Airport taxi delays pose significant challenges to airlines, passengers, and environmental sustainability. Addressing these delays requires synchronizing uncertain airside operations, including pushback sequencing, taxiway movements, and runway take-offs. This article presents the intelligent departure metering advisory tool (I-MATE), an artificial intelligence (AI)-based decision support system that recommends pushback timings to air traffic controllers (ATCOs) to reduce taxi delays while balancing downstream runway throughput. I-MATE's recommendation outcomes unfold over a longer time horizon compared to other AI-driven decision support systems in air traffic control, such as conflict resolution tools, posing a greater cognitive challenge for ATCOs. We conducted validation experiments to assess the efficacy and acceptability of I-MATE in assisting ATCOs to manage airside traffic. The study revealed a spectrum of compliance with I-MATE recommendations among ATCOs, highlighting the complex interplay between human behavior and AI-driven decision support systems. While ATCOs rated I-MATE highly for usefulness and reliability, concerns regarding transparency and explainability emerged. ATCO feedback also emphasized the importance of considering individual differences and human factors in the design of such systems. This research underscores the value of AI-based decision support systems in complex, dynamic environments, particularly for addressing the cognitive challenge of balancing long-term outcomes with immediate actions.

The fusion of LiDAR and camera sensors has demonstrated significant effectiveness in achieving accurate detection for short-range tasks in autonomous driving. However, this fusion approach could face challenges when dealing with long-range detection scenarios due to disparity between sparsity of LiDAR and high-resolution camera data. Moreover, sensor corruption introduces complexities that affect the ability to maintain robustness, despite the growing adoption of sensor fusion in this domain. We present SaViD, a novel framework comprised of a three-stage fusion alignment mechanism designed to address long-range detection challenges in the presence of natural corruption. The SaViD framework consists of three key elements: the Global Memory Attention Network (GMAN), which enhances the extraction of image features through offering a deeper understanding of global patterns; the Attentional Sparse Memory Network (ASMN), which enhances the inte-gration of LiDAR and image features; and the KNNnectivity Graph Fusion (KGF), which enables the entire fusion of spatial information. SaViD achieves superior performance on the long-range detection Argoverse-2 (AV2) dataset with a performance improvement of 9.87% in AP value and an improvement of 2.39% in mAPH for L2 difficulties on the Waymo Open dataset (WOD). Comprehensive experiments are carried out to showcase its robustness against 14 natural sensor corruptions. SaViD exhibits a robust performance improvement of 31.43% for AV2 and 16.13% for WOD in RCE value compared to other existing fusion-based methods while considering all the corruptions for both datasets. Our code is available at SaVil).

Automated decision support tools are increasingly important for assisting pilots and air traffic controllers in managing aircraft operations under complex scenarios such as convective weather, especially given the increasing traffic density and the limits of human cognitive capacity. Existing automation methods based on geometric heuristics or optimization are efficient and interpretable, but fail to coordinate multiple aircraft and adapt to rapidly evolving airspace conditions. This research presents a decentralized deep reinforcement learning (DRL) framework for multi-aircraft rerouting in thunderstorm-affected environments. Each aircraft acts as an autonomous agent and learns a shared policy via Independent Deep Deterministic Policy Gradient (IDDPG). During training, agents optimize a shared multi-objective reward that encodes safety and efficiency. By learning from diverse multi-agent scenarios, the shared policy captures transferable coordination patterns, enabling agents to generalize across traffic densities and storm configurations. The evaluation results for both simulated and real world airspace scenarios show that the proposed method reduces the aircraft conflict rates to below 1 %, maintains a success of over 95 % in reaching exit waypoints, and produces smoother trajectories with more organized traffic flows compared to baseline methods. These findings demonstrate the potential of the method as a reliable and scalable AI-based decision support tool for real-time multi-aircraft rerouting in convective weather conditions.

Abstract Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central–southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors.

