Journal of Air Transport Management最新文献_第9页

Passenger perceptions of Artificial Intelligence in airline operations: Implications for air transport management 乘客对航空公司运营中人工智能的看法：对航空运输管理的影响

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-07-31 DOI: 10.1016/j.jairtraman.2025.102874

Joan-Francesc Fondevila-Gascón , Óscar Gutiérrez-Aragón , David Lopez-Lopez , Gonzalo Curiel-Barrios , Júlia Alabart-Algueró

Artificial Intelligence (AI) is reshaping the aviation industry, driving efficiency, automation, and innovation across multiple operational domains. This study examines commercial airline passengers’ perceptions of AI’s role in addressing key industry challenges, including air traffic management, predictive maintenance, passenger experience, and sustainability. Using a quantitative approach, a survey was conducted among 320 airline passengers in Spain to assess their attitudes toward AI-driven applications in aviation. The findings reveal strong support for AI in optimizing flight operations, reducing delays, and enhancing security procedures. However, significant skepticism remains regarding AI’s autonomy in decision-making, particularly in pilot replacement and automated flight rerouting. Statistical analyses indicate that younger and frequent travelers exhibit higher confidence in AI’s potential, whereas older passengers demonstrate greater reluctance toward AI-driven automation. Additionally, AI is perceived as a crucial enabler of environmental sustainability, with respondents acknowledging its role in reducing fuel consumption and emissions. These insights provide valuable implications for policymakers, airlines, and technology developers seeking to align AI adoption with passenger expectations while ensuring safety, efficiency, and regulatory compliance. The study highlights the need for a balanced approach that integrates AI’s technological advancements with human oversight to foster trust and acceptance in the future of AI-powered aviation.

人工智能（AI）正在重塑航空业，推动多个运营领域的效率、自动化和创新。本研究调查了商业航空公司乘客对人工智能在解决关键行业挑战方面的作用的看法，包括空中交通管理、预测性维护、乘客体验和可持续性。采用定量方法，对西班牙320名航空公司乘客进行了一项调查，以评估他们对航空领域人工智能应用的态度。研究结果显示，人工智能在优化航班运营、减少延误和加强安全程序方面得到了强有力的支持。然而，对于人工智能在决策方面的自主性，特别是在飞行员替换和自动飞行改道方面，仍然存在很大的怀疑。统计分析表明，年轻和经常旅行的人对人工智能的潜力更有信心，而年长的乘客对人工智能驱动的自动化表现出更大的不情愿。此外，人工智能被认为是环境可持续性的关键推动者，受访者承认其在减少燃料消耗和排放方面的作用。这些见解为政策制定者、航空公司和技术开发人员提供了有价值的建议，他们希望将人工智能的采用与乘客的期望结合起来，同时确保安全、效率和法规遵从性。该研究强调，需要采取一种平衡的方法，将人工智能的技术进步与人类的监督相结合，以促进对人工智能航空未来的信任和接受。

{"title":"Passenger perceptions of Artificial Intelligence in airline operations: Implications for air transport management","authors":"Joan-Francesc Fondevila-Gascón , Óscar Gutiérrez-Aragón , David Lopez-Lopez , Gonzalo Curiel-Barrios , Júlia Alabart-Algueró","doi":"10.1016/j.jairtraman.2025.102874","DOIUrl":"10.1016/j.jairtraman.2025.102874","url":null,"abstract":"<div><div>Artificial Intelligence (AI) is reshaping the aviation industry, driving efficiency, automation, and innovation across multiple operational domains. This study examines commercial airline passengers’ perceptions of AI’s role in addressing key industry challenges, including air traffic management, predictive maintenance, passenger experience, and sustainability. Using a quantitative approach, a survey was conducted among 320 airline passengers in Spain to assess their attitudes toward AI-driven applications in aviation. The findings reveal strong support for AI in optimizing flight operations, reducing delays, and enhancing security procedures. However, significant skepticism remains regarding AI’s autonomy in decision-making, particularly in pilot replacement and automated flight rerouting. Statistical analyses indicate that younger and frequent travelers exhibit higher confidence in AI’s potential, whereas older passengers demonstrate greater reluctance toward AI-driven automation. Additionally, AI is perceived as a crucial enabler of environmental sustainability, with respondents acknowledging its role in reducing fuel consumption and emissions. These insights provide valuable implications for policymakers, airlines, and technology developers seeking to align AI adoption with passenger expectations while ensuring safety, efficiency, and regulatory compliance. The study highlights the need for a balanced approach that integrates AI’s technological advancements with human oversight to foster trust and acceptance in the future of AI-powered aviation.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"129 ","pages":"Article 102874"},"PeriodicalIF":3.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750530","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}

