Whether low-cost-carrier passengers spend more on duty-free goods at the airport? Evidence from Incheon International Airport

With a dramatic growth in the low-cost carrier (LCC) traffic around the world, many airports have built budget terminals to accommodate the LCC flights with cheaper airport charge to the airlines but inferior shopping environment and service quality to the passengers. This study aims to answer the following research questions: i). Whether the LCC passengers have higher purchasing power than FSC (full-service carrier) passengers for the airport concessions, particularly the duty-free goods? ii). Would the budget terminal design contribute to or jeopardize the LCC passengers' airport concession expenditure? iii). How could the airport operator maximize its concession revenue by re-designing terminal or re-allocating flight slots? We collect the actual airport duty-free transaction data from Incheon International Airport (ICN) for an empirical investigation. Our empirical evidence first suggests that LCC passengers could have comparable or even higher purchasing power than FSC passengers in consuming duty-free goods at the airport. Among all the LCC passengers, Chinese have the highest purchasing power. Second, the inferior shopping environment and service quality of budget terminal seriously jeopardize the duty-free sales from the LCC passengers. Third, counterfactual analyses show that, if ICN could convert its budget terminal into a conventional terminal, 44 million USD more duty-free sales can be generated per year. Even keeping the current terminal design, re-allocating the LCC flights between the budget terminal and conventional terminal could bring approximately 4.9 million USD more duty-free sales per year. The findings provide managerial implications to ICN and other airports for effective airport revenue management. First, the budget terminal design may not be ideal to accommodate LCC traffic as it damages the concession revenue from particular LCC passengers with high purchasing power. Second, exploring the concession revenue from the growing LCC traffic could help maintain an airport's advantage in aeronautical charge under single-till regulation.     

Relevance of airport accessibility and airport competition

Airport accessibility is an important criterion for airport competition. The relevance of airport accessibility and airport competition was studied in this paper based on the panel data collected from nine large airports in Jiangsu province, China from 2005 to 2014. The results showed that the cost of expense, time and fatigue for the arrival at the airport are proposed to quantify levels of fastness, economy and amenity for the passengers to arrive at airport. The airport accessibility is significantly affected by airport passenger traffic and airline frequency. The passenger traffic can be increased by 2% with 1% increase of airport accessibility based on the analysis results of the nine large airports in Jiangsu province.     

Queue behavioural patterns for passengers at airport terminals: A machine learning approach

Passengers go through different handling processes inside airport terminal buildings. The quality of these processes is usually measured by the time passengers require and by the level of comfort experienced by them. We present an analysis of behavioural patterns in queues at check-in desks and security controls, which are two of the most critical processes regarding passenger service. The passengers' flow is simulated to obtain queue lengths at one busy European airport between 2014 and 2016, supported by real flight data. Simulation is designed as a store-and forward cell-based system, whose parameters have been tuned and validated with real data from observations and empirical capacity and demand studies within the airport. Random Forest algorithms are then implemented to develop different models for each parameter prediction, after a data analysis stage based on statistical and visualization methods. Feature analysis techniques between dependent variables and the target outputs (queue lengths) determine which are the fundamental elements to explain queue behaviour and to predict target variables. We provide a method to forecast behavioural patterns at check-in desks and security controls, to help airport operators to implement adequate response policies. Queue behavioural patterns are captured by Machine Learning models, which can be used to offer improved passenger services (such as real-time predictions for expected waiting time at queues), or can be considered in a dynamic approach for terminal services design (as the entire progress of terminal handling depends on the stochastic behaviour of passengers). This could be a key tool for managing passengers demand and optimise the infrastructure's capacity through resource allocation.     

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.     

Analyzing passenger behavior in airport terminals based on activity preferences

Airport terminals are facilities that provide a variety of activities related to both the preparation of the passengers for their air trip (aeronautical) and their free time inside the terminal (non-aeronautical). In the last years, the number of non-aeronautical activities has substantially increased and significantly diversified both before and after the security checkpoint. The established role of non-aeronautical activities forces planners and managers to better understand passenger behavior. The potential of discrete choice models for the exploration of passenger behavior is analyzed in this paper. For the demonstration of the methodology, Lisbon Humberto Delgado International airport is used as a case study. Data is collected through a revealed and stated preference survey inside the terminal at the area before the security checkpoint. Activity-choice models are developed to identify the factors that affect the choices of the passengers over the area where they conduct non-aeronautical activities. Forecasts show that when increasing the percentage of passengers who conduct the check-in online and have planned their activities before arriving at the airport, the passengers’ preferences to conduct non-aeronautical activities only after the security checkpoint increase. This paper shows the contribution of developing discrete choice models in the better comprehension of passenger decisions over the activities they perform in an airport terminal.     

