Relationship between airport congestion and at-gate delay

A GPSS model is developed for simulating air traffic flow at a major metropolitan airport. It simulates scheduled and non-scheduled flights and incorporates official airline guide flight schedules, Department of Transportation delay statistics, and additional data obtained from all airlines operating out of Detroit Metropolitan Wayne County Airport. Simulation outputs include capacity and delay statistics which can be tabulated by runway, gate, and air carrier. This model can be used to study the effect of weather conditions, scheduling patterns, and runway utilization modes on airport congestion and flight delays. >

The ability of a flight crew to deliver their aircraft to its arrival runway on time is important to the overall efficiency of the National Airspace System (NAS). Over the past several years, the NAS has been stressed almost to its limits resulting in problems such as airport congestion, flight delay, and flight cancellation to reach levels that have never been seen before in the NAS. It is predicted that this situation will worsen by the year 2025, due to an anticipated increase in air traffic operations to one-and-a-half to three times its current level. Improved arrival efficiency, in terms of both capacity and environmental impact, is an important part of improving NAS operations. One way to improve the arrival performance of an aircraft is to enable the flight crew to precisely deliver their aircraft to a specified point at either a specified time or specified interval relative to another aircraft. This gives the flight crew more control to make the necessary adjustments to their aircraft s performance with less tactical control from the controller; it may also decrease the controller s workload. Two approaches to precise time navigation have been proposed: Time-Based Arrivals (e.g., required times of arrival) and Self-Spacing. Time-Based Arrivals make use of an aircraft s Flight Management System (FMS) to deliver the aircraft to the runway threshold at a given time. Self-Spacing enables the flight crew to achieve an ATC assigned spacing goals at the runway threshold relative to another aircraft. The Joint Planning and Development Office (JPDO), a multi-agency initiative established to plan and coordinate the development of the Next Generation Air Transportation System (NextGen), has asked for data for both of these concepts to facilitate future research and development. This paper provides a first look at the delivery performance of these two concepts under various initial and environmental conditions in an air traffic simulation environment.

Airport Congestion in EU: More Investment or Better Pricing?

For a system to be managed, it must be measured. The National Airspace System (NAS), the collection of airspace and airport resources in and under the control of the United States (U.S.), is a very complex system which presents great challenges of measurement and management. One key measure of en route airspace efficiency is flying time, i.e., the amount of time it takes for an aircraft to travel through en route airspace on its flight from origin to destination airport. A number of factors influence flying time, the most obvious being the winds. Other important factors are: traffic congestion, air traffic management (ATM) interventions, route structure, industry strategies, and weather. In this paper, we calculate en route flying time in the aggregate and compare a subset of data for the years 2001 and 2002. We account for aircraft equipment type and adjusted flying time for wind effects. In addition, we have selected for analysis a sample of only good weather days (15 in each of the 2 years). The results show that 2002 has slightly shorter flying times than 2001, on the order of 20–25 seconds shorter. Although we did not investigate the causes for the change, we conjecture that the lower levels of traffic after the Fall of 2001 are causing less congestion and less delay. New automation and procedural initiatives may also be contributing to the improvement. Introduction The Federal Aviation Administration (FAA) is charged with the safe, efficient movement of air traffic in U.S. airspace. In recent years, the FAA has begun initiatives to measure efficiency of airspace usage. The measurements are useful for determining the impact of automation and procedural enhancements. Such measurements are also useful for identifying problem areas in the NAS, which can be targeted for remediation. Changes in efficiency may be examined as a function of time. This study assessed system efficiency using en route flying times, comparing 2001 versus 2002. Although several studies have been performed in the past calculating changes in en route flying distance, the authors are familiar with one time-based analysis in the open literature. Bolczak, et al. used Estimated Time of Arrival (ETA) data to analyze a trend in flying time, and discover some year-to-year changes. Other studies of flight times are in the literature, though they have not compared actual or modeled values across years. An early simulation of Free Flight, Ball, et al. analyzed flying times for flights without the constraints of route structuring. Efficiency may be defined as the level of utilization of a resource, with consideration of the cost or effort undertaken to achieve that level. † Free Flight is an industry/government initiative which provides greater freedom for pilots and airlines to select planned and flown routes and take-off times. Free Flight supports collaboration between airspace managers and airspace users. 1 American Institute of Aeronautics and Astronautics Willemain examined sources of variability in flying times for certain city pairs. The Bureau of Transportation Statistics has collected flight time statistics and hosts a web site which allows the public to access this information. In this study, results are presented in aggregated form, with some detail with respect to city pairs. Differences were found in the flying time metrics: 2002 has slightly lower flying times than 2001. The causes for this difference are not discussed.

