Enhanced Traffic Management System Research Articles

Filtering Enhanced Traffic Management System (ETMS) Altitude Data

Abstract Enhanced Traffic Management System (ETMS) stores all the information gathered by the Federal Aviation Administration (FAA) from aircraft flying in the US airspace. The data stored from each flight includes the 4D trajectory (latitude, longitude, altitude and timestamp), radar data and flight plan information. Unfortunately, there is a data quality problem in the vertical channel and the altitude component of the trajectories contains some isolated samples in which a wrong value was stored. Overall, the data is generally accurate and it was found that only 0.3% of the altitude values were incorrect, however the impact of these erroneous data in some analyses could be important, motivating the development of a filtering procedure. The approach developed for filtering ETMS altitude data includes some specific algorithms for problems found in this particular dataset, and a novel filter to correct isolated bad samples (named Despeckle filter). As a result, all altitude errors were eliminated in 99.7% of the flights affected by noise, while preserving the original values of the samples without bad data. The algorithm presented in this paper attains better results than standard filters such as the median filter, and it could be applied to any signal affected by noise in the form of spikes

Choice of Route Networks: A Qualitative Model for Overland and Overwater Routes

In this paper, airlines and other users' route choice behaviors in the national airspace system of the United States have been examined. A binary logit regression model was used to determine the choice of routes by the users employing instrument flight rules. By categorizing routes into two types-over land and over water-and using a sample of almost 98,500 observations from the Federal Aviation Administration's Enhanced Traffic Management System, it was found that operational characteristics, such as distance, commercial flights, and altitudes of the flights are critical in determining route choice. Inflationary environment, caused by sectoral jet fuel prices and overall economy-wide prices, leads to the choice of overwater routes. Busier months as well as peak times of the day lead to the choice of overwater routes. Finally, airspace redesign tends to make overwater routes more attractive vis a vis than overland routes.

Protocol-Based Conflict Resolution for Air Traffic Control

This paper proposes a simple geometric method for collaborative multiple-aircraft conflict avoidance. We assume that aircraft cruise at constant altitude with varying velocities and thus consider conflict resolution on planar airspace. The multiple-aircraft conflict resolution methodology is presented in two steps; first, we consider an unrealistic but geometrically simple exact conflict, in which the original trajectories of all aircraft intersect at a point, in order to derive a closed-form analytic solution for the required heading change, and then we consider a realistic general conflict, in which conflict points of multiple aircraft do not coincide. Heading change is the main control input for conflict resolution, yet velocity change is also used for a general conflict. The proposed algorithm is based on a protocol, a simple rule for multiple-aircraft conflict resolution, which is easily understandable and implementable by all aircraft involved in the conflict, and provides guarantees of safety within the limits of the model used. We show that this solution is robust to uncertainties in the aircraft’s position, heading, and velocity, as well as to path smoothing, and asynchronous maneuvers. We present simulation results using a dynamic aircraft model for various multiple-aircraft conflict scenarios based on Enhanced Traffic Management System (ETMS) data.

An Airspace-Planning and Collaborative Decision-Making Model: Part II—Cost Model, Data Considerations, and Computations

In Part I of this paper, we presented a large-scale airspace-planning and collaborative decision-making (APCDM) model that is part of a Federal Aviation Administration (FAA)-sponsored effort to enhance the management of the National Airspace System (NAS). Given a set of flights that must be scheduled during some planning horizon, along with alternative surrogate trajectories for each flight, we developed a mixed-integer programming model to select a set of flight plans from among these alternatives, subject to flight safety, air-traffic control workload, and airline equity considerations. The present paper offers insights related to, and a detailed description of, implementing this APCDM model, including the development of a comprehensive cost model, a study for prescribing a set of appropriate parameter values for the overall model, and an investigation on incorporating a suitable set of valid inequalities in the model formulation. Computational results are presented based on several test cases derived from the Enhanced Traffic Management System (ETMS) data provided by the FAA. The results indicate that under plausible probabilistic trajectory error assumptions and with the incorporation of star subgraph convex hull-based valid inequalities, the model offers a viable tool that can be used by the FAA for both tactical and strategic applications.