Combining LiDAR and camera data has shown potential in enhancing short-distance object detection in autonomous driving systems. Yet, the fusion encounters difficulties with extended distance detection due to the contrast between LiDAR’s sparse data and the dense resolution of cameras. Besides, discrepancies in the two data representations further complicate fusion methods. We introduce AYDIV, a novel framework integrating a tri-phase alignment process specifically designed to enhance long-distance detection even amidst data discrepancies. AYDIV consists of the Global Contextual Fusion Alignment Transformer (GCFAT), which improves the extraction of camera features and provides a deeper understanding of large-scale patterns; the Sparse Fused Feature Attention (SFFA), which fine-tunes the fusion of LiDAR and camera details; and the Volumetric Grid Attention (VGA) for a comprehensive spatial data fusion. AYDIV’s performance on the Waymo Open Dataset (WOD) with an improvement of 1.24% in mAPH value(L2 difficulty) and the Argoverse2 Dataset with a performance improvement of 7.40% in AP value demonstrates its efficacy in comparison to other existing fusion-based methods. Our code is publicly available at https://github.com/sanjay-810/AYDIV2

Airside taxi delays have adverse consequences for airports and airlines globally, leading to airside congestion, increased Air Traffic Controller/Pilot workloads, missed passenger connections, and adverse environmental impact due to excessive fuel consumption. Effectively addressing taxi delays necessitates the synchronization of stochastic and uncertain airside operations, encompassing aircraft pushbacks, taxiway movements, and runway take-offs. With the implementation of mixed-mode runway operations (arrivals-departures on the same runway) to accommodate projected traffic growth, complexity of airside operations is expected to increase significantly. To manage airside congestion under increased traffic demand, development of efficient pushback control, also known as Departure Metering (DM), policies is a challenging problem. DM is an airside congestion management procedure that controls departure pushback timings, aiming to reduce taxi delays by transferring taxiway waiting times to gates. Under mixed-mode runway operations, however, DM must additionally maintain sufficient runway pressure—departure queues near runway for take-offs—to utilize available departure slots within incoming arrival aircraft steams. While a high pushback rate may result in extended departure queues, leading to increased taxi-out delays, a low pushback rate can result in empty slots between incoming arrival streams, leading to reduced runway throughput. This study introduces a Deep Reinforcement Learning (DRL) based DM approach for mixed-mode runway operations. We cast the DM problem in a markov decision process framework and use Singapore Changi Airport surface movement data to simulate airside operations and evaluate different DM policies. Predictive airside hotspots are identified using a spatial-temporal event graph, serving as the observation to the DRL agent. Our DRL-based DM approach utilizes pushback rate as agent’s action and reward shaping to dynamically regulate pushback rates for improved runway utilization and taxi delay management under uncertainties. Benchmarking the learnt DRL-based DM policy against other baselines demonstrates the superior performance of our method, especially in high traffic density scenarios. During a typical day at Singapore Changi Airport, DRL-based DM reduces peak taxi times by 1-3 minutes on average, saves 26.6% in fuel consumption, and contributes to more environmentally friendly and sustainable airside operations.

The air traffic control paradigm is shifting from sector-based operations to flow-centric approaches to overcome sectors’ geographical limits. Modeling and predicting intersecting air traffic flows can assist controllers in flow coordination under the flow-centric paradigm. This paper proposes a flow-centric framework – TEMPT: Text-Enriched air traffic flow Modeling and Prediction using Transformers – to identify, represent, and predict intersecting flows in the airspace. Firstly, nominal flow intersections (NFI) are identified through hierarchical clustering of flight trajectory intersections. A flow pattern consistency-based graph analytics approach is proposed to determine the number of NFIs. Secondly, in contrast to the traditional traffic flow feature representation, i.e., numerical time series of flights, this paper proposes a text-enriched flow feature representation to intuitively describe the “flow of flights” in the airspace. More specifically, air traffic flow features are described by a “text paragraph” composed of the time and flight sequences transiting through the NFIs. Finally, a transformer neural network model is adopted to learn the text-enriched flow features and predict the future traffic demand at the NFIs during future time windows. An experimental study was carried out in French airspace to validate the efficacy of TEMPT using one-month ADS-B data in December 2019. Prediction results show that TEMPT outperforms the competitive air traffic flow modeling and prediction approaches: time-series-based Transformers, Long Short-term Memory (LSTM), and Graph Convolutional Networks (GCN), as well as aerodynamic trajectory simulation-based prediction and the historical average.

