Air Traffic Flow Management System Research Articles

Airspace Congestion, flow Relations, and 4-D fundamental Diagrams for advanced urban air mobility

This paper develops theoretical macroscopic air traffic flow models that relate vehicle density and spacing to traffic flow (throughput) measures under different operational parameters for unstructured airspace in the Advanced Air Mobility (AAM) context. Recognizing the role of conflicts in air traffic flow, we relate vehicle density to the frequency of conflict occurrence in airspace using a gas-kinetic analogy. The number of conflicts is then related to vehicle speeds using an average speed loss per conflict. The effects of the speed reductions are coupled with density to explore the fundamental diagram between flow rate and density. The theoretical models are tested and validated with simulated results for a number of parameter levels. The models can be applied for quick predictions of future traffic flow conditions, which will be especially useful for operators or air traffic flow management systems. The theoretical and simulated findings also provide operational and policy insights for AAM operators, planners, and modelers. Notable insights include the critical role of aircraft density in air traffic flow and the variable impact of that density on traffic flow behavior. AAM operators and planners will need to closely manage the airspace density to avoid large numbers of conflicts simultaneously and maintain acceptable travel times and throughputs. Key operational parameters such as aircraft spacing requirements and maximum aircraft speeds were also found to have significant impacts on traffic flow behavior, and offer policy avenues for managing air traffic.

Predictability improvement of Scheduled Flights Departure Time Variation using Supervised Machine Learning

The departure time uncertainty exacerbates the inaccuracy of arrival time estimation and demand for arrival slots, particularly for movements to capacity constrained airports. The Estimated Take-Off Time (ETOT) or Estimated Departure Time(ETD) for each individual flight is currently derived from Air Traffic Flow Management System (ATFMS), which are solely determined based on individual flight plan Estimated Off Block Time(EOBT) or subsequent delays updated by Airline. Even if normal weather conditions prevail, aircraft departure times will differ from ETOTs determined by the ATFMS due to a number of factors such as congestion, early/delayed inbound flight (linked flights), reactionary delays and air traffic flow management slot changes. This paper presents a model that predicts departure time variance based on the previous leg departure time using a combination of exponential moving average and machine learning methods. The model correctly classifies the departure time (Early, On Time, Delay) based on the previous leg departure state, allowing the ATFM system to measure the arrival time of a capacity constrained airport with greater accuracy and better assess demand requirements. The results show that the proposed model with M5P Regression tree provides the best results, with Mean Absolute Error and Root Mean Square Error (RMSE) of 3.43 and 4.83, respectively, indicating a 50% improvement over previous research findings. Whereas, with logistic regression, the classification of departure time (Early, On Time, Delay) is achieved a better accuracy of 91 %, which is higher than previous works.

The Effect of Government Officials' Cultural Bias to their Social Acceptance including a Case Study of Implementing New Air Traffic Flow Management System

The Effect of Government Officials' Cultural Bias to their Social Acceptance including a Case Study of Implementing New Air Traffic Flow Management System

An intelligent recognition model for dynamic air traffic decision-making

Air traffic flow management system (ATFMS) is becoming increasingly important due to the rapid growth of air traffic and serious flight delay nowadays. To aware the air traffic flow density and identify the heat airspace in terminal areas of large hub airports is essential for an ATFMS. Due to numerous parameters in air traffic flow, traditional methods based on one single parameter fail to reflect the true complexity relationship between these parameters. This study aims to develop an intelligent air traffic flow heat airspace recognition model using advanced data science technique for establishing a real-time cloud map in the terminal airspace of airports, which attempts to use machine learning models to represent the complex relationship among these parameters. In the proposed intelligent recognition model, high dimensions of parameters (basic parameters, additional parameters and time parameters) are processed to achieve a comprehensive and accurate situation awareness for support dynamic air traffic decision-making. An aircraft trajectories points clustering method is developed to generate a 4D heat airspace map. The basic parameters and time parameters are used to identify the heat airspaces; the changes of additional parameters which influence the heat airspaces are identified and analyzed by use of grid graphs of flight trajectories; probability fitting graphs are used to verify accuracy of 4D results in order to support air traffic decision-making. A case study on Beijing International Airport (PEK) is conducted to test our model and has obtained two main research findings: there are two areas of PEK that have the high density and there are hot peaks at two different heights; flight trajectories and speed of trajectories also effect on the heat airspace. The study realizes that the proposed 4D heat airspace model is better for detailed and accurate information construction, expression of spatial changes, and visualization of multiple parameters of temporal and spatial density and range. It can assist the decisions on airspace allocation, and also have a definite reference meaning on alleviating the contradiction between the current air traffic demand and airspace resource constraints.

