Journal of Air Transport Management最新文献

Resilience in the skies: Modeling domestic and international air passenger demand in multiple Indian airports 弹性在天空：模拟国内和国际航空客运需求在多个印度机场

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2026-01-15 DOI: 10.1016/j.jairtraman.2026.102971

Punyabeet Sarangi, K.C. Anuj

This study presents a comparative analysis of univariate time-series (ARIMA, SARIMA, ETS) and deep learning models (RNN and LSTM) for forecasting post-COVID domestic and international air passenger demand at eight major Indian airports: Ahmedabad, Bengaluru, Mumbai, Kolkata, Delhi, Hyderabad, Chennai, and Pune. Utilizing quarterly data from 2016 to 2023, performance of time-series and deep learning models is evaluated against actual 2024 air traffic data using MAPE, MAE, and RMSE indices. Results demonstrate that model efficacy is highly context-specific. SARIMA consistently outperforms ARIMA in capturing seasonality, while LSTM excels at modeling non-linear complexities, and ETS proves robust for airports with clear trends. Crucially, a SARIMAX model integrating exogenous drivers, including net domestic product, network connectivity, and operational metrics, significantly enhanced forecasting accuracy, particularly for international travel, underscoring the importance of these drivers. The coefficients reveal several interesting policy scenarios, such as enhancing domestic and international connectivity, particularly at emerging hubs, stimulates passenger growth, while densely populated catchments require investments in multimodal integration to counter negative demand. The findings challenge the presumption of a universal forecasting framework and underscore the inefficiency of relying solely on univariate models, advocating for a tailored approach that incorporates key exogenous variables for resilient air traffic management.

本研究对单变量时间序列（ARIMA、SARIMA、ETS）和深度学习模型（RNN和LSTM）进行了比较分析，用于预测印度8个主要机场（艾哈迈达巴德、班加罗尔、孟买、加尔各答、德里、海德拉巴、金奈和浦那）疫情后的国内和国际航空旅客需求。利用2016年至2023年的季度数据，使用MAPE、MAE和RMSE指数对时间序列和深度学习模型的性能进行了评估。结果表明，模型效能具有高度的情境特异性。SARIMA在捕捉季节性方面一直优于ARIMA，而LSTM在建模非线性复杂性方面表现出色，而ETS在具有明确趋势的机场方面表现出色。至关重要的是，SARIMAX模型整合了外生驱动因素，包括国内净产值、网络连通性和运营指标，显著提高了预测的准确性，特别是对于国际旅行，强调了这些驱动因素的重要性。这些系数揭示了几个有趣的政策情景，例如加强国内和国际连通性，特别是在新兴枢纽，刺激客运量增长，而人口密集的集水区需要投资于多式联运一体化，以抵消负面需求。研究结果挑战了普遍预测框架的假设，并强调了仅仅依赖单变量模型的效率低下，主张采用一种量身定制的方法，将关键的外生变量纳入弹性空中交通管理。

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引用次数: 0

A two-stage stochastic optimization approach for mega-airport departure metering under data-driven taxi-time uncertainty predictions 基于数据驱动的出租车时间不确定性预测的大型机场离场计量两阶段随机优化方法

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2026-01-14 DOI: 10.1016/j.jairtraman.2026.102973

Jiawei Kang, Jie Bao, Junfeng Zhang, Xiaowei Tang, Jiaqi Han, Jiaman He

Over the past decade, mega-airports have experienced a surge in air traffic demand, physical expansion, and increased complexity in apron layouts, leading to a high level of aircraft taxi-time uncertainty and shifting the airport surface management from integrated tower control to dedicated apron control. In this study, a two-stage stochastic optimization framework is developed for mega-airport departure metering (DM), which specializes apron-centric and tower-centric optimization in different stages. Moreover, a data-driven Mixture Density Network (MDN) is built to predict the aircraft taxi-time distribution and characterize the uncertainty levels. A large-scale trajectory dataset is collected from a representative mega-airport in China to illustrate the procedure. The results indicate that the developed two-stage stochastic optimization framework distinguishes tower control and apron control in the DM process, improving the overall flexibility of airport airside operations. The data-driven neural network could better predict the taxi-time uncertainty levels through multimodal probability distributions especially at mega-airport with volatile traffic situations. Furthermore, compared with state-of-the-art DM methods, the two-stage stochastic optimization framework could achieve more robust performance of airport departure management and better trade-off between gate-holding and runway throughput.