The continuous increase in air traffic compels major airports to optimize their resources and enhance their Terminal Maneuvering Airspace (TMA) operations. The primary constraint to increasing airport capacity is the required separation minima between pairs of aircraft arriving through the same approach routes. RECAT-EU is a revised global wake separation minima scheme released by EUROCONTROL in 2018. This suggested a reduction in separation minima between certain aircraft pairs while maintaining safety levels. However, it increases in separation minima scheme complexity by doubling the number of non-minimum radar separation (MRS) values. In this work, a Meta-Heuristic based optimization model is proposed to sequence arrival flights based on the RECAT-EU separation scheme, which can provide an optimized vectoring to ensure flights absorb their assigned delays before reaching the final approach fix. Findings show that the proposed model is able to generate an optimized landing sequence for 50 arrival flights in a computation time of 16 seconds. It also suggests that the proposed algorithm's computational time increases linearly with increasing the number of flights. Furthermore, trajectory vectoring results demonstrate that 85% of the assigned delays could be absorbed by flying the proposed vectored trajectories.

Lack of transparency has been demonstrated to be a stumbling block in building Air Traffic Controller (ATCO) trust towards intelligent decision aids. To address this issue, a runway exit prediction decision aid, with explainability, was developed with the trait of providing explanations involving the top three contributing features to its predictions. To evaluate the influence of the intelligent decision aid's explanations on ATCO Trust, the decision aid was used in a human-in-the-loop study during off-nominal runway exits, utilizing 12 participants and a total of 67 trials. A multi modal approach was adopted with three types of data (questionnaire, behavioural, physiological) being collected in this study to ensure a more comprehensive understanding of the effects of explainability on ATCO trust. The results indicated that higher levels of perceived transparency led to an increase in trust levels, with an accompanied increase in cognitive load and complacency, even with low prediction accuracy by the intelligent decision aid. As such, these effects must be accounted for in designing XAI decision aids, which are defined as decision aids that rationalize their recommendations, for off-nominal events, when attempting to enhance trust levels by increasing transparency.

This work aims to enhance the safety of air traffic in a procedural airspace by air traffic flow management (ATFM) without compromizing air traffic demand. Inc ivil aviation, collision risk is an important indicator for air traffic safety assessment. In this work, we propose a generic ATFM framework based on multiobjective optimization for reducing lateral collision risk of the traffic ina given procedural airspace. T he proposed framework aims to optimize the flight level assignment f or a given set of flight plans such that t he lateral collision risk can be reduced. To achieve this goal, we formulate an optimization problem containing two partially conflicting o bjective functions. We then adopt three well-known evolutionary algorithms, i.e., MODPSO, NSGA-II, and MOEA/D to solve the optimization problem. We specially design some of the operators of those al-gorithms to make them suitable for the optimization problem. We merge the solutions yielded by those three algorithms and filter out the final Pareto solutions. We carry o ut a case study o n the procedural airspace of Singapore flight information region (FIR) with respect to twelve daily traffic data selected from t he real traffic data for December 2019. Experiment results demonstrates that the lateral occupancy which is the key contributor to lateral risk can be reduced by 10.65 % to 93.05 % at a strategic planning level. This research contribute to strategic flight planning by assigning flight levels that m ay reduce t he risk o f collision in procedural airspace.

Airport taxi delays adversely affect airports and airlines around the world leading to airside congestion, increased Air Traffic Controllers/Pilot workload, and adverse environmental impact due to excessive fuel burn. Airport Departure Metering (DM) is an effective approach to contain taxi delays by controlling departure pushback timings. The key idea behind DM is to transfer aircraft waiting time from taxiways to gates. State-of-the-art DM methods use model-based control policies that rely on airside departure modeling to obtain simplified analytical equations. Consequently, these models fail to capture non-stationarity in the airside operations leading to poor performance of control policies under uncertainties. This work proposes model-free and learning-based DM using Deep Reinforcement Learning (DRL) approach to reduce taxi delays while meeting flight schedule constraints. This paper casts the DM problem in a markov decision process framework and develops a representative airport-airside simulator to simulate airside operations and evaluate the learnt DM policy. For effective state representation, this work introduces taxiway hotspot features to account for the spatial-temporal evolution of airside congestion levels. This significantly improves the DM policy convergence rate during training. The performance of the learnt policy is evaluated under different traffic densities with a reduction of approximately 44% in taxi out delays, in medium-density traffic scenarios, which corresponds to 2-minute savings in taxi-out time per aircraft. Furthermore, benchmarking DRL against an evolutionary method and another state-of-the-art simulation-based heuristic demonstrates the superior performance of our method, especially in high traffic density scenarios. With increased traffic density, taxi-time savings achieved by the learnt DM policy increase without a significant decrease in runway throughput. Results, on a typical day of simulated operations at Singapore Changi Airport, demonstrate that DRL can learn an effective DM policy to contain congestion on the taxiways, reduce total fuel consumption by approximately 22% and better manage the airside traffic.