A Stochastic Characterization Based Data Mining Implementation for Airport Arrival and Departure Delay Data

The probabilistic distribution of Airport arrival and departure delays over a selected period of eight months was analyzed using an optimal Generalized Extreme Value (GEV) model in this paper. It is anticipated that quantitative stochastic characterizations of delay data out of our work would improve demand predictions in air traffic flow management systems. Analysis and verification through application of Beijing Capital International Airport’s history flight delay data demonstrate better Goodness of Fit of our optimal GEV based model.

Analysis of Air Traffic Flow Management Effect

With the rapid increase of the vigorous development of China's aviation industry and air traffic flow, air traffic flow management is increasingly outstanding. To evaluate the effect of air traffic flow management system, needs to establish the air traffic flow management system effectiveness evaluation index from the scientific, objective, practical angle. According to the air traffic flow at home and abroad with evaluation index, respectively, from the meaning of statistical method, calculation method, the solution of different angles, the air traffic flow evaluation indicators for the analysis and research.

0–1 integer programming model for procedural separation of aircraft by ground holding in ATFM

This study presents a 0–1 integer programming model for air traffic flow management. The model is used for determining optimum departure times of aircraft so as to avoid aircraft conflicts and to balance capacity and demand on the airports. Standard air traffic control procedural separation minimas were set as conflict criteria, which has never been done before, and this makes it convenient for use in planning of flows in airspace sectors especially without surveillance system. The model was tested on an imaginary scenario, including an airspace sector, airport capacities and flight plans, and solved by a mathematical modelling and optimization software. Considering conflicts before takeoff, as in this study, might prevent riskier and costlier air traffic control measures such as heading, speed and flight level changes. The great need for these measures can be easily inferred from the solution results that presents delay times due to conflict related constraints. Utilizing both standard conflict criteria based on procedural separation minimas used in air traffic control and standard Air Traffic Flow Management strategy based on ground holding of aircraft in planning of flows, makes it easier to adapt the model to the current Air Traffic Flow Management system.

Operational Evaluation of Air Traffic Flow Management in the Philippines

In the past decade, both the economy and air traffic demand have been growing rapidly in the Philippines. As a result, Ninoy Aquino International Airport (NAIA) has had to cope with increasingly heavy traffic. To mitigate the effects of this traffic, an air traffic flow management (ATFM) system was introduced in the Philippines. In this paper, background on introducing the ATFM system is presented and its successful results in reducing the holding time of flights waiting to land at the NAIA are reported on.

Concept of Measurement for En-Route Sector Capacity

ATFM (Air Traffic Flow Management) keeps air traffic flows safe and orderly. When the estimated workload exceeds capacity limit, traffic demand is controlled. In Japan, The ATFM system estimates controller's workload by a lot of parameter that depend on sector characteristics represented by traffic flow. The calculation of the value needs a lot of analysis. Author proposes a simple method for estimating the workload created by the traffic situation.

A Hybrid Model of Neural Network and Grey Theory for Air Traffic Passenger Volume Forecasting

Chinese air traffic passenger volumes have experienced phenomenal growth during the past years. The air traffic volume prediction plays a key role in air traffic flow management system. This paper develops a hybrid model of Neural and Grey Theory for air traffic passenger volume forecasting. The Grey theory is adopted to fit the air traffic data patterns and make the data a higher regularity, and Radical basis function is combined to raise the forecasting accuracy. The model is tested with the Chinese civil aviation passenger volume data from 1998 to 2007 and the result shows that the model is feasible for practical implementations.

Evolutionary Concepts for Decentralized Air Traffic Flow Management

Alternative concepts for modifying the policies and procedures under which the air traffic flow management system operates are described, and an approach to the evaluation of those concepts is discussed. Here, air traffic flow management includes all activities related to the management of the flow of aircraft and related system resources from “block to block.” The alternative concepts represent stages in the evolution from the current system, in which air traffic management decision making is largely centralized within the FAA, to a more decentralized approach wherein the airlines and other airspace users collaborate in air traffic management decision making with the FAA. The emphasis in the discussion is on a viable medium-term partially decentralized scenario representing a phase of this evolution that is consistent with the decision-making approaches embodied in proposed Free Flight concepts for air traffic management. System-level metrics for analyzing and evaluating the various alternatives are defined, and a simulation testbed developed to generate values for those metrics is described. The fundamental issue of modeling airline behavior in decentralized environments is also raised, and an example of such a model, which deals with the preservation of flight bank integrity in hub airports, is presented.

Knowledge-based System For Air Traffic FlowManagement: Timetable Rescheduling AndCentralized Flow Control

A Know ledge-Based System (KBS) has been designed for the Air Traffic Flow Management (ATFM) problem. For timetable rescheduling, the system tends to modify airlines timetable to smooth traffic peaks at airports during rush-hours. For centralized flow control, the system works on-line to forecast the place, time and magnitude of the congestions and to take some actions to prevent these congestions. As an Artificial Intelligence language, Prolog was chosen to develop the prototype of the Knowledge-Based ATFM System. By using this system, Brazilian ATFM, which includes the principal airports of this country, has been studied.