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International Journal of Aviation, Aeronautics, and Aerospace最新文献

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Aviation Fuel Tankering and Sustainability: The Brazilian Scenario 航空加油和可持续性:巴西的情况
IF 0.8 Q4 ENGINEERING, AEROSPACE Pub Date : 2023-01-01 DOI: 10.58940/2374-6793.1786
P. O’Reilly, Fabricio Sulzbacher, Diogo Jardim Coutinho, Maria Petrescu
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引用次数: 0
Historical Research and the case for a Fifth Component of SMS SMS第五部分的历史研究与案例
IF 0.8 Q4 ENGINEERING, AEROSPACE Pub Date : 2023-01-01 DOI: 10.58940/2374-6793.1820
P. Craig
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引用次数: 0
Women and Minorities in Commercial Aviation: A Quantitative Analysis of Data from the United States Bureau of Labor Statistics 商业航空中的妇女和少数民族:对美国劳工统计局数据的定量分析
IF 0.8 Q4 ENGINEERING, AEROSPACE Pub Date : 2023-01-01 DOI: 10.58940/2374-6793.1792
R. Fowler, Alexandra Lundberg, Daniel Siao, C. Smith
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引用次数: 9
Evaluating the Effects of Safety Management Systems (SMS) on Safety Culture Factors in Collegiate Aviation Operations: A Structural Equation Modeling (SEM) Approach 评价安全管理系统(SMS)对大学航空运营安全文化因素的影响:一种结构方程模型(SEM)方法
IF 0.8 Q4 ENGINEERING, AEROSPACE Pub Date : 2023-01-01 DOI: 10.58940/2374-6793.1788
Robert A. Foster, D. Adjekum
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引用次数: 0
Single Pilot Operations and Public Acceptance: A Mixed Methods Study conducted in Greece 单一试点操作和公众接受:在希腊进行的混合方法研究
IF 0.8 Q4 ENGINEERING, AEROSPACE Pub Date : 2023-01-01 DOI: 10.58940/2374-6793.1822
Panagiotis Kioulepoglou, Ilias Makris
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引用次数: 0
Stochastic Optimization to Reduce Aircraft Taxi-in Time at IGIA, New Delhi 新德里IGIA机场减少飞机滑行时间的随机优化
Q4 ENGINEERING, AEROSPACE Pub Date : 2023-01-01 DOI: 10.58940/2374-6793.1832
RAJIB DAS, SAILESWAR GHOSH, RAJENDRA DESAI, PIJUS KANTI BHUIN, STUTI AGARWAL
Since there is an uncertainty in the arrival times of flights, pre-scheduled allocation of runways and stands and the subsequent first-come-first-served treatment results in a sub-optimal allocation of runways and stands, this is the prime reason for the unusual delays in taxi-in times at IGIA, New Delhi.
由于航班到达时间的不确定性,预先安排的跑道和停机位分配以及随后的先到先得处理导致跑道和停机位的次优分配,这是新德里IGIA出租车进入时间异常延误的主要原因。
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引用次数: 0
Learning from Past in the Commercial Air Transport Industry: A Bibliometric Analysis and Systematic Literature Review in the Safety Management Framework 商业航空运输业的前车之鉴:安全管理框架下的文献计量分析与系统文献综述
Q4 ENGINEERING, AEROSPACE Pub Date : 2023-01-01 DOI: 10.58940/2374-6793.1838
Alok Tyagi, Rajesh Tripathi, Soufiane Bouarfa
In the commercial air transport industry, numerous accidents have occurred because stakeholders have failed to learn lessons from the past. The reactive hazard identification (HI) methodology in the current regulatory safety management framework provides a fresh opportunity to negotiate the challenges of the air transportation sector's predicted growth and societal safety expectations. In the regulatory framework, ‘learning from the past’ is related to the reactive method of HI, and it is viewed as a data-driven decision-making process in this review. This literature review aims to investigate academic literature on ‘learning from the past’ in the aviation industry to comprehend an overview and identify the gaps in the scholarly approaches and regulatory provisions. The data sources for the review comprise the research articles indexed in Scopus and Web of Science databases from 2000 to July 2023. This review follows a mixed methodology consisting of a bibliometric analysis using an R tool to develop a broad understanding and, for specific gap identification, a systematic literature review in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. All 283 studies are included in the bibliometric analysis, while only 24 studies are found eligible based on the predefined inclusion/exclusion criteria for systematic review. The results reveal that ‘learning from the past’ has not been explored as a data-driven decision-making process for HI in aviation industry settings. The stagewise barriers in the learning process and the impact of each stage on overall learning remain unexplored. The regulatory provisions associated with safety information from aviation activities' resilient (positive) outcomes for learning from the past purpose, organizational safety culture, and harmonization with the criminal justice system are vulnerable.
