Pub Date : 2024-11-15DOI: 10.1016/j.jairtraman.2024.102707
Ching-Hui Tang, Yi-Hsiang Hsu
We study optimal aircraft seat assignment for infectious diseases in view of the stochastic risk of infection for a passenger assigned to a seat. The stochastic risk is based on the passengers' vaccination status and the different risk probability distributions corresponding to seat locations at window, middle, or aisle. In addition, the influence of groups of passengers who prefer to be seated together on the risk of infection in the cabin is also analyzed. A stochastic programming technique is applied to develop both non-grouped and grouped scenario-based models. The objective is to minimize the risk of infection for the worst-case scenario, as formulated by the Min-Max objective approach. Numerical tests utilizing statistical data from 2369 flights in Taiwan were performed. The results show that the consideration of passengers’ vaccination status during seat assignment is useful, reducing the average risk of infection in the cabin by half. Grouped seat assignment does not seem to have a significant influence on the risk of infection, with an increase of only 1.28 and 1.25 times compared with non-grouped seat assignment. The recommendations are that more heavily vaccinated passengers be assigned to aisle seats, while passengers who have received fewer doses be assigned to window seats. In addition, considering the limited impact of group seating on the risk of infection, it may not be necessary for an airline to decline to accommodate such requests.
{"title":"Stochastic infection risk models for aircraft seat assignment considering passenger vaccination status and seat location","authors":"Ching-Hui Tang, Yi-Hsiang Hsu","doi":"10.1016/j.jairtraman.2024.102707","DOIUrl":"10.1016/j.jairtraman.2024.102707","url":null,"abstract":"<div><div>We study optimal aircraft seat assignment for infectious diseases in view of the stochastic risk of infection for a passenger assigned to a seat. The stochastic risk is based on the passengers' vaccination status and the different risk probability distributions corresponding to seat locations at window, middle, or aisle. In addition, the influence of groups of passengers who prefer to be seated together on the risk of infection in the cabin is also analyzed. A stochastic programming technique is applied to develop both non-grouped and grouped scenario-based models. The objective is to minimize the risk of infection for the worst-case scenario, as formulated by the Min-Max objective approach. Numerical tests utilizing statistical data from 2369 flights in Taiwan were performed. The results show that the consideration of passengers’ vaccination status during seat assignment is useful, reducing the average risk of infection in the cabin by half. Grouped seat assignment does not seem to have a significant influence on the risk of infection, with an increase of only 1.28 and 1.25 times compared with non-grouped seat assignment. The recommendations are that more heavily vaccinated passengers be assigned to aisle seats, while passengers who have received fewer doses be assigned to window seats. In addition, considering the limited impact of group seating on the risk of infection, it may not be necessary for an airline to decline to accommodate such requests.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"122 ","pages":"Article 102707"},"PeriodicalIF":3.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657991","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 : 2024-11-14DOI: 10.1016/j.jairtraman.2024.102704
Hans-Martin Niemeier , Peter Forsyth
The discussion of mitigating climate change has turned towards airports, which are a key element in the overall air transport industry. The paper analyses how measures at the airport level can help to directly or indirectly reduce emissions from the air transport sector. This topic is of relevance because, by now, external costs of the sector are internalised only partially. We distinguish between non-aviation and aviation emissions as well as those from airport access and egress. In addition, airports are regarded as a node by policymakers to reduce emissions. While a reduction of non-aviation emissions is straightforward and also attempted by many airports, the reduction in aviation emissions is mainly controlled by the airlines themselves and airports only have an indirect effect. Applying microeconomics, we analyse how operational factors, pricing and slot regimes can affect output and emissions. In the short run, with busy airports, differentiated charges might only lead to reduced emissions in the US, as the slot systems ration demand well. In the long run new capacity must be assessed by Cost Benefit Analysis with pricing of local and global environmental externalities. While full internalisation of external costs is, in principle, possible it has yet to be achieved. This means that there is a significant task to assess the emissions at airport with a view to enabling stronger policies to reduce emissions.
