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NASA NTRS2019

Complex Dynamics of Air Traffic Flow

Air traffic in the United States has continued to grow at a steady pace since 1980, except for a dip immediately after the tragic events of September 11, 2001. There are different growth scenarios associated both with the magnitude and the composition of the future air traffic. The Terminal Area Forecast (TAF), prepared every year by the FAA, projects the growth of traffic in the United States. Both Boeing and Airbus publish market outlooks for air travel annually. Although predicting the future growth of traffic is difficult, there are two significant trends: heavily congested major airports continue to see an increase in traffic, and the emergence of regional jets and other smaller aircraft with fewer passengers operating directly between non-major airports. The interaction between air traffic demand and the ability of the system to provide the necessary airport and airspace resources can be modeled as a network. The size of the resulting network varies depending on the choice of its nodes. It would be useful to understand the properties of this network to guide future design and development. Many questions, such as the growth of delay with increasing traffic demand and impact of the en route weather on future air traffic, require a systematic understanding of the properties of the air traffic network. There has been a major advance in the understanding of the behavior of networks with a large number of components. Several theories have been advanced about the evolution of large biological and engineering networks by authors in diversified disciplines like physics, mathematics, biology and computer science. Several networks exhibit a scale-free property in the sense that the probabilistic distribution of their nodes as a function of connections decreases slower than an exponential. These networks are characterized by the fact that a small number of components have a disproportionate influence on the performance of the network. Scale-free networks are tolerant to random failure of components, but are vulnerable to selective attack on components. This paper examines two network representations for the baseline air traffic system. A network defined with the 40 major airports as nodes and with standard flight routes as links has a characteristic scale: all nodes have 60 or more links and no node has more than 460 links. Another network is defined with baseline aircraft routing structure exhibits an exponentially truncated scale-free behavior. Its degree ranges from 2 connections to 2900 connections, and 225 nodes have more than 250 connections. Furthermore, those high-degree nodes are homogeneously distributed in the airspace. A consequence of this scale-free behavior is that the random loss of a single node has little impact, but the loss of multiple high-degree nodes (such as occurs during major storms in busy airspace) can adversely impact the system. Two future scenarios of air traffic growth are used to predict the growth of air traffic in the United States. It is shown that a three-times growth in the overall traffic may result in a ten-times impact on the density of traffic in certain parts of the United States.

Scale-free Networks↗

NASA NTRS2011

Traffic Flow Management Wrap-Up

Traffic Flow Management involves the scheduling and routing of air traffic subject to airport and airspace capacity constraints, and the efficient use of available airspace. Significant challenges in this area include: (1) weather integration and forecasting, (2) accounting for user preferences in the Traffic Flow Management decision making process, and (3) understanding and mitigating the environmental impacts of air traffic on the environment. To address these challenges, researchers in the Traffic Flow Management area are developing modeling, simulation and optimization techniques to route and schedule air traffic flights and flows while accommodating user preferences, accounting for system uncertainties and considering the environmental impacts of aviation. This presentation will highlight some of the major challenges facing researchers in this domain, while also showcasing recent innovations designed to address these challenges.

Grabbe, Shon↗

NASA NTRS2009

Integrated Traffic Flow Management Decision Making

A generalized approach is proposed to support integrated traffic flow management decision making studies at both the U.S. national and regional levels. It can consider tradeoffs between alternative optimization and heuristic based models, strategic versus tactical flight controls, and system versus fleet preferences. Preliminary testing was accomplished by implementing thirteen unique traffic flow management models, which included all of the key components of the system and conducting 85, six-hour fast-time simulation experiments. These experiments considered variations in the strategic planning look-ahead times, the replanning intervals, and the types of traffic flow management control strategies. Initial testing indicates that longer strategic planning look-ahead times and re-planning intervals result in steadily decreasing levels of sector congestion for a fixed delay level. This applies when accurate estimates of the air traffic demand, airport capacities and airspace capacities are available. In general, the distribution of the delays amongst the users was found to be most equitable when scheduling flights using a heuristic scheduling algorithm, such as ration-by-distance. On the other hand, equity was the worst when using scheduling algorithms that took into account the number of seats aboard each flight. Though the scheduling algorithms were effective at alleviating sector congestion, the tactical rerouting algorithm was the primary control for avoiding en route weather hazards. Finally, the modeled levels of sector congestion, the number of weather incursions, and the total system delays, were found to be in fair agreement with the values that were operationally observed on both good and bad weather days.

