Multivariate analysis of the impacts of NAS investments: A case study of a capacity expansion at Dallas-Fort Worth Airport

Multivariate statistical analysis is used to estimate the impacts of a major capacity expansion at Dallas-Fort Worth International Airport. The model relates the Daily Flight Time Index (DFTI) to demand, weather, origin airport congestion, and the expansion itself. The effect of the capacity expansion on DFTI is found to depend strongly on visibility. On average, the index in the post-expansion period is 1.3 min less as a consequence of investment. This change includes a larger reduction in departure delay that is offset by an increase in taxi time. Moreover, the reduction caused by the expansion has been more than offset by increases in the DFTI resulting from other factors, notably increased demand and worse weather.     

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 the average and variability of the delays observed at New York’s airports relatively well. Results suggest that delays can be extremely sensitive to even small changes in flight schedules or airport capacity.     

Optimizing flight equencing and gate assignment considering terminal configuration and walking time

Operating airline hub-and-spoke networks (HSN) rather than direct flights among city pairs may significantly reduce supplier cost; however, passengers' travel time may significantly increase due to increased transfer and in-flight time. The costs considered in this study are hub-related and incurred by passengers and aircraft (i.e., passenger transfer, flight dwelling, and gate occupancy). The objective is to minimize the total cost by optimizing flight sequence (i.e., arrivals and departures) and gate assignment, while considering transfer speed, transfer demand, flight size, gate size and terminal configuration. A real-world HSN whose hub airport (HA) is located at Xianyang International Airport (XIY) in Xi'an, China is analyzed. The optimized solutions and their relations to various model parameters are explored.     

Air traffic forecast and its impact on runway capacity. A System Dynamics approach

The uninterrupted growth of air traffic in Colombia has been reinforced since the 1990s by a public policy of liberalization of airspace, and by redirecting public and private investment towards the modernization and updating of airport infrastructures, giving in concession the busiest airports in the country, 19 to date. In the commercial air sector, and in this same period, the flag airline was privatized and new air operators, including low cost airlines (LCC), entered the market. Since 2012, air fares are completely deregulated. Consequently, in the last two and a half decades, passenger transport in Colombia grew by 863%. This important growth rate has been driven and led by the country's main airport, the Bogotá-El Dorado International Airport (BOG), in the capital of Colombia. But some technical reports consider the airport will reach its maximum capacity in the short term mainly because of its inability to expand its airfield (runway system). Due to this circumstance, the aeronautical public authority of Colombia gave course to the planning, design and construction of a new airport (complementary) on the outskirts of the city of Bogotá, which will enter the design phase in 2026/2027. Therefore, it is considered suitable the development of a traffic forecast for BOG in the medium term and to evaluate the impact of future demand on the runway capacity of the airport. Then, and due to the complexity of the air transport forecast, the use of System Dynamics (SD), is considered to be appropriate as an analysis approach. System Dynamics is based on feedback control theory and it is equipped with mathematical computer simulation models, which uses linear and non-linear differential equations. The results suggests a need to expand the airport case study (runway system) after mid-2019, where the current capacity utilization factor is around 100% and two to three runways will be required for the normal operation; after October 2022 the number runways required is set to three until the last period simulated (2023).     

Supply-and-demand models for exploring relationships between smaller airports and neighboring hub airports in the U.S.

