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Implementation of Karolinska sleepiness scale to improve pilots awareness and confidence in fatigue reporting 实施卡罗林斯卡嗜睡量表,提高飞行员疲劳报告的意识和信心
IF 3.6 2区 工程技术 Q2 TRANSPORTATION Pub Date : 2026-06-01 Epub Date: 2026-02-06 DOI: 10.1016/j.jairtraman.2026.102982
Patrick Mullan, Viktoriia Ivannikova
Fatigue among pilots in short-haul, multi-sector night freight operations remains a critical challenge to aviation safety. Irregular schedules and prolonged exposure to the Window of Circadian Low (WOCL) cause cognitive impairments, slower reaction times, and reduced situational awareness. Despite regulatory frameworks such as Fatigue Risk Management Systems (FRMS), cultural and organisational barriers, including fear of punitive action and lack of standardised tools, continue to limit effective fatigue reporting. This study investigates the use of the Karolinska Sleepiness Scale (KSS) as a subjective fatigue management tool in short-haul night freight operations. A mixed-methods approach was applied, combining a six-week survey with semi-structured interviews. The study provides the first systematic evaluation of the KSS not only as a fatigue measurement instrument but also as a communication tool that bridges the gap between pilot experience and FRMS reporting practices in short-haul night freight operations. Pilots used the KSS during operational duties to assess fatigue levels and evaluate its influence on awareness and reporting practices. Findings indicate that the KSS improves pilots’ ability to recognise and communicate fatigue, supports proactive workload management, and fosters collaboration within crews. However, organisational gaps remain, including the absence of integration into Standard Operating Procedures (SOPs), lack of structured training, and hesitancy among less experienced pilots without formal company endorsement. The study recommends the formal adoption of the KSS into SOPs, supported by recurrent training and feedback mechanisms, together with integration of objective measures such as biometrics. Results highlight the potential of the KSS as a cornerstone of fatigue risk management and emphasise the importance of supportive safety cultures in prioritising pilot well-being as a key component of operational safety.
在短途、多航段的夜间货运业务中,飞行员的疲劳仍然是航空安全面临的一个重大挑战。不规律的作息时间和长时间暴露于低昼夜节律窗口(WOCL)会导致认知障碍、反应时间变慢和情境感知能力下降。尽管有诸如疲劳风险管理系统(FRMS)之类的监管框架,但文化和组织障碍,包括对惩罚行动的恐惧和缺乏标准化工具,继续限制有效的疲劳报告。本研究探讨了在短途夜间货运作业中使用卡罗林斯卡睡意量表(KSS)作为主观疲劳管理工具。采用混合方法,将为期六周的调查与半结构化访谈相结合。该研究首次对KSS进行了系统评估,不仅将其作为疲劳测量工具,还将其作为沟通工具,在短途夜间货运操作中弥合飞行员经验与FRMS报告实践之间的差距。飞行员在执行任务期间使用KSS来评估疲劳程度,并评估其对意识和报告做法的影响。研究结果表明,KSS提高了飞行员识别和沟通疲劳的能力,支持主动工作量管理,并促进了机组人员之间的合作。然而,组织差距仍然存在,包括缺乏与标准操作程序(sop)的整合,缺乏结构化培训,以及缺乏正式公司认可的经验不足的飞行员的犹豫。该研究建议在定期培训和反馈机制的支持下,将KSS正式纳入标准操作程序,并结合生物识别等客观措施。研究结果强调了KSS作为疲劳风险管理基石的潜力,并强调了支持性安全文化在优先考虑飞行员健康作为操作安全关键组成部分方面的重要性。
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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 Pub Date : 2026-05-01 Epub 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
Efficiency of Peruvian regional airports: Does the PPP framework make a difference? 秘鲁地区机场的效率:PPP框架会产生影响吗?
IF 3.6 2区 工程技术 Q2 TRANSPORTATION Pub Date : 2026-05-01 Epub Date: 2026-01-19 DOI: 10.1016/j.jairtraman.2026.102972
Victor Chang , Beatriz Tovar
This paper examines whether Public-Private Partnership (PPP) frameworks have improved the technical efficiency of Peruvian regional airports. Using panel data from 17 airports between 2004 and 2017, we estimate a Latent Class Stochastic Frontier Model (LCSFM) based on an input-oriented Cobb-Douglas distance function, which is explicitly controlling for unobserved technological heterogeneity. Airports are classified into two technological groups, with concessionaire status (AdP) serving as a separating variable. Results reveal that airports managed under PPP schemes achieve higher efficiency levels and are more likely to operate with superior technology, with both groups exhibiting increasing returns to scale. Evidence of technological progress, biased towards operational expenditures, suggests that efficiency gains have been driven by labour innovations, outsourcing, and digitalization. These findings highlight the positive role of PPPs in fostering operational improvements and underline the risks of ignoring heterogeneity in regulatory benchmarking. We argue that incorporating heterogeneity-adjusted models into regulatory frameworks could strengthen incentive-based policies, guide infrastructure investments more effectively, and support sustainable development of regional air transport networks.
