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In this article, the system model of the terminal control area is built considering the constraints between different departure routes. The balance for the departure routes in the terminal control area is achieved and the optimal control inputs for the departure flights are also acquired. In particular, the dioid algebra is employed to describe all kinds of concurrent events in the terminal control area and define the interactions between different flight trajectories. In this way, the new optimization model and solution procedure is introduced in order to improve the operation effect of air traffic flow. Finally, different scenarios such as deterministic and stochastic cases are given to demonstrate the performance of the system model. The essence of the system model is allocating optimal time slots to flights coming from different airports. [ABSTRACT FROM AUTHOR]

Abstract: Bad weather is a major contributor to air traffic delays. The ration-by-distance algorithm was proposed to increase the efficiency of ground delay program (GDP) planning in situations where there is uncertainty regarding the duration of the weather conditions that motivated the GDP. This algorithm, however, can treat shorter flights unfairly. We describe a two-stage stochastic, multi-objective integer program for GDP planning. This multi-objective approach allows one to balance equity and efficiency. We provide experimental evidence showing the effectiveness of the solutions generated. [Copyright &y& Elsevier]

Abstract: This paper develops methodologies for generating probabilistic capacity scenarios from two terminal weather forecasts: The Terminal Aerodrome Forecast (TAF) and the San Francisco Marine Initiative (STRATUS) forecast. The scenarios are assessed by using them as inputs to a static stochastic Ground Delay Program (GDP) model to determine efficient Air Traffic Flow Management (ATFM) strategies, and determining the effectiveness of the strategies in reducing the realized cost of delay. We use San Francisco International airport as a case study to quantify the benefit of using weather forecasts in decision making. It is shown that capacity scenarios generated using day-of-operation weather forecasts can reduce the cost of delays by 17%-23% compared to scenarios that do not make use of this information. The paper also compares the cost of delays using strategies determined using the scenarios generated from TAF and STRATUS. It is shown that on average TAF-based scenarios result in delay costs of similar magnitudes to STRATUS-based ones. The methodologies developed using the TAF can be applied to other airports to better plan operations during a GDP. [Copyright &y& Elsevier]

Abstract: This research presents the application of intelligent computing models in Air Traffic Flow Management (ATFM). Firstly, multi-agent system in grid computing environment is applied to deal with the problem of ATFM synchronization. The developed system consists of software agents, which are implemented in a Computational Grid platform for congestions identification, conflicts resolution and agreements negotiation among the participating airports. A metric criterion, called Agent’s Balancing Standard (ABS), is used as a basic index to measure the effectiveness of reducing both the amount of communication among agents and the delay of flights. Secondly, a brief discussion about the Meta-Level Control model is introduced in ATFM issues to improve the efficiency of communication among the agents. The system is developed to analyze the traffic flow information received, identify its importance and process it in the most adequate order. [Copyright &y& Elsevier]

Regulations of air traffic flow are necessary in order to avoid excess aircraft traffic in airports. In this paper, our algorithm is proposed on real-time dynamic departure regulation of air traffic flow by active use of the radar information. The algorithm is apt to the real situation of an airport. The effectiveness of the algorithm is evaluated by computer simulations. The formulas are given for the predicted holding time and for the probability of its occurring in the arrival airport in time unit S which is the shortest arrival time. Dynamic departure regulation method to reduce the holding time is proposed based on the predicted values and the effectiveness of this real-time regulation method is evaluated by computer simulations. © 1998 Scripta Technica. Electron Comm Jpn Pt 1, 81(9): 68–76, 1998 [ABSTRACT FROM AUTHOR]

Air traffic flow management is a new means of promoting efficient and smooth flow of air traffic over a wide airspace, through collaboration with an air traffic control system that controls a wide airspace by dividing the area into a large number of enroute control sectors and terminal airspaces. This paper discusses a real-time dynamic simulation experiment, which was executed to develop an adequate air traffic flow management method, that can cope with the increase of air traffic due to the function expansion program of the Tokyo International Airport. In the experiment, a newly developed air traffic flow management console is connected to the air traffic control simulation facility, and the sectors and terminal airspace are simulated simultaneously. For air traffic flow management, a method is proposed that is a combination of the dynamic flow branching using parallel routes, which has been used effectively in the evaluation of the airspace design for the enroute control sector, and a departure regulation method. The effectiveness of the method is examined by trajectory analysis including the terminal airspace. The effect of the smoothing of the arrival traffic flow on air traffic control processing, as well as the adequate condition for transferring the arriving aircraft between the enroute control and the terminal control, are analyzed. © 1997 Scripta Technica, Inc. Electron Comm Jpn Pt 1, 80 (5): 35–44, 1996 [ABSTRACT FROM AUTHOR]