Airport passenger leakage is the phenomenon of air passengers choosing to travel longer distances to access more extensive air services offered by airlines at an out-of-region hub (or, substitute) airport, instead of using their local airports. Airport leakage can cause further reduction in services offered by airlines at a local airport, thereby causing even further leakage, and so on, which can significantly impact an airport's role in the growth of the local economy. This paper explores the geographic and operational attributes of local-and-substitute airport pairs in the United States, explicitly accounting for the interactive feedback relationship between passenger volumes and air service characteristics that contribute to the onset, persistence, and exacerbation of airport passenger leakage. A two-stage least squares regression model of air passenger demand at small- and medium-sized airports is first presented, where local passengers may travel by vehicle to larger, out-of-region hub airports. The results confirm that airfare and passenger volume relationships exist between the local and substitute airport pairs included in the dataset, and that lower airfares at the substitute airport have a greater impact on airport choices made by larger travel groups. They also suggest the existence of positive feedback in that if an airport attracts increasingly smaller passenger numbers with fewer air services and fewer air services with fewer passengers, without external intervention airport leakage impacts may be irreversible and exacerbate over time. A conceptual market share equilibrium analysis is used to illustrate the mechanisms of a direct two-way feedback relationship between passenger volumes at a local airport and air service characteristics at both the local and substitute airports. With data, this quantitative framework can help guide airport planners in further assessing and verifying suspected passenger leakage issues at their airport. The results suggest that without intervention, airport leakage impacts may be difficult to reverse; further exacerbating the trend are technological advancements that make driving cheaper and easier (connected and autonomous vehicles). However, the results can also guide planners in choosing the types and degrees of infrastructure investments and airline incentives that may be used to expand or retain air services to attract passengers.     

Airline strategies for aircraft size and airline frequency with changing demand and competition: A simultaneous-equations approach for traffic on the north Atlantic

Airport planners need to know the forecast demand on the facilities provided airside at airports. For this they need to know how airlines will deal with traffic in terms of the size of aircraft and frequency of service. In response to increasing demand, airlines may increase capacity by increasing the frequency of flights or they may choose to increase aircraft size. This may yield operating cost economies. If the airports they operate from are capacity constrained they will be limited in the extent that they can change frequency that will limit their ability to compete with the number of frequencies offered. Consequently, these airports are excluded as are major hubs as frequencies will be influenced by connecting passengers. Routes are identified on the north Atlantic that can be analysed and conclusions are suggested on the basis of three stage least-squares estimates for pooled time series-cross section data. An increase in passengers on the whole will result in a larger increase in frequency than in aircraft size but the impact of competition does not yield significant results due to the strategy of excluding certain categories of airport.     

The costs of peak-load demand: An application to Gran Canaria airport

Many airports suffer from peak-load demand problems. To meet unconstrained demand at peak periods, they often invest in extra capacity that may be under used at other times. We use data from the airport in Gran Canaria to illustrate that costs associated with the peak-load problem are not only those related to the new investment. This paper provides a methodology for analyzing the costs arisen due to the peak-load demand, to explore alternative airport policies and to illustrate the problem of congestion at airport terminals. The results suggest that a situation in which airports differentiate charges by peak and off-peak days would be much desirable.     

An assessment of the capacity and congestion levels at European airports

This paper examines the current capacity of the EU's airport infrastructure and the main factors determining that capacity. The nature and role of airport services are detailed. The determination of capacity is examined with discussion of the influence which air traffic control factors, demand characteristics, environmental conditions and engineering design will have on capacity. The methods used to assess delay are detailed along with extensive data sketching the current state of Europe's system of large airports and the extent of infrastructure congestion. The options available to policy makers to improve the management and organisation of capacity are set out and critically discussed.     

A hybrid fuzzy MCDM approach for mitigating airport congestion: A case in Ninoy Aquino International Airport

In this paper, we introduce the application of an integrated fuzzy multi-criteria decision-making (MCDM) model to mitigate airport congestion which affects the on-time performance of airlines, operational reputation of airports, and air travel experience of passengers. In a classical approach, when congestion occurs at the destination airport while the aircraft is en-route, an air traffic flow management action is prompted for implementation. In selecting the most suitable action in the event of airport congestion, the decision must reflect the multiple criteria nature of the problem as well as the uncertainty and vagueness associated with the decision-making process; thus, an integrated fuzzy MCDM is adopted. The applicability of the proposed approach is demonstrated in a case study at Ninoy Aquino International Airport. It is found that stakeholders of the commercial aviation industry favored to apply rerouting, among other actions, as this satisfies aviation safety as the most prioritized criterion.     