引用次数: 0

Time series prediction of airport operational resilience under severe weather conditions 恶劣天气条件下机场运行弹性的时间序列预测

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-07-31 DOI: 10.1016/j.jairtraman.2025.102862

Yuhui Zhang , Lili Liu , Xiong Peng

Airport operational resilience is a crucial metric reflecting an airport’s capacity to adapt to external shocks, essential for maintaining safety and operational efficiency. While there has been research on airport resilience under various severe weather conditions, the specific contributing factors and their impacts remain inadequately explored. This study develops a comprehensive index system that integrates airport performance and meteorological data, using a random forest algorithm to quantify the influence of various factors on airport resilience across five types of severe weather. Furthermore, a PatchTST(Patch time series Transformer)-based time series model improved by the Cauchy loss function is proposed to accurately predict airport operational resilience. Focusing on severe weather events during the period from January 2023 to July 2024 at Dallas-Fort Worth International Airport in the United States. To mitigate multicollinearity, variables with high Pearson correlation and variance inflation factor (VIF) values were removed prior to analysis. Feature importance results reveal that hourly flight movements (HFM) consistently hold the highest importance across weather types, while temperature (TEMP), relative humidity (RHUM) and air pressure (PRES) exhibit relatively higher meteorological influence despite limited overall impact. The optimal Cauchy-PatchTST model, with a look-back window of

L = 36

and a forecast length of

T = 1

, outperforms the traditional PatchTST model with MSE loss, three Transformer-based models and other optimized machine learning algorithms, achieving a 15.49% to 94.10% reduction in MAE on the test set. This study provides critical indicator analysis for airports across various severe weather conditions and offers reliable resilience data to support future operations management.

机场运营弹性是反映机场适应外部冲击能力的关键指标，对保持安全和运营效率至关重要。虽然对各种恶劣天气条件下的机场弹性进行了研究，但具体的影响因素及其影响仍未得到充分探讨。本研究开发了一个综合指标体系，将机场性能和气象数据整合在一起，使用随机森林算法量化各种因素对五种类型恶劣天气下机场恢复力的影响。在此基础上，提出了一种基于Patch时间序列变压器（Patch time series Transformer）的时间序列模型，并对其进行了柯西损失函数的改进，以准确预测机场运行弹性。重点关注2023年1月至2024年7月期间在美国达拉斯-沃斯堡国际机场发生的恶劣天气事件。为了减轻多重共线性，在分析之前去除具有高Pearson相关性和方差膨胀因子（VIF）值的变量。特征重要性结果显示，每小时飞行运动（HFM）在各种天气类型中始终保持最高的重要性，而温度（TEMP）、相对湿度（RHUM）和气压（PRES）表现出相对较高的气象影响，尽管总体影响有限。最优Cauchy-PatchTST模型的回溯窗口为L=36，预测长度为T=1，优于传统的带有MSE损失的PatchTST模型、基于三个变压器的模型以及其他优化的机器学习算法，在测试集上MAE降低了15.49% ~ 94.10%。该研究为各种恶劣天气条件下的机场提供了关键指标分析，并提供了可靠的弹性数据，以支持未来的运营管理。

{"title":"Time series prediction of airport operational resilience under severe weather conditions","authors":"Yuhui Zhang , Lili Liu , Xiong Peng","doi":"10.1016/j.jairtraman.2025.102862","DOIUrl":"10.1016/j.jairtraman.2025.102862","url":null,"abstract":"<div><div>Airport operational resilience is a crucial metric reflecting an airport’s capacity to adapt to external shocks, essential for maintaining safety and operational efficiency. While there has been research on airport resilience under various severe weather conditions, the specific contributing factors and their impacts remain inadequately explored. This study develops a comprehensive index system that integrates airport performance and meteorological data, using a random forest algorithm to quantify the influence of various factors on airport resilience across five types of severe weather. Furthermore, a PatchTST(Patch time series Transformer)-based time series model improved by the Cauchy loss function is proposed to accurately predict airport operational resilience. Focusing on severe weather events during the period from January 2023 to July 2024 at Dallas-Fort Worth International Airport in the United States. To mitigate multicollinearity, variables with high Pearson correlation and variance inflation factor (VIF) values were removed prior to analysis. Feature importance results reveal that hourly flight movements (HFM) consistently hold the highest importance across weather types, while temperature (TEMP), relative humidity (RHUM) and air pressure (PRES) exhibit relatively higher meteorological influence despite limited overall impact. The optimal Cauchy-PatchTST model, with a look-back window of <span><math><mrow><mi>L</mi><mo>=</mo><mn>36</mn></mrow></math></span> and a forecast length of <span><math><mrow><mi>T</mi><mo>=</mo><mn>1</mn></mrow></math></span>, outperforms the traditional PatchTST model with MSE loss, three Transformer-based models and other optimized machine learning algorithms, achieving a 15.49% to 94.10% reduction in MAE on the test set. This study provides critical indicator analysis for airports across various severe weather conditions and offers reliable resilience data to support future operations management.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"129 ","pages":"Article 102862"},"PeriodicalIF":3.6,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739346","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}