本文考察了公私伙伴关系(PPP)框架是否提高了秘鲁地区机场的技术效率。利用2004年至2017年间来自17个机场的面板数据,我们基于输入导向的柯布-道格拉斯距离函数估计了潜在类随机前沿模型(LCSFM),该模型明确控制了未观察到的技术异质性。机场被分为两个技术组,特许经营地位(AdP)是一个分离变量。结果显示,在PPP模式下管理的机场实现了更高的效率水平,更有可能采用卓越的技术运营,两组机场的规模回报都在增加。技术进步的证据偏向于运营支出,表明效率的提高是由劳动力创新、外包和数字化驱动的。这些发现强调了ppp在促进运营改进方面的积极作用,并强调了忽视监管基准异质性的风险。我们认为,将异质性调整模型纳入监管框架可以加强基于激励的政策,更有效地引导基础设施投资,并支持区域航空运输网络的可持续发展。
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引用次数: 0
Award travel in U.S. domestic markets: An analysis of free ticket redemptions 美国国内市场的奖励旅行:免费机票兑换分析
IF 3.6 2区 工程技术 Q2 TRANSPORTATION Pub Date : 2026-05-01 Epub Date: 2026-01-29 DOI: 10.1016/j.jairtraman.2026.102979
Kurt Fuellhart , Will Livsey , Yi Gao
Frequent flyer programs (FFPs) are central to airline strategy, yet little empirical work examines the routes on which free award tickets are redeemed. Using U.S. Department of Transportation Airline Origin and Destination Survey (DB1B) data accessed through Cirium's Diio Mi, this study analyzes general levels of free award travel in the U.S. domestic market from 2000 to 2025. Next, it provides a detailed cross-sectional assessment of free domestic award travel in 2024. Longitudinal results show redemption levels shifting at key industry moments but diverging sharply by carrier over time. Cross-sectional OLS analysis confirms that carrier identity is the strongest predictor of domestic free ticket redemptions, with Southwest exhibiting the highest levels, United the lowest, and Delta rising steadily. Routes with heavy imbalances in passenger origin have significantly higher award shares than those with more balanced routes. Seasonality, market competition, and carrier yield add nuance to the analysis. Our results show that award travel exhibits clear, measurable patterns within domestic markets, with implications for revenue management, competition, and regulatory oversight.
常旅客计划(ffp)是航空公司战略的核心,但很少有实证研究调查免费奖券在哪些航线上兑换。通过Cirium的Diio Mi获取的美国运输部航空出发地和目的地调查(DB1B)数据,本研究分析了2000年至2025年美国国内市场免费奖励旅行的总体水平。接下来,它提供了2024年免费国内奖励旅行的详细横断面评估。纵向结果显示,赎回水平在关键行业时刻发生变化,但随着时间的推移,不同航空公司的赎回水平差异很大。横断面OLS分析证实,航空公司身份是国内免费机票兑换的最强预测因素,西南航空的水平最高,联合航空最低,达美航空稳步上升。客源严重不平衡的航线奖励份额明显高于客源较平衡的航线。季节性因素、市场竞争和运营商收益增加了分析的细微差别。我们的研究结果表明,奖励旅游在国内市场呈现出清晰、可衡量的模式,这对收入管理、竞争和监管监督都有影响。
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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 Pub Date : 2026-05-01 Epub 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
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 Pub Date : 2026-05-01 Epub 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
Beyond route-specific forecasting: An empirical test of two cross-series transfer learning strategies for airline demand with short-data constraints 超越航线特定预测:短数据约束下航空公司需求的两种跨序列迁移学习策略的实证检验
IF 3.6 2区 工程技术 Q2 TRANSPORTATION Pub Date : 2026-05-01 Epub Date: 2026-02-04 DOI: 10.1016/j.jairtraman.2026.102981
Kiljae K. Lee, Ahmed F. Abdelghany
Airline demand forecasting frequently faces a “short-data” problem, where individual routes have limited historical records due to new route launches, seasonal suspensions, or external shocks such as COVID-19. While this constraint impedes the performance of forecasting models trained on single-series data, the presence of numerous parallel routes—a typical characteristic of airline networks—presents a “short-but-wide” data structure, offering a clear opportunity for cross-series transfer learning.
To exploit this opportunity, we propose and empirically validate two competing strategies: a feature-rich Fine-Tuned Global Forecasting Model (FT-GFM) and an adaptation-focused Adapted Model-Agnostic Meta-Learning (Adapted-MAML).
Our analysis uses 28 months of data for 1203 origin–destination pairs arriving at three major U.S. hubs (ATL, DFW, DEN). The feature-rich FT-GFM improved accuracy substantially, reducing SMAPE by an average of 28.45% and outperforming local models on 86.74% of routes. The data-efficient Adapted-MAML achieved even greater gains, reducing SMAPE by 45.88% and outperforming local models on 93.81% of routes, despite using only historical passenger volumes.