{"title":"Addressing the impact of airport pricing, investment and operations on greenhouse gas emissions","authors":"Hans-Martin Niemeier , Peter Forsyth","doi":"10.1016/j.jairtraman.2024.102704","DOIUrl":"10.1016/j.jairtraman.2024.102704","url":null,"abstract":"<div><div>The discussion of mitigating climate change has turned towards airports, which are a key element in the overall air transport industry. The paper analyses how measures at the airport level can help to directly or indirectly reduce emissions from the air transport sector. This topic is of relevance because, by now, external costs of the sector are internalised only partially. We distinguish between non-aviation and aviation emissions as well as those from airport access and egress. In addition, airports are regarded as a node by policymakers to reduce emissions. While a reduction of non-aviation emissions is straightforward and also attempted by many airports, the reduction in aviation emissions is mainly controlled by the airlines themselves and airports only have an indirect effect. Applying microeconomics, we analyse how operational factors, pricing and slot regimes can affect output and emissions. In the short run, with busy airports, differentiated charges might only lead to reduced emissions in the US, as the slot systems ration demand well. In the long run new capacity must be assessed by Cost Benefit Analysis with pricing of local and global environmental externalities. While full internalisation of external costs is, in principle, possible it has yet to be achieved. This means that there is a significant task to assess the emissions at airport with a view to enabling stronger policies to reduce emissions.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"122 ","pages":"Article 102704"},"PeriodicalIF":3.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.jairtraman.2024.102693
Sien Chen , Yinghua Huang
Purpose
A key issue of making upgrade decisions is to match the most relevant upgrade offers to the right customers at the right time. To optimize upgrade strategies and profitability, companies seek to break “data silos” between themselves and other business partners for a more holistic view of customers' consumption experiences. However, multi-source data fusion may lead to potential privacy leakage. To overcome these two challenges in data silos and privacy protection, this study introduced a privacy-preserving federated learning (FL) approach and explained the process of using FL in modeling airline passengers’ willingness to pay for upgrade offers.
Design/methodology/approach
Federated learning is a new confidential computing technique that allows companies to train a model cooperatively by exchanging model parameters instead of the actual raw data, which might include customers' privacy sensitive information. Using a case study of a Chinese airline company, this study demonstrated how FL-based upgrade models using multi-source data can be developed to improve the accuracy of predicting customers' willingness to pay for upgrades while preserving customers’ personal data privacy.
Findings
Comparing with traditional unilateral model using single-source data, the federated logistic regression and SecureBoost models demonstrate better model performance. This indicates that the proposed FL approach can enhance the accuracy of modeling airline passengers' willingness to pay for upgrade offers while preserving passengers’ data privacy. The findings also show that the FL-based models generally took longer running time than the traditional unilateral model due to the design of FL approach in ensuring data privacy.
Originality
This study contributes to the literature of upgrade optimization by introducing the new FL approach for developing machining learning models to predict customers’ reaction to upgrade offers. Although we focus on the airline industry in our case study, the proposed FL approach can be applied to other industries with a similar issue of upgrade optimization such as hotels or cruise lines, and car rental.
