An Integrated Gate Turnaround Management Concept Leveraging Big Data Analytics for NAS Performance Improvements

"Gate Turnaround" plays a key role in the National Air Space (NAS) gate-to-gate performance by receiving aircraft when they reach their destination airport, and delivering aircraft into the NAS upon departing from the gate and subsequent takeoff. The time spent at the gate in meeting the planned departure time is influenced by many factors and often with considerable uncertainties. Uncertainties such as weather, early or late arrivals, disembarking and boarding passengers, unloading/reloading cargo, aircraft logistics/maintenance services and ground handling, traffic in ramp and movement areas for taxi-in and taxi-out, and departure queue management for takeoff are likely encountered on the daily basis. The Integrated Gate Turnaround Management (IGTM) concept is leveraging relevant historical data to support optimization of the gate operations, which include arrival, at the gate, departure based on constraints (e.g., available gates at the arrival, ground crew and equipment for the gate turnaround, and over capacity demand upon departure), and collaborative decision-making. The IGTM concept provides effective information services and decision tools to the stakeholders, such as airline dispatchers, gate agents, airport operators, ramp controllers, and air traffic control (ATC) traffic managers and ground controllers to mitigate uncertainties arising from both nominal and off-nominal airport gate operations. IGTM will provide NAS stakeholders customized decision making tools through a User Interface (UI) by leveraging historical data (Big Data), net-enabled Air Traffic Management (ATM) live data, and analytics according to dependencies among NAS parameters for the stakeholders to manage and optimize the NAS performance in the gate turnaround domain. The application will give stakeholders predictable results based on the past and current NAS performance according to selected decision trees through the UI. The predictable results are generated based on analysis of the unique airport attributes (e.g., runway, taxiway, terminal, and gate configurations and tenants), and combined statistics from past data and live data based on a specific set of ATM concept-of-operations (ConOps) and operational parameters via systems analysis using an analytic network learning model. The IGTM tool will then bound the uncertainties that arise from nominal and off-nominal operational conditions with direct assessment of the gate turnaround status and the impact of a certain operational decision on the NAS performance, and provide a set of recommended actions to optimize the NAS performance by allowing stakeholders to take mitigation actions to reduce uncertainty and time deviation of planned operational events. An IGTM prototype was developed at NASA Ames Simulation Laboratories (SimLabs) to demonstrate the benefits and applicability of the concept. A data network, using the System Wide Information Management (SWIM)-like messaging application using the ActiveMQ message service, was connected to the simulated data warehouse, scheduled flight plans, a fast-time airport simulator, and a graphic UI. A fast-time simulation was integrated with the data warehouse or Big Data/Analytics (BAI), scheduled flight plans from Aeronautical Operational Control AOC, IGTM Controller, and a UI via a SWIM-like data messaging network using the ActiveMQ message service, illustrated in Figure 1, to demonstrate selected use-cases showing the benefits of the IGTM concept on the NAS performance

A statistical and probabilistic approach for improving efficiency in Air Traffic Flow management

This thesis presents a novel approach based on statistics and probability theory to improve Air Traffic Flow Management (ATFM) efficiency. This work proposes four main contributions, which are briefly described below. A procedure for determining the Airport capacity using an estimated probability that a given number of movements can occur is shown. This procedure is a probabilistic approach that employs the statistical information of aircraft arrivals and departures that are collected over a given time interval. The procedure is demonstrated using historical arrival and departure data to estimate the capacity of London Heathrow airport. A procedure to establish a strategic arrival schedule is developed. An implicit feature of this probabilistic procedure is that it takes into account the uncertainty in the arrival and departure times at a given airport. The methodology has been applied at Glasgow International Airport to design a strategic schedule that reduces either the probability of conflict of the arrivals or the length of the landing slots necessitated by flights. The benefits that are expected through the application of this methodology are a reduction of airborne delays and/or an increase of the airport capacity. Thus a safer and more efficient system is achieved. A decision support tool for Air Traffic Flow Management (ATFM) is developed. This tool is designed to allow air traffic controllers to organise an air traffic flow pattern using a ground holding strategy. During the daily planning of air traffic flow unpredictable events such as adverse weather conditions and system failures occur, necessitating airborne and ground-hold delays. These delays are used by controllers as a means of avoiding 4D status conflicts. Of the two methods, ground hold delays are preferred because they are safer, less expensive and cause even less pollution. In this thesis a method of estimating the duration of a ground-hold for a given flight is developed. The proposed method is novel in the use of a real time stochastic analysis. The method is demonstrated using Glasgow International Airport. The results presented show how a ground hold policy at a departure airport can increase capacity and minimise conflicts at a destination airport. A methodology to regulate the dispatch of aircraft through any congested sector and Terminal Manoeuvring Areas (TMA) in order to reduce conflicts is presented. This methodology, based on statistics and probability theory, presents a new schedule at the strategic planning level that will ensure, with a high probability, that the aircraft will comply with the established separation minima during their route and during their approach holding patterns. Air Traffic Control Centres (ATC) will be less likely to be overloaded and thus a minimisation of the penalty imposed on aircraft by the operators will be achieved

