A generic framework for modeling airport operations at a macroscopic level,

International audienceIn this paper, a general approach for modeling airport operations is presented. Airport operations have been extensively studied in the last decades ranging from airspace, airside and landside operations. Due to the nature of the system, simulation techniques have emerged as a powerful approach for dealing with the variability of these operations. However, in most of the studies, the different elements are studied in an individual fashion. The aim of this paper, is to overcome this limitation by presenting a methodological approach where airport operations are modeled together, such as airspace and airside. The contribution of this approach is that the resolution level for the different elements is similar therefore the interface issues between them is minimized. The framework can be used by practitioners for simulating complex systems like airspace-airside operations or multi-airport systems. The framework is illustrated by presenting a case study analyzed by the authors

Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie

Existing and Required Modeling Capabilities for Evaluating ATM Systems and Concepts

ATM systems throughout the world are entering a period of major transition and change. The combination of important technological developments and of the globalization of the air transportation industry has necessitated a reexamination of some of the fundamental premises of existing Air Traffic Management (ATM) concepts. New ATM concepts have to be examined, concepts that may place more emphasis on: strategic traffic management; planning and control; partial decentralization of decision-making; and added reliance on the aircraft to carry out strategic ATM plans, with ground controllers confined primarily to a monitoring and supervisory role. 'Free Flight' is a case in point. In order to study, evaluate and validate such new concepts, the ATM community will have to rely heavily on models and computer-based tools/utilities, covering a wide range of issues and metrics related to safety, capacity and efficiency. The state of the art in such modeling support is adequate in some respects, but clearly deficient in others. It is the objective of this study to assist in: (1) assessing the strengths and weaknesses of existing fast-time models and tools for the study of ATM systems and concepts and (2) identifying and prioritizing the requirements for the development of additional modeling capabilities in the near future. A three-stage process has been followed to this purpose: 1. Through the analysis of two case studies involving future ATM system scenarios, as well as through expert assessment, modeling capabilities and supporting tools needed for testing and validating future ATM systems and concepts were identified and described. 2. Existing fast-time ATM models and support tools were reviewed and assessed with regard to the degree to which they offer the capabilities identified under Step 1. 3 . The findings of 1 and 2 were combined to draw conclusions about (1) the best capabilities currently existing, (2) the types of concept testing and validation that can be carried out reliably with such existing capabilities and (3) the currently unavailable modeling capabilities that should receive high priority for near-term research and development. It should be emphasized that the study is concerned only with the class of 'fast time' analytical and simulation models. 'Real time' models, that typically involve humans-in-the-loop, comprise another extensive class which is not addressed in this report. However, the relationship between some of the fast-time models reviewed and a few well-known real-time models is identified in several parts of this report and the potential benefits from the combined use of these two classes of models-a very important subject-are discussed in chapters 4 and 7

Applying complexity science to air traffic management

Complexity science is the multidisciplinary study of complex systems. Its marked network orientation lends itself well to transport contexts. Key features of complexity science are introduced and defined, with a specific focus on the application to air traffic management. An overview of complex network theory is presented, with examples of its corresponding metrics and multiple scales. Complexity science is starting to make important contributions to performance assessment and system design: selected, applied air traffic management case studies are explored. The important contexts of uncertainty, resilience and emergent behaviour are discussed, with future research priorities summarised

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

Network Congestion Control of Airport Surface Operations

The reduction of taxi-out times at airports has the potential to substantially reduce delays and fuel consumption on the airport surface, and to improve the air quality in surrounding communities. The taxiway and runway systems at an airport determine its maximum possible departure throughput, or the number of aircraft departures that it can handle per unit time. Current air traffic control procedures allow aircraft to push from their gates and enter the taxiway system as soon as they are ready. As this pushback rate approaches the maximum departure throughput of the airport, runway queues grow longer and surface congestion increases, resulting in increased taxi-out times

Data Analysis of Delays in Airline Networks

Cost-optimized airline resource schedules often imply a lack of delay tolerance in case of unforeseen disruptions, e.g. late check-ins, technical defects or airport and airspace congestion. Therefore, the consideration of timeliness and robustness has become an important topic in robust resource scheduling and a wide range of sophisticated scheduling approaches has been developed in recent years. However, these approaches depend on assumptions made concerning delay occurrences. A better understanding of delay mechanisms may lead to a better trade-off between cost-efficiency and robustness and is therefore the purpose of this paper. We provide a data-driven detection of decision rules for daytime delay trends, depending on spatio-temporal attributes. The focus is on interpretable rules whose prediction accuracy is compared to random forests as a non-parametric, automated modeling approach. The obtained results give an insight into both the nature of primary delay occurrence and the methodical potential of delay prediction in the context of robust resource scheduling

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