Grabbe, Shon R.↗

NASA NTRS2014

Traffic Flow Management and Optimization

This talk will present an overview of Traffic Flow Management (TFM) research at NASA Ames Research Center. Dr. Rios will focus on his work developing a large-scale, parallel approach to solving traffic flow management problems in the national airspace. In support of this talk, Dr. Rios will provide some background on operational aspects of TFM as well a discussion of some of the tools needed to perform such work including a high-fidelity airspace simulator. Current, on-going research related to TFM data services in the national airspace system and general aviation will also be presented.

linear programming↗

NASA NTRS2001

The Development of the Multi-Center Traffic Management Advisor (MCTMA): Traffic Flow Management Research in a Multi-Facility Environment

The Traffic Management Advisor (TMA) is a decision-support tool for traffic managers and air traffic controllers that provides traffic flow visualization and other flow management tools. TMA creates an efficiently sequenced and safely spaced schedule for arrival traffic that meets but does not exceed specified airspace system constraints. TMA is being deployed at selected facilities throughout the National Airspace System in the US as part of the FAA's Free Flight Phase 1 program. TMA development and testing, and its current deployment, focuses on managing the arrival capacity for single major airports within single terminal areas and single en route centers. The next phase of development for this technology is the expansion of the TMA capability to complex facilities in which a terminal area or airport is fed by multiple en route centers, thus creating a multicenter TMA functionality. The focus of the multi-center TMA (McTMA) development is on the busy facilities in the Northeast comdor of the US. This paper describes the planning and development of McTMA and the challenges associated with adapting a successful traffic flow management tool for a very complex airspace.

Lee, Katharine K.↗

NASA NTRS2010

Traffic Flow Management Using Aggregate Flow Models and the Development of Disaggregation Methods

A linear time-varying aggregate traffic flow model can be used to develop Traffic Flow Management (tfm) strategies based on optimization algorithms. However, there are no methods available in the literature to translate these aggregate solutions into actions involving individual aircraft. This paper describes and implements a computationally efficient disaggregation algorithm, which converts an aggregate (flow-based) solution to a flight-specific control action. Numerical results generated by the optimization method and the disaggregation algorithm are presented and illustrated by applying them to generate TFM schedules for a typical day in the U.S. National Airspace System. The results show that the disaggregation algorithm generates control actions for individual flights while keeping the air traffic behavior very close to the optimal solution.

Sun, Dengfeng↗

NASA NTRS2009

Massively Parallel Dantzig-Wolfe Decomposition Applied to Traffic Flow Scheduling

Optimal scheduling of air traffic over the entire National Airspace System is a computationally difficult task. To speed computation, Dantzig-Wolfe decomposition is applied to a known linear integer programming approach for assigning delays to flights. The optimization model is proven to have the block-angular structure necessary for Dantzig-Wolfe decomposition. The subproblems for this decomposition are solved in parallel via independent computation threads. Experimental evidence suggests that as the number of subproblems/threads increases (and their respective sizes decrease), the solution quality, convergence, and runtime improve. A demonstration of this is provided by using one flight per subproblem, which is the finest possible decomposition. This results in thousands of subproblems and associated computation threads. This massively parallel approach is compared to one with few threads and to standard (non-decomposed) approaches in terms of solution quality and runtime. Since this method generally provides a non-integral (relaxed) solution to the original optimization problem, two heuristics are developed to generate an integral solution. Dantzig-Wolfe followed by these heuristics can provide a near-optimal (sometimes optimal) solution to the original problem hundreds of times faster than standard (non-decomposed) approaches. In addition, when massive decomposition is employed, the solution is shown to be more likely integral, which obviates the need for an integerization step. These results indicate that nationwide, real-time, high fidelity, optimal traffic flow scheduling is achievable for (at least) 3 hour planning horizons.