Growth and Operating Patterns of Regional Jets in the United States

Data concerning the growth and utilization of regional jets between 1998 and 2003 are presented and analyzed. During this period, daily regional jet operations increased by 356%. These additional flights were primarily used to enter new midrange markets that were previously too far to serve efficiently with a turboprop and too small to warrant a traditional jet. Analysis of aircraft situational display to industry operational data also shows an increase in the stage length of regional jet flights; for example, at Dallas‐Fort Worth, the regional jet catchment basin increased from 299 n mile to 868 n mile. To better understand the reasons for the rapid growth of regional jets, an analysis of the economics of regional flight was performed. This analysis shows that airlines benefit from regional jets primarily because of the fee-per-departure payment structure and their current utilization patterns. HE number of regional jets in the national airspace system (NAS) has grown significantly over the past 10 years. Regional jets are small, 30‐100 seat aircraft capable of flying up to 1000 n mile and have been growing in number at a nearly exponential rate since 1994 (Ref. 1). This increase represents a major change in the composition of the national fleet and may affect the efficiency of the air traffic control system, as well as the dynamics of the airline industry. This paper examines the evolution of regional jet utilization to better understand their growth and operating patterns, as well as possible future trends. The goal just outlined was reached in two steps. The first was to analyze flight track data and develop methods to visualize and quantify any changes in the flight patterns of regional jets. Once this was accomplished, the second step was to analyze aircraft economics, aircraft utilization, airline scope clause agreements, and other factors to develop an understanding and explanation of the observed patterns. II. Methodology A. Data Sources To perform an analysis of emerging regional jet trends and compare them to those of other aircraft, this study made use of actual flight data from the aircraft situational display to industry (ASDI) feed. The ASDI feed provides aircraft position and speed information during flight, in addition to the flight origin, destination, and departure/arrival times. The ASDI feed is compiled from the Federal Aviation Administration’s (FAA’s) Enhanced Traffic Management System (ETMS) and is made available to vendors, who can then pass the data on to other interested parties. This study used ASDI data for one Thursday in January, between 1998 and 2003, resulting in a total of 6 days. Each data sample is from the same month and weekday to eliminate the effects of seasonal and weekly variations in airline operations. The specific dates were chosen to represent typical January days. In particular,

Common Trajectory Modeling for National Airspace System: Decision Support Tools

Federal Aviation Administration air traffic operations utilize several types of Decision Support Tools (DSTs) to provide for separation and management of air traffic in the National Airspace System (NAS). Some DSTs include the Enhanced Traffic Management System (ETMS), User Request Evaluation Tool (URET), and the Center-TRACON Automation System (CTAS) Traffic Management Advisor (TMA). A common element of these DSTs is a trajectory modeling function that predicts flight status (e.g., aircraft position and altitude) over time. Modeling techniques vary among the tools because modelers were custom made for the tools, which have different operational requirements.Despite differences in operational requirements, there are some common functional components within these trajectory modeling functions. Flight plan route processing, for example, is performed similarly among the DSTs. Airspace adaptation data, such as airway definitions, are used by all DSTs. Identification of the common functional components and underlying data elements opens the potential for their consolidation as common services in a NAS infrastructure that supports all DST applications, or at least more than a single tool. Common services and data are expected to provide several benefits to subscribing DSTs, including compatible decisions, increased interoperability, and development and maintenance cost savings.Research was conducted to identify common trajectory modeling components within a selected set of NAS DSTs. This paper reports the results of the research. The paper introduces the topic of common trajectory modeling; identifies a set of trajectory modeling services, characteristics, and data found to be common across the DSTs studied; presents architectural alternatives for implementing the common services; and defines a process for selecting a common approach for implementing the services.

