scholarly journals An improved 4D trajectory modeling approach for traffic management

2021 ◽  
Author(s):  
Zhou Shen ◽  
Xiaomo Yu
Keyword(s):  
Traffic Flow ◽  
Traffic Safety ◽  
Data Model ◽  
Traffic Management ◽  
Air Transportation ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Trajectory Prediction ◽  
Air Traffic Flow ◽  
Aircraft Performance

Abstract Under the premise that the capability of existing air transportation system can no longer meet the demand of air traffic flow, 4D trajectory operation based on accuracy is the basis of future air traffic management (ATM) system to achieve the optimization of flight trajectory. This article investigates the establishment of a data model system based on aircraft performance and operation procedures, which can be applied to 4D trajectory prediction to greatly reduce or avoid the possibility of flight conflicts in the air, enhance air traffic safety and improve air traffic flow.

The Airline's Perception of Air Traffic Management

1990 ◽  
Vol 43 (2) ◽  
pp. 204-208 ◽  
Author(s):  
Colin Hume
Keyword(s):  
Traffic Flow ◽  
Traffic Management ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Flow Management ◽  
Air Traffic Flow Management ◽  
Air Traffic Flow ◽  
Traffic Flow Management ◽  
Reverse Situation

The situation today can be described as very frustrating for a variety of reasons. Air traffic flow-management (ATFM) has dominated the scene for many years since its conception in 1980. At that time, the principles of ATFM were directed at ensuring that temporary or isolated sector overloads could be handled by ATC and only when broad, prolonged overloads were expected was ATFM activated. Today, we have the reverse situation, where ATFM is active throughout 16 h or more during each day. The system as such was never intended or planned to cope with such a burden and the results are seen in a variety of forms, including departure delays as shown in Fig. 1.

A Trajectory Prediction Tool to Support Air Traffic Management

2009 ◽  
Author(s):  
Domenico Accardo ◽  
Antonio Moccia ◽  
Luigi Fiorillo ◽  
Alberto Leardi ◽  
Giuseppe Maresca
Keyword(s):  
Traffic Management ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Prediction Tool ◽  
Trajectory Prediction

A framework of indicators for the potential influence of Air Navigation Services on air traffic safety in Europe

2008 ◽  
Vol 222 (1) ◽  
pp. 57-78
Author(s):  
H H de Jong ◽  
F Preti ◽  
G W H van Es
Keyword(s):  
Traffic Safety ◽  
Traffic Management ◽  
Statistical Significance ◽  
Safety Data ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Safety Performance ◽  
Weak Links ◽  
Potential Influence ◽  
Air Navigation Services

This paper outlines a proposal for a framework of indicators developed with the aim to improve European safety performance monitoring of Air Navigation Services. The extension of scope from the usual choice of Air Traffic Management to Air Navigation Services has been made to address the complication that Air Traffic Management is a different service from Communication, Navigation, and Surveillance, but intimately connected with it. The framework considers the potential influence of Air Navigation Services on air traffic safety, and it uses accidents, their causal/contributing factors, and incidents related to these services as source data for the indicators. Those occurrence categories are considered for which Air Navigation Services have the potential to improve risk. This approach is independent of the notion of a service's contribution to occurrences, which is normally used, but which suffers from a considerable degree of subjectivity. In the data flows from air traffic operations to safety performance indicators, weak links are human incident reporting, varying proportions of incidents actually investigated sufficiently well plus different ways to perform the investigations, and differences in interpretation in providing overviews of the resulting safety data on the level of States. In view of these weaknesses, conditions are developed to prevent data of insufficient quality from being used. The paper mentions a number of aspects to consider when using the indicators. Before drawing conclusions, statistical significance and the existence of reporting bias need to be assessed. The paper finishes with a discussion of the relation of the framework with existing targets and indicates how the framework could support deriving appropriate targets and performance of safety assessments.

A Method for Predicting Air Traffic Network Flow by Short-Term Probability

2015 ◽  
Vol 713-715 ◽  
pp. 1500-1503
Author(s):  
Jin Feng Kong ◽  
Yu Wang
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Network Flow ◽  
Traffic Management ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Main Task ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Probability Prediction

The main task of air traffic management is making adjustments when traffic flow reaches to the maximum capacity of air traffic control. It aims to increase the use ratio of airport and airspace. Air traffic flow prediction is one important part of air traffic management. Usually, air traffic prediction is divided into deterministic flow prediction and probability prediction .We research on the probability prediction on the basis of deterministic flow prediction. By analyzing aircrafts’ experienced flight time distribution, giving experienced distribution function and computing method. The data analysis shows that the accuracy rate increased by 22%.