Rios, Joseph Lucio↗

NASA NTRS1997

Evolutionary Concepts for Decentralized Air Traffic Flow Management

Alternative concepts for modifying the policies and procedures under which the air traffic flow management system operates are described, and an approach to the evaluation of those concepts is discussed. Here, air traffic flow management includes all activities related to the management of the flow of aircraft and related system resources from 'block to block.' The alternative concepts represent stages in the evolution from the current system, in which air traffic management decision making is largely centralized within the FAA, to a more decentralized approach wherein the airlines and other airspace users collaborate in air traffic management decision making with the FAA. The emphasis in the discussion is on a viable medium-term partially decentralized scenario representing a phase of this evolution that is consistent with the decision-making approaches embodied in proposed Free Flight concepts for air traffic management. System-level metrics for analyzing and evaluating the various alternatives are defined, and a simulation testbed developed to generate values for those metrics is described. The fundamental issue of modeling airline behavior in decentralized environments is also raised, and an example of such a model, which deals with the preservation of flight bank integrity in hub airports, is presented.

Adams, Milton↗

NASA NTRS2010

Interaction Between Strategic and Local Traffic Flow Controls

The loosely coordinated sets of traffic flow management initiatives that are operationally implemented at the national- and local-levels have the potential to under, over, and inconsistently control flights. This study is designed to explore these interactions through fast-time simulations with an emphasis on identifying inequitable situations in which flights receive multiple uncoordinated delays. Two operationally derived scenarios were considered in which flights arriving into the Dallas/Fort Worth International Airport were first controlled at the national-level, either with a Ground Delay Program or a playbook reroute. These flights were subsequently controlled at the local level. The Traffic Management Advisor assigned them arrival scheduling delays. For the Ground Delay Program scenarios, between 51% and 53% of all arrivals experience both pre-departure delays from the Ground Delay Program and arrival scheduling delays from the Traffic Management Advisor. Of the subset of flights that received multiple delays, between 5.7% and 6.4% of the internal departures were first assigned a pre-departure delay by the Ground Delay Program, followed by a second pre-departure delay as a result of the arrival scheduling. For the playbook reroute scenario, Dallas/Fort Worth International Airport arrivals were first assigned pre-departure reroutes based on the MW_2_DALLAS playbook plan, and were subsequently assigned arrival scheduling delays by the Traffic Management Advisor. Since the airport was operating well below capacity when the playbook reroute was in effect, only 7% of the arrivals were observed to receive both rerouting and arrival scheduling delays. Findings from these initial experiments confirm field observations that Ground Delay Programs operated in conjunction with arrival scheduling can result in inequitable situations in which flights receive multiple uncoordinated delays.

Grabbe, Son↗

NASA NTRS2003

Analysis of ETMS Data Quality for Traffic Flow Management Decisions

The data needed for air traffic flow management decision support tools is provided by the Enhanced Traffic Management System (ETMS). This includes both the tools that are in current use and the ones being developed for future deployment. Since the quality of decision support provided by all these tools will be influenced by the quality of the input ETMS data, an assessment of ETMS data quality is needed. Motivated by this desire, ETMS data quality is examined in this paper in terms of the unavailability of flight plans, deviation from the filed flight plans, departure delays, altitude errors and track data drops. Although many of these data quality issues are not new, little is known about their extent. A goal of this paper is to document the magnitude of data quality issues supported by numerical analysis of ETMS data. Guided by this goal, ETMS data for a 24-hour period were processed to determine the number of aircraft with missing flight plan messages at any given instant of time. Results are presented for aircraft above 18,000 feet altitude and also at all altitudes. Since deviation from filed flight plan is also a major cause of trajectory-modeling errors, statistics of deviations are presented. Errors in proposed departure times and ETMS-generated vertical profiles are also shown. A method for conditioning the vertical profiles for improving demand prediction accuracy is described. Graphs of actual sector counts obtained using these vertical profiles are compared with those obtained using the Host data for sectors in the Fort Worth Center to demonstrate the benefit of preprocessing. Finally, results are presented to quantify the extent of data drops. A method for propagating track positions during ETMS data drops is also described.

