An analysis of the application of AI to the development of intelligent aids for flight crew tasks

This report presents the results of a study aimed at developing a basis for applying artificial intelligence to the flight deck environment of commercial transport aircraft. In particular, the study was comprised of four tasks: (1) analysis of flight crew tasks, (2) survey of the state-of-the-art of relevant artificial intelligence areas, (3) identification of human factors issues relevant to intelligent cockpit aids, and (4) identification of artificial intelligence areas requiring further research

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

Workload balancing for flight dispatcher scheduling

Unlike other airline operations planning problems, optimization in flight dispatching is not common in literature. Flight dispatchers are centrally located and monitor multiple flights in different places simultaneously. Their work involves planning fuel requirements, routing, and weather monitoring, both before and during a flight. An area of opportunity exists in the assignment of work amongst dispatchers. A desk contains a series of flights, and is served by a dispatcher or a series of dispatchers working consecutive shifts. In this work, we do not consider shifts and instead focus on assigning flights amongst a set number of desks. Our goal is to balance the workload of each desk, which is measured by the sum of each desk’s maximum workload throughout the day. Two formulations are presented that model the assignment of flights to desks, which we call the Flight Dispatching Problem. The Flight Dispatcher Schedule Formulation (FDSF) assigns flights amongst a set number of desks. The Set Covering Formulation (SCF) selects from known schedules (the assignment of flights to a single desk) to cover all flights with the specified number of desks (i.e., schedules). The base implementation solves the SCF using a column generation approach that creates new schedules with each iteration. Additional variants are also modelled where we limit which flights are assigned to the same desk. Testing is performed on European Airline Data and American Airlines data. The instances range in size from 46 to 297 flights in one day. We find that the FDSF solves to optimality quickly for small instances but not for the larger ones. The base implementation converges within two hours for the small and mid-size instances. Gaps are reduced using an improvement heuristic in some cases. For the larger instances, neither implementation solves within two hours and the gaps after that time are very large. Constraining the flight assignments provides trade-offs between computation time (which is typically faster) and solution quality (which is typically worse). We also tested the case where load varies throughout the flight. For the base implementation, most of the computation time for larger instances is spent in the pricing problem. In some cases, this is improved by generating multiple columns in each iteration instead of just one. The solution of the pricing problem is an area where future work could be focused to improve the computational performance. Other areas for future work include modelling dispatch zones instead of decomposing the problem by zones, changing the balance metric in the objective, incorporating uncertainty, and including the shift component of the dispatching problem

Presentations from the MIT/Industry Cooperative Research Program Annual Meeting, 1991

Cover titleMay 1991Includes bibliographical reference

North American Bird Strike Advisory System: Strategic Plan

The Strategic Plan has been accepted for inclusion in 2009 Bird Strike North America Conference (11th Joint meeting of Bird Strike Committee USA & Canada, Victoria BC, Canada, 14-17 September 2009). The international aviation community recognizes the high human and economic costs associated with bird strikes. Hundreds of lives and millions of dollars have been lost in recent years because of this problem. Notably, aviation experts in North America recognize the importance and availability of potential solutions for this problem. Several models and systems such and the USAF’s Bird Avoidance Model (BAM) and the Avian Hazard Advisory System (AHAS) as well as the technological development of advanced radar and communications systems have made great progress in addressing the problem of bird strikes. However, many have argued that further and much greater advancement could be made if the current fragmented and competitive efforts could be consolidated in a single cooperative venture. This strategic plan is the initial step in a process of consolidating and integrating the various United States and Canadian civil and military efforts in order to develop and implement North American Bird Strike Advisory System. The plan has been developed based on the collected wisdom and technical knowledge of the top personnel and organizations in the field of aviation safety. If implemented, the plan will represent a critical first step leading to the realization of a North American Bird Strike Advisory System that will help protect aviators and their equipment from the deadly and costly effects of bird hazards. The plan outlines the architecture of a notional bird strike advisory system for North America. It identifies the key agencies that must be involved in the development of the system. It establishes a top level schedule and identifies six key goals in developing an integrated system. The plan describes more detailed objectives and activities required to accomplish these goals. Recommendations are made regarding which agencies might most effectively take the lead in integrating various activities needed to accomplish each goal. It proposes a 5 year budget of approximately $16,000,000 in order to support the initial phases of the effort. The strategic plan and its appendices also outline in considerable detail the key technical challenges, risks, and suggested organizational and technological solutions for these problems. While reviewing this strategic plan, it’s important to remember that it is not a detailed blueprint for developing and implementing the final system. Rather, it is a starting point for an evolving project and system that can be continuously developed and improved as technology and organizational systems become more advanced. The relatively modest budget proposed is essentially a “down payment” for the more robust system that will evolve based on this initial consolidation and integration effort. The plan represents an important first step in moving beyond fragmented competitive approaches to consolidated and integrated system that will save hundreds of lives and prevent a great deal of economic loss associated with destroyed or damaged aircraft and equipment. The plan highlights the many advantages of an integrated and consolidated bird strike advisory system. One such advantage is the improvement in the accuracy and fidelity of bird avoidance information to users in the aviation community. Another advantage of the proposed system is the synergistic use of data from new and existing radar and other systems to enhance reporting on bird activity without compromising the current effectiveness of those systems. Throughout the plan, the development of a robust communications infrastructure and network is described to enhance the timeliness and scope of bird advisory information delivery.publishe