scholarly journals Modeling Air Traffic Situation Complexity with a Dynamic Weighted Network Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hongyong Wang ◽  
Ziqi Song ◽  
Ruiying Wen
Keyword(s):  
Traffic Flow ◽  
Traffic Management ◽  
Flow Characteristics ◽  
Air Traffic Management ◽  
Air Traffic ◽  
System Capacity ◽  
Traffic Situation ◽  
Flight Delays ◽  
Weighted Network ◽  
Potential Applications

In order to address the flight delays and risks associated with the forecasted increase in air traffic, there is a need to increase the capacity of air traffic management systems. This should be based on objective measurements of traffic situation complexity. In current air traffic complexity research, no simple means is available to integrate airspace and traffic flow characteristics. In this paper, we propose a new approach for the measurement of air traffic situation complexity. This approach considers the effects of both airspace and traffic flow and objectively quantifies air traffic situation complexity. Considering the aircraft, waypoints, and airways as nodes, and the complexity relationships among these nodes as edges, a dynamic weighted network is constructed. Air traffic situation complexity is defined as the sum of the weights of all edges in the network, and the relationships of complexity with some commonly used indices are statistically analyzed. The results indicate that the new complexity index is more accurate than traffic count and reflects the number of trajectory changes as well as the high-risk situations. Additionally, analysis of potential applications reveals that this new index contributes to achieving complexity-based management, which represents an efficient method for increasing airspace system capacity.

A Multi-Agent Simulation of Collaborative Air Traffic Flow Management

2009 ◽  
pp. 357-381 ◽  
Author(s):  
Shawn R. Wolfe ◽  
Peter A. Jarvis ◽  
Francis Y. Enomoto ◽  
Maarten Sierhuis ◽  
Bart-Jan van Putten
Keyword(s):  
Traffic Flow ◽  
Traffic Management ◽  
Air Traffic ◽  
Simulation Design ◽  
Flow Management ◽  
Air Traffic Flow Management ◽  
Air Traffic Flow ◽  
Traffic Flow Management ◽  
Agent Simulation ◽  
Traffic Management System

Today’s air traffic management system is not expected to scale to the projected increase in traffic over the next two decades. Enhancing collaboration between the controllers and the users of the airspace could lessen the impact of the resulting air traffic flow problems. The authors summarize a new concept that has been proposed for collaborative air traffic flow management, the problems it is meant to address, and our approach to evaluating the concept. The authors present their initial simulation design and experimental results, using several simple route selection strategies and traffic flow management approaches. Though their model is still in an early stage of development, these results have revealed interesting properties of the proposed concept that will guide their continued development, refinement of the model, and possibly influence other studies of traffic management elsewhere. Finally, they conclude with the challenges of validating the proposed concept through simulation and future work.

A regression model for terminal airspace delays

2017 ◽  
Vol 121 (1239) ◽  
pp. 680-692 ◽  
Author(s):  
F. Aybek Çetek ◽  
Y.M. Kantar ◽  
A. Cavcar
Keyword(s):  
Regression Model ◽  
Traffic Flow ◽  
Traffic Management ◽  
Air Traffic ◽  
Fast Time ◽  
Total Delay ◽  
Traffic Demand ◽  
Flight Operations ◽  
Entry Points ◽  
Air Traffic Flow

ABSTRACTAir Traffic Management (ATM) research generally focuses on achieving a safer, more effective and economical air traffic system. The current airspace system has become increasingly strained as the demand for air travel has steadily grown. Innovative, proactive and multi-disciplinary approaches to research are needed to solve flight congestion and delays as a consequence of this rapid growth. As a result of this growth, air traffic flow becomes more complex, especially in Terminal Airspaces (TMA) where climb and descent manoeuvres of departing and arriving flights take place around airports. As air traffic demand exceeds the capacity in a TMA, the resultant congestion leads to delays that spread all over the system. Therefore, the reduction of delays is critical for airspace designers to increase customer satisfaction and the perception of service quality. Numerous studies have been conducted to reduce delays within TMAs. This research focuses on defining the causes of delays quantitatively through statistical analysis. The first step was to create a fast-time simulation model of sample airspace for collecting delay data. After building up this model using the SIMMOD fast-time ATM simulation tool, simulation experiments were run to produce various traffic scenarios and to generate traffic delay data. The number of airports, entry points, fixes and flight operations in airspace and the probability of wide-body aircraft were considered as independent variables. The correlations between the considered variables were analysed, and the total delay data was modelled using a linear regression model. The findings of regression model present a statistical approach for airspace designers and air traffic flow planners.

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