Classical approaches for modelling aircraft taxi-speed assume constant speed or use a turning rate function to approximate taxi-timings for taxiing aircraft. However, those approaches cannot predict spatio-temporal component of aircraft-taxi trajectory due to a lack of consideration of the complexity and stochasticity of airport-airside movements and interactions. This research adopts the Generative Adversarial Imitation Learning (GAIL) algorithm for aircraft taxi-speed modelling, while considering multiple operational factors including surrounding traffic on the ground and target take-off time. The proposed model can learn and reproduce the ground movement patterns in a real-world dataset under different circumstances. In addition, the characteristics of the taxi-speed model are also analyzed, especially focusing on handling conflict scenarios with surrounding traffic. Finally, the travel-time of the aircraft from starting to target positions are compared with baseline models and actual taxiing data. The proposed model outperforms all the baseline models with a significant margin. In terms of spatial completion (SC), it achieves up to 97.1% for arrivals and 88.3% for departures. The results also show significantly high performance for temporal completion. The model achieves a stable performance with low Root Mean Square Error (RMSE) (16.8 seconds for arrivals, 32.4 seconds for departures) and Mean Absolute Percentage Error (MAPE) (4.4% for arrivals and 7.6% for departures). Our model’s errors are 72% lower for arrivals and 48% lower for departures when compared to other baseline models.

Air transport contributes significantly to the globalization and world economic. With the increasing demand for both passengers and air cargo, future airspace may encounter unprecedented traffic pressure. It is always the paramount commitment of air transport to ensure flying safety. In the face of increasing traffic demand, it is pertinent to investigate how to reduce en-route collision risk without compromising the traffic demand. In this paper, we propose a multiobjective optimization based method to reduce the technical vertical risk (TVR) by controlling en-route air traffic speed. The suggested method simultaneously optimizes two objectives. The first one aims to minimize the TVR while the second tries to minimize the traffic delay. As the modeled optimization problem is non-convex and the two objectives conflict with each other, we therefore introduce two well-known multiobjective evolutionary algorithms named NSGA-II and NSGA-III and modify some of their operators to solve the proposed optimization problem. Finally, we carry out experiments on sixteen real-world daily traffic sample data that cover en-route flights within the Singapore flight information region (FIR). Experiments demonstrate that by optimizing the proposed problem using the introduced algorithms we obtain a set of speed control suggestions each of which can reduce the TVR for the Singapore FIR. This work will contribute both to strategical and tactical air traffic management as the aviation players can make the preferred choices based on the solutions yielded by the introduced algorithms.

To investigate the robustness of complex networks in face of disturbances can help prevent potential network disasters. Percolation on networks is a potent instrument for network robustness analysis. However, existing percolation theories are primarily developed for interdependent or multilayer networks. Little attention is paid to multipartite networks which are an indispensable part of complex networks. In this article, we theoretically explore the robustness of multipartite networks under node failures. We put forward the generic percolation theory for gauging the robustness of multipartite networks with arbitrary degree distributions. Our developed theory is capable of quantifying the robustness of multipartite networks under either random or target node attacks. Our theory unravels the second order phase transition phenomenon for multipartite networks. In order to verify the correctness of the proposed theory, simulations on computer generated multipartite networks have been carried out. The experiments demonstrate that the simulation results coincide quite well with that yielded by the proposed theory.