引用次数: 0

Profit Efficiency: Insight into airline business models and strategic choices 利润效率：洞察航空公司的商业模式和战略选择

IF 3.9 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-07-22 DOI: 10.1016/j.jairtraman.2025.102863

Fecri Karanki , Roger Schaufele

Following a challenging start to the 21st century, airlines rebounded to achieve record profits in the aftermath of the Great Recession (2007–2009). While profitability refers to the absolute financial gains of a firm, profit efficiency is a measure of how effectively a firm converts its resources into maximum potential profit, given its operating environment and input prices. These distinct economic concepts raise key questions about airline strategies: Can airlines maximize their profits? Which business models achieve higher profit efficiency? What factors influence their profit efficiency? This study addresses these questions using a stochastic profit efficiency model based on data from U.S. airlines spanning from 2009 to 2019. Our findings reveal that the U.S. airline industry exhibits an average profit efficiency of 93.2 %. Low-Cost Carriers (LCCs) have a higher mean efficiency score of 98.7 % while Full-Service Airlines (FSAs) follow them with 95.3 %. Ultra-Low-Cost Carriers (ULCCs) have the lowest profit efficiency at 86.1 %. Finally, LCCs have demonstrated more stable profit efficiency over the years. In addition, ancillary revenues positively impact the profit efficiency, indicating higher markup resulting from add-on pricing. The strategies implemented after the Great Recession—such as capacity discipline and mergers—have significantly increased profit efficiency while the airport network expansion result in lower profit inefficiency. Overall, this study highlights the extent of profit efficiency for the U.S. airline industry and identifies the key factors influencing it.

在经历了21世纪充满挑战的开端之后，航空公司在经济大衰退（2007-2009）之后反弹，实现了创纪录的利润。盈利能力是指企业的绝对财务收益，而利润效率是衡量企业在其经营环境和投入价格的情况下，如何有效地将其资源转化为最大潜在利润。这些截然不同的经济概念提出了有关航空公司战略的关键问题：航空公司能否实现利润最大化？哪种商业模式的利润效率更高？影响他们利润效率的因素是什么？本研究使用基于2009年至2019年美国航空公司数据的随机利润效率模型来解决这些问题。我们的研究结果显示，美国航空业的平均利润效率为93.2%。低成本航空公司（lcc）的平均效率得分更高，为98.7%，而全服务航空公司（FSAs）的平均效率得分为95.3%。超低成本航空公司（ulcc）的利润效率最低，为86.1%。最后，低成本航空公司多年来表现出更稳定的利润效率。此外，辅助收入正向影响利润效率，表明附加定价导致更高的加价。经济大衰退后实施的运力约束和合并等战略显著提高了利润效率，而机场网络扩张则降低了利润效率。总体而言，本研究突出了美国航空业的利润效率程度，并确定了影响它的关键因素。

{"title":"Profit Efficiency: Insight into airline business models and strategic choices","authors":"Fecri Karanki , Roger Schaufele","doi":"10.1016/j.jairtraman.2025.102863","DOIUrl":"10.1016/j.jairtraman.2025.102863","url":null,"abstract":"<div><div>Following a challenging start to the 21st century, airlines rebounded to achieve record profits in the aftermath of the Great Recession (2007–2009). While profitability refers to the absolute financial gains of a firm, profit efficiency is a measure of how effectively a firm converts its resources into maximum potential profit, given its operating environment and input prices. These distinct economic concepts raise key questions about airline strategies: Can airlines maximize their profits? Which business models achieve higher profit efficiency? What factors influence their profit efficiency? This study addresses these questions using a stochastic profit efficiency model based on data from U.S. airlines spanning from 2009 to 2019. Our findings reveal that the U.S. airline industry exhibits an average profit efficiency of 93.2 %. Low-Cost Carriers (LCCs) have a higher mean efficiency score of 98.7 % while Full-Service Airlines (FSAs) follow them with 95.3 %. Ultra-Low-Cost Carriers (ULCCs) have the lowest profit efficiency at 86.1 %. Finally, LCCs have demonstrated more stable profit efficiency over the years. In addition, ancillary revenues positively impact the profit efficiency, indicating higher markup resulting from add-on pricing. The strategies implemented after the Great Recession—such as capacity discipline and mergers—have significantly increased profit efficiency while the airport network expansion result in lower profit inefficiency. Overall, this study highlights the extent of profit efficiency for the U.S. airline industry and identifies the key factors influencing it.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"129 ","pages":"Article 102863"},"PeriodicalIF":3.9,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679478","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}

