Multi-Objective Evolutionary Algorithm Based on Decomposition for Air Traffic Flow Network Rerouting Problem

This paper builds on top of a collaborative ATFM framework previously published, which demonstrated a potential in significant pre-tactical system delay reduction over the current European ATFM practices. In this paper, we aim to raise the technology readiness level (TRL) of the original works, focusing on improving the tractability of its DCB optimizer. To this end, we apply a Dantzig-Wolfe (DW) decomposition approach and use high performance computing (HPC) techniques to attempt the solution in a short time. The main contribution of this paper is to mitigate the computational burden faced by previous approaches, which will make all the contributions demonstrated therein more operationally feasible.

Air Traffic Management (ATM) is defined as a broad domain that consists of complex activities such as Air Traffic Flow Management (ATFM). Within this context this paper presents two prototypes of a module (MAAD) assembled in a decision support device that is based on Artificial Intelligence, which generates suggestions for air traffic flow control according to each traffic saturation scenario. The device known as Distributed Decision Support System for Air Traffic Flow Management (SISCONFLUX) was built with a computational agent based on Reinforcement Learning (Module MAAD), which uses Q-learning algorithm. Two prototypes were built and one of them incorporated the specialist's (human agent) experience. The performance of the prototypes was evaluated through a case study modeled from real traffic demands verified on certain dates. The results obtained in the case study are promising; the behavior of the prototypes demonstrated that the Q-learning algorithm converged in a satisfying way. The prototypes generated actions that contributed effectively to the reduction of saturation in the air traffic scenarios under test.

Inherent uncertainties of the air transportation system can induce unexpected anomalies in its operations such as deviations in flight schedules, sudden imbalances of demands and capacities, higher workload for Air Traffic Controllers (AT-Cos), etc. Current Air Traffic Flow Management (ATFM) models rarely consider uncertainty in their algorithms, and generally focus on minimizing the flight delays under deterministic constraints. Thus, to bridge this gap, the thesis proposes several frameworks for en-route ATFM, while scrutinizing different uncertainty components present in air transportation system. Generally, uncertainty exists in two major forms, i.e., capacity uncertainty and demand uncertainty, which result in the stochastic behavior of the Air Traffic Network (ATN).

The high cost of flight delays for airlines has motivated scientific research in air traffic flow management (ATFM). The majority of ATFM models in the literature are deterministic and do not take into account stochastic factors such as weather. In this paper, new stochastic programming models for ATFM are proposed. The models include as tactical control options: ground holding, airborne holding and rerouting. To solve the models, a new heuristic method that takes advantage of the problem structure is derived and illustrated. Computational results show that the heuristic method provides practical computation times. Furthermore, the value of the stochastic solution is up to 14% for cases where adverse weather affects a significant part of the network. This implies that using the proposed approach to make air traffic flow decisions can lead to tangible monetary benefits for airlines.

Decision support system in tactical air traffic flow management for air traffic flow controllers

Demand uncertainty is one critical form of uncertainty which has an adverse effect on Air traffic flow management (ATFM). This is mainly due to the deviation in departure time and aircraft speed from their scheduled values. This may lead to the arrival of aircraft to certain routes at unscheduled times, causing an unexpected demand on those routes. The uncertainty of demand creates several difficulties in air transportation systems, including higher workloads for air traffic controllers, higher delays, travel costs, as well as safety risk. In this study, we propose a robust flight routing and scheduling scheme while considering both departure and speed uncertainty present in the air traffic network. Following robust optimization, we ensure that the capacity violations are eliminated from the system. The ATFM problem is formulated as a Mixed Integer Quadratic Programming (MIQP) problem with the objective of minimizing expected total delay of the system while maintaining required in-trail separation between aircraft even under uncertainty. In addition, we use an optimal flight level assignment method and speed assignment strategy to minimize the system delay and to fully utilize the system capacity. Furthermore, a greedy strategy with parallel computation is presented with the problem decomposed into a set of maximum independent sets to reduce the computational complexity in solving large-scale ATFM problems. With the experimental results, we demonstrate the effectiveness of the model.

A study on basic research priorities and development suggestions for the digital transformation of air traffic management

The rapid advancement of cutting-edge technology has reshaped the global dynamics of the airline sector, elevating the significance of service quality, effectiveness, and protection. As a vital contributor to national economic growth and a public utility, aviation's expansion has directly impacted businesses in the service sector, leading to increased operational opportunities for hotels, restaurants, and travel agencies. This article addresses the challenges within Air Traffic Management (ATM), focusing on the aviation industry's primary concerns. With the continuous growth of aviation traffic, there is a pressing need to enhance security, productivity, and environmental sustainability. The article aims to stimulate business innovation and collaboration in advanced study areas such as dynamic airspace management (DAM), air traffic flow management (ATFM), and collaborative/non-collaborative surveillance. The proposed frameworks, based on Clean Sky and NextGen, incorporate 4D Trajectory Optimization techniques, advanced surveillance technologies, and data link connections to establish a foundation for ATM industry development. Additionally, the research explores adaptive Human-Machine Interface and Interaction (HMI2) forms to automate the assessment and negotiation of aircraft intentions, thereby improving the efficiency and security of ATM operations. The study also addresses specific requirements for cooperative and non-cooperative Detect-and-Avoid (DAA) systems for Remotely Piloted Aircraft Systems (RPAS) within the evolving CNS+A process, ensuring their safe and unrestricted access to all airspace types.

In this paper, we develop an analytical approach to planning air traffic flow management (TFM) strategies under uncertain weather. To do that, we model the dynamics of an air traffic flow under uncertain weather as a discrete jump-linear system, which enacts the tracking of transient traffic congestion statistics. We then conduct a sensitivity analysis of traffic congestion to disturbances introduced by mean inflow rate and flow restriction constraint. The integrated framework of jump-linear modeling and sensitivity analysis permits optimal planning for flow redistribution and flow restriction redesign. We demonstrate the design procedure through an example of a major flow splitting into multiple flows that enter a bank of weather zones.