Air traffic is playing a leading role in the global economical growth. Air traffic is indispensable from airport networks which accommodate the traffic demands. Note that airport networks are confronted with intractable uncertainties such as severe meteorological conditions, random mechanical failures of aircraft instruments, terrorist attacks, etc., which give rise to the failures of the components of airport networks. It is of great significance to improve the robustness of airport networks to component failures as the failures can cause staggering economical losses. Existing works either employ network rewire mechanism or add more links to an airport network to enhance the robustness of the given network. In this paper, we provide a counter-intuitive way to enhance the robustness of airport networks. Specifically, we propose to remove links from a given airport network to improve its robustness in face of perturbations. To do so, we develop a single-objective genetic algorithm to locate the links of an airport network whose removal will increase its robustness. Experimental studies on six realworld airport networks validate the feasibility of the proposed research idea. This work provides a new perspective for aviation decision makers to manage airports and air routes, and therefore sheds new light towards robust airspace design.

A key safety indicator for airspace is its collision risk estimate, which is compared against a target level of safety to provide a quantitative basis for judging the safety of operations in airspace. However, this quantitative basis fails to provide any insight regarding the magnitude, location, and timing of the risk of collision, distributed within a given airspace. In this paper, we propose a methodology for the identification of collision risk hot spots in a given airspace. The proposed methodology consists of processing air traffic data and developing traffic routes based on entry and exit points within the airspace. These routes and other flight information are then used to project air-traffic crossings and cluster potential collisions. The proposed method then estimates the collision risk for each identified cluster, culminating in risk assessment for the entire airspace. The model extends and adopts the state-of-the art clustering models, systemically identifies airspace collision risk hot spots, and further analyses hot spots by analyzing cluster features (number of points and contribution to overall risk) with flight levels and time of day. Experiments were conducted using one-month traffic data (25 440 flights) from Bahrain en-route airspace. By visualizing crossing points and clustering them in a 2-D geographic information system model we are able to identify collision risk hot spots, which contribute significantly to overall collision risk.

The agent computing paradigm is rapidly emerging as one of the powerful technology to deal with the uncertainty in dynamic environment. Recently, traditional learning classifier system are challenged by changes in the context. In this paper, an anticipatory agent based on the Anticipatory Learning Classifier System (ACS) for learning in changing environments is presented. This research aims to develop an agent learning architecture using anticipatory system that will enable intelligent agent to be able to detect environmental changes, adapt functionality at run-time to achieve goal. For achieving the intended target, an extension to the ACS framework called “Greedy Covering” to the ACS framework have been proposed. The novelty of the approach is in determining the changes in the environment and to generate optimal rules to adapt and reestablish the optimal policy to reach the goal state. The proposed algorithm is evaluated on several synthetic maze design and simulate a variety of changing environments. Experiment results indicate that up to 65% changes in an environment the ACS with the greedy covering can reestablish the optimal performance without increasing the number of classifiers.

With the continued growth in Air Traffic, researchers are investigating innovative ways to increase airspace capacity while maintaining safety. A key safety indicator for an airspace is its Collision Risk estimate, which is compared against a Target Level of Safety (TLS) to provide a quantitative basis for judging the safety of operations in an airspace. However this quantitative value does not give an insight into the overall collision risk picture for an airspace, and how the risk changes given the interaction of a multitude of factors such as sector/traffic characteristics and controllers actions for flow management. In this paper, we propose an evolutionary framework with multi-objective optimization to evolve collision risk of air traffic scenarios. We attempt to identify, through evolutionary mechanism, the flight events resulting from Air Traffic Controller's actions that can lead to higher collision risks, thereby identifying the contributing factors or the events leading to collision risk. Computational experiments were conducted in an hi-fidelity air traffic simulation environment with collision risk model. Results indicate that “risk-free” traffic scenarios having collision risk below TLS can become “risk-prone” by few flight events, with Climb and Turn maneuvers, specifically during entering and exiting a sector, contributing significantly to increased collision risk.