The growing excess demand for airport capacity makes an efficient use of capacity at congested airports in Europe in increasingly urgent issue. The key question addressed here focuses on the perspectives for a more efficient use of existing airport capacity through various market based options, especially with regard to hub airports in Europe. To that end the following steps are taken. First, the existing allocation system for scarce airport capacity is described and the inefficiencies in the system are identified. More efficient market based approaches are introduced, ranging from congestion pricing to primary slot auctioning and secondary slot trading. The exemption of the ‘grey’ slot trading market in London is explained. Thereafter, we describe the controversy between the two objectives of slot allocation, i.e. efficient use of slots in the upstream market and sufficient competition in the downstream air transport markets. The substantially different approaches chosen by the US and the EU with respect to preferred market based slot allocation options is analysed in this context of conflicting objectives. We also take account of the Commission’s decision to tolerate slot trading at coordinated airports in Europe. Finally, we discuss the impact of secondary slot trading at hub airports in Europe. We confront the slot trading approach with the hubbing system operated at major congested airports in Europe. Since hubbing results in a daily sequence of peak and off-peak periods, the use of hub airport capacity is inefficient, especially during periods of idle capacity. Finally, we address whether slot trading will improve an efficient use of hub airport capacity and if so, how will the hubbing system adapt to these new conditions. We will use Amsterdam Airport as an example.

Scheduling limits have become commonplace at congested airports throughout the world. This paper describes, first, a demand smoothing (DS) optimization model that generates a new flight schedule at busy U.S. airports in response to the introduction of scheduling limits. Given an existing schedule that has been created through the airlines’ scheduling choices, the model produces a feasible modified schedule that obeys the scheduling limits specified for an airport without canceling any flights. At the same time, the model respects all aircraft itineraries, so that aircraft fly their original daily routes through the airport network. In addition, it preserves all passenger connections, in the sense that any connecting passenger will be able to connect between exactly the same two flights under the modified schedule as under the existing one. We then demonstrate, through a detailed example based on operations at the hub airport of Newark Liberty International (EWR), an approach that combines the DS model with a new special-purpose network queuing model to estimate both the local and nationwide effects of alternative scheduling limits at a major airport. We examine the relationship between the scheduled demand and the capacity at EWR to identify plausible scheduling limits that may have a significant impact on delays. The DS model shows that, with a small schedule displacement of 30 minutes or less for any flight during the day, it is possible to obtain a feasible schedule that obeys slot limits as low as the airport’s capacity under Instrument Flight Rules. We find that the local delay savings that would result from introducing such scheduling limits at EWR can be of the order of 20% for arrivals and 50% for departures on busy days; we may also expect a reduction of 20% in delays propagated from EWR to the rest of the U.S. network of airports.

The current demand in the National Airspace System (NAS) is rapidly approaching the system capacity. This capacity limit causes significant congestion and delay in off-nominal conditions, such as inclement weather. It will also constrain future growth in the aviation sector. Transformation of the current system can relieve this constraint. Examples of such transformation include (i) flight schedule shifting to periods of low demand or alternate airports, (ii) air traffic control (ATC) productivity enhancements, (iii) new procedures and airspace redesigns, and (iv) improvements in weather nowcasting and forecasting. Planners are now investigating such NAS architecture strategies. This paper describes research and plans regarding our investigation of future NAS performance in inclement weather days. Our NAS simulation allows researchers and decision makers to hypothesize strategies in various weather conditions and traffic demand levels to quantify the combined impact on the NAS. The results of the simulation will guide decisions regarding the value of investments in weather forecasting technology and NAS procedures. Our simulation approach is to use (i) archived weather and traffic schedule data with (ii) weather and traffic forecasting models and (iii) tactical weather vectoring and stochastic traffic flow management capabilities.