{"title":"A privacy-preserving federated learning approach for airline upgrade optimization","authors":"Sien Chen , Yinghua Huang","doi":"10.1016/j.jairtraman.2024.102693","DOIUrl":"10.1016/j.jairtraman.2024.102693","url":null,"abstract":"<div><h3>Purpose</h3><div>A key issue of making upgrade decisions is to match the most relevant upgrade offers to the right customers at the right time. To optimize upgrade strategies and profitability, companies seek to break “data silos” between themselves and other business partners for a more holistic view of customers' consumption experiences. However, multi-source data fusion may lead to potential privacy leakage. To overcome these two challenges in data silos and privacy protection, this study introduced a privacy-preserving federated learning (FL) approach and explained the process of using FL in modeling airline passengers’ willingness to pay for upgrade offers.</div></div><div><h3>Design/methodology/approach</h3><div>Federated learning is a new confidential computing technique that allows companies to train a model cooperatively by exchanging model parameters instead of the actual raw data, which might include customers' privacy sensitive information. Using a case study of a Chinese airline company, this study demonstrated how FL-based upgrade models using multi-source data can be developed to improve the accuracy of predicting customers' willingness to pay for upgrades while preserving customers’ personal data privacy.</div></div><div><h3>Findings</h3><div>Comparing with traditional unilateral model using single-source data, the federated logistic regression and SecureBoost models demonstrate better model performance. This indicates that the proposed FL approach can enhance the accuracy of modeling airline passengers' willingness to pay for upgrade offers while preserving passengers’ data privacy. The findings also show that the FL-based models generally took longer running time than the traditional unilateral model due to the design of FL approach in ensuring data privacy.</div></div><div><h3>Originality</h3><div>This study contributes to the literature of upgrade optimization by introducing the new FL approach for developing machining learning models to predict customers’ reaction to upgrade offers. Although we focus on the airline industry in our case study, the proposed FL approach can be applied to other industries with a similar issue of upgrade optimization such as hotels or cruise lines, and car rental.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"122 ","pages":"Article 102693"},"PeriodicalIF":3.9,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.jairtraman.2024.102684
Simon Okwir , Kaveh Amouzgar , Amos HC. Ng
Understanding delay conditions and making accurate predictions are essential for optimizing turnaround and taxi times, which in turn reduces fuel consumption and lowers CO2 emissions in airport operations. However, while existing research has explored the impact of various prediction models on airport operations, it often overlooks the performance of Collaborative Decision Making (CDM) variables when discussing delay conditions. The implementation of CDM at major European airports has led to a milestone-based approach within airport operations, particularly in the turnaround operations, segmenting these operations with unique features. The purpose of this paper is to systematically investigate the efficacy of various machine learning techniques, such as linear regression, regression trees, random forests, elastic nets, and multi-layer perceptrons (MLP), in accurately predicting delay categories within the CDM framework. For this purpose, we analyzed CDM operational data from Madrid Airport, with at least 166,185 flight observations. Our findings illustrate a training methodology on how different models vary in prediction accuracy when applied to CDM operational data. We applied the SHAP (SHapley Additive exPlanations) method for feature importance analysis of all our independent variables to interpret the output of our machine learning models. Our results indicate that linear regression and elastic nets are the most effective machine learning models for achieving high prediction accuracy within the CDM framework. To test their robustness, we extended the analysis with predictions for better schedule times for taxi times on arrival and depature for selected runways using a different dataset. Our results contribute by showcasing a training methodology, highlighting how elastic net model as the best-performing model can be adopted for turnaround operations. In conclusion, we discuss the implications of our results for runway demand policies and use of airport resources such as gate & runaway allocation.