Presentations from the 1994 MIT/industry cooperative research program annual meeting

Cover titleMay 199

Space station data system analysis/architecture study. Task 2: Options development, DR-5. Volume 2: Design options

The primary objective of Task 2 is the development of an information base that will support the conduct of trade studies and provide sufficient data to make key design/programmatic decisions. This includes: (1) the establishment of option categories that are most likely to influence Space Station Data System (SSDS) definition; (2) the identification of preferred options in each category; and (3) the characterization of these options with respect to performance attributes, constraints, cost and risk. This volume contains the options development for the design category. This category comprises alternative structures, configurations and techniques that can be used to develop designs that are responsive to the SSDS requirements. The specific areas discussed are software, including data base management and distributed operating systems; system architecture, including fault tolerance and system growth/automation/autonomy and system interfaces; time management; and system security/privacy. Also discussed are space communications and local area networking

Machine learning in airline crew pairing to construct initial clusters for dynamic constraint aggregation

The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of flight legs separated by connection time and rest periods that starts and ends at the same base. Because of the extensive list of complex rules and regulations, determining whether a sequence of flights constitutes a feasible pairing can be quite difficult by itself, making CPP one of the hardest of the airline planning problems. In this paper, we first propose to improve the prototype Baseline solver of Desaulniers et al. (2020)2020) by adding dynamic control strategies to obtain an efficient solver for large-scale CPPs: Commercial-GENCOL-DCA. These solvers are designed to aggregate the flights covering constraints to reduce the size of the problem. Then, we use machine learning (ML) to produce clusters of flights having a high probability of being performed consecutively by the same crew. The solver combines several advanced Operations Research techniques to assemble and modify these clusters, when necessary, to produce a good solution. We show, on monthly CPPs with up to 50 000 flights, that Commercial-GENCOL-DCA with clusters produced by ML-based heuristics outperforms Baseline fed by initial clusters that are pairings of a solution obtained by rolling horizon with GENCOL. The reduction of solution cost averages between 6.8% and 8.52%, which is mainly due to the reduction in the cost of global constraints between 69.79% and 78.11%

Proceedings of the Air Transportation Management Workshop

The Air Transportation Management (ATM) Workshop was held 31 Jan. - 1 Feb. 1995 at NASA Ames Research Center. The purpose of the workshop was to develop an initial understanding of user concerns and requirements for future ATM capabilities and to initiate discussions of alternative means and technologies for achieving more effective ATM capabilities. The topics for the sessions were as follows: viewpoints of future ATM capabilities, user requirements, lessons learned, and technologies for ATM. In addition, two panel sessions discussed priorities for ATM, and potential contributions of NASA to ATM. The proceedings contain transcriptions of all sessions

Computational optimization of networks of dynamical systems under uncertainties: application to the air transportation system

To efficiently balance traffic demand and capacity, optimization of air traffic management relies on accurate predictions of future capacities, which are inherently uncertain due to weather forecast. This dissertation presents a novel computational efficient approach to address the uncertainties in air traffic system by using chance constrained optimization model. First, a chance constrained model for a single airport ground holding problem is proposed with the concept of service level, which provides a event-oriented performance criterion for uncertainty. With the validated advantage on robust optimal planning under uncertainty, the chance constrained model is developed for joint planning for multiple related airports. The probabilistic capacity constraints of airspace resources provide a quantized way to balance the solution’s robustness and potential cost, which is well validated against the classic stochastic scenario tree-based method. Following the similar idea, the chance constrained model is extended to formulate a traffic flow management problem under probabilistic sector capacities, which is derived from a previous deterministic linear model. The nonlinearity from the chance constraint makes this problem difficult to solve, especially for a large scale case. To address the computational efficiency problem, a novel convex approximation based approach is proposed based on the numerical properties of the Bernstein polynomial. By effectively controlling the approximation error for both the function value and gradient, a first-order algorithm can be adopted to obtain a satisfactory solution which is expected to be optimal. The convex approximation approach is evaluated to be reliable by comparing with a brute-force method.Finally, the specially designed architecture of the convex approximation provides massive independent internal approximation processes, which makes parallel computing to be suitable. A distributed computing framework is designed based on Spark, a big data cluster computing system, to further improve the computational efficiency. By taking the advantage of Spark, the distributed framework enables concurrent executions for the convex approximation processes. Evolved from a basic cloud computing package, Hadoop MapReduce, Spark provides advanced features on in-memory computing and dynamical task allocation. Performed on a small cluster of six workstations, these features are well demonstrated by comparing with MapReduce in solving the chance constrained model