引用次数: 0

Route expansion trends, performances and driving factors of Chinese low-cost carriers 中国低成本航空公司航线扩张趋势、表现及驱动因素

IF 3.9 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-07-21 DOI: 10.1016/j.jairtraman.2025.102861

Chuntao Wu, Xiaohe He, Wenjing Xue

引用次数: 0

Multi-agent task allocation and path planning for autonomous ground support equipment 自主地面保障设备多智能体任务分配与路径规划

IF 3.9 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-07-18 DOI: 10.1016/j.jairtraman.2025.102855

Manouk van der Zwan , Gülçin Ermiş, Alexei Sharpanskykh

We aim to contribute to the automation of ground handling tasks using autonomous ground support equipment (GSE) at airports. Automation of airside operations has recently become critical for the airports to achieve higher levels of safety and efficiency under growing traffic demand and requires solving a complex scheduling and path planning problem. To address this problem, we present a multi-agent task allocation and path planning model for handling airside operations on the apron. In the problem, the ground handling tasks are to be allocated to the equipment, the trips of vehicles should be scheduled within specific time windows considering the flight schedules, and the collisions of vehicles on the apron and service roads should be avoided. We present a centralized multi-agent task allocation and routing model which aims to optimize the allocation and routing of various types of ground handling tasks over a heterogeneous set of GSE vehicles. We convert the allocation and routing problem into vehicle routing problem with time windows, pick-ups, deliveries and solve the problem using a warm start mixed integer linear programming (MILP) model. We also introduce a nonlinear objective function which converts the MILP model into a mixed integer nonlinear programming (MINLP) model, to minimize the time service locations at the stands are occupied. Then, we solve the corresponding path finding problem to find collision free paths for the GSE, by the multi-agent path finding model. The proposed model outperforms the decentralized approach in previous research regarding the allocation rate of assigning tasks to vehicles and the performance indicators of finding conflict free paths, and in CPU time. The mean deviations from shortest paths were considerably small in path planning which means that the solution quality was high. Furthermore, the CPU time of allocating tasks has been reduced by 48% compared to the CPU time of decentralized allocation.

我们的目标是利用机场的自主地面支持设备（GSE）为地面处理任务的自动化做出贡献。在日益增长的交通需求下，空侧操作自动化对机场实现更高水平的安全和效率至关重要，需要解决复杂的调度和路径规划问题。为了解决这个问题，我们提出了一个多智能体任务分配和路径规划模型来处理停机坪上的空侧操作。在问题中，将地面服务任务分配给设备，考虑航班时刻表，在特定的时间窗口内安排车辆的行程，避免停机坪与服务道路上车辆的碰撞。我们提出了一个集中的多智能体任务分配和路由模型，该模型旨在优化异构GSE车辆上各种类型地勤任务的分配和路由。我们将分配和路径问题转化为有时间窗的车辆路径问题，采用热启动混合整数线性规划（MILP）模型求解。同时引入非线性目标函数，将MILP模型转化为混合整数非线性规划（MINLP）模型，使服务点占用时间最小化。然后，通过多智能体寻路模型，解决相应的寻路问题，为GSE寻找无碰撞路径。在任务分配率、寻找无冲突路径的性能指标和CPU时间方面，该模型优于以往研究中的分散方法。在路径规划中，与最短路径的平均偏差相当小，这意味着解决方案的质量很高。此外，分配任务的CPU时间比分散分配的CPU时间减少了48%。

