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

Uncertainty management at the airport transit view

Air traffic networks, where airports are the nodes that interconnect the entire system, have a time-varying and stochastic nature. An incident in the airport environment may easily propagate through the network and generate system-level effects. This paper analyses the aircraft flow through the Airport Transit View framework, focusing on the airspace/airside integrated operations. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Manoeuvring Area concept. Aircraft operations are characterised by different temporal milestones, which arise from the combination of a Business Process Model for the aircraft flow and the Airport Collaborative Decision-Making methodology. Relationships between factors influencing aircraft processes are evaluated to create a probabilistic graphical model, using a Bayesian network approach. This model manages uncertainty and increases predictability, hence improving the system's robustness. The methodology is validated through a case study at the Adolfo Suárez Madrid-Barajas Airport, through the collection of nearly 34,000 turnaround operations. We present several lessons learned regarding delay propagation, time saturation, uncertainty precursors and system recovery. The contribution of the paper is two-fold: it presents a novel methodological approach for tackling uncertainty when linking inbound and outbound flights and it also provides insight on the interdependencies among factors driving performance

Reduction of Uncertainty Propagation in the Airport Operations Network

[EN] Airport operations are a complex system involving multiple elements (ground access, landside, airside and airspace), stakeholders (ANS providers, airlines, airport managers, policy makers and ground handling companies) and interrelated processes. To ensure appropriate and safe operation it is necessary to understand these complex relationships and how the effects of potential incidents, failures and delays (due to unexpected events or capacity constraints) may propagate throughout the different stages of the system. An incident may easily ripple through the network and affect the operation of the airport as a whole, making the entire system vulnerable. A holistic view of the processes that also takes all of the parties (and the connections between them) into account would significantly reduce the risks associated with airport operations, while at the same time improving efficiency. Therefore, this paper proposes a framework to integrate all relevant stakeholders and reduce uncertainty in delay propagation, thereby lowering the cause-effect chain probability of the airport system (which is crucial for the operation and development of air transport). Firstly, we developed a model (map) to identify the functional relationships and interdependencies between the different stakeholders and processes that make up the airport operations network. This will act as a conceptual framework. Secondly, we reviewed and characterised the main causes of delay. Finally, we extended the system map to create a probabilistic graphical model, using a Bayesian Network approach and influence diagrams, in order to predict the propagation of unexpected delays across the airport operations network. This will enable us to learn how potential incidents may spread throughout the network creating unreliable, uncertain system states. Policy makers, regulators and airport managers may use this conceptual framework (and the associated indicators) to understand how delays propagate across the airport network, thereby enabling them to reduce system vulnerability, and increase its robustness and efficiency.Rodríguez Sanz, Á.; Gómez Comendador, F.; Arnaldo Valdés, R. (2016). Reduction of Uncertainty Propagation in the Airport Operations Network. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 36-78. https://doi.org/10.4995/CIT2016.2016.3484OCS367

Data-driven modeling of systemic delay propagation under severe meteorological conditions

The upsetting consequences of weather conditions are well known to any person involved in air transportation. Still the quantification of how these disturbances affect delay propagation and the effectiveness of managers and pilots interventions to prevent possible large-scale system failures needs further attention. In this work, we employ an agent-based data-driven model developed using real flight performance registers for the entire US airport network and focus on the events occurring on October 27 2010 in the United States. A major storm complex that was later called the 2010 Superstorm took place that day. Our model correctly reproduces the evolution of the delay-spreading dynamics. By considering different intervention measures, we can even improve the model predictions getting closer to the real delay data. Our model can thus be of help to managers as a tool to assess different intervention measures in order to diminish the impact of disruptive conditions in the air transport system.Comment: 9 pages, 5 figures. Tenth USA/Europe Air Traffic Management Research and Development Seminar (ATM2013

A Data-Driven Air Transportation Delay Propagation Model Using Epidemic Process Models

In air transport network management, in addition to defining the performance behavior of the system’s components, identification of their interaction dynamics is a delicate issue in both strategic and tactical decision-making process so as to decide which elements of the system are “controlled” and how. This paper introduces a novel delay propagation model utilizing epidemic spreading process, which enables the definition of novel performance indicators and interaction rates of the elements of the air transportation network. In order to understand the behavior of the delay propagation over the network at different levels, we have constructed two different data-driven epidemic models approximating the dynamics of the system: (a) flight-based epidemic model and (b) airport-based epidemic model. The flight-based epidemic model utilizing SIS epidemic model focuses on the individual flights where each flight can be in susceptible or infected states. The airport-centric epidemic model, in addition to the flight-to-flight interactions, allows us to define the collective behavior of the airports, which are modeled as metapopulations. In network model construction, we have utilized historical flight-track data of Europe and performed analysis for certain days involving certain disturbances. Through this effort, we have validated the proposed delay propagation models under disruptive events