Passengers perceptions of airport branding strategies: The case of Tom Jobim International Airport – RIOgaleão,Brazil

This study examines the passengers perceptions of Tom Jobim International Airport – RIOgaleão branding strategies and its impact to their experiences in light of the airport branding elements proposed by the literature. To achieve these aims it was conducted a semi-structured interview with the airport's branding manager and a questionnaire with the passengers (n = 92). The findings showed that the airport's new management company has been employing tourist branding strategies using a mix of elements, but it is possible that these actions have had the main objective of raising the airport to international service standards, whereby the operation of terminals by large companies is a trend. It was also concluded that some of the airport branding elements (i.e. logos and slogans) are well developed and represent a higher impact on passenger experience, while others (i.e. retail pricing strategies) require further attention and new approaches in order to improve passenger experience in the terminal.     

Does a reward program affect customers’ behavioural intention of visiting the airport? A case study of Singapore Changi Airport

An understanding of the behavioural intentions of customers in visiting airports may help airport management to boost airport retail revenue (i.e. non-aeronautical revenue) by increasing customer spending at the airports. This study aims to examine the connection between airport reward programs and customers' behavioural intentions of visiting the airport, attempting to bring some benefits and credits for implementing reward programs in the airport sector by using Singapore Changi Airport as a case study. Two types of customer behavioural intentions (i.e. time spent at the airport and various purposes of visiting the airport) were investigated in the study to examine the effects of Changi Airport's reward program (i.e. Changi Rewards). An on-line survey was conducted, and the participants' responses were analysed via a structural equation modelling approach. The results showed that the attractive benefits of Changi Rewards could have a positive influence on arousing customers' intentions to engage with the programme. In addition, customers' intentions to engage with the programme can have a positive effect on increasing passengers' time spent at the airport and inducing customers to visit the airport for leisure purposes. Managerial implications to the airport sector are discussed.     

Brazilian airport economics from a geographical perspective

This paper discusses the revenue and expenditure functions of a sample of Brazil’s airports administered by Infraero, the Brazilian State enterprise responsible for administering the country’s major airports. It aims to identify what volume of movement at the airports associates with positive net earnings, i.e., the break-even point between revenue and expenditure, in view of aspects of airport movement and geographical factors. It examines airports serving regular airlines and with movement of up to 8 million passengers in 2010. Revenues and expenditures are explained by total passengers embarked and disembarked at each airport and by the potential of the airport’s ‘anchor city’. Multiple regression analysis achieved a high level of explanation for the dependent variables studied, i.e., revenues and expenditures. The model explains 81% and 91% of the variations in revenues and expenditures, respectively. The analysis shows that, considering the ranking of cities, Brazilian airports with less than 2 million passengers tend to operate at a financial loss, those with between 2 and 3 million passengers are at the transition stage between positive and negative earnings, while airports with more than 3 million passengers tend to make gains. However, the simulation modelling shows that the break-even point between expenditure and revenue functions can vary considerably when different ‘city potentials’ are considered. In this respect, the modelling offers investors a tool for analyzing passenger demand risk in the light of expectations for Brazilian cities’ potential.     

Market segmentation analysis for airport access mode choice modeling with mixed logit

Accessing airports can be considered as a crucial issue since passengers need not miss their flights. This issue makes the mode choice to access the airports important to study on and develop policies regarding it. Many studies show destination type as domestic or international affects the airport access mode choice, along with other factors. In this study, we investigate the effect of destination type of mode choice using mixed logit, using market segmentation approach. Market segmentation regarding destination type as domestic or international is a first in airport access mode choice modeling. Revealed-preference data was collected by face-to-face passenger surveys at Ataturk International Airport in Istanbul, Turkey, in 2015. We did market segmentation analysis for Multinomial Logit (MNL) and Mixed Logit (ML) models. When MNL and ML models were compared, it was observed that ML was superior to MNL. Further, results of market segmentation analysis revealed that using segmented models produced more accurate results than using the pooled model; both in MNL and ML. This finding was also supported by the value of time estimates; there were significant differences between domestic and international travel markets in terms of airport access mode choice. These results showed that different transportation policies may be introduced for domestic and international traveler segments, which also were explained.     