{"title":"Multi-agent task allocation and path planning for autonomous ground support equipment","authors":"Manouk van der Zwan , Gülçin Ermiş, Alexei Sharpanskykh","doi":"10.1016/j.jairtraman.2025.102855","DOIUrl":"10.1016/j.jairtraman.2025.102855","url":null,"abstract":"<div><div>We aim to contribute to the automation of ground handling tasks using autonomous ground support equipment (GSE) at airports. Automation of airside operations has recently become critical for the airports to achieve higher levels of safety and efficiency under growing traffic demand and requires solving a complex scheduling and path planning problem. To address this problem, we present a multi-agent task allocation and path planning model for handling airside operations on the apron. In the problem, the ground handling tasks are to be allocated to the equipment, the trips of vehicles should be scheduled within specific time windows considering the flight schedules, and the collisions of vehicles on the apron and service roads should be avoided. We present a centralized multi-agent task allocation and routing model which aims to optimize the allocation and routing of various types of ground handling tasks over a heterogeneous set of GSE vehicles. We convert the allocation and routing problem into vehicle routing problem with time windows, pick-ups, deliveries and solve the problem using a warm start mixed integer linear programming (MILP) model. We also introduce a nonlinear objective function which converts the MILP model into a mixed integer nonlinear programming (MINLP) model, to minimize the time service locations at the stands are occupied. Then, we solve the corresponding path finding problem to find collision free paths for the GSE, by the multi-agent path finding model. The proposed model outperforms the decentralized approach in previous research regarding the allocation rate of assigning tasks to vehicles and the performance indicators of finding conflict free paths, and in CPU time. The mean deviations from shortest paths were considerably small in path planning which means that the solution quality was high. Furthermore, the CPU time of allocating tasks has been reduced by 48% compared to the CPU time of decentralized allocation.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"129 ","pages":"Article 102855"},"PeriodicalIF":3.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655329","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}

引用次数: 0

An investigation of the relationship among weather, low-level wind shear and aircraft go-around at Jeju International Airport in Korea 济州岛国际机场天气、低层风切变与飞机复飞关系的研究

IF 3.9 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-07-18 DOI: 10.1016/j.jairtraman.2025.102860

Jinho Cho , Yonghwa Lee , Hojong Baik , Janghoon Park

Wind shear (WS) refers to an abrupt change in wind speed and/or direction, whether in a vertical or horizontal direction. In particular, low-level wind shear (hereafter LLWS) is a type of WS that occurs at or below an altitude of approximately 1600 ft (500 m) and thus affects aircraft operations during landing or take-off phases. Jeju International Airport (CJU) is well-known for experiencing frequent LLWS and consequent occurrence of go-around (GA) operations (also referred to as missed approach). LLWS is known to be elusive and thus difficult to predict. Most previous studies are concerned with elucidating LLWS from a meteorological angle, without considering its potential effects on flight operations. In this study, we investigate the weather conditions that lead to LLWS at CJU airport and then seek the linkage between LLWS and go-around operations. General weather information and flight records containing aircraft speed, altitude, and specific weather observations during GA at CJU airport are collected. We empirically categorize five wind patterns that contribute to severe LLWS and necessitate go-around operations. In this paper, we drive a probability table that summarizes the chances of go-around operations according to the wind direction and speed. We also discuss limitations and areas for future research.

风切变（WS）是指风速和/或风向的突然变化，无论是垂直方向还是水平方向。特别是低空风切变（low-level wind shear，以下简称LLWS）是一种发生在大约1600英尺（500米）或以下的WS，从而影响飞机在着陆或起飞阶段的操作。济州国际机场（CJU）因频繁发生LLWS和复飞（GA）操作（也称为误进）而闻名。众所周知，LLWS是难以捉摸的，因此难以预测。以往的大多数研究都是从气象角度来阐述LLWS，而没有考虑其对飞行操作的潜在影响。在本研究中，我们调查了导致CJU机场LLWS的天气条件，然后寻求LLWS与复飞之间的联系。一般天气资料和飞行记录，包括飞机速度、高度和特别天气观测。根据经验，我们将导致严重LLWS的五种风模式进行了分类，并需要进行复飞操作。在本文中，我们建立了一个概率表，根据风向和风速总结了复飞操作的机会。我们还讨论了局限性和未来研究的领域。

{"title":"An investigation of the relationship among weather, low-level wind shear and aircraft go-around at Jeju International Airport in Korea","authors":"Jinho Cho , Yonghwa Lee , Hojong Baik , Janghoon Park","doi":"10.1016/j.jairtraman.2025.102860","DOIUrl":"10.1016/j.jairtraman.2025.102860","url":null,"abstract":"<div><div>Wind shear (WS) refers to an abrupt change in wind speed and/or direction, whether in a vertical or horizontal direction. In particular, low-level wind shear (hereafter LLWS) is a type of WS that occurs at or below an altitude of approximately 1600 ft (500 m) and thus affects aircraft operations during landing or take-off phases. Jeju International Airport (CJU) is well-known for experiencing frequent LLWS and consequent occurrence of go-around (GA) operations (also referred to as missed approach). LLWS is known to be elusive and thus difficult to predict. Most previous studies are concerned with elucidating LLWS from a meteorological angle, without considering its potential effects on flight operations. In this study, we investigate the weather conditions that lead to LLWS at CJU airport and then seek the linkage between LLWS and go-around operations. General weather information and flight records containing aircraft speed, altitude, and specific weather observations during GA at CJU airport are collected. We empirically categorize five wind patterns that contribute to severe LLWS and necessitate go-around operations. In this paper, we drive a probability table that summarizes the chances of go-around operations according to the wind direction and speed. We also discuss limitations and areas for future research.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"129 ","pages":"Article 102860"},"PeriodicalIF":3.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655328","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}

