Reducing Runway Incursions: The Role of Collaboration, Education, and Training

Runway incursions are a major threat to aviation safety and can cause major delays and collisions that have significant human and financial implications for airlines. This study investigated how training, education, and collaboration may be improved to reduce the occurrence of runway incursions at airports. Data collection involved interviews, a focus group, and document analysis to explore the participants’ perceptions. The interviews and focus group involved a purposive sample of 12 pilots, air traffic controllers, airport administrators, and ground personnel. The interviews and focus group transcripts were chunked, coded, and patterns sought to form five key themes addressing the research question: exercising key safety practices, effective communication, a greater focus on scenario-based training, need for greater standardization, and more collaboration and partnership among stakeholders. The findings have the potential to influence Federal Aviation Administration’s (FAA) decision-making through resource allocation for improving runway safety, as well as to inform the prevention of runway incursions through improvements to education, training, and collaboration

Predicting Pilot Misperception of Runway Excursion Risk Through Machine Learning Algorithms of Recorded Flight Data

The research used predictive models to determine pilot misperception of runway excursion risk associated with unstable approaches. The Federal Aviation Administration defined runway excursion as a veer-off or overrun of the runway surface. The Federal Aviation Administration also defined a stable approach as an aircraft meeting the following criteria: (a) on target approach airspeed, (b) correct attitude, (c) landing configuration, (d) nominal descent angle/rate, and (e) on a straight flight path to the runway touchdown zone. Continuing an unstable approach to landing was defined as Unstable Approach Risk Misperception in this research. A review of the literature revealed that an unstable approach followed by the failure to execute a rejected landing was a common contributing factor in runway excursions. Flight Data Recorder data were archived and made available by the National Aeronautics and Space Administration for public use. These data were collected over a four-year period from the flight data recorders of a fleet of 35 regional jets operating in the National Airspace System. The archived data were processed and explored for evidence of unstable approaches and to determine whether or not a rejected landing was executed. Once identified, those data revealing evidence of unstable approaches were processed for the purposes of building predictive models. SAS™ Enterprise MinerR was used to explore the data, as well as to build and assess predictive models. The advanced machine learning algorithms utilized included: (a) support vector machine, (b) random forest, (c) gradient boosting, (d) decision tree, (e) logistic regression, and (f) neural network. The models were evaluated and compared to determine the best prediction model. Based on the model comparison, the decision tree model was determined to have the highest predictive value. The Flight Data Recorder data were then analyzed to determine predictive accuracy of the target variable and to determine important predictors of the target variable, Unstable Approach Risk Misperception. Results of the study indicated that the predictive accuracy of the best performing model, decision tree, was 99%. Findings indicated that six variables stood out in the prediction of Unstable Approach Risk Misperception: (1) glideslope deviation, (2) selected approach speed deviation (3) localizer deviation, (4) flaps not extended, (5) drift angle, and (6) approach speed deviation. These variables were listed in order of importance based on results of the decision tree predictive model analysis. The results of the study are of interest to aviation researchers as well as airline pilot training managers. It is suggested that the ability to predict the probability of pilot misperception of runway excursion risk could influence the development of new pilot simulator training scenarios and strategies. The research aids avionics providers in the development of predictive runway excursion alerting display technologies

Modelling airport surface safety: a framework for a holistic airport safety management

Airports are complex systems involving the continuous interaction of human operators with the physical infrastructure, technology and procedures to ensure the safe and efficient conduct of flights. From an operational perspective, airport surface operations (i.e. runway and taxiway operations) require the interaction of five main stakeholders (i.e. crew or pilots, air traffic control, airport operator, ground handling and regulator) both to facilitate the ground movement of aircraft and vehicles, and to maintain the surface in a working condition. The complexity of these operations makes the runway and taxiway system vulnerable and presents a risk of failure with the consequent potential for the occurrence of accidents. Therefore, the development and implementation of an effective Safety Management System (SMS) are required to ensure the highest level of safety for surface operations. A SMS is a systematic approach to managing safety based on the four cornerstones of safety policy and objectives, risk management, assurance, and safety promotion. Although the International Civil Aviation Organisation (ICAO) provides the global legislative framework for SMS, the relevant regulations are still to be established at the national level with the consequence that practical guidance on the development and implementation of SMS is rare, and reliable tools to support SMS are lacking. The consequence of this is that the current approach to surface safety management is piecemeal and not integrated. Typically, a single accident and incident type is investigated from the perspective of an individual stakeholder with the consequence that resulting proposals for safety mitigation measures are biased and limited in terms of their impact. In addition, the industry is characterised by non-standardised data collection and investigation practices, insufficient or missing definitions, differing reporting levels, and a lack of a coherent and standardised structure for efficient coding and analysis of safety data. Since these shortcomings are a major barrier to the required holistic and integrated approach to safety management, this thesis addresses the four cornerstones of SMS and recommends major enhancements. In particular, a framework for a holistic airport surface safety management is proposed. The framework comprises the static airport architecture, a process model of surface operations, the determination of causal factors underlying failure modes of these operations, a macroscopic scenario tool and a functional relationship model. Safety data and other data sources feed the framework and a dedicated data pre-processing strategy ensures its validity. Unlike current airport surface safety management practices, the proposed framework assesses the safety of the operations of all relevant actors. Firstly, the airport architecture is modelled and the physical and functional variability of airports defined. Secondly, a process model of surface operations is developed, which captures the tasks of the stakeholders and their interactions with physical airport surface infrastructure. This model serves as a baseline model and guides the further development of the airport SMS. To manage the safety of surface operations, the causes of accidents and incidents must be identified and their impacts understood. To do so, a reference data set combining twelve databases from airlines, airport operators, Air Navigation Service Providers (ANSPs), ground handling companies and regulators is collected. Prior to its analysis, the data is assessed for its quality, and in particular, for its internal validity (i.e. precision), external validity (i.e. accuracy) and in terms of reporting levels. A novel external data validation framework is developed and each database is rated with a data quality index (DQI). In addition, recommendations for reporting systems and safety policies are given. Subsequently, the data is analysed for causal factors across stakeholders and the contribution of the individual actors are highlighted. For example, the analysis shows that the various stakeholders capture different occurrence types and underlying causal factors, often including information that is of potential use for another party. The analysis is complemented by interviews, observations and statistical analysis, and the results are summarised in a new taxonomy. This taxonomy is applicable to all relevant stakeholders and is recommended for operational safety risk management. After the airport surface operations have been modelled and the drivers to safety identified, the results are combined, resulting in a macroscopic scenario tool which supports the management of change (i.e. safety assurance), training and education, and safety communication (i.e. safety promotion) functions of the SMS. Finally, a structured framework to assess the functional relationship between airport surface accidents / incidents and their underlying causal factors is proposed and the system is quantified in terms of safety. Compared to the state-of-the-art safety assessments that are biased and limited in terms of their impact, the holistic approach to surface safety allows modelling the safety impact of each system component, their interactions and the entire airport surface system architecture. The framework for a holistic airport surface safety management developed in this thesis delivers a SMS standard for airports. The standard exceeds international requirements by standardizing the two SMS core functions (safety risk management and safety assurance) and integrating safety-relevant information across all relevant stakeholders. This allows a more effective use of safety information and provides an improved overview on, and prediction of, safety risks and ultimately improves the safety level of airports and their stakeholders. Furthermore, the methodology employed in this thesis is flexible and could be applied to all aspects of aviation SMS and system analysis.Open Acces