{"title":"Exploring prediction accuracy for optimal taxi times in airport operations using various machine learning models","authors":"Simon Okwir , Kaveh Amouzgar , Amos HC. Ng","doi":"10.1016/j.jairtraman.2024.102684","DOIUrl":"10.1016/j.jairtraman.2024.102684","url":null,"abstract":"<div><div>Understanding delay conditions and making accurate predictions are essential for optimizing turnaround and taxi times, which in turn reduces fuel consumption and lowers CO<sub>2</sub> emissions in airport operations. However, while existing research has explored the impact of various prediction models on airport operations, it often overlooks the performance of Collaborative Decision Making (CDM) variables when discussing delay conditions. The implementation of CDM at major European airports has led to a milestone-based approach within airport operations, particularly in the turnaround operations, segmenting these operations with unique features. The purpose of this paper is to systematically investigate the efficacy of various machine learning techniques, such as linear regression, regression trees, random forests, elastic nets, and multi-layer perceptrons (MLP), in accurately predicting delay categories within the CDM framework. For this purpose, we analyzed CDM operational data from Madrid Airport, with at least 166,185 flight observations. Our findings illustrate a training methodology on how different models vary in prediction accuracy when applied to CDM operational data. We applied the SHAP (SHapley Additive exPlanations) method for feature importance analysis of all our independent variables to interpret the output of our machine learning models. Our results indicate that linear regression and elastic nets are the most effective machine learning models for achieving high prediction accuracy within the CDM framework. To test their robustness, we extended the analysis with predictions for better schedule times for taxi times on arrival and depature for selected runways using a different dataset. Our results contribute by showcasing a training methodology, highlighting how elastic net model as the best-performing model can be adopted for turnaround operations. In conclusion, we discuss the implications of our results for runway demand policies and use of airport resources such as gate & runaway allocation.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"122 ","pages":"Article 102684"},"PeriodicalIF":3.9,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.jairtraman.2024.102694
Xin Zhao , Ulrika Ziverts , Henrik Ekstrand , Maria Ullvetter , Peter Lukic , Anette Näs , Esbjörn Olsson , Martin Ridal , Åke Johansson , Martin Wall , Olivier Petit , Tomas Grönstedt
This paper presents a study of using site-specific statistical meteorological data in the construction of curved flight procedures to explore its potential in reducing the environmental impact of air traffic near the airports. In the study, the statistical meteorological data which covers a 10-year period of time, from 2009 to 2018, have been collected for the air space centred two major Swedish airports, Arlanda at Stockholm (ESSA) and Landvetter at Göteborg (ESGG). Two procedure design practices, one is an area navigation (RNAV) standard instrument departure (SID) procedure from runway 08 of Arlanda airport and another is a required navigation performance authorization required (RNP AR) approach procedure heading to the runway 03 at Landvetter airport, have been performed and analyzed. Applying the 95th percentile wind speed from statistical meteorological data, instead of the ICAO standardized tailwind component (TWC), offers varying benefits depending on the specific case. For the RNAV SID procedure from ESSA, the part of the designed departure path which is outside of the regulated noise dispersion area is significantly reduced. Whilst for the RNP AR approach procedure to ESGG, the smaller turning radius resulted from the lower TWC which is calculated from the local meteorological data makes it possible to avoid flying over an inhabited area. Besides the notable potential of noise impact reduction, flight distance shortening of 3.7 NM (RNAV SID ESSA case) and 1 NM (RNP AR ESGG case) compared to the same procedures designed on ICAO standard TWC have been observed. In general, the presented results are positive in supporting the use of local meteorological data in planning curved flight procedures during departures and approaches. A validation performed using an A320 full flight simulator has confirmed the operability of the ESGG RNP AR procedure from the design practice. In the full flight simulator, even with the 100th percentile wind condition from the collected statistical meteorological data, the designed RNP AR approach procedure can be operable considering RNP 0.3 corridor while a 30° bank angle is required for approximately 20 s during the turn.