引用次数: 0

Reconsidering airport economic impact assessments: A bottom-up comparative analysis of Belgian airports 重新考虑机场经济影响评估：比利时机场自下而上的比较分析

IF 3.9 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-07-17 DOI: 10.1016/j.jairtraman.2025.102854

Jolien Pauwels , Sven Buyle , Wouter Dewulf , Bart Jourquin

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.

本研究分析并比较了比利时五个商业机场对其所在地区和国家的经济影响。这些机场代表了不同的类型，包括比利时的主要机场，布鲁塞尔机场和四个区域机场：一个低成本区域机场（布鲁塞尔南沙勒罗瓦机场），一个专业货运区域机场（利瓦里奇机场）和两个小型区域机场（安特卫普机场和奥斯坦德-布鲁日机场）。经济影响是通过投入产出分析来衡量的，投入产出分析在直接、间接和诱导水平上评估增加值和就业。为了提高准确性，我们采用了一种自下而上的方法，通过NACE分类将公司层面的就业和增加值数据与投入产出框架联系起来。此外，引入蒙特卡罗灵敏度分析来加强我们研究结果的稳健性。我们的研究结果表明，根据机场规模和运营重点，各机场的经济贡献存在显著差异，其中利弗里奇机场的货运专业化产生了特别强烈的区域影响。这些发现引发了基于经济影响对机场补贴的更广泛讨论。除了比利时的情况，我们自下而上的方法为更精确的机场影响评估提供了一个可复制的框架。

{"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":"2025-07-17","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}

引用次数: 0

A systematic review of general aviation accident factors, effects and prevention 通用航空事故因素、影响及预防的系统综述

IF 3.9 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-07-12 DOI: 10.1016/j.jairtraman.2025.102859

Emma Sheffield, Seung-Yong Lee, Yahua Zhang

General aviation, which comprises civilian aviation but excludes scheduled airline services and commercial air transport operations, continues to carry a poor safety record with a significantly higher accident rate compared to that of commercial aviation. This systematic literature review examined 46 studies to identify the most prominent causal and contributing factors to fixed-wing general aviation accidents, as well as effective measures to improve safety. Thematic analysis revealed five key themes: Human Factors (26), Training Deficiencies (21), Aircraft Characteristics (13), Pilot Characteristics (11), and Phase of Flight (9). Flight in low visibility conditions, such as instrument meteorological conditions (IMC) or night flight were consistently associated with increased fatality risk. Pilots operating under visual flight rules (VFR) without an instrument rating were especially vulnerable during inadvertent IMC encounters. Training deficiencies were another key factor, especially the lack of recurrent training in emergency procedures. Accident risk was also higher during specific flight phases: take-off, landing, and low-altitude manoeuvring were frequently associated with fatal stall/spin events due to minimal recovery margins. To mitigate these risks, targeted recurrent training is essential. Routine practice with certified flight instructors, alongside the use of approved flight simulators and distance learning, can improve both technical and non-technical pilot proficiency. Future research should investigate improved training methods, including the use of emerging technologies such as virtual and augmented reality, to enhance skill retention and reduce accident rates in general aviation.

通用航空包括民用航空，但不包括定期航空服务和商业航空运输业务，其安全记录仍然很差，事故率明显高于商业航空。本文对46项研究进行了系统的文献回顾，以确定固定翼通用航空事故最突出的因果和促成因素，以及提高安全的有效措施。专题分析揭示了五个关键主题：人为因素（26）、训练不足（21）、飞机特性（13）、飞行员特性（11）和飞行阶段(9)。在低能见度条件下飞行，如仪表气象条件（IMC）或夜间飞行，始终与死亡风险增加有关。飞行员在目视飞行规则（VFR）下操作，没有仪表评级，在无意的IMC遭遇中特别脆弱。缺乏培训是另一个关键因素，特别是缺乏应急程序方面的经常性培训。在特定的飞行阶段，事故风险也更高：起飞、着陆和低空操纵经常与致命的失速/旋转事件相关，因为恢复余地很小。为了减轻这些风险，有针对性的经常性培训是必不可少的。与持证飞行教官一起进行常规练习，同时使用经批准的飞行模拟器和远程学习，可以提高飞行员的技术和非技术熟练程度。未来的研究应调查改进的培训方法，包括使用虚拟和增强现实等新兴技术，以提高通用航空的技能保留和降低事故率。

