A scenario approach to airport evaluation in remote communities: with particular reference to the Pilbara region of Australia

Coordinated development of aviation facilities and services is critical for geographically remote communities. Improvements in aviation capability can be assisted by an analytical base for determining the implications of alternative configurations of air services in terms of links to be served, airport/aerodrome investment, type of flight equipment and flight frequency. We outline a method for identifying airport supply configurations to meet air service demand to and from the Pilbara region of north-west Australia. The approach emphasises minimum levels of demand required from a community in order to justify provision of air services of a given scenario. The method is influenced by the paucity of data on demand in remote communities, and the consequent risk of relying solely on demand-side forecasts of patronage levels. It is therefore particularly useful in assessing transport systems associated with remote resource development projects which are notable for the rapidity of change which they can bring. The approach has relevance to a wide range of transport applications.     

Evaluating the efficiency of the Essential Air Service program in the United States

Essential Air Service (EAS) is a federally funded program in the United States that provides connecting, commercial air service between rural communities and their nearest large or medium commercial hub airport. During fiscal year 2010, $170 million dollars were spent to provide this service to 107 communities in the US. However, with significant variations in subsidies to each airport (ranging from $427,757 to $3,082,403) and marked differences in passengers served, there are serious concerns regarding the overall efficiency of the EAS program. The purpose of this paper is to use data envelopment analysis integrated in a geographic information system for evaluating service efficiencies at the community level. Policy implications and strategies to improve the EAS program are discussed.     

Airline’s choice of aircraft size – Explanations and implications

When facing a growth in demand, airlines tend to respond more by means of increasing frequencies than by increasing aircraft size. At many of the world’s largest airports there are fewer than 100 passengers per air transport movement, although congestion and delays are growing. Furthermore, demand for air transport is predicted to continue growing but aircraft size is not. This paper aims to investigate and explain this phenomenon, the choice of relatively small aircraft. It seems that this choice is associated mainly with the benefits of high frequency service, the competitive environment in which airlines operate and the way airport capacity is allocated and priced. Regression analysis of over 500 routes in the US, Europe and Asia provides empirical evidence that the choice of aircraft size is mainly influenced by route characteristics (e.g. distance, level of demand and level of competition) and almost not at all by airport characteristics (e.g. number of runways and whether the airport is a hub or slot coordinated). We discuss the implications of this choice of aircraft size and suggest that some market imperfections exist in the airline industry leading airlines to offer excessive frequency on some routes and too low frequency on others.     

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.     

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.     

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.     

Impact of slot controls with a market-based allocation mechanism at San Francisco International Airport

This paper presents an empirical analysis of the possible impact of the application of slot controls as a demand-management measure at San Francisco International Airport (SFO). We examine the specific case of slot controls allocated with a market-based mechanism. The paper first describes the nature of the delay problem at SFO and how slot controls would work to manage demand, briefly reviewing their history in the US. We then describe the methodology used to analyze their potential impact given the assumption of a market-based allocation mechanism and present our findings on their predicted impacts on arrival delays and the nature and level of scheduled passenger air service at the airport. Finally, we present conclusions as to the overall potential of slot controls to alleviate delay at SFO and their non-delay consequences. The methods presented in the paper facilitate a detailed analysis of the incidence of the impact of slot controls on passengers, airlines and individual communities that depend on SFO for access to the national air transportation system.     

Demand for rail travel to and from airports

Rail access to airports is becoming increasingly important for both train operators and the airports themselves. This paper reports analysis of inter-urban rail demand to and from Manchester and Stansted Airports and the sensitivity of this market segment to growth in air traffic and the cost and service quality of rail services. The estimated demand parameters vary in an expected manner between outward and inward air travellers as well as between airport users and general rail travellers. These parameters can be entered into the demand forecasting framework widely used in the rail industry in Great Britain to provide an appropriate means of forecasting for this otherwise neglected market segment. The novel features of this research, at least in the British context, are that it provides the first detailed analysis of aggregate rail flows to and from airports, it has disaggregated the traditional generalised time measure of rail service quality in order to estimate separate elasticities to journey time, service headway and interchange, and it has successfully explored departures from the conventional constant elasticity position.     

Estimating the demand responses for different sizes of air passenger groups

This paper investigates the sensitivity of demand for air travel by singleton passengers, couples, and families. It examines how the demand for air travel by these groups is potentially different. In this study, a compound Poisson structure of the demand of different passenger groups is considered, and aggregate demand observations and maximum likelihood procedures are used to decompound the processes and estimate demand sensitivity of each group of customers to price, time, season, and the economic cycle. The methodology is applied to Canadian market data and the results indicate there are significant differences among the different groups of customers.     