本文介绍了一项关于在构建曲线飞行程序时使用特定地点统计气象数据的研究，以探索其在减少机场附近空中交通对环境影响方面的潜力。在这项研究中，收集了以瑞典两大机场（斯德哥尔摩的阿兰达机场（ESSA）和哥德堡的兰德维特机场（ESGG））为中心的空域的气象统计数据，时间跨度为 10 年（2009 年至 2018 年）。对两种程序设计实践进行了分析，一种是从阿兰达机场08号跑道出发的区域导航（RNAV）标准仪表起飞（SID）程序，另一种是前往兰德维特机场03号跑道的必要导航性能授权（RNP AR）进近程序。根据具体情况，采用统计气象数据中的第 95 百分位数风速而非国际民航组织标准化尾风分量（TWC）可带来不同的益处。就 ESSA 的 RNAV SID 程序而言，设计的离港路径中位于受管制的噪声扩散区域之外的部分明显减少。而对于飞往 ESGG 的 RNP AR 进近程序，由于根据当地气象数据计算的 TWC 较低，因此转弯半径较小，可以避免飞越居民区。与根据国际民航组织标准 TWC 设计的相同程序相比，除了显著降低噪声影响的潜力外，还观察到飞行距离缩短了 3.7 NM（RNAV SID ESSA 案例）和 1 NM（RNP AR ESGG 案例）。总体而言，所提供的结果对在离港和进港过程中使用当地气象数据规划曲线飞行程序具有积极意义。使用 A320 全飞行模拟器进行的验证证实了 ESGG RNP AR 程序在设计实践中的可操作性。在全飞行模拟器中，即使在收集到的统计气象数据的第100百分位风况下，所设计的RNP AR进近程序在考虑到RNP 0.3走廊的情况下也是可操作的，而在转弯过程中需要约20秒的30°倾角。
{"title":"Curved flight procedure construction with site-specific statistical meteorological data: A Swedish example","authors":"Xin Zhao , Ulrika Ziverts , Henrik Ekstrand , Maria Ullvetter , Peter Lukic , Anette Näs , Esbjörn Olsson , Martin Ridal , Åke Johansson , Martin Wall , Olivier Petit , Tomas Grönstedt","doi":"10.1016/j.jairtraman.2024.102694","DOIUrl":"10.1016/j.jairtraman.2024.102694","url":null,"abstract":"<div><div>This paper presents a study of using site-specific statistical meteorological data in the construction of curved flight procedures to explore its potential in reducing the environmental impact of air traffic near the airports. In the study, the statistical meteorological data which covers a 10-year period of time, from 2009 to 2018, have been collected for the air space centred two major Swedish airports, Arlanda at Stockholm (ESSA) and Landvetter at Göteborg (ESGG). Two procedure design practices, one is an area navigation (RNAV) standard instrument departure (SID) procedure from runway 08 of Arlanda airport and another is a required navigation performance authorization required (RNP AR) approach procedure heading to the runway 03 at Landvetter airport, have been performed and analyzed. Applying the 95th percentile wind speed from statistical meteorological data, instead of the ICAO standardized tailwind component (TWC), offers varying benefits depending on the specific case. For the RNAV SID procedure from ESSA, the part of the designed departure path which is outside of the regulated noise dispersion area is significantly reduced. Whilst for the RNP AR approach procedure to ESGG, the smaller turning radius resulted from the lower TWC which is calculated from the local meteorological data makes it possible to avoid flying over an inhabited area. Besides the notable potential of noise impact reduction, flight distance shortening of 3.7 NM (RNAV SID ESSA case) and 1 NM (RNP AR ESGG case) compared to the same procedures designed on ICAO standard TWC have been observed. In general, the presented results are positive in supporting the use of local meteorological data in planning curved flight procedures during departures and approaches. A validation performed using an A320 full flight simulator has confirmed the operability of the ESGG RNP AR procedure from the design practice. In the full flight simulator, even with the 100th percentile wind condition from the collected statistical meteorological data, the designed RNP AR approach procedure can be operable considering RNP 0.3 corridor while a 30° bank angle is required for approximately 20 s during the turn.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"121 ","pages":"Article 102694"},"PeriodicalIF":3.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1016/j.jairtraman.2024.102692
Lynnette Dray , Joanna Kuleszo , Roger Teoh , Marc Stettler , James Stewart , Andreas Schäfer
Models of air freight are often constrained by a lack of available data. This study brings together different sources of air freight supply and demand data to address this gap. To study air freight operations, we combine schedules, flight tracking data and country-level databases of passenger and freight movements to produce estimates of global flight segment-level capacity and load factors in freighter aircraft and passenger holds for 2019–2021. To study true origin-ultimate destination air freight demand, a freight mode choice model by commodity group is developed for 2019 to fill gaps in mode information in international and national trade datasets, and estimates are made for 2019 and 2021. Initial comparisons of supply and demand data demonstrate that air freight journeys differ significantly from passenger journeys, typically including more flight legs (roughly, around 2, compared to 1.2 for passengers) and greater leg distances (2.2–2.5 times average passenger distance), with significant asymmetry in commodity flows and operations to and from individual countries and regions. These differences persist in 2021, despite COVID-19 induced shifts towards carrying more air freight in freighter aircraft. This research forms a first step towards making available an integrated database of estimated global air freight flows by commodity.