Chatterji, Gano B.↗

NASA NTRSDate not supplied

Analysis of Traffic Flow in Structured Urban Airspace Networks with MFD-based Feedback Control

This research delves into applying the Macroscopic Fundamental Diagram (MFD) concept to structured airspace networks for comprehensive aggregate modeling and introduces a feedback-based departure function aimed at optimizing traffic flow. Previous studies have rarely examined structured airspace networks featuring non-stationary vehicles through the MFD perspective. We devised a scenario grounded in practical applications, featuring a multi-lane network with explicit lane-changing behavior. The MFD effectively captured the open-loop response, displaying a low-scatter, unimodal curve on the flow versus occupancy plot. Drawing inspiration from the ground transportation ramp-metering strategies, a proportional-integral-based controller was developed. Extensive simulation outcomes suggest that feedback control, informed by MFD, holds significant potential for managing traffic flow in Urban Air Mobility (UAM) environments; a reduction of 80% in the peak number of vehicles in a holding pattern was observed for a slight reduction in throughput in this study.

MFD↗

NASA NTRSDate not supplied

Analysis of Traffic Flow in Structured Urban Airspace Networks with MFD-based Feedback Control

MFD↗

NASA NTRS2008

A Performance Assessment of an Airborne Separation Assistance System Using Realistic Complex Traffic Flows

This paper presents the results from a study that investigates the performance of a tactical Airborne Separation Assistance System (ASAS) in en route airspace, under varying demand levels, with realistic traffic flows. The ASAS concept studied here allows flight crews of equipped aircraft to perform separation from other air traffic autonomously. This study addresses the tactical aspects of an ASAS using aircraft state data (i.e. position and velocity) to detect and resolve projected conflicts. In addition, use of a conflict prevention system helps ASAS-equipped aircraft avoid maneuvers that may cause new conflicts. ASAS-capable aircraft are equipped with satellite-based navigation and Automatic Dependent Surveillance Broadcast (ADS-B) for transmission and receipt of aircraft state data. In addition to tactical conflict detection and resolution (CD&R), a complete, integrated ASAS is likely to incorporate a strategic CD&R component with a longer look-ahead time, using trajectory intent information. A system-wide traffic flow management (TFM) component, located at the FAA command center helps aircraft to avoid regions of excessive traffic density and complexity. A Traffic Alert and Collision Avoidance System (TCAS), as used today is the system of last resort. This integrated approach avoids sole reliance on the use of the tactical CD&R studied here, but the tactical component remains a critical element of the complete ASAS. The focus of this paper is to determine to what extent the proposed tactical component of ASAS alone can maintain aircraft separation at demand levels up to three times that of current traffic. The study also investigates the effect of mixing ASAS-equipped aircraft with unequipped aircraft (i.e. current day) that do not have the capability to self-separate. Position and velocity data for unequipped aircraft needs to be available to ASASequipped. Most likely, for this future concept, state data would be available from instrument flight rules (IFR) aircraft, equipped with at least ADS-B transmission capability. The objective is to reduce the number of losses of separation to a minimum and investigate the limits of tactical-only CD&R. Thus, the objective is not, expressly, to achieve zero losses of separation with tactical ASAS because this is one component of an integrated ASAS.

Smith, Jeremy C.↗

NASA NTRS2011

Interaction of Airspace Partitions and Traffic Flow Management Delay with Weather

The interaction of partitioning the airspace and delaying flights in the presence of convective weather is explored to study how re-partitioning the airspace can help reduce congestion and delay. Three approaches with varying complexities are employed to compute the ground delays.In the first approach, an airspace partition of 335 high-altitude sectors that is based on clear weather day traffic is used. Routes are then created to avoid regions of convective weather. With traffic flow management, this approach establishes the baseline with per-flight delay of 8.4 minutes. In the second approach, traffic flow management is used to select routes and assign departure delays such that only the airport capacity constraints are met. This results in 6.7 minutes of average departure delay. The airspace is then partitioned with a specific capacity. It is shown that airspace-capacity-induced delay can be reduced to zero ata cost of 20percent more sectors for the examined scenario.

