A dynamic rerouting model for air traffic flow management

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.

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.

An Agent-Based Approach to Aircraft Conflict Resolution with Constraints

Air traffic management (ATM) is crucial to reduce environmental impacts, delays, and operational issues in crowded airspace. Traditional ATM methods often fail to address CO2 and non-CO2 emissions, particularly in contrail formation, which significantly affects climate change. This study introduces a fusion framework for climate and sensor failure detection for the cluster-based air traffic flow management (CSFDCA) model, which merges environmental cost optimization with advanced network theory. CSFDCA has a two-layer air traffic network, allowing for detailed analysis of node importance based on traffic flow and proximity. It aims for route planning by integrating environmental costs, considering both contrail effects and CO2 emissions. CSFDCA model uses GWP and aCCFs to evaluate environmental impacts. Simulations show that the CSFDCA model, based on CSFDCA, the existing ATM methods, achieves lower flight times and effectively avoids contrail zones. It improved traffic distribution and network resilience, balancing operational efficiency with environmental concerns and contributing to the long-term goals of aviation sustainability amidst rising global air traffic.

This paper explores how applying a basic management science principle to air traffic management operations can lead to revised roles and responsibilities that allow operations to be handled by the most appropriate participants. Previous research that applied the principle to local departure and arrival management is reviewed; the application is then expanded to regional and national air traffic management. The paper examines the operational and engineering implications of the resulting concept, contrasted with current operations and tools, and identifies where additional research or development is needed. Examples are provided that illustrate the resulting activities at the various air traffic management facilities across a broad range of look-ahead timeframes. I. Introduction HE High-Density Area Departure and Arrival Management (HDDAM) concept was developed in the context of arrival and departure air traffic management in high-density airspace such as Metroplex areas. The management science principles underlying HDDAM appear to be applicable to National Airspace System (NAS) operations at any level, including all phases of planning and flight. This paper explores some operational and engineering implications of such an application to local, regional, and NAS-wide air traffic operations. As described elsewhere, 1-3 HDDAM redefines the decision making processes within the Air Route Traffic Control Center (ARTCC), the Terminal Radar Approach Control (TRACON), and multiple Air Traffic Control Towers (ATCTs) for high-density area departure/arrival traffic management. The decision making authority proposed for each facility reflects the information available to each decision maker and the impact of the decision on their operating environment. Specifically, each facility is responsible for defining the capacity of its air traffic management resources (such as airports, airspace, airways) and requesting resources for flights to use. As shown in Fig. 1 for a single Metroplex, the TRACON has a central role in these interactions, serving as a “broker” between the facilities with the resources and the facilities with requests for use of those resources that are not within their scope of control, while also accounting for the TRACON’s ability to deliver the flights to the resources in question. ATCTs are responsible for requesting resources for departing flights and the ARTCC for requesting resources for arriving flights, as they each possess the best information regarding the status of these flights. Because of the uncertainties present in predicting individual flight departure readiness and inbound air traffic arrival times, the TRACON does not assign specific departure and arrival slots to individual flights. Instead, the TRACON allocates slots to the airports (for departures) and ARTCC (for arrivals) so they can make flight-specific assignments as deemed best for their individual operating environments. This effectively decouples the decisions of the individual ATCTs and the ARTCC, giving each facility increased flexibility to handle situations dynamically as they develop. Air traffic management operations are most effective when the decision makers work as a team. The underlying principle of HDDAM is that this can be accomplished best when all facilities’ roles and responsibilities are based on the management science principle that allows the appropriate level of autonomy at each facility. The principle states that decisions should be assigned to those who are already performing other tasks with the relevant data—those who know the most about the factors affecting the decision—and will be most directly affected by the consequences of 1 Lead Multi-Discipline System Engineer, TFM System Engineering and Evolution, 7515 Colshire Drive, Mailstop

Generating day-of-operation probabilistic capacity scenarios from weather forecasts

In order to solve the short-term air traffic flow management problem (ST-TFMP) efficiently, we present an algorithm using Lagrangian relaxation technique. The primal problem is decoupled by relaxing the air route capacity constraints, and then the corresponding dual problem is solved by subgradient method. We also propose a heuristic procedure in combination with the knowledge of air traffic management. It forces the dual solution from infeasible to feasible by adding non-coupled constraints. At last, the algorithm is validated by practical data from the Beijing ATC Center of the Civil Aviation Administration of China (CAAC). The computational results show that the algorithm can improve the performance of ST-TFMP.