Investigation of environmental justice analysis in airport planning practice from 2000 to 2010

Municipal airport owners and the Federal Aviation Administration (FAA) regularly evaluate capacity and demand to decide if and when airports need more infrastructure. New infrastructure can alter the profile of noise, emissions, and land use, which may affect the quality of life for airport-adjacent communities. When the FAA and airport owners initiate infrastructure expansion, they must conduct environmental justice analysis to measure the distribution of negative externalities on nearby communities. This research investigates the environmental justice methodologies and narratives reported in planning documents for nineteen airport capacity expansions planned or deployed from 2000 to 2010 in the United States. The mixed-methods approach analyzes airport operations data, spatial demographic data, and planning artifacts to determine whether the environmental justice analyses were robust. This research proposes alternative metrics, the ‘Risk of disproportionate impact’ and ‘Capacity strain’, to further contextualize the presence of protected population groups alongside capacity needs. The main finding of the study is that the planning documents did not consistently detect environmental justice impacts, nor did they consistently confer importance to those impacts when high proportions of protected populations were detected. As a result, the social costs of collective airport expansion are unclear and likely underestimated. This study identifies two limitations that undermined the environmental justice analysis throughout the airport sample: (1) inconsistent methodological choices impeded the detection of impacts and, (2) narrative interpretations tended to ‘null’ the finding even when impacts were detected.     

Latent temporal preferences: An application to airline travel

An essential element of demand modeling in the airline industry is the representation of time of day demand—the demand for a given itinerary as a function of its departure or arrival times. It is an important datum that drives successful scheduling and fleet decisions. There are two key components to this problem: the distribution of the time of day demand and how preferred travel time influences itinerary choice. This paper focuses on estimating the time of day distribution. Our objective is to estimate it in a manner that is not confounded with air travel supply; is a function of the characteristics of the traveler, the trip, and the market; and accounts for potential measurement errors in self-reported travel time preferences. We employ a stated preference dataset collected by intercepting people who were booking continental US trips via an internet booking service. Respondents reported preferred travel times as well as choices from a hypothetical set of itineraries. We parameterize the time of day distribution as a mixture of normal distributions (due to the strong peaking nature of travel time preferences) and allow the mixing function to vary by individual characteristics and trip attributes. We estimate the time of day distribution and the itinerary choice model jointly in a manner that accounts for measurement error in the self-reported travel time preferences. We find that the mixture of normal distributions fits the time of day distribution well and is behaviorally intuitive. The strongest covariates of travel time preferences are party size and time zone change. The methodology employed to treat self-reported travel time preferences as potentially having error contributes to the broader transportation time of day demand literature, which either assumes that the desired travel times are known with certainty or that they are unknown. We find that the error in self-reported travel time preferences is statistically significant and impacts the inferred time of day demand distribution.     

A multi-criteria decision support methodology for implementing truck operation strategies

This study proposes a multi-criteria decision support methodology to enable the prioritization of potential alternative transportation system operations strategies and then demonstrates the effectiveness of the methodology using a case study involving truck operations. The primary feature of this methodology is its ability to help policymakers consider economic, public, and private sector standpoints simultaneously. The economic criterion is cost to the public sector where four criteria related to truck impacts on the transportation system are incorporated. These are traffic congestion, safety hazards, air pollution, and pavement damage. In addition, reliability and productivity are regarded as metrics representing the private sector viewpoint since they can significantly affect profitability. The methodology combines qualitative and quantitative aspects of these standpoints. In order to demonstrate the applicability of this methodology, a corridor with some of the highest truck traffic in the US is selected as a case study and three forms of left lane restrictions for trucks are considered. For qualitative analysis, survey data were collected from two groups classified as public agency and transportation industry professionals who are experts in trucking. In addition, a micro traffic simulation model was used to produce various performance measurements that can describe quantitative impacts. As a result, the methodology provides a rational argument for prioritizing potential alternative truck strategies.     

The integration of Taiwanese and Chinese air networks for direct air cargo services

The two sides of the Taiwan Strait perform mutually dependent but complementary activities in the global manufacturing supply-chain. As a result, trade between Taiwan and China grew in double digits annually in the 1990s. With growing economic ties, direct air links are inevitable. In this research, we analyzed government documents and interviewed the air cargo carriers and airlines that currently serve the Taiwan–China air cargo market. This information enabled us to tabulate the trade, estimate the airport-to-airport air cargo demand and calibrate the international and domestic freight tariffs. We used a connectivity measurement and classified Chinese airports into national, regional and local classes in a hub-and-spoke air cargo network. We developed a mathematical model and a branch-and-bound algorithm. The results showed that at least two transit airports are economically necessary for a Taiwan–China air link. Shanghai and Xiamen were always the top two transit airports. The third airport would be Changsha if the decision becomes three air-links. These links are different from the top three passenger transit airports, Fuzhou, Xiamen and Shanghai, even though the cost saving is moderate.     