Lee, Hak-Tae↗

DOE OSTI2026

APSO-enhanced algebraic derivative estimation approach for real-time traffic flow prediction on critical road sections during wildfire evacuation

In rapid-onset disaster scenarios such as wildfires, evacuation traffic often significantly deviates from historical patterns, rendering conventional data-driven forecasting methods less effective. To address this challenge, we propose an improved algebraic derivative estimation (ADE) incorporating particle swarm optimization (PSO) for real-time traffic flow prediction. Our approach dynamically adjusts the ADE prediction time window at each step by minimizing a cost function based on the mean and variance of accumulated forecasting errors within the window, thereby balancing bias and variability. We evaluate the method using traffic data from the January 2025 California wildfires, focusing on key road segments critical for large-scale evacuations. The results demonstrate that our approach surpasses established machine learning and deep learning models—XGBoost, LSTM, and GRU—in predictive accuracy and maintains high computational efficiency. Notably, the proposed method eliminates the need for offline model training. Moreover, rapid PSO-based tuning enables real-time deployment, which provides a crucial advantage in scenarios where evacuation timings and road closures change dynamically. In conclusion, these findings highlight the benefits of the PSO-enhanced ADE framework for emergency traffic management, where rapid, data-sparse forecasts are essential for effective evacuation planning.

Algebraic derivative estimation↗

NASA NTRS2010

Incorporating User Preferences Within an Optimal Traffic Flow Management Framework

The effectiveness of future decision support tools for Traffic Flow Management in the National Airspace System will depend on two major factors: computational burden and collaboration. Previous research has focused separately on these two aspects without consideration of their interaction. In this paper, their explicit combination is examined. It is shown that when user preferences are incorporated with an optimal approach to scheduling, runtime is not adversely affected. A benefit-cost ratio is used to measure the influence of user preferences on an optimal solution. This metric shows user preferences can be accommodated without inordinately, negatively affecting the overall system delay. Specifically, incorporating user preferences will increase delays proportionally to increased user satisfaction.

Rios, Joseph Lucio↗

NASA NTRS2018

Insights Gained from the Ongoing Integrated Arrival/Departure/Surface (IADS) Traffic Flow Operational Prototype

The Insights Gained from the Ongoing Integrated Arrival/Departure/Surface (IADS) Traffic Flow Operational Prototype presentation will include a definition of the learning objectives for the audience, an overview/explanation of the ATD-2 project, an overview of the ATC/Operator data exchange and integration that is foundational for advanced surface capability, and a description of the ATC/Operator data exchange and integration research questions and insights. It will also give an overview of the overhead stream around CLT and a preview of the operational integration benefits, research questions, and insights related to the overhead stream. It will continue with a surface metering process flow overview, a description of local surface demand/capacity imbalance, the benefits of collaborative surface metering, the associated research questions and insights, and wrap up with the next steps planned for ATD-2.

collaborative operational environment↗

NASA NTRS1997

Decentralized and Tactical Air Traffic Flow Management

This project dealt with the following topics: 1. Review and description of the existing air traffic flow management system (ATFM) and identification of aspects with potential for improvement. 2. Identification and review of existing models and simulations dealing with all system segments (enroute, terminal area, ground) 3. Formulation of concepts for overall decentralization of the ATFM system, ranging from moderate decentralization to full decentralization 4. Specification of the modifications to the ATFM system required to accommodate each of the alternative concepts. 5. Identification of issues that need to be addressed with regard to: determination of the way the ATFM system would be operating; types of flow management strategies that would be used; and estimation of the effectiveness of ATFM with regard to reducing delay and re-routing costs. 6. Concept evaluation through identification of criteria and methodologies for accommodating the interests of stakeholders and of approaches to optimization of operational procedures for all segments of the ATFM system.

Odoni, Amedeo R.↗