Modelling the effects of capacity constraints on air travellers’ airport choice

This paper analyses the effects of limited capacity on air travellers’ airport choice. The analysis is based on a market-segment specific airport choice model that accounts for limited capacities. The region of Stuttgart in Germany serves as a case study to examine the impact of limited airport capacity on air travellers’ airport choice. Air travellers’ choice depends on the supply of flights and accessibility of the airports in their choice set as well as on their preferences and willingness-to-pay. To elaborate the effects of limited airport capacity, scenarios relating to the capacity situation at airports serving the air travel demand of the Stuttgart region are analysed. This paper reveals the mutual dependence among airports. Capacity constraints at one airport cause spill-over effects and thus influence air travel demand served at other airports. In some cases this may even lead to new capacity constraints elsewhere.     

Airport capacity management: A review and bibliometric analysis

Over the past two decades, the aviation sector has seen dramatic growth in demand with limited support in terms of supply, due to under-development of the larger airport infrastructure. This has led to congestion and delays at major airports across the world. The topic of ‘airport capacity management’ (ACM) has thereby attracted considerable scholarly attention. From our review of extant literature, we find an extensive repository of research work related to ACM, vis a vis its inherent role and the expected output. Nevertheless, extant literature has failed to conduct a systematic literature review on ACM in order to understand its evolution over time. Therefore, we analyze ACM literature in detail to grasp and evaluate the work being done thus far, using bibliometric analysis. Our analysis reveals that airport capacity, congestion, competition and ground holding problems are important keywords related to ACM. Further, using co-citation analysis we identify 5 broad clusters and thought pattern of researchers. We also propose a flowchart of literature with major research area related to ACM. Finally, we note that the Journal of Air Transport Management (JATM) with 1086 citations and 109 documents tops the list of journals publishing papers on ACM.     

An application of the airport service quality model in South Africa

Passengers’ expectations of service quality at airports are becoming increasingly important as air traffic grows. This research investigated passengers’ perceptions of airport service quality at O.R. Tambo International Airport, South Africa. An existing model placing the concept of passenger expectations central to measuring an airport’s specific level of performance was applied. The results proved significant in terms of the investment made by the airport in staff training and highlighted areas for improvement. The results show that business travellers and leisure travellers have different opinions regarding the importance of services offered by airports and of the level of performance at ORTIA in particular. Significant differences also occur in the perceptions of frequent travellers and infrequent travellers.     

Warehouse location problems for air freight forwarders: a challenge created by the airport relocation

The move of Hong Kong International Airport from the city centre to a suburban area in July 1998 provided sufficient capacity to meet the increasing demand of passenger and air-cargo flows in Hong Kong in the foreseeable future. However, the move has adverse side effects such as causing the readjustment of many existing systems and creating many imminent strategic problems. One of such problems is the warehouse location of freight forwarders: they have to decide whether they should locate their warehouses in the new airport, in current locations, or in new locations somewhere in the city. This paper delineates the results of a survey conducted in the summer of 1997 before the airport was relocated. The survey asked what decisions the freight forwarders had made and how they evaluated some potential warehouse locations. The paper also considers two mathematical models, which explain and predict, respectively, the location pattern of freight forwarder warehouses before and after the relocation of the airport.     

Is increasing aircraft size common practice of airlines at congested airports?

If the overall demand for air transport grows, but additional airport capacity is not available at congested airports, we could assume that airlines will offer flights with more seats in order to cope with the demand. An analysis of frequency and average seat capacity developments at congested, and not yet congested airports, has shown that the hypothesis of bigger aircraft being used in congested situations is valid in most instances, although not at all airports. The objective of this paper is to report on an analysis of the development of average seat capacity at congested airports, in contrast to the situation at not yet congested airports, and to find out the reasons for airlines increasing the number of seats at congested airports, by means of a statistical model using variables including the degree of airport congestion and average flight distance.     