<div class="htmlview paragraph">“As we handle more operations and passengers in the air, we must make certain we have the capacity to handle increased traffic on the ground.”</div> <div class="htmlview paragraph">- Jane Garvey, FAA Administrator (4/20/98)</div> <div class="htmlview paragraph">The FAA Technical Center (Aviation System Analysis and Modeling Branch, ACT-520) has been responsive to the FAA Airport Capacity Program customers for the past 22 years, developing, testing, and applying airfield and airspace simulation models. More than 90 capacity studies have been completed with ACT-520 personnel contributing their technical expertise to the Airport Design Teams. The teams are comprised of FAA personnel, airport operators, air carriers, other airport users and aviation industry representatives at major airports throughout the US.</div> <div class="htmlview paragraph">Initial studies focused on modeling airport operations from final approach, taxi, gate operations and departure processing. Later in the program, local airspace studies were included in some airport study efforts. Both airport and airspace studies include either a full scale design team study or a less detailed tactical initiative. Recently, National Airspace System (NAS) modeling personnel joined the group providing system modeling expertise in the NAS Architecture and Flight 2000 programs.</div> <div class="htmlview paragraph">An arsenal of analysis tools and computer simulation programs has been developed and tested to accomplish each task. The group has experience using the models they have developed or improved upon (ADSIM, RDSIM, RQSIM, etc.), have obtained from other FAA entities (SIMMOD, NASPAC, RAAM, etc.) and have acquired under contract or agreements (The Airport Machine, TAAM, MIT Delays Model, etc.). This modeling capability permitted the group to perform simulations of improvements in the categories of airfield, facilities and equipment, ATC operational, and airport policy improvements. The delay effects of aircraft demand (schedules), fleet mix, runway configurations, weather, aircraft separation standards, etc., were measured in the studies.</div> <div class="htmlview paragraph">Future studies will require new model development particularly in the arena of system wide impacts measured on an annual basis. The requirement for an analyst to know both the model inputs and the working of the model's code will continue to apply for a successful study. As in the past, no universal model will answer every question and existing models will require enhancements to reflect unique requirements. Because of the increases in computing speed and memory, the fidelity of simulations can be enhanced in the near future through new hardware acquisitions.</div> <div class="htmlview paragraph">ACT 520's past participation in this enterprise has generated an understanding of the interrelationships of various airport and airline systems. Our seven senior analysts and eight junior analysts combined with their professional expertise are ready to address the FAA Administrator's concerns about capacity.</div>

In recent years, flight delay costs the air transportation industry millions of dollars and has become a systematic problem. Understanding the behavior of flight delay is thus critical. This paper focuses on how flight delay is affected by operation-, time-, and weather-related factors. Different econometric models are developed to analyze departure and arrival delay. The results show that compared to departure delay, arrival delay is more likely to be affected by previous delays and the buffer effect. Block buffer presents a reduction effect seven times greater than turnaround buffer in terms of flight delays. Departure flights suffer more delays from convective weather than arrival flights. Convective weather at the destination airport for flight delay has a greater impact than at the original airport. In addition, sensitivity analysis of flight delays from an aircraft utilization perspective is conducted. We find that the effect of delay propagation on flight delay differs by aircraft utilization. This impact on departure delay is greater than the impact on arrival delay. In general, specific to the order of flights, the previous delay increases the impact on flight on-time performance as a flight flies a later leg. Buffer time has opposite effects on departure and arrival delay, with the order increasing. A decrease in buffer time with the order increasing, however, still has a greater reduction effect on departure delay than arrival delay. Specific to the number of flights operated by an aircraft, the more flights an aircraft flies in a day, the more the on-time performance of those flights will suffer from the previous delay and buffer time generally.

The performance metric used to evaluate on-time performance in the US airline industry is flight-based, measuring the number of flight legs with arrival delay of 15 minutes or more. We analyze airline passenger operations and schedule performance and conclude that this flight-based performance metric does not accurately reflect delays to passengers, primarily because it does not recognize the long passenger delays resulting from flight leg cancellations and missed connections. Using passenger bookings and flight operations data from a major US airline, we develop a Passenger Delay Calculator to compute passenger delays and to establish relationships between passenger delays and cancellation rates, flight leg delay distributions, load factors, and flight schedule design. Using the insights gained in our analysis, we define new passenger-centric metrics to address the shortcomings of existing flight-based metrics and more accurately evaluate schedule reliability.

*† Simulations of the National Airspace System (NAS) including the effects of inclement weather pose several challenges for researchers. First, one must efficiently model the flight of thousands of aircraft flying in the NAS, which is a challenge for simulation technology of today. Second, simulations must be diverse enough to model a broad range of possible types of weather days, from clear weather to severe weather days. Finally, simulations must include national control strategies that occur in response to weather conditions. In this paper, we investigate a series of models that allows us to simulate the effects of a broad range of weather conditions on the NAS. Furthermore, we present models that are appropriate for modeling the effects of weather on the Next Generation Air Transportation System (NGATS).

