Congestion Mitigation through Schedule Coordination at JFK: An Integrated Approach

Most flight delays are created by large temporary or long-term imbalances between demand and capacity at the busiest airports. Absent large increases in capacity, airport congestion can only be mitigated through improvements in the utilization of available capacity and the implementation of demand management measures. This paper presents an integrated approach that jointly optimizes the airport’s flight schedule at the strategic level and the utilization of airport capacity at the tactical level, subject to scheduling and capacity constraints. The capacity utilization part involves controlling the runway configuration and the balance of arrival and departure service rates to minimize congestion costs. The schedule optimization reschedules a selected set of flights to reduce the demand-capacity mismatches while minimizing interference with airline competitive scheduling. We develop an original iterative solution algorithm that integrates airport stochastic queue dynamics and a Dynamic Programming model of airport operating procedures into an Integer Programming model of flight rescheduling. The algorithm is shown to converge in reasonable computational times and is thus implementable in practice. Extensive computational results for JFK Airport suggest that very substantial delay reductions can be achieved through limited changes in airline schedules. It is also shown that the proposed integrated approach to airport congestion mitigation performs significantly better than the typical sequential approach where scheduling and operational decisions are made separately

Endogenous Control of Service Rates in Stochastic and Dynamic Queuing Models of Airport Congestion

Airport congestion mitigation requires reliable delay estimates. This paper presents an integrated model of airport congestion that combines a tactical model of capacity utilization into a strategic queuing model. The model quantifies the relationships between flight schedules, airport capacity and flight delays, while accounting for the way arrival and departure service rates can be controlled over the day to maximize operating efficiency. We show that the model estimates well the average and variability of the delays observed at New York’s airports. Results suggest that delays can be extremely sensitive to even small changes in flight schedules or airport capacity

A Joint Planning, Management and Operations Framework for Airport Infrastructure

Many airports around the world are actively considering development or expansion projects. Such projects can spur tremendous benefits but are investment-intensive and span several decades from conception to completion. We formulate the associated dynamic, complex decision-making problems using a broad systems frame. We propose a conceptual framework that links airport infrastructure investments and airport management and operations in a time-expanded, state-contingent problem. To develop this framework we consider the social and policy objectives for well functioning air transportation infrastructure, the decision levers available to stakeholders, the influence of the institutional field and regulatory context on these decisions, and the key performance measures that operationalize system ilities. Our framework integrates literature from investments under uncertainty, airport demand management, and airport operating procedures. Four case examples of airports in Delhi, Charlotte, London and New York illustrate decision-making in the context of our framework. We argue for a more integrated approach to decision-making while evaluating investments in greenfield airports or capacity expansions

Airport Congestion Mitigation through Dynamic Control of Runway Configurations and of Arrival and Departure Service Rates under Stochastic Operating Conditions

The high levels of flight delays require the implementation of airport congestion mitigation tools. In this paper, we optimize the utilization of airport capacity at the tactical level in the face of operational uncertainty. We formulate an original Dynamic Programming model that selects jointly and dynamically runway configurations and the balance of arrival and departure service rates at a busy airport to minimize congestion costs, under stochastic queue dynamics and stochastic operating conditions. The control is exercised as a function of flight schedules, of arrival and departure queue lengths and of weather and wind conditions. We implement the model in a realistic setting at JFK Airport. The exact Dynamic Programming algorithm terminates within reasonable time frames. In addition, we implement an approximate one-step look-ahead algorithm that considerably accelerates the execution of the model and results in close-to-optimal policies. In combination, these solution algorithms enable the on-line implementation of the model using real-time information on flight schedules and meteorological conditions. The application of the model shows that the optimal policy is path-dependent, i.e., it depends on prior decisions and on the stochastic evolution of arrival and departure queues during the day. This underscores the theoretical and practical need for integrating operating stochasticity into the decision-making framework. From comparisons with an alternative model based on deterministic queue dynamics, we estimate the benefit of considering queue stochasticity at 5% to 20%. Finally, comparisons with advanced heuristics aimed to imitate actual operating procedures suggest that the model can yield significant cost savings, estimated at 20% to 30%

A System Level Study of New Wake Turbulence Separation Concepts and Their Impact on Airport Capacity