{"title":"A systematic review of general aviation accident factors, effects and prevention","authors":"Emma Sheffield, Seung-Yong Lee, Yahua Zhang","doi":"10.1016/j.jairtraman.2025.102859","DOIUrl":"10.1016/j.jairtraman.2025.102859","url":null,"abstract":"<div><div>General aviation, which comprises civilian aviation but excludes scheduled airline services and commercial air transport operations, continues to carry a poor safety record with a significantly higher accident rate compared to that of commercial aviation. This systematic literature review examined 46 studies to identify the most prominent causal and contributing factors to fixed-wing general aviation accidents, as well as effective measures to improve safety. Thematic analysis revealed five key themes: Human Factors (26), Training Deficiencies (21), Aircraft Characteristics (13), Pilot Characteristics (11), and Phase of Flight (9). Flight in low visibility conditions, such as instrument meteorological conditions (IMC) or night flight were consistently associated with increased fatality risk. Pilots operating under visual flight rules (VFR) without an instrument rating were especially vulnerable during inadvertent IMC encounters. Training deficiencies were another key factor, especially the lack of recurrent training in emergency procedures. Accident risk was also higher during specific flight phases: take-off, landing, and low-altitude manoeuvring were frequently associated with fatal stall/spin events due to minimal recovery margins. To mitigate these risks, targeted recurrent training is essential. Routine practice with certified flight instructors, alongside the use of approved flight simulators and distance learning, can improve both technical and non-technical pilot proficiency. Future research should investigate improved training methods, including the use of emerging technologies such as virtual and augmented reality, to enhance skill retention and reduce accident rates in general aviation.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"128 ","pages":"Article 102859"},"PeriodicalIF":3.9,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606098","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}

引用次数: 0

Dare to Fly? Analyzing psychological reactions and travel attitudes of Chinese social media users post-aviation accidents through text mining 敢飞吗？通过文本挖掘分析中国社交媒体用户在航空事故后的心理反应和旅行态度

IF 3.9 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-07-11 DOI: 10.1016/j.jairtraman.2025.102858

Yunxiao Guo , Jingqiang Tong , Junrong Zhao , Wei Pan , Xiayu Du , Jinghan Hu , Qingfeng Yang , Fangfang Wen , Zhihong Ren

Background

Commercial air travel is essential to modern society, but aviation accidents can have profound psychological impacts. The public's emotional responses and attitudes toward air travel are shaped by factors such as the severity of the incident and its social repercussions.

Objectives

The study analyzes how the Chinese public's concerns, emotional responses, attributions, and attitudes toward air travel change following aviation accidents with varying severity and social impact. Additionally, it extends the application of the emotion-social information model in disaster scenarios and online behavior, providing valuable insights for crisis management.

Method

The study employs the LDA topic model to identify public concerns, sentiment analysis with an affective dictionary, and Naive Bayes for emotional attribution. Comments before and after the incidents were analyzed using Wilcoxon rank tests to assess changes in attitudes toward air travel.

Results

Public attention focused on victims, causes, and crew responses. Severe accidents elicited sadness, while less severe incidents prompted disgust and fear. Positive societal impacts from accidents improved emotions but did not significantly affect travel attitudes. Negative emotions led to a notable shift in attitudes toward air travel, while positive emotions had a limited effect.

Conclusions

The study provides valuable insights into the emotional dynamics following aviation accidents, enhancing the understanding of the emotion-social information model. It highlights the lasting impact of negative emotions on travel attitudes and the limited influence of positive emotions, with implications for psychological interventions and crisis management.