On the effects of airport capacity expansion under responsive airlines and elastic passenger demand

This paper discusses the results of modeling and simulation performed by FAA’s Performance Analysis and Strategy office to forecast delays at thirty major US airports six months ahead of time. In order to have reliable delay forecasts, it is important that the simulation model validates well with historic actual data. As part of the validation efforts, we recognize that an aircraft’s movement in the US National Airspace System (NAS) involves its interaction with various resources along the flight path, such as airport runways, arrival and departure fixes, airspace sectors, and terminal gates. When multiple aircraft compete for access to these resources, their movements are impeded due to operational and safety considerations, resulting in flight delays. The objective of this study is to understand how various resources contribute to total flight delays, and how sensitive the delays are to changes in resource capacities. The results to date indicate that reduced airport runway capacities play a significant role in overall delays, but arrival and departure fixes and airspace sector constraints do not. Our use of historical airport AARs and ADRs for runway capacities, which may inherently account for fix constraints, yields delays that agree very well with actual values reported in ASPM. However, care has to be taken to use and interpret these rates when there are runway additions or closures. One area in which all NAS-wide models fail to replicate the behavior of the NAS is gate-out delay. Our simulation includes modeled distributions to capture gate-out delays, but they were found to capture only a fraction of actual gate-out delays. Terminal gate constraints were added to verify if they yielded large gate-in delays that would result in additional gate-out delays. No significant additional gate-out delays were obtained, although we gained better insight into how gate-in delays may occur. We are continuing to research potential NAS interactions that would account for the gap in predicting gate-out delays. Additional simulation model enhancements are planned to account for traffic flow initiatives such as GDP, as well as itinerary adjustments due to flight cancellation and substitutions.

The U.S. National Airspace System (NAS), its interconnected operations, and resultant dynamics (e.g., flight delays, route configurations) can be understood at different scales. At the resolution of the entire NAS, a component (e.g., Denver Center) could be considered as one homogeneous node, with dependencies on other components (e.g., adjacent centers) and exogenous stakeholders (e.g., Air Traffic Control System Command Center, airline network operations centers). Understanding the connected behavior of the NAS in a data-driven and adaptive way is critical for rigorously determining whether interventions, strategic or tactical, were successful. However, within NAS components flight operations induce a multitude of relationships between parts of the airspace at many levels. To capture, analyze, and build upon such a connected and multi-scaled system, we require graph-based network models at varying resolutions, which can be adapted to fit a particular analysis use case. As an example, graphical representation at a higher resolution within a component may be required to capture nuanced behavior in analyses focused on local perturbations. In this work, we present a pipeline that constructs flexible graph-structured data from flight trajectories, and leverage this for different case studies within the NAS, all focused on evaluating different aspects of traffic flow management, and at different scales.

This paper studies the impacts on aviation and related costs of a snow storm in the northeastern United States that occurred on October 29, 2011, focusing on the question of what would have happened had an accurate forecast been available to airlines and the traveling public at least twenty-four hours before the actual event. Snow storms are remarkably difficult to forecast. In the case of the October 29th 2011 event, snow fell over a larger area than initially forecast, the event moved through the Northeast faster than predicted, and the storm occurred in the middle of the day, maximizing its impact on air travel. A mitigating factor is that the event occurred on a Saturday, a day less traveled than other days of the week. The unexpected nature of the storm caused twenty-nine flights to be diverted to Bradley International Airport (BDL) near Hartford, Connecticut. One diverted flight remained at BDL for over seven hours before continuing to its intended destination. Additionally, all major Northeast airports had reduced capacity, as some runways closed for snow removal operations while others operated under Instrument Meteorological Conditions (IMC) for an extended time. The impact was larger than the aviation system was prepared to handle in part due to the fact that the weather forecasts were accurate only within two hours of the storm's onset in the Northeast. The question we seek to answer is the extent to which the impact can be mitigated had an accurate forecast been available twenty-four hours in advance. To study the question, we configured NASA's Airspace Concepts Evaluation System (ACES) to replicate the day with a two-hour forecast, and a second time with a hypothetical twenty-four hour perfect forecast. The forecasts as well as actual conditions not only in the Northeast, but throughout the National Airspace System (NAS), were provided by the Air Force Environmental Data Cube Support System group [1]. The ACES agents involved in airline decision making, particularly the Airline Operations Center (AOC) agent, contain logic that mimics an airline's decision making logic (cancellation, flight delay, or diversion) given a forecast of future conditions. By providing the AOC agent a two-hour perfect forecast and a twenty-four hour perfect forecast, the flight impact can be estimated. We monetize the flight impact in two ways, one from the airlines' perspective and another from the passengers' perspective. The airline monetization is achieved using standard cost of airline operations as derived from other studies [2]. Passenger monetization is computed using a study of the impact of flight delays and cancellations on passenger costs [3]. The costs are compared, and a classic tradeoff between airline and passenger costs associated with imperfect weather forecasts is observed.