Airport demand management: The operations research and economics perspectives and potential synergies

Airport demand management aims to mitigate air traffic congestion by limiting the imbalances between demand and capacity at busy airports through administrative measures (e.g., slot controls) or economic incentives (e.g., congestion pricing, slot auctions). This paper provides an integrated synthesis of the contributions of the fields of operations research/management science (OR/MS) and economics on the subject matter. From an operating standpoint, assessing the benefits of demand management requires estimates of airport capacity and models of airport on-time performance. From a managerial standpoint, the design of demand management mechanisms can be supported by decision-making models of flight scheduling. From an economic standpoint, the welfare impact of congestion pricing, slot controls and slot auctions depends on the market structure at the airport. This paper proposes an integrated framework that underscores the interdependencies between these operating, managerial and economic aspects to foster cross-disciplinary approaches toward more effective demand management policies at busy airports worldwide.     

Research review of air traffic management

As air transport demand keeps growing more quickly than system capacity, efficient and effective management of system capacity becomes essential to the operation of the future global air traffic system. Although research in the past two decades has made significant progress in relevant research fields, e.g. air traffic flow management and airport capacity modelling, research loopholes in air traffic management still exist and links between different research areas are required to enhance the system performance of air traffic management. Hence, the objective of this paper is to review systematically current research in the literature about the issue of air traffic management to prioritize productive research areas. Papers about air traffic management are discussed and categorized into two levels: system and airport. The system level of air transport research includes two main topics: air traffic flow management and airspace research. On the airport level, research topics are: airport capacity, airport facility utilization, aircraft operations in the airport terminal manoeuvring area as well as aircraft ground operations research. Potential research interests to focus on in the future are the integration between airspace capacity and airport capacity, the establishment of airport information systems to use airport capacity better, and the improvement in flight schedule planning to improve the reliability of schedule implementation.     

A decision support system for total airport operations management and planning

The airport planning and decision making process exhibits various trade‐offs and complications due to the large number of stakeholders having different, and sometimes conflicting, objectives regarding the assessment of airport performance. As a result, the airport performance assessment necessitates the use of advanced modelling capabilities and decision support systems or tools in order to capture the multifaceted aspects, interests and measures of airport performance like capacity, delays, safety, security, noise and cost‐effectiveness. Presently, airport decision makers lack decision support tools able to provide an integrated view of total airport (both airside and landside) operations and analyse at a reasonable effort and decision‐oriented manner the various trade‐offs involved among different airport performance measures. The objective of this paper is twofold: (i) to describe the decision‐oriented modelling framework and development process of a decision support system for total airport operations management and planning, and (ii) to demonstrate the decision support capabilities and basic modelling functionalities of the proposed system. Copyright © 2010 John Wiley & Sons, Ltd.     

Predicting and planning airport acceptance rates in metroplex systems for improved traffic flow management decision support

Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability.     

Apron capacity at hub airports—the impact of wave‐system structure

At hub airports, dominant airlines/alliance coordinate their flights in time with the aim of increasing the number (and quality) of connections, thus producing a wave‐system in traffic schedules. This paper addresses the impact of concentrating aircraft into waves on airport apron capacity. Existing models for apron capacity estimation are based on the number of stands, stand occupancy time, and demand structure, differing between representative groups of aircraft served at an airport. Criteria for aircraft grouping are aircraft type and/or airline and/or type of service (domestic, international, etc.). Modified deterministic analytical models proposed in this paper also take into account the wave‐system parameters, as well as runway capacity. They include the impact of these parameters on the number of flights in wave, stand occupancy time, and consequently apron capacity. Numerical examples illustrate the difference between apron capacity for an origin–destination airport and a hub airport, under the same conditions; utilization of the theoretical apron capacity at a hub airport, given the wave‐system structure; and utilization of the apron capacity at a hub airport when point‐to‐point traffic is allowed to use idle stands. Furthermore, the influence of different assignment strategies for aircraft stands in the case of hub airports is also discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

Light Rail Rapid Transit systems for more sustainable ground accessibility of airports

This paper investigates the potential of Light Rail Rapid Transit (LRRT) to mitigate the environmental and social burden of ground access systems of an airport. This implies, on the one hand, LRRT's capability in mitigating externalities in terms of noise, air pollution/climate change, traffic incidents/accidents and congestion of airport ground access systems and, on the other, the provision of sufficient capacity to accommodate generally increasing volumes of both air passenger and airport employee demand by connecting the airport to its core catchment area. A methodology for assessing the capability of LRRT operating as an airport ground access system is developed. This methodology consists of models to analyze and predict demand and capacity for an LRRT system and models to quantify the externalities of particular airport ground access systems as well as assessing their prospective savings thanks to the introduction of an LRRT system. The methodology is applied to a large European airport – Amsterdam Schiphol (the Netherlands) – using a ‘what-if?’ scenario approach.     