在过去的十年中，大型机场经历了空中交通需求的激增、物理扩张和停机坪布局的复杂性增加，导致飞机滑行时间的高度不确定性，并将机场地面管理从综合塔台控制转变为专用停机坪控制。本文建立了大型机场离场计量的两阶段随机优化框架，在不同阶段分别进行以停机坪为中心和以塔台为中心的优化。此外，建立了数据驱动的混合密度网络（MDN）来预测飞机滑行时间分布并表征不确定性水平。为了说明这一过程，我们从中国一个具有代表性的大型机场收集了大规模的轨迹数据集。结果表明，所建立的两阶段随机优化框架区分了DM过程中的塔台控制和停机坪控制，提高了机场空侧运行的整体灵活性。数据驱动的神经网络可以通过多模态概率分布更好地预测滑行时间的不确定性水平，特别是在交通状况不稳定的大型机场。此外，与最先进的决策优化方法相比，两阶段随机优化框架可以实现更稳健的机场离港管理性能，更好地平衡登机口等待和跑道吞吐量。

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引用次数: 0

Evolution of China’s intercontinental air network affected by the COVID-19 pandemic and geopolitical disruptions 受新冠肺炎疫情和地缘政治干扰影响的中国洲际航空网络演变

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2026-01-09 DOI: 10.1016/j.jairtraman.2025.102963

Weichen Peng , Weicheng Wang , Xueyan Bai , Xiangru Wu

This study investigates the heterogeneous responses of Chinese and foreign airlines to the compound disruptions from the COVID-19 pandemic and geopolitical tensions, focusing on the restructuring of China’s international direct air transport network. Utilizing airline-route-level data from 2019 to 2024, the research employs a probit model to analyse route service decisions in the China–North America and China–Europe markets. The empirical findings reveal divergent recovery patterns shaped by distinct underlying drivers. In the China–Europe market, Chinese airlines, benefiting from sustained access to Russian airspace, assumed a dominant role, whereas foreign airlines faced a slower recovery constrained by costly operational detours. In contrast, the recovery in the China–North America market remained suppressed for all carriers due to persistent geopolitical tensions. Strategically, foreign airlines became more likely to serve more competitive routes and to avoid routes already operated by alliance partners, compared to the pre-disruption period. Conversely, Chinese airlines exhibited a greater likelihood of serving less competitive routes and routes already operated by other alliance members. These findings underscore how asymmetric operational constraints and geopolitical factors reshape aviation networks through carrier-specific strategies, offering critical insights for policymakers and managers.

本研究调查了中外航空公司对2019冠状病毒病大流行和地缘政治紧张局势复合中断的异质反应，重点研究了中国国际直航运输网络的重组。该研究利用2019年至2024年航空公司航线级数据，采用probit模型分析了中国-北美和中国-欧洲市场的航线服务决策。实证结果显示，不同的潜在驱动因素形成了不同的复苏模式。在中欧市场，受益于持续进入俄罗斯领空的中国航空公司占据了主导地位，而外国航空公司则因成本高昂的运营弯路而面临较慢的复苏。相比之下，由于持续的地缘政治紧张局势，中国-北美市场的复苏对所有航空公司来说都受到抑制。从战略上讲，与混乱前的时期相比，外国航空公司更有可能服务更具竞争力的航线，并避开已经由联盟伙伴运营的航线。相反，中国航空公司更有可能服务竞争不那么激烈的航线和其他联盟成员已经运营的航线。这些发现强调了不对称操作约束和地缘政治因素如何通过航母特定战略重塑航空网络，为政策制定者和管理者提供了重要见解。

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引用次数: 0

Promoting transition towards sustainable air transport systems: A hybrid decision support system for effective national-level performance evaluation 促进向可持续航空运输系统过渡：用于有效国家级绩效评估的混合决策支持系统

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2026-01-07 DOI: 10.1016/j.jairtraman.2025.102964

Karahan Kara , Galip Cihan Yalçın , Emre Kadir Özekenci , Günal Bilek , Vladimir Simic , Çağatay Tunçsiper , Dragan Pamucar

