Scanning the Issue

2008

Due to a dramatic increase in air traffic around the globe, the tasks for air traffic controllers have increased multifold. This work aims to develop an air traffic control to prioritize landing sequences assigned to planes when, unexpectedly, a large number of planes approach the airfield. Two alternatives are proposed: one is based on rule-based expert system and another on artificial neural networks. The author shows that each of the models helps to optimize the prioritization of overall landing requests, with exception only to a situation of a larger number of emergencies. Further, a combination of these approaches is discussed to show that it does help in minimizing time to handle landing sequences during emergencies.

ArXiv, 2018

Air Traffic Flow and Capacity Management (ATFCM) is one of the constituent parts of Air Traffic Management (ATM). The goal of ATFCM is to make airport and airspace capacity meet traffic demand and, when capacity opportunities are exhausted, optimise traffic flows to meet the available capacity. One of the key enablers of ATFCM is the accurate estimation of future traffic demand. The available information (schedules, flight plans, etc.) and its associated level of uncertainty differ across the different ATFCM planning phases, leading to qualitative differences between the types of forecasting that are feasible at each time horizon. While abundant research has been conducted on tactical trajectory prediction (i.e., during the day of operations), trajectory prediction in the pre-tactical phase, when few or no flight plans are available, has received much less attention. As a consequence, the methods currently in use for pre-tactical traffic forecast are still rather rudimentary, often ...

2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019

Air traffic flow Management aims to control route network flow on regions in which the service is provided. One important challenge is to guarantee that aircraft are able to departure and land safely, with minimum delay. To satisfy this requirement, several restrictive measures are employed, such as: ground delay programs, en-route deceleration, holding patterns at intermediate altitudes (descent and approach phases), and diversions to less congested airports. The problem becomes how to better distribute these measures among the involved entities. In this paper, a new model is proposed to tackle this problem, named RELEASE, which utilizes reinforcement learning techniques to train agents to better negotiate their delays with other aircraft. To do so, the negotiation process was modeled as a Markov Decision Process. The aircraft is considered as agent and then trained to negotiate its position in the environment with other aircraft through time offers with an associated reward. The learning behavior was compared to a reference model. In the case studies, the agents were able to achieve similar results as the reference model, and even getting better results for agents with greater operational costs than their predecessors.

AIAA Guidance, Navigation, and Control Conference, 2009

In this paper, algorithms are presented that model increased collaboration between the air traffic service provider and airspace users on flight route and delay decisions. These decisions are part of the traffic flow management function that constrains demand below capacity. Currently, users cannot make changes to the route or delay of a flight close to or after departure time and instead must send requests to the service provider who attempts to accommodate the users based on congestion and workload limitations. To mitigate this limitation, the algorithms model a new collaboration scheme. First, users directly implement their flight route and delay decisions, when the flight is further from the congested airspace, without sending a request to the service provider. The service provider can override their action when the flight becomes closer to the congested airspace. Second, users send flight ranking, route ranking and location-to-absorb-delay preferences to the service provider. The service provider may reject these preferences if needed. The algorithms are used to study whether increasing users' responsibility and increasing their preferences would prevent maintaining demand below capacity. To prevent demand from exceeding capacity the algorithms impose limits, such as available routes and imposed flow rates, on user decisions. A simulation case demonstrates the impact of the collaboration schemes on reducing demand below capacity within an en-route center. Preliminary results indicate that aircraft delay and, to a larger extent, passenger delay are reduced. However, congestion is reduced by a smaller amount when user preferences are considered by the service provider. Giving users responsibility according to service provider limits and delay feedback did not increase congestion.

IEEE Access, 2019

Flight schedules are highly sensitive to delays and witness these events on a very frequent basis. In an interconnected and interdependent air transportation system, these delays can magnify and cascade as the flight itineraries progress, causing reactionary delays. The airlines, passengers and airports bear the negative economic implications of such phenomenon. The current research draws motivation from this behavior and develops a multi-agent based method to predict the reactionary delays of flights, given the magnitude of primary delay that the flights witness at the beginning of the itinerary. Every flight is modeled as an agent which functions in a dynamic airport environment, receives information about other agents and updates its own arrival and departure schedule. To evaluate the performance of the method, this paper carries out a case study on the flights in Southeast Asia, which covers eleven countries. The model is tested on a six-month ADS-B dataset that is collected for the calendar year 2016. Through the reactionary delay values predicted by the multi-agent based method, the flights are first classified as delayed or un-delayed in terms of departure. The classification results show an average accuracy of 80.7%, with a delay classification threshold of 15 minutes. Further, a delay multiplier index is evaluated, which is a ratio of the total delays (primary+reactionary delays) and the primary delays for each aircraft. The majority of delay multiplier values range between 1-1.5, which signifies that for except a few outliers, the primary delays do not significantly cascade into reactionary delays for the flights in Southeast Asia. The outliers represent scenarios where primary delays magnify and propagate as reactionary delays over subsequent flight legs. Therefore, the proposed method can assist in better flight scheduling by identifying itineraries which experience higher reactionary delays. INDEX TERMS Air traffic management, agent-based method, ground delay analysis, Southeast Asian airports, reactionary delays.