Dynamic Airspace Sectorization (DAS) is a future concept in Air Traffic Management. Its main goal is to increase airspace capacity by reshaping - thus optimizing - airspace sector boundaries based on the specifics of different air traffic situations, weather conditions and other factors. The primary objective for the optimization is to balance and reduce the workload of Air Traffic Controllers (ATCs). Many researchers have made efforts in this topic in the past years. However, air traffic changes continually, and DAS has to be adaptive to each change; be it in terms of aircraft density, dynamic routes, fleet mix, etc. Therefore, instead of sectorizing the airspace each time a change occurs, we should re-sectorize it by maintaining maximum similarities between each sectorization. In this paper, we propose a multi-objective evolutionary computation methodology to re-sectorize an airspace. We use a similarity measure between the existing sectorization and the re-sectorization as an objective to maximize during the evolution.We test the methodology with different air traffic conditions with four objective functions: minimize ATC task load standard deviation, maximize average flight sector time, maximize the minimum distance between traffic crossing points and sector boundaries, and maximize the similarity of two airspace sectorizations. Experimental results show that our re-sectorization method is able to perform airspace re-sectorization under different changes in the air traffic, while satisfying the predefined objectives.

What makes a combinatorial optimization problem hard? The concept of phase transition was introduced in combinatorial decision problems to explain that not all NP-Complete problems are hard, and that there exists a phase transition from solvable to unsolvable problems, within which hard problems exist. Phase transition has been studied using randomly generated problems in which variables have uniform distributions across the different constraints. Real-world problems demonstrate different distributions, however. This paper reveals the relationship between the difficulty of a 3-SAT problem and graph properties. It establishes for the first time a link between the theory of phase-transition in 3-SAT and phase transitions in complex systems and networks. This paper also addresses the question of whether the phase transition phenomenon exists for non-uniform randomly generated problems. A positive answer to this question means in principle that (1) we can generate test problems for combinatorial optimization that are not uniform; (2) we can generate test problems that resemble hard versions of real-world problems; (3) we can identify the features that we need to look for in a problem to test whether or not it is hard. We use a method that we call Adversarial Evolution (AE). In AE, an evolutionary computation method is used to generate hard problem instances by evolving solutions towards the failure of an algorithm and the phase transition region.

An efficient design of a Multi-Objective Learning Classifier System for multi-flight navigation is presented. A classifier is represented by a set of rules, which are used to simultaneously navigate all the flights in the airspace. Navigation of a flight is based on the relation of the flight with factors of the air traffic environment such as wind, storm as well as other flights. This system continually learns and refines the rules of classifiers by a multi-objective optimization algorithm - NSGAII - to discover the trade-off set of classifiers which navigate flights without any conflict, minimal distance of flying, minimal discomfort defined by storm level and the time duration of flights passing through storm areas, and minimizing total delay time of flights. We propose to detect conflicts between flights by grouping trajectory segments in 3-D (abscissa-x, ordinate-y, and time-t) boxes. The conflict detection is only implemented in a box, thus the number of conflict detection times approximates to the number of conflicts. Further, conflicts between flights are resolved using a hill climber by propagating delays in the takeoff time of conflicting flights. The advantage of the proposed system is that the classifier outputs its rules in a symbolic representation, making the overall process transparent to the user and reusable. Moreover, the system successfully discovered rules in all runs to optimize its performance.

Evaluating conflict resolution algorithms in the air-traffic domain is a challenging task. These algorithms are usually tested using a pair of aircraft or a limited number of geometries involving multiple aircraft. Our previous work demonstrated the use of evolutionary computation for risk assessment of air-traffic conflict detection algorithms using a red-teaming (or playing the devil) approach. This paper extends our previous work to conflict resolution and investigate the effect of symmetry in the representation on the performance of the evolutionary operators.

In this paper, we introduce the air traffic operations and management simulator (ATOMS), which is an air traffic and airspace modeling and simulation system for the analysis of free-flight concepts. This paper describes the design, architecture, functionality, and applications of the ATOMS. It is an intent-based simulator that discretizes the airspace in equal-sized hyper-rectangular cells to maintain intent reference points. It can simulate end-to-end airspace operations and air navigation procedures for conventional air traffic, as well as for free flight. Atmospheric and wind data that are modeled in the ATOMS result in accurate trajectory predictions. The ATOMS uses a multiagent-based modeling paradigm for modular design and easy integration of various air traffic subsystems. A variety of advanced air traffic management (ATM) concepts that are envisioned in free flight are prototyped in the ATOMS, including airborne separation assurance (ASA), cockpit display of traffic information (CDTI), weather avoidance, and decision support systems (DSSs). Experimental results indicate that advanced ATM concepts make a sound case for free flight; however, there is a need to investigate and understand their complex interaction under nonnominal scenarios.