Air transport plays a pivotal role in enhancing economic development by supporting trade, tourism, and regional competitiveness. The growing environmental concerns and social expectations have necessitated the transition towards sustainable air transport systems. Sustainable air transport refers to aviation activities that balance environmental, economic, and social objectives, aiming to minimize carbon emissions, promote renewable energy usage, and enhance socio-economic welfare. In this study, a novel multi-criteria decision-making (MCDM)-based decision support system (DSS) is proposed to evaluate the sustainable air transport performance of the European countries. The main objective of this research is to develop a comprehensive and integrative framework for measuring and ranking the sustainable air transport performance of nations. A hybrid method, termed fractional fuzzy–ranking comparison-response to criteria weighting (RANCOM)–response to criteria weighting (RECA)–ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS), is introduced. DSS consists of five main stages: expert-based subjective weighting using fractional fuzzy RANCOM, objective weighting via RECA, aggregation of weights, and final performance ranking through the RATGOS method. The results indicate that Germany ranks highest, while Cyprus has the lowest sustainable air transport performance among the evaluated countries. The criterion "commercial aircraft fleet by age of aircraft" is determined to have the highest importance among the sustainable air transport performance indicators. The study provides a comprehensive, replicable framework for policymakers and stakeholders aiming to monitor and improve sustainable aviation systems. It contributes to the literature by addressing the gap in national-level sustainable air transport performance evaluation.

航空运输通过支持贸易、旅游和区域竞争力，在促进经济发展方面发挥着关键作用。日益增长的环境问题和社会期望使向可持续航空运输系统过渡成为必要。可持续航空运输是指平衡环境、经济和社会目标的航空活动，旨在减少碳排放，促进可再生能源的使用，并提高社会经济福利。本研究提出了一种基于多准则决策（MCDM）的决策支持系统（DSS）来评估欧洲国家的可持续航空运输绩效。本研究的主要目标是制定一个全面和综合的框架，用于衡量和排名各国的可持续航空运输绩效。介绍了分数阶模糊排序比较-标准加权响应(RANCOM) -标准加权响应(RECA) -最优解相似比几何均值排序技术。决策支持系统包括五个主要阶段：基于专家的主观加权，采用分数模糊随机抽样，通过RECA进行客观加权，通过RATGOS方法进行权重聚合，最后通过RATGOS方法进行最终性能排名。结果表明，德国排名最高，而塞浦路斯的可持续航空运输绩效在评估国家中最低。在可持续航空运输绩效指标中，“按飞机机龄划分的商用飞机机队”被确定为最重要的标准。该研究为旨在监测和改进可持续航空系统的决策者和利益相关者提供了一个全面的、可复制的框架。通过解决国家层面可持续航空运输绩效评估的差距，为文献做出了贡献。

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引用次数: 0

Transit oriented development under the influence of urban air mobility: A public transit-based vertiport siting method 城市空中交通影响下的交通导向发展：基于公共交通的垂直选址方法

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-12-29 DOI: 10.1016/j.jairtraman.2025.102962

Renwei Liu , Haishan Xia , Lu Li , Qi Li , Sitong Liu

The emergence of electric vertical takeoff and landing (eVTOL) technology has enabled large-scale implementation of urban air mobility (UAM) within cities. This study proposes a vertiport-siting optimization method based on public transit stations to better utilize existing urban resources for UAM development. The study also maximizes the combined strengths of UAM's flexibility and efficiency with the high-capacity characteristics of traditional transit systems, addressing future mobility demands that feature both decentralization and urban agglomeration. The proposed method minimizes travel costs between public transit stations and vertiport locations while incorporating population density as a weighting factor to enhance the objective function. Two siting strategies were compared regarding travel costs and spatial service coverage. Using Shenzhen's Bao'an District as a case study, results indicate that the population-weighted optimization approach reduces distance-based travel costs by 12.6 % and improves coverage in core functional zones by approximately 20 %, significantly enhancing spatial service efficiency. This study diverges from traditional infrastructure-led siting approaches by emphasizing the foundational role of public transit systems in shaping UAM networks. It introduces a three-dimensional urban planning concept that integrates transit-oriented development (TOD) with UAM, offering a new pathway for reshaping urban spatial structures and improving spatial efficiency in future cities.