Conflict‐free time‐based trajectory planning for aircraft taxi automation with refined taxiway modeling

To mitigate airport congestion caused by increasing air traffic demand, the trajectory‐based surface operations concept has been proposed to improve surface movement efficiency while maintaining safety. It utilizes decision support tools to provide optimized time‐based trajectories for each aircraft and uses automation systems to guide surface movements and monitor their conformance with assigned trajectories. Whether the time‐based trajectories can be effectively followed so that the expected benefits can be guaranteed depends firstly on whether these trajectories are realistic. So, this paper first deals with the modeling biases of the network model typically used for taxi trajectory planning via refined taxiway modeling. Then it presents a zone control‐based dynamic routing and timing algorithm upon the refined taxiway model to find the shortest time taxi route and timings for an aircraft. Finally, the presented algorithm is integrated with a sequential planning framework to continuously decide taxi routes and timings. Experimental results demonstrate that the solution time for an aircraft can be steadily around a few milliseconds with timely cleaning of expired time windows, showing potential for real‐time decision support applications. The results also show the advantages of the proposed methodology over existing approaches. Copyright © 2015 John Wiley & Sons, Ltd.     

A Heuristic Algorithm for the Allocation of Airport Runway System Capacity

Abstract

This paper develops a heuristic algorithm for the allocation of airport runway capacity to minimise the cost of arrival and departure aircraft/flight delays. The algorithm is developed as a potential alternative to optimisation models based on linear and integer programming. The algorithm is based on heuristic (‘greedy’) criteria that closely reflect the ‘rules of thumb’ used by air traffic controllers. Using inputs such as arrival and departure demand, airport runway system capacity envelopes and cost of aircraft/flight delays, the main output minimises the cost of arrival and departure delays as well as the corresponding interdependent airport runway system arrival and departure capacity allocation. The algorithm is applied to traffic scenarios at three busy US airports. The results are used to validate the performance of the proposed heuristic algorithm against results from selected benchmarking optimisation models.     

An aggregate demand model for air passenger traffic in the hub-and-spoke network

In this paper, we build an aggregate demand model for air passenger traffic in a hub-and-spoke network. This model considers the roles of airline service variables such as service frequency, aircraft size, ticket price, flight distance, and number of spokes in the network. It also takes into account the influence of local passengers and social-economic and demographic conditions in the spoke and hub metropolitan areas. The hub airport capacity, which has a significant impact on service quality in the hub airport and in the whole hub-and-spoke network, is also taken into consideration.Our demand model reveals that airlines can attract more connecting passengers in a hub-and-spoke network by increasing service frequency than by increasing aircraft size in the same percentage. Our research confirms the importance of local service to connecting passengers, and finds that, interestingly, airlines’ services in the first flight leg are more important to attract passengers than those in the second flight segment. Based on data in this study, we also find that a 1% reduction of ticket price will bring about 0.9% more connecting passengers, and a 1% increase of airport acceptance rate can bring about 0.35% more connecting passengers in the network, with all else equal. These findings are helpful for airlines to understand the effects of changing their services, and also useful for us to quantify the benefits of hub airport expansion projects.At the end of this paper, we give an example as an application to demonstrate how the developed demand model could be used to valuate passengers’ direct benefit from airport capacity expansion.     

Effect of local operational constraints on runway capacity - A case study

The estimation of runway capacity is important in airport planning and operational analysis. Standard procedures for capacity determination typically assume that there is no constraint on aircraft operations and do not provide good estimates when constraints exist. This paper presents a study of runway capacity at Singapore Changi Airport in which local operational constraints are taken into account. In addition, the impacts on capacity due to marine vessel crossings in a shipping channel near the airport, and the timing for implementation of simultaneous, independent instrument approach procedures are also investigated. The levels of annual aviation demand that could be served without excessive delays to aircraft under various operating scenarios are estimated.     