Symbolic representation of scenarios in Bologna airport on virtual reality concept

This paper is a part of a big Project named Retina Project, which is focused in reduce the workload of an ATCO. It uses the last technological advances as Virtual Reality concept. The work has consisted in studying the different awareness situations that happens daily in Bologna Airport. It has been analysed one scenario with good visibility where the sun predominates and two other scenarios with poor visibility where the rain and the fog dominate. Due to the study of visibility in the three scenarios computed, the conclusion obtained is that the overlay must be shown with a constant dimension regardless the position of the aircraft to be readable by the ATC and also, the frame and the flight strip should be coloured in a showy colour (like red) for a better control by the ATCO

The future of UAS: standards, regulations, and operational experiences [workshop report]

This paper presents the outcomes of "The Future of UAS: Standards, Regulations and Operational Experiences" workshop, held on the 7th and 8th of December, 2006 in Brisbane, Queensland, Australia. The goal of the workshop was to identify recent international activities in the Unmanned Airborne Systems (UAS) airspace integration problem. The workshop attracted a broad cross-section of the UAS community, including: airspace and safety regulators, developers, operators and researchers. The three themes of discussion were: progress in the development of standards and regulations, lessons learnt from recent operations, and advances in new technologies. This paper summarises the activities of the workshop and explores the important outcomes and trends as perceived by the authors

Decision making under uncertainties for air traffic flow management

A goal of air traffic flow management is to alleviate projected demand-capacity imbalances at airports and in en route airspace through formulating and applying strategic Traffic Management Initiatives (TMIs). As a new tool in the Federal Aviation Administration\u27s NextGen portfolio, the Collaborative Trajectory Options Programs (CTOP) combines many components from its predecessors and brings two important new features: first, it can manage multiple constrained regions in an integrated way with a single program; second, it allows flight operators to submit a set of desired reroute options (called a Trajectory Options Set or TOS), which provides great flexibility and efficiency. One of the major research questions in TMI optimization is how to determine the planned acceptance rates for airports or congested airspace regions (Flow Constrained Areas or FCA) to minimize system-wide costs. There are two important input characteristics that need to be considered in developing optimization models to set acceptance rates in a CTOP: first, uncertain airspace capacities, which result from imperfect weather forecast; second, uncertain demand, which results from flights being geographically redistributed after their TOS options are processed. Although there are other demand disturbances to consider, such as popup flights, flight cancellations, and flight substitutions, their effect on demand estimates at FCAs will likely be far less than that of rerouting from TOSs. Hence, to cope with capacity and demand uncertainties, a decision-making under uncertainty problem needs to be solved. In this dissertation, three families of stochastic programming models are proposed. The first family of models, which are called aggregate stochastic models and are formulated as multi-commodity flow models, can optimally plan ground and air delay for groups of flights given filed route choice of each flight. The second family of models, which are called disaggregate stochastic models and directly control each individual flight, can give the theoretical lower bounds for the very general reroute, ground-, and air-holding problem with multiple congested airspace regions and multiple route options. The third family of models, called disaggregate-aggregate models, can be solved more efficiently compared with the second class of models, and can directly control the queue size at each congested region. Since we assume route choice is given or route can be optimized along with flight delay in a centralized manner, these three families of models, although can provide informative benchmarks, are not compatible with current CTOP software implementation and have not addressed the demand uncertainty problem. The simulation-based optimization model, which can use stochastic programming models as part of its heuristic, addresses the demand uncertainty issue by simulating CTOP TOS allocation in the optimization process, and can give good suboptimal solution to the practical CTOP rate planning problem. Airline side research problems in CTOP are also briefly discussed in this dissertation. In particular, this work quantifies the route misassignment cost due to the current imperfect Relative Trajectory Cost (RTC) design. The main contribution of this dissertation is that it gives the first algorithm that optimizes the CTOP rate under demand and capacity uncertainty and is compatible with the Collaborative Decision Making (CDM) CTOP framework. This work is not only important in providing much-needed decision support capabilities for effective application of CTOP, but also valuable for the general multiple constrained airspace resources multiple reroutes optimization problem and the design of future air traffic flow management program