Data Mining for Business …, 2009

IEEE Transactions on Intelligent Transportation Systems, 2008

Arrival sequencing and scheduling (ASS) at airports is an NP-hard problem. Much effort has been made to use permutation-representation-based genetic algorithms (GAs) to tackle this problem, whereas this paper attempts to design an efficient GA based on a binary representation of arriving queues. Rather than using the order and/or arriving time of each aircraft in the queue to construct chromosomes for GAs, this paper uses the neighboring relationship between each pair of aircraft, and the resulted chromosome is a 0-1-valued matrix. A big advantage of this binary representation is a highly efficient uniform crossover operator, which is normally not applicable to those permutation representations. The strategy of receding horizon control (RHC) is also integrated into the new GA to attack the dynamic ASS problem. An extensive comparative simulation study shows that the binary-representation-based GA outperforms the permutationrepresentation-based GA.

Transportation Research Procedia

The increasing number of air operations is a challenge for air traffic controllers. The organization of air traffic can be achieved by better aligning the planes for landing or sequencing. Sequencing problem is commonly found in many areas of science, industry and economics. The schedule of tasks in air transport is also important in the integration of traffic, as it allows passengers, not only direct flights, but also efficient interchanges. Generally, the problem of sequencing tasks is to determine the order of execution of tasks on machines (CPUs) so as to minimize (or maximize) the value of a given criterion. The problem of optimally determining the order of landing operations is noticeable both in official regulations and scientific publications. Aiming to develop the optimum sequencing of aircraft landing process, support procedures implemented at airports. In the early stages of air traffic sequencing, extended arrival management (AMAN) and feature-based navigation (PBN) are used to extend the planning horizon. It is possible to sequence traffic both during the flight and early descent. However, there are no universal sequencing methods. Research is still needed in this area. The article discusses the process of sequencing landing aircraft, taking into account the minimization of the schedule length. It represents the desired number of landing operations in the shortest possible time. The application of theoretical algorithms has been verified and a methodology has been developed for determining the order of landing operations, providing the shortest possible execution of all operations. On the basis of the computerized algorithm of sequencing landing aircraft with regard to the minimization of the ranking, calculations were made to check the validity of the algorithm. The results were compared with the times achieved using probabilistic sequencing problems.

International journal of advanced computer science and applications/International journal of advanced computer science & applications, 2024

Sequencing efficiently the departure traffic remains among the critical parts of air traffic management these days. It not only reduces delays and congestion at hold points, but it also enhances airport operations, improves traffic planning, and increases capacity. This research paper proposes an approach, that employs a genetic algorithm (GA), to help air traffic controllers in organizing a sequence for the departure traffic based on the standard instrument departures (SIDs) configuration. A scenario with randomly assigned types, SIDs, and departure times was applied to a set of aircraft in a terminal area with a four-SID configuration to assess the performance of the suggested GA. Subsequently, a comparison with the standard method of First Come First Served (FCFS) was conducted. The testing data revealed promising results in terms of the total spent time to reach a specified altitude after takeoff.

2020

This work presents an analysis of arrival sequencing at Stockholm Arlanda airport. Thesequencing of arrivals is very important part of air traffic control management and assuressafe space and time distancing of arriving aircraft. In this work we use historical flight datafrom Opensky Network database. The historical flight data contains the information about allthe arrivals of the year 2018. The aim of this work is to propose the key performanceindicators (KPIs) for evaluation of the arrival sequencing at Stockholm Arlanda airport. Thethree KPIs we are considering in this work are the minimum time to final, spacing deviationand sequence pressure. We choose data subsets of different size representing different trafficsituations. We visualize the results and summarize them in tables which assures better clarityfor the comparison of the same KPIs for different data subsets. In addition, we demonstratehow the proposed KPIs can be used for evaluation of optimization results from related ...