Predictability impacts of airport surface automation

Past evaluations of airport surface operations automation technologies have focused on capacity utilization, delay mitigation and fuel efficiency impacts. Predictability, while recognized as an important operational performance goal, has received little attention. One reason could be that applicable predictability metrics have not been developed in the context of airport surface operations management. This research fills the gap by proposing metrics for predictability performance evaluation. Using results from a SARDA human-in-the-loop simulation conducted at NASA Ames’ Future Flight Central, we present a comprehensive assessment of the predictability impacts of airport surface automation. A wide range of the impacts is considered, which includes variability in taxi-out time, predictability of take-off time and take-off sequence, entropy of the airfield state, and perceived predictability from users.     

A comment on W. Wei and M. Hansen’s benefit estimation in “An aggregate demand model for air passenger traffic in the hub-and-spoke network”

This comment analyzes the demand model and method used by W. Wei and M. Hansen for computing the benefits of an airport capacity extension. The log-linear specification and coefficients of the demand function imply that the airport extension’s positive effect on demand decreases with the volume of traffic and associated congestion. If this unlikely result is accepted, the surplus method used by Wei and Hansen underestimates the users’ investment benefit. The note argues that the marginal willingness to pay for a capacity extension should rather increase with congestion. If this is the case, the method used by Wei and Hansen could actually overestimates the benefit.A better specification would rather allow for a function concave to the origin in order to account for capacity constraints, and also set a maximum finite price. Above all, a fair evaluation of the users’ benefit should be based on a function which explicitly includes a variable, or function of variables, that permits an estimation of the users’ cost of congestion.     

Development and demonstration of an integrated decision support system for airport performance analysis

Airport decision makers are frequently facing complex decision-making problems related to airport planning, design, and operations. The airport decision-making process is further perplexed by the large number of stakeholders having different, and sometimes conflicting, objectives regarding the assessment of the airport performance. Despite the rich experience in both models and tools for airport performance analysis, existing models and tools address only fragmented parts of the airport decision-making process. At present, airport stakeholders lack models and tools able to provide an integrated view of the total airport processes and analyze the tradeoffs between the various measures of airport effectiveness. The objective of this paper is threefold: (i) to introduce the concept of total airport performance analysis, (ii) to describe the development of a Decision Support System capable of performing integrated airport analysis, and (iii) to demonstrate the capabilities of this Decision Support System by analyzing a real-world airport planning case of the Athens International Airport.     

A generic system dynamics based tool for airport terminal performance analysis

Decision making for airport terminal planning, design and operations is a challenging task, since it should consider significant trade-offs regarding alternative operational policies and physical terminal layout concepts. Existing models and tools for airport terminal analysis and performance assessment are too specific (i.e., models of specific airports) or general simulation platforms that require substantial airport modelling effort. In addition, they are either too detailed (i.e., microscopic) or too aggregate (i.e., macroscopic), affecting, respectively, the flexibility of the model to adapt to any airport and the level of accuracy of the results obtained. Therefore, there is a need for a generic decision support tool that will incorporate sufficient level of detail for assessing airport terminal performance. To bridge this gap, a mesoscopic model for airport terminal performance analysis has been developed, that strikes a balance between flexibility and realistic results, adopting a system dynamics approach. The proposed model has a modular architecture and interface, enabling quick and easy model building and providing the capability of being adaptable to the configuration and operational characteristics of a wide spectrum of airport terminals in a user-friendly manner. The capabilities of the proposed model have been demonstrated through the analysis of the Athens International Airport terminal.     

Air traffic assignment to reduce population noise exposure using activity-based approach

Aircraft noise has been regarded as one of the major environmental issues related to air transport. Many airports have introduced a variety of measures to reduce its impact. Several air traffic assignment strategies have been proposed in order to allocate noise more wisely. Even though each decision regarding the assignment of aircraft to routes should consider population exposure to noise, none of the air traffic assignment strategies has addressed daily migrations of population and number of people exposed to noise. The aim of this research is to develop a mathematical model and a heuristic algorithm that could assign aircraft to departure and arrival routes so that number of people exposed to noise is as low as possible, taking into account temporal and spatial variations in population in an airport’s vicinity. The approach was demonstrated on Belgrade airport to show the benefits of the proposed model. Numerical example showed that population exposure to noise could be reduced significantly by applying the proposed air traffic assignment model. As a consequence of the proposed air traffic assignment, overall fuel consumption increased by less than 1%.     