ATM automation: guidance on human technology integration

© Civil Aviation Authority 2016Human interaction with technology and automation is a key area of interest to industry and safety regulators alike. In February 2014, a joint CAA/industry workshop considered perspectives on present and future implementation of advanced automated systems. The conclusion was that whilst no additional regulation was necessary, guidance material for industry and regulators was required. Development of this guidance document was completed in 2015 by a working group consisting of CAA, UK industry, academia and industry associations (see Appendix B). This enabled a collaborative approach to be taken, and for regulatory, industry, and workforce perspectives to be collectively considered and addressed. The processes used in developing this guidance included: review of the themes identified from the February 2014 CAA/industry workshop1; review of academic papers, textbooks on automation, incidents and accidents involving automation; identification of key safety issues associated with automated systems; analysis of current and emerging ATM regulatory requirements and guidance material; presentation of emerging findings for critical review at UK and European aviation safety conferences. In December 2015, a workshop of senior management from project partner organisations reviewed the findings and proposals. EASA were briefed on the project before its commencement, and Eurocontrol contributed through membership of the Working Group.Final Published versio

Online Learning for Ground Trajectory Prediction

This paper presents a model based on an hybrid system to numerically simulate the climbing phase of an aircraft. This model is then used within a trajectory prediction tool. Finally, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm is used to tune five selected parameters, and thus improve the accuracy of the model. Incorporated within a trajectory prediction tool, this model can be used to derive the order of magnitude of the prediction error over time, and thus the domain of validity of the trajectory prediction. A first validation experiment of the proposed model is based on the errors along time for a one-time trajectory prediction at the take off of the flight with respect to the default values of the theoretical BADA model. This experiment, assuming complete information, also shows the limit of the model. A second experiment part presents an on-line trajectory prediction, in which the prediction is continuously updated based on the current aircraft position. This approach raises several issues, for which improvements of the basic model are proposed, and the resulting trajectory prediction tool shows statistically significantly more accurate results than those of the default model.Comment: SESAR 2nd Innovation Days (2012

Flight Awareness Collaboration Tool Development

NASA is developing the Flight Awareness Collaboration Tool (FACT) to support airline and airport operations during winter storms. The goal is to reduce flight delays and cancellations due to winter weather. FACT concentrates relevant information from the Internet and FAA databases on one screen for easy access. It provides collaboration tools for those managing the winter weather event including the airline operations center, airport authority (runway treatment), the Federal Aviation Administration air traffic control tower, and de-icing operators. Prediction tools are being added to improve FACT capabilities including one that anticipates changes in airport departure rates from weather forecasts. We have formed two user teams from affected airports to guide the design and evaluate the web-based prototype. Future work includes adding more automated capabilities and conducting a simulation to evaluate FACT in a realistic environment

Seeing the invisible: from imagined to virtual urban landscapes

Urban ecosystems consist of infrastructure features working together to provide services for inhabitants. Infrastructure functions akin to an ecosystem, having dynamic relationships and interdependencies. However, with age, urban infrastructure can deteriorate and stop functioning. Additional pressures on infrastructure include urbanizing populations and a changing climate that exposes vulnerabilities. To manage the urban infrastructure ecosystem in a modernizing world, urban planners need to integrate a coordinated management plan for these co-located and dependent infrastructure features. To implement such a management practice, an improved method for communicating how these infrastructure features interact is needed. This study aims to define urban infrastructure as a system, identify the systematic barriers preventing implementation of a more coordinated management model, and develop a virtual reality tool to provide visualization of the spatial system dynamics of urban infrastructure. Data was collected from a stakeholder workshop that highlighted a lack of appreciation for the system dynamics of urban infrastructure. An urban ecology VR model was created to highlight the interconnectedness of infrastructure features. VR proved to be useful for communicating spatial information to urban stakeholders about the complexities of infrastructure ecology and the interactions between infrastructure features.https://doi.org/10.1016/j.cities.2019.102559Published versio