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

Cruise-Efficient Short Takeoff and Landing (CESTOL): Potential Impact on Air Traffic Operations

The National Aeronautics and Space Administration (NASA) is investigating technological and operational concepts for introducing Cruise-Efficient Short Takeoff and Landing (CESTOL) aircraft into a future US National Airspace System (NAS) civil aviation environment. CESTOL is an aircraft design concept for future use to increase capacity and reduce emissions. CESTOL provides very flexible takeoff, climb, descent and landing performance capabilities and a high-speed cruise capability. In support of NASA, this study is a preliminary examination of the potential operational impact of CESTOL on airport and airspace capacity and delay. The study examines operational impacts at a subject site, Newark Liberty Intemational Airport (KEWR), New Jersey. The study extends these KEWR results to estimate potential impacts on NAS-wide network traffic operations due to the introduction of CESTOL at selected major airports. These are the 34 domestic airports identified in the Federal Aviation Administration's Operational Evolution Plan (OEP). The analysis process uses two fast-time simulation tools to separately model local and NAS-wide air traffic operations using predicted flight schedules for a 24-hour study period in 2016. These tools are the Sen sis AvTerminal model and NASA's Airspace Concept Evaluation System (ACES). We use both to simulate conventional-aircraft-only and CESTOL-mixed-with-conventional-aircraft operations. Both tools apply 4-dimension trajectory modeling to simulate individual flight movement. The study applies AvTerminal to model traffic operations and procedures for en route and terminal arrival and departures to and from KEWR. These AvTerminal applications model existing arrival and departure routes and profiles and runway use configurations, with the assumption jet-powered, large-sized civil CESTOL aircraft use a short runway and standard turboprop arrival and departure procedures. With these rules, the conventional jet and CESTOL aircraft are procedurally separated from each other geographically and in altitude during tenninal airspace approach and departure operations, and each use a different arrival runway. AvTeminal implements its unique Focal-point Scheduling Process to sequence, space and delay aircraft to resolve spacing and overtake conflicts among flights in the airspace and airport system serving KEWR. This Process effectively models integrated arrival and departure operations. AvTerminal assesses acceptance rates and delay magnitude and causality at selected locations, including en route outer boundary fixes, tenninal airspace arrival and departure boundary fixes, terminal airspace arrival merge and departure diverge fixes, and runway landing and takeoff runways. The analysis compares the resulting capacity impacts, flight delays and delay sources between CESTOL and conventional KEWR operations. AvTerminal quantitative results showed that CESTOL has significant capability to increase airport arrival acceptance rates (35-40% at KEWR) by taking advantage of otherwise underused airspace and runways where available. The study extrapolates the AvTerminal-derived KEWR peak arrival and departure acceptance rates to estimate capacity parameter values for each of the OEP airports in the ACES modeling of traffic through the entire NAS network. The extrapolations of acceptance rates allow full, partial or no achievement of CESTOL capacity gains at an OEP airport as determined by assessments of the degree to which local procedures allow leveraging of CESTOL capabilities. These assessments consider each OEP airport's runway geometries, runway system configurations, airport and airspace operations, and potential CESTOL traffic loadings. The ACES modeling, simulates airport and airspace spacing constraints imposed by airport runway system, terminal and en route air traffic control and traffic flow management operations using airport acceptance rates representing conventional-aircraft-only and CESTOL-mixed operations. CEOL aircraft are assumed to have Mach 0.8, and alternatively Mach 0.7, cruise speeds to examine compatibility with conventional aircraft operations in common airspace. The ACES results provides estimates of CESTOL delay impact NAS-wide and at OEP airports due to changes in OEP airport acceptance rates and changes in en route airspace potential conflict rates. Preliminary results show meaningful nationwide delay reductions (20%) due to CESTOL operations at 34 major domestic airports

Complexity challenges in ATM

After more than 4 years of activity, the ComplexWorld Network, together with the projects and PhDs covered under the SESAR long-term research umbrella, have developed sound research material contributing to progress beyond the state of the art in fields such as resilience, uncertainty, multi-agent systems, metrics and data science. The achievements made by the ComplexWorld stakeholders have also led to the identification of new challenges that need to be addressed in the future. In order to pave the way for complexity science research in Air Traffic Management (ATM) in the coming years, ComplexWorld requested external assessments on how the challenges have been covered and where there are existing gaps. For that purpose, ComplexWorld, with the support of EUROCONTROL, established an expert panel to review selected documentation developed by the network and provide their assessment on their topic of expertise