An application of a rank ordered probit modeling approach to understanding level of interest in autonomous vehicles

Surveys of behavior could benefit from information about people’s relative ranking of choice alternatives. Rank ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alternative) to better understand their relative preferences. Despite the widespread interest in collecting data on and modeling people’s preferences for choice alternatives, rank-ordered data are rarely collected in travel surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. This paper presents a rank ordered probit modeling approach that overcomes limitations associated with prior approaches in analyzing rank ordered data. The efficacy of the rank ordered probit modeling methodology is demonstrated through an application of the model to understand preferences for alternative configurations of autonomous vehicles (AV) using the 2015 Puget Sound Regional Travel Study survey data set. The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including age, gender, household income, education, employment and household structure, significantly influencing preference for alternative configurations of AV adoption, ownership, and shared usage. The ability to estimate rank ordered probit models offers a pathway for better utilizing rank ordered data to understand preferences and recognize that choices may not be absolute in many instances.     

Path flow estimator for planning applications in small communities

This paper presents an alternative planning framework to model and forecast network traffic for planning applications in small communities, where limited resources debilitate the development and applications of the conventional four-step travel demand forecasting model. The core idea is to use the Path Flow Estimator (PFE) to estimate current and forecast future traffic demand while taking into account of various field and planning data as modeling constraints. Specifically, two versions of PFE are developed: a base year PFE for estimating the current network traffic conditions using field data and planning data, if available, and a future year PFE for predicting future network traffic conditions using forecast planning data and the estimated base year origin–destination trip table as constraints. In the absence of travel survey data, the proposed method uses similar data (traffic counts and land use data) as a four-step model for model development and calibration. Since the Institute of Transportation Engineers (ITE) trip generation rates and Highway Capacity Manual (HCM) are both utilized in the modeling process, the analysis scope and results are consistent with those of common traffic impact studies and other short-range, localized transportation improvement programs. Solution algorithms are also developed to solve the two PFE models and integrated into a GIS-based software called Visual PFE. For proof of concept, two case studies in northern California are performed to demonstrate how the tool can be used in practice. The first case study is a small community of St. Helena, where the city’s planning department has neither an existing travel demand model nor the budget for developing a full four-step model. The second case study is in the city of Eureka, where there is a four-step model developed for the Humboldt County that can be used for comparison. The results show that the proposed approach is applicable for small communities with limited resources.     

Modeling taxi drivers’ decisions for improving airport ground access: John F. Kennedy airport case

Taxi service is an important component of airport ground access, which affects the economic competitiveness of an airport and its potential positive impact on the surrounding region. Airports across the globe experience both taxi shortages and excesses due to various factors such as the airport’s proximity to the city center, timing and frequency of flights, and the fare structure. Since taxi drivers are independent entities whose decisions affect the taxi supply at airports, it is important to understand taxi drivers’ decision mechanisms in order to suggest policies and to maintain taxi demand and supply equilibrium at the airports. In this paper, New York City (NYC) taxi drivers’ decisions about airport pick-ups or cruising for customers at the end of each trip is modeled using logistic regression based on a large taxi GPS dataset. The presented approach helps to quantify the potential impacts of parameters and to rank their influence for policy recommendations. The results reveal that spatial variables (mainly related to proximity) have the highest impact on taxi drivers’ airport pickup decisions, followed by temporal, environmental and driver-shift related variables. Along with supplementary information from unstructured taxi driver interviews, the model results are used to suggest policies for the improvement of John F. Kennedy (JFK) airport’s ground access and passenger satisfaction, i.e. the implementation of taxi driver frequent airport server punch cards and a time-specific ride share program.     

Level of service analysis for airport baggage claim with a case study of the Calgary International Airport

This work presents a methodology to evaluate the level of service (LOS) of an airport baggage claim. A psychometric scaling technique is employed to obtain quantitative data from qualitative data (user responses as a function of five rating categories). Regression analysis is applied to obtain a statistically valid relationship between user responses and observed operational indicators. The proposed model is developed from a case study of the West Jet baggage claim area at the Calgary International Airport – Canada, where 62 passengers have been observed and interviewed. The collected data allowed the proposal of standards, which indicate tolerable limits as a function of various LOS ranges (A–E). The results are useful for managers and designers on assessing the LOS offered in a terminal, but also during the planning and design stages of an airport. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

Performance based clustering for benchmarking of US airports

Managing service operations is gaining significant attention in both academic and practitioner circles. In this broad area, performance evaluation and process improvement of airlines and air carriers has been the focus of several studies. Although efficient airport operations are critical for improved performance of airlines and air carriers, few studies have focused on airport performance measurement. This study evaluates the operational efficiencies of 44 major US airports across 5 years using multi-criteria non-parametric models. These efficiency scores are treated by a clustering method in identifying benchmarks for improving poorly performing airports. Efficiency measures are based on four resource input measures including airport operational costs, number of airport employees, gates and runways, and five output measures including operational revenue, passenger flow, commercial and general aviation movement, and total cargo transportation. The methodology presented here can be generalized to other industries and institutions.     

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.