Queuing Model for Taxi-Out Time Estimation

Flights incur a large percentage of their delays on the ground during the departure process between their scheduled departure from the gate and takeoff. Because of the large uncertainties associated with them, these delays are difficult to predict and account for, hindering the ability to effectively manage the Air Traffic Control (ATC) system. This paper presents an effort to improve the accuracy of estimating the taxi-out time, which is the duration between pushback and takeoff. The method was to identify the main factors that affect the taxi-out time and build an estimation model that takes the most important ones into account. An analysis conducted at Boston Logan International Airport identified the runway configuration, the airline/terminal, the downstream restrictions and the takeoff queue size as the main causal factors that affect the taxi-out time. Of these factors the takeoff queue size was the most important one, where the queue size that an aircraft experienced was measured as the number of takeoffs that took place between its pushback time and its takeoff time. Consequently, a queuing model was built to estimate the taxi-out time at Logan Airport based on queue size estimation. For each aircraft, the queuing model assumes knowledge of the number of departure aircraft present on the airport surface at its pushback time and estimates its takeoff queue size by predicting the amount of passing that it may experience on the airport surface during its taxi out. The prediction performance of the queuing model was compared at Logan Airport with a running average model, which represents the baseline used currently in the Enhanced Traffic Management System (ETMS). The running average model uses a fourteen-day average as the estimate of the taxi-out time. The queuing model improved the mean absolute error in the taxi-out time estimation by approximately twenty percent and the accuracy rate by approximately ten percent, over the fourteen-day running average model.

Reversing course: Issues in modeling legacy systems

The Enhanced Traffic Management System (ETMS) is a legacy system used by the Federal Aviation Administration (FAA) to support air traffic flow management. Air traffic flow management is the strategic control of air traffic to minimize delays and congestion and maximize the throughput of aircraft throughout the National Airspace System (NAS). This paper discusses the reasons for modeling a legacy system, problems and advantages encountered in modeling an operational system, and describes the construction of a simulation model of ETMS. Originally written in Pascal to run on Apollo workstations under the Aegis Domain operating system, ETMS has been converted to C/C++ and ported to HP servers and workstations running HP-UX, a POSIX-compliant version of UNIX. The objectives of the modeling task were to assess performance of the ported system and to provide a basis for evaluating a possible redesign/re-architecture of the system. The initial plan was to develop one or two models aimed at the network aspects (both LAN and WAN) of ETMS at a relatively high level, and then to develop a more detailed model to look at specific workstation/server issues. As is shown in this paper, issues of existing system design and documentation and the availability (or the lack) of data continually arose. Nevertheless, a reasonable set of working assumptions were derived which allowed modeling and evaluation to proceed. Thus, the quantitative and qualitative results obtained provided information and lessons learned that can be built upon. Moreover, the second of the stated goals (to provide a basis for possible redesign) was also achieved because there is now a baseline for future design/architecture studies. The focus of this paper is to provide insights into the issues involved in modeling an existing system rather than the results of the model itself.

Technical Note—A Dynamic Programming Framework for the Global Flow Control Problem in Air Traffic Management

This paper presents a network model which can serve as a framework for generating optimal global congestion-relieving strategies for the flow control problem in air traffic management. The general network model presented here addresses the global problem of optimally combining a number of local strategies to relieve multiple interdependent occurrences of congestion. While several algorithms and techniques have been or are being developed which deal with congestion at one air traffic control element for one time period, the actual flow control problem is concerned with congestion in a dynamic setting occurring at a number of different elements which are interrelated in a complex manner. The network model presented here is designed to work with the Enhanced Traffic Management System (ETMS) project under development at the Transportation Systems Center (TSC) for the Federal Aviation Administration (FAA).

Advanced Traffic Management System automation

The Transportation Systems Center (TSC) of the US Department of Transportation utilized state-of-the-art advancements and pioneered automation concepts in delivering the Enhanced Traffic Management System (ETMS), which is serving the nationwide needs of the Federal Aviation Administration's (FAA's) Traffic Management System. The author describes a multiple-part computer complex that supports new concept developments while permitting tested concepts and newly automated functions to be quickly implemented and used operationally by the FAA in daily traffic management activities. Noting the impact of ETMS, an explanation of traffic management functions highlights the role of automation. A description of the five-phase TSC R&D program is followed by explanations of the innovative aircraft situation display and how the ETMS works. The ETMS distributed architecture design, redundancy, networking, and fail-safe characteristics are presented, leading up to the achievement of a compressed life cycle to operational service. The author highlights design factors contributing significantly to rapid development, low cost, flexible system design, operational integrity, and timely operational enhancements.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>