电动垂直起降（eVTOL）技术的出现使城市空中交通（UAM）在城市内的大规模实施成为可能。本研究提出了一种基于公共交通站点的垂直选址优化方法，以更好地利用现有城市资源进行UAM发展。该研究还最大限度地发挥了UAM的灵活性和效率与传统交通系统的高容量特点的综合优势，解决了以分散化和城市群为特征的未来交通需求。该方法最大限度地减少了公共交通车站和垂直机场之间的交通成本，同时将人口密度作为加权因素来增强目标函数。在旅行成本和空间服务覆盖率方面比较了两种选址策略。以深圳市宝安区为例，研究结果表明，人口加权优化方法使基于距离的出行成本降低12.6%，使核心功能区的覆盖范围提高约20%，显著提高了空间服务效率。这项研究与传统的以基础设施为主导的选址方法不同，强调公共交通系统在塑造UAM网络中的基础作用。它引入了一种立体的城市规划理念，将交通导向发展（TOD）与UAM相结合，为重塑城市空间结构和提高未来城市的空间效率提供了新的途径。

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引用次数: 0

An alternative approach to understanding airline customers’ attitudes and behaviors 了解航空公司客户态度和行为的另一种方法

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-12-29 DOI: 10.1016/j.jairtraman.2025.102960

Somaye Mansouri , Saeed Moradpour , Catherine Prentice

The aviation industry is facing challenges in meeting the evolved expectations of travelers in the post-COVID era in which their expectations are expressed in their online reviews. Unprecedented proliferation of passenger reviews on online platforms has resulted in a deluge of unstructured data. Hence, in order to assess customers’ attitudes and behaviors, this study utilized machine learning technique including topic modelling and sentiment analysis through analyzing passenger review data extracted from Skytrax for the top 100 low-cost and flagship airlines in 2024. Two most popular topic modelling methods, Latent Dirichlet Allocation and BERTopic were applied to identify recurrent themes. The results show that flight delays, schedule-related issues, communication gaps, and lack of pricing transparency were underscored by passengers as major shortcomings. Low-cost carrier passengers were more focused on operational reliability; whereas, flagship airline passengers were mainly concerned about extra charges and quality of services. The sentiment analysis reveals that cultural and regional differences have influenced customer satisfaction regarding the airline types, continents, cabin comfort, handling of baggage, and in-flight service provision. This research contributes to existing service quality research by extending the traditional methods of assessing customer attitudes and behaviors. The findings provide insightful guidance for airlines to improve service offerings and attract customer patronage.

在后新冠肺炎时代，旅客的期望通过在线评论来表达，航空业在满足旅客的期望方面面临着挑战。在线平台上前所未有的乘客评论激增，导致非结构化数据泛滥。因此，为了评估客户的态度和行为，本研究利用机器学习技术，包括主题建模和情感分析，通过分析从Skytrax中提取的2024年前100名低成本和旗舰航空公司的乘客评论数据。两种最流行的主题建模方法，潜狄利克雷分配和BERTopic被用于识别重复主题。结果显示，航班延误、与时间表相关的问题、沟通差距和缺乏定价透明度是乘客强调的主要缺点。低成本航空公司的乘客更关注运营可靠性；然而，旗舰航空公司的乘客主要关注的是额外收费和服务质量。情感分析显示，文化和地区差异影响了航空公司类型、大洲、客舱舒适度、行李处理和机上服务提供方面的客户满意度。本研究扩展了传统的顾客态度和行为评估方法，为现有的服务质量研究做出了贡献。研究结果为航空公司改善服务和吸引客户惠顾提供了有见地的指导。

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引用次数: 0

An improved optimization algorithm for solving arrival aircraft scheduling problem in the Terminal Maneuvering Area 一种求解终端机动区到港飞机调度问题的改进优化算法

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-12-24 DOI: 10.1016/j.jairtraman.2025.102961

Ying Huo , Daniel Delahaye , Huijuan Yang , Maolin Wang

The Terminal Maneuvering Area (TMA) is one of the most complex and congested airspace segments, where tools like the Arrival Manager (AMAN) are used to manage inbound traffic and provide accurate, efficient scheduling for each flight. The associated optimization problem is NP-hard, requiring advanced algorithms to meet performance demands in both computational time and solution quality. Heuristic algorithms, such as Simulated Annealing (SA), are known for their ability to provide fast, near-optimal solutions in large and complex state spaces. In our previous work, simulation-based optimization using SA was employed, where information of all flights was integrated into each simulation, resulting in a computationally intensive evaluation process. In this study, we propose a more efficient method by leveraging the inherent safety dependencies between neighboring flights in the operation.By focusing on the performance of individual flights and their immediate impact on adjacent flights, the optimization process becomes more targeted, eliminating the need to integrate all flight data at once. This improves both efficiency and flexibility. To demonstrate the advantages of a selective structure in Simulated Annealing, we introduce Selective Simulated Annealing (SSA) and compare it to the Standard Simulated Annealing algorithm (OSA), highlighting their distinct features. A case study at Paris-Charles de Gaulle (CDG) Airport is used to analyze the performance of both algorithms. Key parameter adjustments are examined to gain insights into their optimization behaviors. The comparison reveals that SSA significantly outperforms OSA, delivering faster computation and reducing delays by 50%.