{"title":"Global air freight flow data for aviation policy modelling","authors":"Lynnette Dray , Joanna Kuleszo , Roger Teoh , Marc Stettler , James Stewart , Andreas Schäfer","doi":"10.1016/j.jairtraman.2024.102692","DOIUrl":"10.1016/j.jairtraman.2024.102692","url":null,"abstract":"<div><div>Models of air freight are often constrained by a lack of available data. This study brings together different sources of air freight supply and demand data to address this gap. To study air freight operations, we combine schedules, flight tracking data and country-level databases of passenger and freight movements to produce estimates of global flight segment-level capacity and load factors in freighter aircraft and passenger holds for 2019–2021. To study true origin-ultimate destination air freight demand, a freight mode choice model by commodity group is developed for 2019 to fill gaps in mode information in international and national trade datasets, and estimates are made for 2019 and 2021. Initial comparisons of supply and demand data demonstrate that air freight journeys differ significantly from passenger journeys, typically including more flight legs (roughly, around 2, compared to 1.2 for passengers) and greater leg distances (2.2–2.5 times average passenger distance), with significant asymmetry in commodity flows and operations to and from individual countries and regions. These differences persist in 2021, despite COVID-19 induced shifts towards carrying more air freight in freighter aircraft. This research forms a first step towards making available an integrated database of estimated global air freight flows by commodity.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"121 ","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1016/j.jairtraman.2024.102689
Ioan Gaitan , Gawon Yun , Harry Joo , Sunil Hwang
Military pilots can play important roles in saving aviation fuel by operating aircraft in an environmentally sustainable manner. Based on the theory of planned behavior (TPB), this study investigates the factors associated with cargo pilots’ fuel-saving intentions during logistics missions. We collected 108 survey responses, including 62 from the United States Air Force (USAF) and 46 from the Republic of Korea Air Force (ROKAF). Confirmatory factor analysis (CFA) and structural equation modeling (SEM) using the survey data show partial support for the relationships between three antecedents (attitude, subjective norms, and perceived behavioral control) and behavioral intention. Contrary to existing studies involving TPB, which are mostly about personal choices with some degree of freedom, the results suggest that the impact of subjective norms is greater than that of attitude in this study context, which can be explained by the rigid military culture and strict air traffic control including specific routes, altitudes, and speeds mandated by Air Combat Command. The theoretical and practical contributions of this study provide insights into how subjective norms influence intentions across different contexts, extending the applicability of TPB to industries with rigid organizational cultures and tight operational controls, such as the airline industry.
{"title":"Understanding military pilots’ fuel-saving intentions for supporting logistics missions","authors":"Ioan Gaitan , Gawon Yun , Harry Joo , Sunil Hwang","doi":"10.1016/j.jairtraman.2024.102689","DOIUrl":"10.1016/j.jairtraman.2024.102689","url":null,"abstract":"<div><div>Military pilots can play important roles in saving aviation fuel by operating aircraft in an environmentally sustainable manner. Based on the theory of planned behavior (TPB), this study investigates the factors associated with cargo pilots’ fuel-saving intentions during logistics missions. We collected 108 survey responses, including 62 from the United States Air Force (USAF) and 46 from the Republic of Korea Air Force (ROKAF). Confirmatory factor analysis (CFA) and structural equation modeling (SEM) using the survey data show partial support for the relationships between three antecedents (attitude, subjective norms, and perceived behavioral control) and behavioral intention. Contrary to existing studies involving TPB, which are mostly about personal choices with some degree of freedom, the results suggest that the impact of subjective norms is greater than that of attitude in this study context, which can be explained by the rigid military culture and strict air traffic control including specific routes, altitudes, and speeds mandated by Air Combat Command. The theoretical and practical contributions of this study provide insights into how subjective norms influence intentions across different contexts, extending the applicability of TPB to industries with rigid organizational cultures and tight operational controls, such as the airline industry.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"121 ","pages":"Article 102689"},"PeriodicalIF":3.9,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417345","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 : 2024-10-01DOI: 10.1016/j.jairtraman.2024.102688
Xin Li , Chi Wei Su
This study proposes a new joint quantile impulse response function (jQIRF) and applies that function to investigate the impact of multiple uncertainty shocks on the volatility of China's airline industry. The jQIRF not only allows one to examine the joint impact of multiple factors on the target variable; the impact of these factors on specific quantiles or states of the target variable can also be examined. The empirical results show that, compared to traditional IRF, the proposed jQIRF successfully reveals the positive impact of multiple uncertainties on the volatility of the airline industry and obtains a narrower confidence interval for IRF. The jQIRF also successfully corrects the overestimation bias caused by simple aggregation in generalized IRF. In addition, empirical results at different quantiles show the existence of a “leverage effect” in the impact of uncertainty on airline volatility. This means that, in more volatile market environments, the positive joint impact of uncertainties is stronger. However, research that has focused on individual airline stocks indicates that the airlines appear to be capable of implementing measures to stabilize stock volatility, thereby mitigating the negative impact of uncertainties on the airline industry. Overall, the proposed jQIRF and empirical conclusions in this paper help to more accurately assess the impact of multiple factors on the airline industry from a joint perspective. This ability is beneficial for both policymakers and investors.
