network (taxiways, runways, gates) to provide insights into traffic flow dynamics and mechanisms of congestion formation, spread, and dissipation. Traditional approaches – such as microsimulation methods and queuing models – are often either computationally demanding or focus on specific components (like runways), limiting their ability to capture broader network interactions and reducing their operational feasibility. This study proposes an alternative approach, adapting the Macroscopic Fundamental Diagram (MFD) to model airside traffic using three-dimensional aircraft trajectory data. By analyzing aggregate traffic variables – flow, density, and speed – the MFD provides a computationally efficient means of understanding airside congestion patterns and supports informed decision-making. This paper presents a novel methodology for constructing airside MFDs using A-SMGCS data from Singapore Changi Airport. The study also investigates the spatial and temporal factors contributing to congestion, offering insights into how congestion patterns develop and evolve under varying operational conditions. In the temporal domain, even during low-demand periods, departure and arrival banks contribute to congestion. Additionally, this study analyzes the impact of weather conditions on the airside network flow, highlighting the effects of variable wind and adverse weather, such as rain and thunderstorm, on airside congestion. In the spatial domain, traffic inhomogeneity – an uneven distribution of traffic on the airside network – reduces overall flow, particularly during congestion. These findings highlight the potential to improve airside capacity utilization and mitigate congestion by distributing traffic more evenly across both temporal and spatial domains, i.e., minimizing schedule banks and ensuring a balanced allocation of taxi routes.
{"title":"Spatial–temporal dynamics of airside congestion: A Macroscopic Fundamental Diagram perspective","authors":"Hasnain Ali, Xuan Tao Hoo, Van-Phat Thai, Duc-Thinh Pham, Sameer Alam","doi":"10.1016/j.jairtraman.2025.102850","DOIUrl":"10.1016/j.jairtraman.2025.102850","url":null,"abstract":"<div><div>Airport airside congestion, driven by the growing imbalance between air traffic demand and constrained capacity, presents significant operational challenges that affect efficiency, safety, and environmental impact. Effectively addressing this requires models that capture the complex interactions within the airside network (taxiways, runways, gates) to provide insights into traffic flow dynamics and mechanisms of congestion formation, spread, and dissipation. Traditional approaches – such as microsimulation methods and queuing models – are often either computationally demanding or focus on specific components (like runways), limiting their ability to capture broader network interactions and reducing their operational feasibility. This study proposes an alternative approach, adapting the Macroscopic Fundamental Diagram (MFD) to model airside traffic using three-dimensional aircraft trajectory data. By analyzing aggregate traffic variables – flow, density, and speed – the MFD provides a computationally efficient means of understanding airside congestion patterns and supports informed decision-making. This paper presents a novel methodology for constructing airside MFDs using A-SMGCS data from Singapore Changi Airport. The study also investigates the spatial and temporal factors contributing to congestion, offering insights into how congestion patterns develop and evolve under varying operational conditions. In the temporal domain, even during low-demand periods, departure and arrival banks contribute to congestion. Additionally, this study analyzes the impact of weather conditions on the airside network flow, highlighting the effects of variable wind and adverse weather, such as rain and thunderstorm, on airside congestion. In the spatial domain, traffic inhomogeneity – an uneven distribution of traffic on the airside network – reduces overall flow, particularly during congestion. These findings highlight the potential to improve airside capacity utilization and mitigate congestion by distributing traffic more evenly across both temporal and spatial domains, i.e., minimizing schedule banks and ensuring a balanced allocation of taxi routes.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"128 ","pages":"Article 102850"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-07-07DOI: 10.1016/j.jairtraman.2025.102856
Jungho Baek , Soojoong Nam
This article investigates how fluctuations in oil prices influence air travel demand in Korea and Japan, considering short- and long-term impacts. Using both ARDL and NARDL models, the study reveals that oil prices significantly influence air travel demand, with asymmetrical impacts more pronounced in Korea than in Japan. While both countries show short-term sensitivity to oil prices, Korea also experiences long-term effects. Economic growth and exchange rates are also critical factors affecting air travel demand. These findings suggest tailored policy approaches for Korea and Japan to enhance the resilience of their aviation sectors in response to oil price changes.
{"title":"Do oil price fluctuations influence air travel Demand? Symmetric and asymmetric insights from Korea and Japan","authors":"Jungho Baek , Soojoong Nam","doi":"10.1016/j.jairtraman.2025.102856","DOIUrl":"10.1016/j.jairtraman.2025.102856","url":null,"abstract":"<div><div>This article investigates how fluctuations in oil prices influence air travel demand in Korea and Japan, considering short- and long-term impacts. Using both ARDL and NARDL models, the study reveals that oil prices significantly influence air travel demand, with asymmetrical impacts more pronounced in Korea than in Japan. While both countries show short-term sensitivity to oil prices, Korea also experiences long-term effects. Economic growth and exchange rates are also critical factors affecting air travel demand. These findings suggest tailored policy approaches for Korea and Japan to enhance the resilience of their aviation sectors in response to oil price changes.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"128 ","pages":"Article 102856"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate prediction of milestone times in aircraft turnaround operations is crucial for enhancing flight on-time performance and airport operational efficiency within the airport collaborative decision-making framework. This study proposed a multi-output gradient boosting regression tree-based model in a cascaded framework to dynamically predict crucial milestone times of aircraft turnaround operations, with predictions continuously updated throughout the operational timeline. A comprehensive feature set, incorporating flight-related attributes and hierarchical information transmission features from preceding predictions, was developed using operational data from a study airport. The results demonstrate the effectiveness of the proposed method with an initial prediction accuracy higher than 80% within ±5 min for the actual turnaround activity times. Prediction performance improves progressively as turnaround operations advance, with over 60% of activities ultimately attaining prediction accuracy above 95% within the same threshold. Feature importance analysis indicates significant differences in feature contributions to different milestones of the ground handling process. This methodology provides stakeholders with actionable insights to support airport collaborative decision-making initiatives, enabling delay minimization and reduced slot wastage.