终端机动区（TMA）是最复杂、最拥挤的空域之一，在这里，像到达管理器（AMAN）这样的工具被用来管理入境交通，并为每个航班提供准确、高效的调度。相关的优化问题是np困难的，需要先进的算法来满足计算时间和解决方案质量的性能要求。启发式算法，如模拟退火（SA），以其在大型复杂状态空间中提供快速、接近最优解决方案的能力而闻名。在我们之前的工作中，采用了基于SA的模拟优化，将所有航班的信息集成到每个模拟中，从而导致计算密集型的评估过程。在本研究中，我们提出了一种更有效的方法，利用相邻航班之间的内在安全依赖关系。通过关注单个航班的性能及其对相邻航班的直接影响，优化过程变得更有针对性，消除了一次整合所有航班数据的需要。这提高了效率和灵活性。为了证明选择性结构在模拟退火中的优势，我们引入了选择性模拟退火算法（SSA），并将其与标准模拟退火算法（OSA）进行了比较，突出了它们的独特之处。以巴黎戴高乐机场为例，分析了两种算法的性能。关键参数调整检查，以获得洞察他们的优化行为。比较表明，SSA明显优于OSA，提供更快的计算速度并减少50%的延迟。

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引用次数: 0

Research on the classification and optimization strategies of civil aviation customer service based on BERTopic and the Kano model 基于BERTopic和Kano模型的民航客户服务分类与优化策略研究

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-12-23 DOI: 10.1016/j.jairtraman.2025.102959

Heyong Wang, Yuanhao Chen

This study presents an innovative data-driven framework for optimizing airline customer service by integrating BERTopic topic modeling, SnowNLP sentiment analysis, and the Kano model. Unlike traditional approaches relying on surveys or subjective judgment, this method analyzes real customer service dialogues to extract 15 key service topics, assess satisfaction and attention scores, and categorize them into basic, expected, attractive, and indifferent needs. Findings show that Children's Ticket Inquiries, Pet Transportation, Baggage Regulations, and Seat Selection fall under basic needs, requiring prioritized investment to prevent dissatisfaction. Flight Rescheduling, Membership Verification, and Medical Refunds are expected needs that demand targeted improvements to enhance satisfaction. Promotional Inquiries and Expedited Services are attractive needs where innovative enhancements can create surprise and delight. Indifferent needs such as Standard Refunds, Meal Services, and Lost Item Handling require only baseline quality maintenance. Guided by the principle of demand-oriented resource allocation, the study proposes tailored optimization strategies for each category. This framework reveals latent customer priorities and transforms unstructured dialogue data into actionable insights, offering both theoretical contributions and practical implications for improving service quality and competitive positioning in the civil aviation sector.

本研究提出了一个创新的数据驱动框架，通过集成BERTopic主题建模、SnowNLP情感分析和Kano模型来优化航空公司的客户服务。与依赖调查或主观判断的传统方法不同，该方法分析真实的客户服务对话，提取15个关键服务主题，评估满意度和注意力得分，并将其分为基本需求、预期需求、吸引需求和无关需求。调查结果显示，儿童机票查询、宠物运输、行李规定和座位选择属于基本需求，需要优先投资以防止不满。航班改签、会员验证和医疗退款是预期的需求，需要有针对性的改进以提高满意度。促销咨询和加急服务是有吸引力的需求，创新的增强功能可以创造惊喜和快乐。诸如标准退款、餐饮服务和遗失物品处理等无关紧要的需求只需要基本的质量维护。以需求为导向的资源配置原则为指导，针对每个类别提出了有针对性的优化策略。该框架揭示了潜在的客户优先级，并将非结构化的对话数据转化为可操作的见解，为提高服务质量和在民用航空领域的竞争定位提供了理论贡献和实践意义。

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引用次数: 0

Global airline productivity analysis for the year 2022 2022年全球航空公司生产力分析

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-12-23 DOI: 10.1016/j.jairtraman.2025.102955

Xiaoqian Sun , Changhong Zheng , Xiaowen Fu , Martin Dresner , Anming Zhang , Sebastian Wandelt