Reforming air traffic control: an assessment from the American perspective

This paper examines institutional and economic reform of the ways in which air traffic control (ATC) services are provided in the US. It also contrasts the European and US ATC systems in terms of size, scope, cost and organizational form. The paper suggests that many of the congestion and delay problems experienced in the US result from the inefficient provision and use of air traffic capacity in the airport area, and these conditions are likely to continue or worsen if economic principles are not used to organize and provide ATC services. The paper notes that, while Europe has advanced more rapidly in the organizational and economic reform of providing ATC services, other problems remain. Because most large European airports have slot controls to limit demand in the airport area, its ATC congestion is more pronounced in the en route environment.     

Analysing and modelling some effects of solutions for matching the airport runway system capacity to demand

The paper deals with analysing and modelling some effects of three solutions for matching the airport runway system (landing) capacity to corresponding demand. These are: i) charging congestion applied to the NY LaGuardia airport (New York, USA); ii) deployment of the innovative operational procedures supported by the new technologies developing in the scope of European SESAR (Single European Sky ATM Research) and U.S. NextGen (Next Generation) program applied to the system of two closely-spaced parallel runways at Dubai International airport (Dubai, UAE (United Arab Emirates)); and iii) building the new additional runway(s) applied to the runway system of three London airports - Heathrow, Gatwick, and Stansted (London, UK).The results have shown that each of the considered solutions can contribute to more efficient and effective matching of the airport runway (landing) capacity to the current and expected (prospectively growing) airside demand under given (specified) conditions.     

Competitive advantage analysis and strategy formulation of airport city development—The case of Taiwan

In the past decade, the focus of international airport development has shifted from a transportation hub towards a multi-functional aero metropolis. An airport city serves not only as an index of a country’s performance in development, but also plays the role as impetus of national industries and a gateway to economic globalization. Through literature review, secondary data analysis and interviews with focus groups and experts, this paper explores development features, operation strategies and competitive advantages of various airport cities in newly industrialized economies. This research also proposes a novel approach to strategy formulation, which utilizes the theory of competitive advantage of nations (a revised diamond model), SWOT analysis and strategy matching using the TOWS matrix and competitive benchmarking. The case study of Taoyuan International Airport illustrates the applicability of the proposed approach in systematic competitive analysis and strategy formulation of airport city development. The strategic planning put forward not only provides practical reference for systematic operation of related units, but also inspires a new research model and direction of study for airport city development.     

Price,capacity and concession period decisions of Pareto-efficient BOT contracts with demand uncertainty

In this paper, we study the impact of demand uncertainty on the build-operate-transfer (BOT) contract design by optimizing a bi-objective problem via three critical decisions: toll, capacity and concession period. We derive the optimums and identify the public and private sector’s economic incentives. We find that the optimal length of concession period and the service quality of the infrastructure depend on the two parties’ operational costs and negotiation powers. Under mild conditions, we prove that the government will build a larger capacity but charge less than the private sector. Furthermore, the efficiency of BOT contract is improved with demand uncertainty.     

Offsite passenger service facilities: A viable option for airport access?

Offsite passenger service facilities transfer passengers from a common location to an airport and may provide additional services such as baggage handling and passenger check-in. Although they are conceptually promising, there is a lack of modern methods specifically tailored to forecast their demand. This paper reports on the development of two sequential models that forecast demand for an offsite facility. Models were calibrated based on data collected at six airports in California, Maryland, Massachusetts, and Virginia. The results showed that likely candidates for an offsite facility are airports that are not easily accessible, and departing air passengers with early morning flight departure times and high variability in ground travel times to the airport. A case study demonstrates the application of the models to Virginia’s Richmond International Airport.     

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement.Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.