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

Data sharing is the keystone to maintain the continuous operation of air traffic management (ATM). As situational awareness and collaborative decision-making become significant components of smart ATM, data sharing capability becomes a constraint on the development of ATM. The existing ATM data-sharing solutions have the following issues: 1) data-sharing efficiency is low; 2) data security has not received sufficient attention; and 3) access control is difficult to implement. Therefore, a security data-sharing model for ATM based on cloud-blockchain fusion is proposed, which realizes data security storage, intelligent service governance, and data authorized access. Firstly, a set of metafile storage structures is defined to realize the separate storage of data and metadata, which provides integrity verification and encrypted storage. Besides, using attribute-based encryption and service retrieval algorithms, the data publishing, discovery, and invocation functions based on smart contracts are realized. Finally, this scheme realizes secure data sharing in the ATM environment, and the computational cost and time cost are also kept at an acceptable level compared with other existing schemes.

Air Traffic Management decisions affect the interests of a diverse group of organizations and individuals. Agents, including individual aircraft, airlines, and ground control centers, look for conflictfree options that meet their own interests without depreciating other agents' operations. A cooperative structure for interactions of agents has been defined on the basis of Principled Negotiation. This method allows distributed decision-making for all users while providing a fair coordination through Air Traffic Management systems. The major interest of Principled Negotiation relies in its mutual gain approach to selection of options. Agents negotiate options with a traffic coordinator that ensures coordination of all users preferred operations while satisfying safety criteria. Thus, the model is flexible in responding to the needs and goals of agents, and it maintains safety. It provides users with different interests the freedom to optimize their operations, it includes Air Traffic Flow Management processes, and it certifies safe aircraft separations. A computer simulation dedicated to evaluating the benefits of such a cooperative structure is under development. The simulation provides trajectorybased traffic modeling with freedom of routing, communication, and negotiation modeling. It incorporates intelligent behavior modeling and incorporates airborne collision avoidance systems. First results show that Principled Negotiation is appropriate for coordination in a multi-agent/multi-interest system such as the Aircraft/Airspace System.

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.

This chapter reviews the International Civil Aviation Organization air traffic management (ATM) work programme along with ATM developments taking place around the world. It examines the various elements of the ATM system and suggests some of die research, trials and demonstrations. Airspace management is traditionally recognized mainly as involving a dynamic or tactical sharing of airspace by civil and military users. Air Traffic Services itself is comprised of several subelements, one of these being air traffic control. The increasing number of long-range interregional flights may require integration of regional Air Traffic Flow Management systems and procedures. Free Right is the guiding vision, mission and operational concept of the Federal Aviation Administration, based on Communications, Navigation, Surveillance/Air Traffic Management systems (CNS/ATM). Africa is composed of many delicate national economies separated by vast wilderness areas. The main scope of Airspace management will be toward a strategic planning function of airspace infrastructure, and flexibility of airspace use.

The Air Traffic Flow Management (ATFM) is considered as an effective method for air safety and efficiency guarantee. However, as one of the vital part of flight efficiency, fuel consumption is easily affected by external factors induced by various uncertainties such as severe weather conditions, which impact decision making process. Therefore, a distributionally robust mixed integer programming model for ATFM problem (DR-ATFM) is introduced in this paper to handle the uncertainty of fuel consumption while minimizing the total cost of fuel consumption, flight cancellation and flight delays. By exploiting the moment information of the fuel consumption data, a moment ambiguity set is constructed which characterizes the actual distribution of fuel consumption influctation. Based on this, an equivalent reformulation of DR-ATFM is derived to transform the problem into a mathematically solvable one, which is further solved through a cutting plane-based decomposition algorithm proposed in this paper. Finally, the effectiveness and robustness of the method are verified based on computational results of small-sized instances.

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.

One of the most restrictive aspects in today’s Air Traffic Management (ATM) system is that aircraft are still largely limited to flying routes defined by fixed, ground-based navaids. With current air travel experiencing increasing amount of delay, it is evident that this inflexible jet route system will not be able to handle future demand, especially when jet routes get closed by hazardous weather constraints. However, not having any route structure in the AS might not be a viable approach either as it could lead to high complexity and low traffic pattern predictability. For use in the ext Generation ATM system ( extGen), the etwork Flow Organizer ( FO) has been designed as a Traffic Management Initiative (TMI) to control the topology of the air traffic flow network and its flow properties. The FO alleviates en route congestion by creating airspace capacity through dense, automatically monitored traffic flows. This is in contrast to other TMIs that restrict capacity, such as Airspace Flow Program, miles-in-trail, ground delays and ground stops. The FO forms and dissolves flows based on demand and the necessity to resolve demand-capacity imbalances, thus providing structure only when needed without unnecessarily constraining traffic. Such flows allow parallel lanes of traffic, creating high traffic throughput with low complexity and workload. The flows are dynamically relocated throughout the day to be safe from weather hazards and to avoid Special Use Airspace. A concept for protected slot templates is established to allow conflict free intersection of flows and turning at intersections.