A large neighborhood search heuristic to establish an optimal ad-hoc hubbing strategy in the wake of a large-scale airport outage

Abrupt airport outages can cause diversions and fuel-critical situations for flights, leading to costly passenger misconnections. We develop a large neighborhood search heuristic to optimize the rerouting of flights bound for a disrupted airport to a hub airport that is not disrupted, with the goal of accommodating passengers on existing flights departing the non-disrupted hub. The objective of the heuristic is to identify and reroute flights to the ad-hoc hub(s) – non-disrupted hub airport(s) – that minimize the sum of passenger travel time and wait time. We minimize the passenger cost as the sum of passenger travel time to the diversion airport and wait time for a connecting flight at the ad-hoc hub airport, subject to on-board fuel and diversion airport capacity constraints. We use the heuristic to determine how a coordinated traffic management strategy could have diverted flights immediately following a real-world airport outage.     

Passenger waiting time in an airport and expenditure carried out in the commercial area

A methodology to estimate the relationship between the expenditure in the commercial area of an airport and the passenger waiting time to board is developed. It shows that the more time spent in the airport, the greater consumption by passengers. This functional dependence is analysed for the travellers who travel for holiday purposes and of those who do so for business. As an example of application of this methodology the results shown are obtained from Asturias Airport (Spain).     

Measuring airport service quality: A multidimensional approach

Currently, airports are expected to be operated as self-sufficient service organizations providing efficient and high-quality services to a variety of customers. In this context, improving airport service quality (ASQ) has become paramount. However, due to the complexity of the airport service environment, an effective process of measuring and analyzing passenger perceptions of ASQ is not easily achieved. Generic scales for perceived service quality might not cover some particularities of the passenger–airport interaction. Furthermore, while some measurement practices have been developed within the airport industry, there has been only limited consideration for validity and reliability. These concerns are certainly relevant to avoid misinterpreting passenger perceptions. In view of these concerns, this paper has a twofold objective. First, to fit a measurement model for perceived ASQ built on typical service measures within the airport industry. Second, to test for the model's equivalence across groups of passengers. Sample data from an extensive survey applied at a major Brazilian airport was used for confirmatory factor analysis. The results suggested that a six-factor structure provides a meaningful multi-item measurement model for perceived ASQ. The model was validated for international and domestic departing passengers with respect to its factorial structure and metric invariance. The proposed measurement model could be considered an alternative for a multidimensional approach in the context of airport performance measurement regarding service quality. Finally, the findings from this research might contribute to the discussion on passenger perceptions of ASQ, particularly concerning its multidimensionality and the need to review current practices for ASQ analysis.     

Processing passengers efficiently: An analysis of airport processing times for international passengers

With immense and growing pressure on stakeholders in international airport terminals to process passengers faster than previously, there is a great benefit to understanding which factors affect passenger processing times and in which situations. In addition, storing and analysing the collected data in batch is itself a difficult and time consuming task that could be made much simpler with sequential analysis. We aim to present a method for airport managers to discover which variables are important to understanding passenger processing times and identifying problematic passenger profiles without the need for high computational capacity and full historical datasets.In this paper we introduce Bayesian hierarchical models as a method of sequentially processing data, reducing computation time and obviating storage of large amounts of raw data. We use a range of exploratory models to identify which variables are important to predicting passenger processing time using a dataset from a day of operations at an international airport terminal, then compare a range of regression models. A Bayesian hierarchical regression model based on the model of best fit discovered through exploration is then applied to two subsets of data. We demonstrate that sequential updating based on daily data achieves similar results to batch processing based on full historical datasets and can therefore be used as an alternative in appropriate circumstances. Using the presented models, we find that the airline operating a flight is the most important variable to determining passenger processing time, followed by each passenger's age, sex and nationality. We demonstrate that in our dataset, the passenger profiles correlated with higher mean processing times overall were not the same as those passengers most problematic for meeting processing time targets.     