{"title":"Evaluating the impact of multiple uncertainty shocks on China's airline stocks volatility: A novel joint quantile perspective","authors":"Xin Li , Chi Wei Su","doi":"10.1016/j.jairtraman.2024.102688","DOIUrl":"10.1016/j.jairtraman.2024.102688","url":null,"abstract":"<div><div>This study proposes a new joint quantile impulse response function (jQIRF) and applies that function to investigate the impact of multiple uncertainty shocks on the volatility of China's airline industry. The jQIRF not only allows one to examine the joint impact of multiple factors on the target variable; the impact of these factors on specific quantiles or states of the target variable can also be examined. The empirical results show that, compared to traditional IRF, the proposed jQIRF successfully reveals the positive impact of multiple uncertainties on the volatility of the airline industry and obtains a narrower confidence interval for IRF. The jQIRF also successfully corrects the overestimation bias caused by simple aggregation in generalized IRF. In addition, empirical results at different quantiles show the existence of a “leverage effect” in the impact of uncertainty on airline volatility. This means that, in more volatile market environments, the positive joint impact of uncertainties is stronger. However, research that has focused on individual airline stocks indicates that the airlines appear to be capable of implementing measures to stabilize stock volatility, thereby mitigating the negative impact of uncertainties on the airline industry. Overall, the proposed jQIRF and empirical conclusions in this paper help to more accurately assess the impact of multiple factors on the airline industry from a joint perspective. This ability is beneficial for both policymakers and investors.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"121 ","pages":"Article 102688"},"PeriodicalIF":3.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356967","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 : 2024-09-28DOI: 10.1016/j.jairtraman.2024.102683
Qihang Xu, Yutian Pang, Yongming Liu
Addressing the complexities of modern Air Traffic Management (ATM), this paper introduces a novel framework for dynamic airspace sectorization, tailored to enhance efficiency and safety in congested airspaces. Central to this framework is the WP-ConvLSTM model, an innovative deep learning approach equipped with attention mechanisms. This model excels in accurately predicting workload dynamics, a critical factor in managing air traffic flow. To implement sectorization, we adopt a constrained K-means clustering technique for spatial division, followed by a refinement process involving Support Vector Machine (SVM) algorithms for precise boundary generation. Further optimization of sector boundaries is achieved through an evolutionary algorithm, ensuring both flexibility and stability in airspace divisions. Our methodology was thoroughly evaluated using real-world data from one of the busiest airspaces, demonstrating significant improvements in workload prediction accuracy and airspace sector management. The findings highlight the model’s robustness in practical scenarios, offering a scalable solution for ATM challenges. We conclude with a recognition of the study’s limitations and propose avenues for future research to build upon our findings, particularly in enhancing real-time data integration and adapting to evolving air traffic patterns.