Including local air pollution in airport efficiency assessment: A hyperbolic-stochastic approach

We examine data from Italian airports covering 2005–2008 to include local environmental effects in airport efficiency assessment. We consider both desirable outputs such as aircraft, passengers, and freight movements and some undesirable outputs of airport operations associated with local air pollution. We estimate both a classical distance function with no undesirable output, and a hyperbolic distance function. By comparing the estimated efficiency scores with these two frontiers we show that airport efficiency increases when local air pollution is included in the analysis. Moreover, we show a fleet-mix effect because airports with similar aircraft movements exhibit large variations in the amount of pollution per flight. Last, we find that there is complementarity between desirable and undesirable output: a 1% decrease in pollution has an opportunity cost of a 0.67% reduction in both passenger and freight traffic.     

Measuring inaccuracy in travel demand forecasting: methodological considerations regarding ramp up and sampling

Project promoters, forecasters, and managers sometimes object to two things in measuring inaccuracy in travel demand forecasting: (1) using the forecast made at the time of making the decision to build as the basis for measuring inaccuracy and (2) using traffic during the first year of operations as the basis for measurement. This paper presents the case against both objections. First, if one is interested in learning whether decisions about building transport infrastructure are based on reliable information, then it is exactly the traffic forecasted at the time of making the decision to build that is of interest. Second, although ideally studies should take into account so-called demand “ramp up” over a period of years, the empirical evidence and practical considerations do not support this ideal requirement, at least not for large-N studies. Finally, the paper argues that large samples of inaccuracy in travel demand forecasts are likely to be conservatively biased, i.e., accuracy in travel demand forecasts estimated from such samples would likely be higher than accuracy in travel demand forecasts in the project population. This bias must be taken into account when interpreting the results from statistical analyses of inaccuracy in travel demand forecasting.     

Airport taxi planning: Lagrangian decomposition

The airport taxi planning (TP) module is a decision tool intended to guide airport surface management operations. TP is defined by a flow network optimization model that represents flight ground movements and improves aircraft taxiing routes and schedules during periods of aircraft congestion. TP is not intended to operate as a stand‐alone tool for airport operations management: on the contrary, it must be used in conjunction with existing departing and arriving traffic tools and overseen by the taxi planner of the airport, also known as the aircraft ground controller. TP must be flexible in order to accommodate changing inputs while maintaining consistent routes and schedules already delivered from past executions. Within this dynamic environment, the execution time of TP may not exceed a few minutes. Classic methods for solving binary multi‐commodity flow networks with side constraints are not efficient enough; therefore, a Lagrangian decomposition methodology has been adapted to solve it. We demonstrate TP Lagrangian decomposition using actual data from the Madrid‐Barajas Airport. Copyright © 2011 John Wiley & Sons, Ltd.     

Stochastic capacity expansion models for airport facilities

It is important and also challenging to plan airport facilities to meet future traffic needs in a rapidly changing environment, which is characterized by various uncertainties. One key issue in airport facility development is that facility performance functions (delay levels as functions of capacity utilization rates) are nonlinear, which complicates the solution method design. Potential demand fluctuations in a deregulated aviation market add another dimension to the decision making process. To solve this problem, a deterministic total cost minimization model is proposed and then extended into stochastic programs, by including uncertainties in traffic forecasts. After the exploration of properties of the delay cost function, an Outer-Approximation (OA) technique which is superior to the existing discrete approximation is designed. After model enhancements, an efficient solution framework based on the OA technique is used to solve the model to its global optimality by interactively generating upper and lower bounds to the objective. Computational tests demonstrate the validity of developed models and efficiency of proposed algorithms. The total cost is reduced by 18.8% with the stochastic program in the numerical example.     

A modeling framework for impact assessment of urban transport systems

An integrated software tool environment is presented, and a methodology is proposed for the operational support of the local authority, for analysis of the impact of transport measures in terms of network energy consumption and pollutant emissions. It is based on work done by the European Union within the save program (specific actions for vigorous energy efficiency)—Slam project (supporting local authorities methodology). As background, the Slam project is described, with the principal aspects and needs of environmental and traffic network management. The central section defines a methodology able to support technicians in recognizing the traffic asset and decision makers in evaluating interventions on urban transport infrastructures or technological systems. The role of the different models and their interactions with the transport telematics services currently active on the Florence (Italy) network is discussed. Finally, the procedure for calculating the traffic impacts on energy consumption is described with the help of a test case, the evaluation of a dedicated bus corridor in Florence.