{"title":"Dynamic prediction of aircraft turnaround milestone times using a cascaded gradient boosting model for improved airport collaborative decision-making","authors":"Xiaowei Tang , Jiaqi Wu , Cheng-Lung Wu , Ye Ding , Shengrun Zhang","doi":"10.1016/j.jairtraman.2025.102842","DOIUrl":"10.1016/j.jairtraman.2025.102842","url":null,"abstract":"<div><div>Accurate prediction of milestone times in aircraft turnaround operations is crucial for enhancing flight on-time performance and airport operational efficiency within the airport collaborative decision-making framework. This study proposed a multi-output gradient boosting regression tree-based model in a cascaded framework to dynamically predict crucial milestone times of aircraft turnaround operations, with predictions continuously updated throughout the operational timeline. A comprehensive feature set, incorporating flight-related attributes and hierarchical information transmission features from preceding predictions, was developed using operational data from a study airport. The results demonstrate the effectiveness of the proposed method with an initial prediction accuracy higher than 80% within ±5 min for the actual turnaround activity times. Prediction performance improves progressively as turnaround operations advance, with over 60% of activities ultimately attaining prediction accuracy above 95% within the same threshold. Feature importance analysis indicates significant differences in feature contributions to different milestones of the ground handling process. This methodology provides stakeholders with actionable insights to support airport collaborative decision-making initiatives, enabling delay minimization and reduced slot wastage.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"128 ","pages":"Article 102842"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-07-04DOI: 10.1016/j.jairtraman.2025.102846
João Basilio Tarelho Szenczuk , Rogéria de A. Gomes , Jorge M.R. Silva
This study employs a statistical modeling approach to commercial aviation fuel consumption and flight time to assess air traffic management (ATM) performance. The study investigates the operational factors that impact ATM efficiency, exploring airport-specific performance. Using datasets from Automatic Dependent Surveillance - Broadcast (ADS-B) surveillance systems, the Brazilian air transport statistical database, and meteorological data, the research develops linear regression models to quantify the effects of traffic intensity, weather conditions, and airspace structure on fuel consumption and flight time. The data covers the Brazilian domestic market from 2018 to 2022, totaling more than 1.3 million flights analyzed. The findings suggest differences in the impacts of traffic intensity and adverse weather conditions among the busiest airports in Brazil. Some airports had better efficiency levels for the same traffic intensity level, while the airspace structure’s impact was somewhat more similar in all major airports. At SBGR, for example, the busiest airport in Brazil, the traffic intensity during arrivals caused about 74 kilograms of extra fuel per flight, while the airspace structure was associated with about 160 kilograms of extra fuel per flight. This research offers insights into quantifying potential savings from ATM improvements by providing a data-driven approach.
{"title":"Estimating the impacts of traffic intensity, weather conditions, and airspace structure on fuel consumption and flight time of Brazilian commercial aviation","authors":"João Basilio Tarelho Szenczuk , Rogéria de A. Gomes , Jorge M.R. Silva","doi":"10.1016/j.jairtraman.2025.102846","DOIUrl":"10.1016/j.jairtraman.2025.102846","url":null,"abstract":"<div><div>This study employs a statistical modeling approach to commercial aviation fuel consumption and flight time to assess air traffic management (ATM) performance. The study investigates the operational factors that impact ATM efficiency, exploring airport-specific performance. Using datasets from Automatic Dependent Surveillance - Broadcast (ADS-B) surveillance systems, the Brazilian air transport statistical database, and meteorological data, the research develops linear regression models to quantify the effects of traffic intensity, weather conditions, and airspace structure on fuel consumption and flight time. The data covers the Brazilian domestic market from 2018 to 2022, totaling more than 1.3 million flights analyzed. The findings suggest differences in the impacts of traffic intensity and adverse weather conditions among the busiest airports in Brazil. Some airports had better efficiency levels for the same traffic intensity level, while the airspace structure’s impact was somewhat more similar in all major airports. At SBGR, for example, the busiest airport in Brazil, the traffic intensity during arrivals caused about 74 kilograms of extra fuel per flight, while the airspace structure was associated with about 160 kilograms of extra fuel per flight. This research offers insights into quantifying potential savings from ATM improvements by providing a data-driven approach.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"128 ","pages":"Article 102846"},"PeriodicalIF":3.9,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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