商业航空旅行对现代社会至关重要，但航空事故会产生深远的心理影响。公众对航空旅行的情绪反应和态度受到事件严重性及其社会影响等因素的影响。目的：研究分析不同严重程度和社会影响的航空事故后，中国公众对航空旅行的关注、情绪反应、归因和态度是如何变化的。此外，它扩展了情绪-社会信息模型在灾难场景和在线行为中的应用，为危机管理提供了有价值的见解。方法采用LDA主题模型识别公众关注，情感词典进行情感分析，朴素贝叶斯进行情感归因。使用Wilcoxon等级测试对事件前后的评论进行分析，以评估对航空旅行态度的变化。结果公众的注意力集中在受害者、原因和船员的反应上。严重的事故会引起悲伤，而不那么严重的事故则会引起厌恶和恐惧。事故带来的积极社会影响改善了情绪，但对旅行态度没有显著影响。负面情绪导致人们对航空旅行的态度发生了显著变化，而积极情绪的影响有限。结论本研究为航空事故后情绪动态提供了有价值的见解，增强了对情绪-社会信息模型的理解。它强调了负面情绪对旅行态度的持久影响和积极情绪的有限影响，对心理干预和危机管理具有启示意义。

{"title":"Dare to Fly? Analyzing psychological reactions and travel attitudes of Chinese social media users post-aviation accidents through text mining","authors":"Yunxiao Guo , Jingqiang Tong , Junrong Zhao , Wei Pan , Xiayu Du , Jinghan Hu , Qingfeng Yang , Fangfang Wen , Zhihong Ren","doi":"10.1016/j.jairtraman.2025.102858","DOIUrl":"10.1016/j.jairtraman.2025.102858","url":null,"abstract":"<div><h3>Background</h3><div>Commercial air travel is essential to modern society, but aviation accidents can have profound psychological impacts. The public's emotional responses and attitudes toward air travel are shaped by factors such as the severity of the incident and its social repercussions.</div></div><div><h3>Objectives</h3><div>The study analyzes how the Chinese public's concerns, emotional responses, attributions, and attitudes toward air travel change following aviation accidents with varying severity and social impact. Additionally, it extends the application of the emotion-social information model in disaster scenarios and online behavior, providing valuable insights for crisis management.</div></div><div><h3>Method</h3><div>The study employs the LDA topic model to identify public concerns, sentiment analysis with an affective dictionary, and Naive Bayes for emotional attribution. Comments before and after the incidents were analyzed using Wilcoxon rank tests to assess changes in attitudes toward air travel.</div></div><div><h3>Results</h3><div>Public attention focused on victims, causes, and crew responses. Severe accidents elicited sadness, while less severe incidents prompted disgust and fear. Positive societal impacts from accidents improved emotions but did not significantly affect travel attitudes. Negative emotions led to a notable shift in attitudes toward air travel, while positive emotions had a limited effect.</div></div><div><h3>Conclusions</h3><div>The study provides valuable insights into the emotional dynamics following aviation accidents, enhancing the understanding of the emotion-social information model. It highlights the lasting impact of negative emotions on travel attitudes and the limited influence of positive emotions, with implications for psychological interventions and crisis management.</div></div>","PeriodicalId":14925,"journal":{"name":"Journal of Air Transport Management","volume":"128 ","pages":"Article 102858"},"PeriodicalIF":3.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596434","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}

引用次数: 0

Spatial–temporal dynamics of airside congestion: A Macroscopic Fundamental Diagram perspective 空侧拥堵的时空动态：宏观基本图视角

IF 3.9 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-07-10 DOI: 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.

由于空中交通需求和有限容量之间日益不平衡，机场空侧拥堵带来了重大的运营挑战，影响了效率、安全和环境影响。有效地解决这一问题需要模型来捕捉空侧网络（滑行道、跑道、登机口）内复杂的相互作用，以提供对交通流动力学和拥堵形成、传播和消散机制的见解。传统的方法——如微模拟方法和排队模型——通常要么需要计算，要么关注特定的组件（如跑道），限制了它们捕捉更广泛的网络交互的能力，降低了它们的操作可行性。本研究提出了一种替代方法，采用宏观基本图（MFD）利用三维飞机轨迹数据来模拟空侧交通。通过分析综合交通变量——流量、密度和速度——MFD提供了一种计算效率高的方法来理解空侧拥堵模式，并支持明智的决策。本文提出了一种利用新加坡樟宜机场的a - smgcs数据构建空侧mfd的新方法。该研究还调查了导致拥堵的空间和时间因素，为拥堵模式在不同运营条件下的发展和演变提供了见解。在时间域，即使在低需求时期，出发银行和到达银行也会造成拥堵。此外，本研究还分析了天气条件对空侧网络流量的影响，强调了可变风和恶劣天气（如降雨和雷暴）对空侧拥堵的影响。在空间领域，交通的不均匀性——空侧网络上交通的不均匀分布——减少了总体流量，特别是在拥堵期间。这些发现强调了通过在时间和空间领域更均匀地分配交通来提高空侧容量利用率和缓解拥堵的潜力，即最小化时间表银行和确保出租车路线的平衡分配。

{"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":"2025-07-10","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}

引用次数: 0