The results validate both strategies as effective solutions and show that, in data-constrained environments, Adapted-MAML's meta-learned initialization yields superior route-level forecasting accuracy compared with FT-GFM's feature-driven approach. These findings provide actionable guidance for airlines on selecting appropriate cross-series transfer learning strategies to mitigate the short-data problem and enhance operational resilience under uncertainty.
航空公司需求预测经常面临“短数据”问题,即由于新航线的开通、季节性停运或COVID-19等外部冲击,单个航线的历史记录有限。虽然这种约束阻碍了单系列数据训练的预测模型的性能,但大量平行航线的存在(航空网络的典型特征)呈现出“短而宽”的数据结构,为跨系列迁移学习提供了明显的机会。为了利用这一机会,我们提出并实证验证了两种相互竞争的策略:一种是特征丰富的微调全局预测模型(FT-GFM),另一种是以适应为中心的自适应模型不确定元学习(adaptive - maml)。我们的分析使用了到达美国三个主要枢纽(ATL, DFW, DEN)的1203对始发目的地的28个月数据。特征丰富的FT-GFM大大提高了精度,平均降低了28.45%的SMAPE,在86.74%的路线上优于本地模型。数据高效的adaptive - maml取得了更大的收益,在仅使用历史客运量的情况下,将SMAPE降低了45.88%,在93.81%的航线上优于本地模型。结果验证了这两种策略都是有效的解决方案,并表明,在数据受限的环境中,与FT-GFM的特征驱动方法相比,adaptive - maml的元学习初始化产生了更高的路线级预测精度。这些研究结果为航空公司选择合适的跨系列迁移学习策略以缓解短数据问题和增强不确定条件下的运营弹性提供了可操作的指导。
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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 Pub Date : 2026-05-01 Epub 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
Time-series risk forecasting of airport conflict hotspot with SA-GRU 基于SA-GRU的机场冲突热点时间序列风险预测
IF 3.6 2区 工程技术 Q2 TRANSPORTATION Pub Date : 2026-05-01 Epub Date: 2026-01-28 DOI: 10.1016/j.jairtraman.2026.102977
Wen Tian , Yuchen Li , Xuefang Zhou , Jinghui Sun , Xv Shi
Airport surface operations increasingly confront collision risks from intricate layouts, vehicle-aircraft interactions, and dense mixed traffic flows. This study develops a predictive framework for conflict hotspot identification by integrating topological, network vulnerability, and traffic complexity metrics into a composite risk evaluation system. A hybrid method combining composite weighting and improved TOPSIS first identifies latent hotspots through node-level risk assessments. Temporal risk patterns are then extracted via principal component analysis of hotspot features, with future risk trajectories predicted using a GRU network enhanced by self-attention mechanisms. Validated through Shenzhen Bao'an International Airport simulations, the proposed SA-GRU model reduces RMSE by 9.14–11.55 % against benchmark models (HA/ARIMA/SVR/LSTM/GRU). Analysis reveals significant spatiotemporal variations in hotspot risks, where daily trends show similar risk fluctuation patterns across zones but differ substantially in intensity. High-risk areas dynamically shift across operational phases, emphasizing the necessity of time-sensitive predictions. The framework enables proactive identification of critical conflict zones through predictive risk monitoring, demonstrating practical potential for optimizing airport surface management. By translating multidimensional operational data into actionable safety insights, this methodology supports intelligent decision-making for collision prevention and resource allocation in complex aviation environments, while remaining adaptable to diverse airport configurations.
机场地面运营越来越多地面临着复杂布局、车-机相互作用和密集混合交通流带来的碰撞风险。本研究通过将拓扑、网络脆弱性和流量复杂性指标整合到一个复合风险评估系统中,构建了冲突热点识别的预测框架。一种结合复合加权和改进TOPSIS的混合方法首先通过节点级风险评估识别潜在热点。然后通过热点特征的主成分分析提取时间风险模式,并使用自关注机制增强的GRU网络预测未来风险轨迹。通过深圳宝安国际机场仿真验证,SA-GRU模型与基准模型(HA/ARIMA/SVR/LSTM/GRU)相比,RMSE降低了9.14 - 11.55%。分析显示,热点风险存在显著的时空差异,各区域的日趋势显示出相似的风险波动模式,但强度差异很大。高风险区域在操作阶段之间动态变化,强调了时间敏感预测的必要性。该框架能够通过预测性风险监测主动识别关键冲突区域,展示优化机场地面管理的实际潜力。通过将多维运行数据转化为可操作的安全见解,该方法支持在复杂航空环境中进行碰撞预防和资源分配的智能决策,同时保持对不同机场配置的适应性。
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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 Pub Date : 2026-05-01 Epub 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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Journal of Air Transport Management
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