{"title":"Dynamic airspace sectorization with machine learning enhanced workload prediction and clustering","authors":"Qihang Xu, Yutian Pang, Yongming Liu","doi":"10.1016/j.jairtraman.2024.102683","DOIUrl":"10.1016/j.jairtraman.2024.102683","url":null,"abstract":"<div><div>Addressing the complexities of modern Air Traffic Management (ATM), this paper introduces a novel framework for dynamic airspace sectorization, tailored to enhance efficiency and safety in congested airspaces. Central to this framework is the WP-ConvLSTM model, an innovative deep learning approach equipped with attention mechanisms. This model excels in accurately predicting workload dynamics, a critical factor in managing air traffic flow. To implement sectorization, we adopt a constrained K-means clustering technique for spatial division, followed by a refinement process involving Support Vector Machine (SVM) algorithms for precise boundary generation. Further optimization of sector boundaries is achieved through an evolutionary algorithm, ensuring both flexibility and stability in airspace divisions. Our methodology was thoroughly evaluated using real-world data from one of the busiest airspaces, demonstrating significant improvements in workload prediction accuracy and airspace sector management. The findings highlight the model’s robustness in practical scenarios, offering a scalable solution for ATM challenges. We conclude with a recognition of the study’s limitations and propose avenues for future research to build upon our findings, particularly in enhancing real-time data integration and adapting to evolving air traffic patterns.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"121 ","pages":"Article 102683"},"PeriodicalIF":3.9,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356968","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}
Airlines continuously seek strategic ways to impress their passengers and build memorable experiences. Inflight food services as an imperative interaction, the research included healthy and environmentally sustainable attributes (e.g., production method, product origin), reflecting the emphasis on longevity/well-being and sustainability to design optimal inflight food bundles as well as the brand and taste attributes. This exploratory research investigated differences in inflight food preferences between frequent and occasional flyers. A total of 16 full-profile pairwise comparison questions of mixed levels for five attributes were given to the sample of international flight passengers (n = 490). The participants were asked to rate inflight food bundles using a 9-point Likert scale. The results indicate that healthy and environmentally sustainable inflight foods will be preferred over brand-named or tasty foods when served onboard. There were notable differences in inflight food preferences between occasional and frequent flyers. While both flyers preferred low-calorie and tasty foods made with nationally sourced ingredients, frequent flyers highly preferred branded foods made with organically grown ingredients. In contrast, occasional flyers desired generic branded foods made with conventionally grown ingredients. The study findings are discussed further to help airline marketers build a desirable inflight food bundle.
{"title":"Do healthy and environmentally sustainable inflight foods matter to international flight passengers? Frequent vs. occasional flyers","authors":"Eunmin (Min) Hwang , Yen-Soon Kim , Seyhmus Baloglu , Carola Raab","doi":"10.1016/j.jairtraman.2024.102687","DOIUrl":"10.1016/j.jairtraman.2024.102687","url":null,"abstract":"<div><div>Airlines continuously seek strategic ways to impress their passengers and build memorable experiences. Inflight food services as an imperative interaction, the research included healthy and environmentally sustainable attributes (e.g., production method, product origin), reflecting the emphasis on longevity/well-being and sustainability to design optimal inflight food bundles as well as the brand and taste attributes. This exploratory research investigated differences in inflight food preferences between frequent and occasional flyers. A total of 16 full-profile pairwise comparison questions of mixed levels for five attributes were given to the sample of international flight passengers (n = 490). The participants were asked to rate inflight food bundles using a 9-point Likert scale. The results indicate that healthy and environmentally sustainable inflight foods will be preferred over brand-named or tasty foods when served onboard. There were notable differences in inflight food preferences between occasional and frequent flyers. While both flyers preferred low-calorie and tasty foods made with nationally sourced ingredients, frequent flyers highly preferred branded foods made with organically grown ingredients. In contrast, occasional flyers desired generic branded foods made with conventionally grown ingredients. The study findings are discussed further to help airline marketers build a desirable inflight food bundle.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"121 ","pages":"Article 102687"},"PeriodicalIF":3.9,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327725","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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