Despite various planning efforts, airspace capacity can sometimes be exceeded, typically because of disruptive events. Air traffic flow management (ATFM) is the process of managing flights in this situation. In this paper, we present an ATFM model that accounts for different rerouting options (path rerouting and diversion) and preexisting en route flights. The model proposes having a central authority to control all decisions, which is then compared with current practice. We also consider interflight and interairline fairness measures in the network. We use an exact approach to solve small- to medium-sized instances, and we propose a modified fix-and-relax heuristic to solve large-sized instances. Allowing a central authority to control all decisions increases network efficiency compared with the case where the ATFM authority and airlines control decisions independently. Our experiments show that including different rerouting options in ATFM can help reduce delays by up to 8% and cancellations by up to 23%. Moreover, ground delay cost has much more impact on network decisions than air delay cost, and network decisions are insensitive to changes in diversion cost. Furthermore, the analysis of the tradeoff between total network cost and overtaking cost shows that adding costs for overtaking can significantly improve fairness at only a small increase in total system cost. A balanced total cost per flight among airlines can be achieved at a small increase in the network cost (0.2%–3.0%) when imposing airline fairness. In conclusion, the comprehensiveness of the model makes it useful for analyzing a wide range of alternatives for efficient ATFM.

Air traffic flow management under emission policies: Analyzing the impact of sustainable aviation fuel and different carbon prices

<p>Convective weather represents a significant disruption to air traffic flow management (ATFM) operations. Thunderstorms are the cause for a substantial amount of delay in both the en-route and airport environment. Before the day of operations, poor prediction capability of convective weather prohibits traffic managers from considering weather mitigation strategies during the pre-tactical phase of ATFM planning. As a result, convective weather is mitigated tactically, possibly leading to excessive delays.  </p><p>The skill of weather forecasting has greatly improved in recent years. Hi-resolution weather models can predict the future state of the atmosphere for some weather parameters. However, incorporating the output from these sophisticated weather products into an ATFM solution that provides easily interpreted information by the air traffic managers remains a challenge. </p><p>This paper combines data from high-resolution numerical weather predictions with actual storm observations from lightning detecting and satellite images. It applies supervised machine learning techniques such as binary classification, multiclass classification, and regression to train neural networks to predict the occurrence, severity, and altitude of thunderstorms. The model predictions are given up to 36hr in advance, within timeframes necessary for pre-tactical planning of ATFM, providing traffic managers with valuable information for developing weather mitigation plans. </p>

In order to make a sensible prediction on the air traffic flow management with conditions of wave-off and bolter, a system dynamic model for the night recovery operations of embarked aircrafts is built to ensure the adaption of air traffic flow with the capacity of air control at each phase of the recovery operations. The model aims at the characteristics of multiple feedbacks, delays and complex time varying, builds a stock flow diagram and operation model with impact factors of the night recovery system, and is simulated in Vensim® Personal Learning Edition 5.9. The simulation shows a reasonable prediction result for the night recovery of embarked aircrafts with conditions of bolter and wave-off and can provide a theoretical basis for scheduling the air traffic flow management of embarked aircrafts formation recovery.

The problem of high similarity in attack data leading to unsatisfactory detection results of air traffic control network security attack behavior is addressed. This article designs a new clustering detection method for air traffic control network security attack behavior. Set the characteristic state of air traffic control network security attack behavior, obtain the set of air traffic control network security attack behavior characteristics through recursive feature addition method, and extract the characteristics of air traffic control network security attack behavior by determining the degree of feature criticality. Calculate the expected information gain and entropy value of feature data, determine the information gain of feature data, and reduce the interference of similar feature data. Introduce an automatic encoder in artificial intelligence algorithms to encode and decode the characteristics of air traffic control network security attack behavior, and achieve dimensionality reduction processing of air traffic control network security attack behavior data. Based on the above processing, a Unsupervised learning algorithm for clustering detection of air traffic control network security attacks is designed. Firstly, determine the distance between clustering clusters of air traffic control network security attack behavior characteristics, calculate the clustering threshold, and construct the initial clustering center. Then, recalculate the new mean of all feature objects in each cluster as the new cluster center point. Secondly, traverse all objects in the clustering cluster of air traffic control network security attack behavior feature data. Finally, clustering detection of air traffic control network security attack behavior is completed through the calculation of the objective function. The experiment takes three sets of experimental attack behavior datasets as the test subjects, with detection rate, false detection rate, and recall rate as the test indicators, and selects three similar methods for comparative testing. The experimental results show that the detection rate of the proposed method remains around 98%, the false detection rate remains below 1%, and the recall rate is above 97%. It has been proven that the proposed method can improve the detection performance of air traffic control network security attack behavior.

Since it is safer and less expensive to absorb delays on the ground, many models have been developed to assign ground holding delays optimally in the general network of airports, so that the total (ground plus airborne) delay costs of all fights are minimized. Still, the undeniable fact remains that airborne delays cannot be totally avoided. When there are airborne holds, such as when the local airspace capacity has a sudden decrease due to bad weather or other unpredicted disruptions, it is important to quickly minimize the costs of airborne holds. Much research has been conducted to explore ways to minimize airborne holds. Motivated by this, after analyzing the network structure of air traffic control areas and the dynamic nature of air traffic flow, this paper presents a model based on multicommodity dynamic network flow for short-term air traffic flow management. The model was validated by practical data from the Beijing ATC Center of the Civil Aviation Administration of China (CAAC).