在商业航空运输业,由于利益相关者未能从过去吸取教训,已经发生了许多事故。当前安全管理监管框架中的反应性危险识别(HI)方法为航空运输业的预期增长和社会安全期望带来的挑战提供了新的谈判机会。在监管框架中,“从过去学习”与HI的反应性方法有关,在本综述中,它被视为数据驱动的决策过程。本文献综述旨在调查航空业“从过去学习”的学术文献,以了解概述并确定学术方法和监管规定中的差距。本综述的数据来源包括2000年至2023年7月在Scopus和Web of Science数据库中检索的研究文章。本综述采用了一种混合方法,包括使用R工具进行文献计量分析以获得广泛的理解,以及根据系统综述和元分析(PRISMA)指南的首选报告项目进行系统文献综述,以确定具体的差距。所有283项研究都纳入了文献计量学分析,而根据系统评价的预定义纳入/排除标准,只有24项研究符合条件。结果表明,“从过去学习”尚未被探索为航空工业背景下高绩效的数据驱动决策过程。学习过程中的阶段障碍以及每个阶段对整体学习的影响仍未被探索。与航空活动的安全信息相关的监管规定是脆弱的,这些信息来自于从过去的目的中学习的弹性(积极)成果、组织安全文化以及与刑事司法系统的协调。
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引用次数: 0
ADS-B Communication Interference in Air Traffic Management 空中交通管理中的ADS-B通信干扰
IF 0.8 Q4 ENGINEERING, AEROSPACE Pub Date : 2023-01-01 DOI: 10.58940/2374-6793.1821
George Ray
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引用次数: 0
Factors Affecting Passengers’ Acceptance of Single Pilot Operations: A Qualitative Study conducted in Greece 影响乘客接受单一试点运营的因素:在希腊进行的定性研究
IF 0.8 Q4 ENGINEERING, AEROSPACE Pub Date : 2023-01-01 DOI: 10.58940/2374-6793.1800
Panagiotis Kioulepoglou, I. Makris
Aviation is undeniably one of the fastest-growing industries worldwide (ICAO, 2022). Analysts predict that despite a brief―yet considerable―pause, brought on by COVID-19 (Gao, 2022;IATA, 2022), global aviation traffic is increasing and will possibly threefold in the next 30 years (Gössling & Humpe, 2020;ICAO, 2023). Furthermore, this tremendous increase in air travel will also trigger a considerable need for personnel and will unavoidably lead to a significant shortage of pilots which has been forecasted by many sources in the past (Bennett & Vijaygopal, 2021;Champ, 2022;Meredith, 2019;Moehle & Clauss, 2015;Rice et al., 2014). Specifically, trends showed that the world will be lacking approximately 800,000 pilots by the year 2037 (Caraway, 2020). Put another way, the demand for airline pilots is going to outweigh the supply by far. Unfortunately, the high entry cost of new pilots to the industry and the relatively low salaries are averting a quick fix to this problem (Lutte, 2014) © 2022, International Journal of Aviation, Aeronautics, and Aerospace.All Rights Reserved.
不可否认,航空业是全球增长最快的行业之一(国际民航组织,2022年)。分析人士预测,尽管新冠肺炎造成了短暂但相当大的停顿(Gao,2022;国际航空运输协会,2022),但全球航空运输量正在增加,未来30年可能会增加三倍(Gössling和Humpe,2020;国际民航组织,2023)。此外,航空旅行的巨大增长也将引发对人员的巨大需求,并将不可避免地导致飞行员的严重短缺,这在过去已经被许多消息来源预测过(Bennett和Vijaygopal,2021;Champ,2022;Meredith,2019;Moehle和Clauss,2015;Rice等人,2014)。具体而言,趋势显示,到2037年,世界将缺乏约80万名飞行员(Caraway,2020)。换句话说,到目前为止,对航空公司飞行员的需求将超过供应。不幸的是,新飞行员进入该行业的高昂成本和相对较低的工资阻碍了这个问题的快速解决(Lutte,2014)©2022,《国际航空、航空和航天杂志》。保留所有权利。
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引用次数: 1
Aircraft Damage Classification by using Machine Learning Methods 基于机器学习方法的飞机损伤分类
IF 0.8 Q4 ENGINEERING, AEROSPACE Pub Date : 2023-01-01 DOI: 10.58940/2374-6793.1810
Tuzun Tolga Inan
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引用次数: 0
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International Journal of Aviation, Aeronautics, and Aerospace
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