Indicadores de desempenho da segurança de processos nas operações de pátio em aeroportos

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2017.Os pátios de aeronaves são usualmente descritos como locais perigosos nos quais pessoas, aeronaves, veículos e equipamentos interagem em um ambiente normalmente congestionado e sob grande pressão. Os custos globais decorrentes de ocorrências nos pátios de aeronaves são estimados em mais de US$4 bilhões anuais para os transportadores aéreos. Apesar de se observar um aumento da preocupação da indústria e da academia com as operações das aeronaves em solo, a maior atenção ainda é dedicada às operações na área de manobras. Este trabalho se propõe a contribuir para o aprimoramento da segurança de processos nas operações do pátio de aeronaves por meio da proposição de um sistema de indicadores de desempenho da segurança. A avaliação da segurança é um desafio em muitas indústrias e um componente fundamental para o bom funcionamento de um sistema de gerenciamento da segurança operacional. O primeiro passo para a proposição dos indicadores foi a identificação daquilo que deveria ser objeto de medição. Para isso, foram realizadas entrevistas semiestruturadas com especialistas e uma revisão sistemática da literatura, a fim de identificar os elementos que influenciam o risco nas operações de pátio. Em seguida, a relação entre esses elementos foi detalhadamente analisada por meio da análise Bow tie. Um total de 62 elementos foram identificados e submetidos à avaliação de profissionais da aviação civil por meio de um questionário eletrônico. Com base no resultado da avaliação foi realizada uma análise de agrupamentos (cluster analysis) por meio da qual os elementos foram divididos em quatro grupos de prioridade distintos. Indicadores foram propostos para os onze elementos integrantes do grupo de maior prioridade. Os indicadores propostos foram avaliados com base em requisitos identificados na literatura. A análise dos dados do questionário possibilitou ainda identificar que os elementos associados a indicadores reativos tendem a ser considerados mais importantes para o monitoramento da segurança. Identificou-se ainda uma diferença significativa na opinião dos representantes do órgão regulador e da indústria em relação a determinado grupo de elementos.Airport aprons are usually described as hazardous locations in which humans, aircrafts, vehicles and ground support equipment interact in a congested and pressured environment. The annual worldwide costs arising from ground accidents and incidents are estimated at more than$ 4 billion for air carriers. Although industry and academia have shown increased concern to on-ground risks, the greatest attention is still devoted to operations in the maneuvering area. The purpose of this study is to contribute to the improvement of process safety in ramp operations through the proposal of a set of safety performance indicators. Safety assessment is a challenge in many industries and an essential component of for the proper functioning of a safety management system. The initial step in the development of the indicators was the identification of the key issues of concern. In order to identify the elements that influence the risk in ramp operations, semistructured interviews with specialists and a systematic literature review were conducted. Then, the relationship between these elements was reviewed in detail using the Bow tie analysis. A total of 62 elements have been identified and submitted to the assessment of civil aviation professionals through an electronic questionnaire. Based on the results of the evaluation, a cluster analysis was carried out through which the elements were divided into four distinct priority groups. Indicators were proposed for the eleven elements of the highest priority group. The proposed indicators were then evaluated based on requirements identified in the literature. The analysis of the questionnaire data also made it possible to identify that elements associated with reactive indicators tend to be considered more important for safety performance monitoring. There was also a significant difference in the opinion of the representati