Does the nature of airport terminal service activities matter? Processing and non-processing service quality,passenger affective image and satisfaction

The surge in air transport demand and the increasingly competitive and volatile market dynamics due to airline deregulation are rapidly transforming airports’ character into multi-service firms and destinations. As a result, service performance measurement of significant systems and their consequent impact on airport users are crucial in creating better airport service design, operation and management for sustainable competitive advantage. The present study 1) assesses the applicability of the Airport Indicators of Passenger Experience (AIPEX) model on Shanghai Pudong International Airport (PVG), and 2) tests a theoretical model that explores the direct and indirect relationships among airport service quality, passenger affective image and satisfaction, as well as the moderating mechanism of passenger type (travel purpose) in these associations. The results indicate that, the AIPEX model fits the PVG context for airport service performance assessment. Also, the theoretical model suggests robust direct associations among processing/non-processing domains and passenger satisfaction, as well as the processing domain and passenger affective image, except non-processing domain and passenger affective image relation. Moreover, significant mediating and moderating effects of passenger affective image and travel purpose on the significant positive direct associations are found. Further, implications for theory and practice are 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.     

Assessing airport terminal performance using a system dynamics model

Current models for airport terminal performance assessment require substantial modeling effort to be customized to the configuration of a particular airport terminal and to reflect adequately the alternative airport operational policies adopted in a user-friendly way. Therefore, there is a need to develop a generic, yet flexible decision-support tool that will facilitate high-level decision-making related to fundamental changes in the structure and operation of the airport terminal system. This paper presents a generic and easily customizable system dynamics based tool for assessing the performance of the Athens International Airport passenger terminal under different demand and resource deployment scenaria.     

Route optimization for energy efficient airport shuttle operations – A case study from Dallas Fort worth International Airport

The objective of this research is to reduce energy consumption from intra airport shuttle operations by optimizing routes and schedules, without compromising on passenger travel experience. To achieve this objective, we propose an optimization model that generates optimal airport shuttle routes for a given set of constraints and a discrete-event simulator that evaluates the optimal shuttle routes in a stochastic environment to understand the tradeoffs between the amount of time passengers wait for shuttles, and shuttle energy consumption. The proposed optimization model and stochastic simulation are tested using shuttle route data provided by the Dallas Fort Worth International Airport. Results indicate that optimized routes can lead to a 20% energy reduction in shuttle operations with a modest 2-min increase in average shuttle wait times. The optimization model and simulator presented here are designed to be generalizable and can be adapted to optimize shuttle operations at any major airport.     

The effects of service quality dimensions and passenger characteristics on passenger's overall satisfaction with an airport

The objective of this paper is twofold. First, to identify service quality dimensions related to airports. Second, to examine the effects of those dimensions on passenger's overall satisfaction with an airport together with variables related to passenger characteristics. Data from an extensive survey applied in a main Brazilian international airport were used. Exploratory factor analysis was applied to extract dimensions of airport service quality as perceived by the passengers. The effects on the overall satisfaction level were estimated using a probabilistic approach. Findings underline implications regarding the use of meaningful service dimensions instead of a large set of variables as predictors of passenger satisfaction. Moreover, the study stresses the need for considering how passenger characteristics may be related to different perceived levels of service quality.     

北京大兴国际机场轨道交通专线延时运营方案设计研究

随着北京大兴国际机场夜间到港旅客量的增加,机场轨道交通运营时长已难以满足客运需求。为提升线路服务水平,延长夜间运营时间,同时减少运力浪费,需要平衡线路夜间客运需求、维检修需求及运营成本,制定延时运营方案。通过挖掘北京大兴国际机场及大兴机场线客流变化规律,分析线路夜间客运需求,结合大兴机场线夜间维检修要求,探讨延时运营对设备设施作业的影响,并进一步研究延时运营成本的构成以及成本计算方式。最后通过对比、分析不同的延时运营方案对夜间维修时间的影响、增开列次与路网换乘情况、运营成本的增加情况,提出当前大兴机场线宜采取上行单向延长运营30 min的方案,并结合大兴机场线实际情况设计线路延时运营阶段的列车运行方案。     

Future airport terminals: New technologies promise capacity gains

The changing dynamics of passenger processes in future airport terminals resulting from pressures from both the demand and supply side are analyzed in this paper. Short and long term (beyond 2020) developments are studied following technology advances and business plans of airlines and airports. Key technologies affecting the central passenger processing functions include identity management and biometrics, Near Field Communications, Big Data analytics and smartphone applications. A simulation model is developed and used to assess the impact of forthcoming changes on the airport's departure hall. Lisbon Portela airport is used as case study. It is shown that passenger process times at the check-in and security checkpoints are significantly reduced, due to the introduction of passenger facilitation processes, under a range of behavioral, technological and policy uncertainties. The most salient implication of these reductions is the quantified capacity gains in the building which question the need for terminal expansion.