Investigating the productivity of airlines in these times is crucial to compare airlines’ performance against peers during a period marked by significant changes in travel demand, operational protocols, and market dynamics. In this study, we present the results of an airline benchmark for the year 2022. Specifically, we collected data for 76 out of 200 largest airlines according to the number of transported passengers in the year 2022. To perform a comprehensive productivity analysis among these airlines, we use eight indicators from the literature, including aggregated ones, such as Total Factor Productivity (TFP) and Residual Total Factor Productivity (RTFP), as well as more specific indicators, such as labor productivity and fuel productivity. As a result of our investigation, we report on the outperforming airlines in two distinct categories: Full-service carriers and low-cost airlines. We believe that this benchmark is a natural complement to existing work on airport benchmarking and will help researchers as well as policy makers to guide airlines towards efficient and sustainable air transportation.

在旅行需求、运营协议和市场动态发生重大变化的时期，调查航空公司的生产力对于将航空公司的表现与同行进行比较至关重要。在本研究中，我们提出了2022年航空公司基准的结果。具体来说，我们根据2022年运送乘客的数量收集了200家最大航空公司中的76家的数据。为了对这些航空公司进行全面的生产率分析，我们使用了文献中的八个指标，包括综合指标，如全要素生产率（TFP）和剩余全要素生产率（RTFP），以及更具体的指标，如劳动生产率和燃料生产率。根据我们的调查，我们将表现优异的航空公司分为两类：全服务航空公司和低成本航空公司。我们相信，这一基准是对现有机场基准工作的自然补充，将有助于研究人员和政策制定者指导航空公司实现高效和可持续的航空运输。

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引用次数: 0

Fairness metric-based RTA adjustment in the presence of wind uncertainty 存在风不确定性时基于公平度量的RTA调整

IF 3.6 2区工程技术 Q2 TRANSPORTATION

Journal of Air Transport Management

Pub Date : 2025-12-19 DOI: 10.1016/j.jairtraman.2025.102958

Hyunho Jung , John-Paul Clarke

Planning flights in windy conditions is a significant challenge in air transportation. Wind data, derived from updated hourly forecasts available prior to departure, serve as the basis for route planning. However, ensuring timely arrival at the destination airport remains difficult due to inaccuracies and uncertainties in these meteorological forecasts. This challenge is compounded in scenarios involving multi-aircraft, as each flight seeks to minimize resource consumption such as flight time and fuel. Therefore, this study has two aims. First, it seeks to optimize airspeeds while accounting for estimated wind uncertainty in order to conserve fuel while adhering to the Required Time of Arrival (RTA) in single-aircraft operations. The first proposed methodology, referred to as the Optimal Airspeed Search Model, integrates principles from Stochastic Programming (SP) and Receding Horizon Control (RHC) frameworks to determine the most suitable airspeed. To achieve this, the flight is segmented, and a backward-propagation strategy is employed to determine airspeeds for all flight segments. The second aim considers scenarios involving multi-aircraft, assuming in-flight negotiation between aircraft and air traffic control centers to achieve fairness among flights. Using the fairness metric-based model, flight RTAs are adjusted to minimize the fairness metric value while maintaining equitable outcomes. The proposed solution optimizes airspeed, reduces fuel consumption, and negotiates fairness among multi-aircraft under RTA constraints using an integer programming approach with a branch-and-bound optimization method.

在有风的条件下规划飞行是航空运输的重大挑战。根据出发前每小时的最新预报得来的风向数据，可作为路线规划的基础。然而，由于这些气象预报的不准确性和不确定性，确保及时到达目的地机场仍然很困难。在涉及多架飞机的情况下，这一挑战变得更加复杂，因为每次飞行都力求最大限度地减少飞行时间和燃料等资源消耗。因此，本研究有两个目的。首先，它寻求在考虑估计风不确定性的情况下优化空速，以便在保持单架飞机所需到达时间（RTA）的同时节省燃料。第一种提出的方法被称为最优空速搜索模型，它结合了随机规划（SP）和后退地平线控制（RHC）框架的原理来确定最合适的空速。为了实现这一点，对飞行进行分段，并采用反向传播策略来确定所有飞行段的空速。第二个目标考虑了涉及多架飞机的场景，假设飞机和空中交通管制中心之间在飞行中进行协商，以实现航班之间的公平。使用基于公平度量的模型，调整航班rta以最小化公平度量值，同时保持公平结果。该方案采用分支定界优化的整数规划方法，在RTA约束下优化空速、降低燃油消耗和多机间的公平性。

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引用次数: 0