The air transportation industry continues to grow worldwide, but demand is often limited by available airspace and airport capacity. This thesis focuses on evaluating new air traffic procedures: specifically, new and emerging wake turbulence separation rules that could potentially increase runway capacity based on today’s knowledge of wake vortex turbulence and technological capabilities. While legacy wake separation rules establish aircraft-classes based on weight of aircraft, these new separation rules can define separation standards by considering other aircraft parameters and dynamic wind conditions. A fast-time runway system model is developed for studying these wake separation rules, using Monte-Carlo simulations, to provide accurate and realistic runway capacity estimates based on the randomness of arrival and departure operations. A total of nine new proposed wake separation rules are analyzed in detail, which include both distance-based and time-based methods, as well as static and dynamic concepts. Seven of the busiest and most delayed U.S. airports are selected as case studies for the illustration of runway capacity benefits enabled by these new wake separation rules: Boston (BOS), New York J.F. Kennedy (JFK), New York LaGuardia (LGA), Newark (EWR), San Francisco (SFO), Los Angeles (LAX), and Chicago O’Hare (ORD). For a detailed capacity analysis, the new wake separation rules are tested under the most constraining runway configurations at each of these airports. The results indicate that increasing the number of aircraft wake categories can increase runway capacity, but the added benefits become smaller with each new category added. A five-or six-category wake separation system can capture most of the runway capacity that can be achieved with a static pair-wise system. Additionally, shifting wake category boundaries between airports as a function of local fleet mix can provide additional runway capacity benefits, meaning that airport specific wake separation rules can increase capacity over a universal separation rule system. Among the new wake separation rules, the results indicate that reducing wake separations further from current minimum separations (separation values of 2NM or less) can shift the operational bottleneck from the approach path to the runway, as runway occupancy time becomes the limiting factor for inter-arrival separations. The findings from the time-based separation rule demonstrate that switching from distance-based separations to time-based separations in strong headwind conditions can recover significant lost capacity. Time-based separation rules can be of great value 4 to increase operational reliability and capacity predictability at airports in all weather conditions. Moreover, the results also indicate that a reduction in minimum separations enabled by dynamic wind and aircraft information can offer marginal runway capacity benefits over the capacity enabled by static pair-wise wake separations, as more and more aircraft pairs become limited by runway occupancy time. Therefore, a joint effort is needed for reducing both wake separations and runway occupancy in order to accommodate future air traffic demand.This project was funded under the FAA NEXTOR II Center of Excellence

The Rebound Effect in the Aviation Sector

The rebound effect, i.e., the (partial) offset of the energy efficiency improvement potential due to a reduction in marginal usage costs and the associated increase in consumer demand, has been extensively studied for residential energy demand and automobile travel. This study presents a quantitative estimate of the rebound effect for an air traffic network including the 22 busiest airports, which serve 14 of the highest O-D cities within the domestic U.S. aviation sector. To satisfy this objective, passenger flows, aircraft operations, flight delays and the resulting energy use are simulated. Our model results indicate that the average rebound effect in this network is about 19%, for the range of aircraft fuel burn reductions considered. This is the net impact of an increase in air transportation supply to satisfy the rising passenger demand, airline operational effects that further increase supply, and the mitigating effects of an increase in flight delays. Although the magnitude of the rebound effect is small, it can be significant for a sector that has comparatively few options for reducing greenhouse gas emissions

A Concept for Robust, High Density Terminal Air Traffic Operations

This paper describes a concept for future high-density, terminal air traffic operations that has been developed by interpreting the Joint Planning and Development Office s vision for the Next Generation (NextGen) Air Transportation System and coupling it with emergent NASA and other technologies and procedures during the NextGen timeframe. The concept described in this paper includes five core capabilities: 1) Extended Terminal Area Routing, 2) Precision Scheduling Along Routes, 3) Merging and Spacing, 4) Tactical Separation, and 5) Off-Nominal Recovery. Gradual changes are introduced to the National Airspace System (NAS) by phased enhancements to the core capabilities in the form of increased levels of automation and decision support as well as targeted task delegation. NASA will be evaluating these conceptual technological enhancements in a series of human-in-the-loop simulations and will accelerate development of the most promising capabilities in cooperation with the FAA through the Efficient Flows Into Congested Airspace Research Transition Team

Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster

Integrated and joint optimisation of runway-taxiway-apron operations on airport surface

Airports are the main bottlenecks in the Air Traffic Management (ATM) system. The predicted 84% increase in global air traffic in the next two decades has rendered the improvement of airport operational efficiency a key issue in ATM. Although the operations on runways, taxiways, and aprons are highly interconnected and interdependent, the current practice is not integrated and piecemeal, and overly relies on the experience of air traffic controllers and stand allocators to manage operations, which has resulted in sub-optimal performance of the airport surface in terms of operational efficiency, capacity, and safety. This thesis proposes a mixed qualitative-quantitative methodology for integrated and joint optimisation of runways, taxiways, and aprons, aiming to improve the efficiency of airport surface operations by integrating the operations of all three resources and optimising their coordination. This is achieved through a two-stage optimisation procedure: (1) the Integrated Apron and Runway Assignment (IARA) model, which optimises the apron and runway allocations for individual aircraft on a pre-tactical level, and (2) the Integrated Dynamic Routing and Off-block (IDRO) model, which generates taxiing routes and off-block timing decisions for aircraft on an operational (real-time) level. This two-stage procedure considers the interdependencies of the operations of different airport resources, detailed network configurations, air traffic flow characteristics, and operational rules and constraints. The proposed framework is implemented and assessed in a case study at Beijing Capital International Airport. Compared to the current operations, the proposed apron-runway assignment reduces total taxiing distance, average taxiing time, taxiing conflicts, runway queuing time and fuel consumption respectively by 15.5%, 15.28%, 45.1%, [58.7%, 35.3%, 16%] (RWY01, RWY36R, RWY36L) and 6.6%; gated assignment is increased by 11.8%. The operational feasibility of this proposed framework is further validated qualitatively by subject matter experts (SMEs). The potential impact of the integrated apron-runway-taxiway operation is explored with a discussion of its real-world implementation issues and recommendations for industrial and academic practice.Open Acces