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Airport passenger terminal performance assessment provides a valuable feedback to airport managers. Researchers and practitioners alike have recognized that measuring terminal performance through purely operational approaches (i.e., based on airport ability to process passengers and baggage) is not sufficient. Innovative techniques studying passenger needs and their perception of service quality have been developed during the last couple of decades. A new generation of terminal assessment models incorporating issues, such as comfort, convenience, and ambience in the evaluation models has emerged. Existing models vary according to the type of decisions supported, evaluation perspective, type of measurements, and evaluation approach used. This paper reviews the state of the art and state-of-practice regarding methods and techniques used for assessing the performance of airport passenger terminals, identifies their capabilities and limitations, and proposes issues that should be further researched.
The importance of passenger satisfaction for enhancing airport passenger terminal performance and generating top commercial revenues is widely recognized. Recently, the Airport Cooperative Research Program (ACRP) proposed funding for research activities with the objective to develop innovative concepts for terminal planning and design centred at superior passenger satisfaction (1).
Two major trends dictate the need for research for airport passenger terminal performance assessment, namely: i) privatisation and liberalisation of airport operations (2) and ii) the continuous increase of air traffic demand (3,4). Airports, in order to compete effectively in the emerging competitive market landscape, should set their targets so as to balance strategies for accommodating additional demand and providing adequate service quality to passengers.
In the academic literature and in professional practice, airport performance is most commonly assessed from three perspectives: passenger, airline, and airport authorities (5). Given that passengers are the end customer and constitute the main source of airport revenues, their point of view merits further attention. The service quality provided by an airport terminal is also affecting airline terminal selection decisions. Gaining insight on how airlines assess terminal performance also provides useful information to airport managers.
The objective of this paper is threefold: first to provide an overview of the state-of-the-art and state-of-practice in the area of airport passenger terminal performance assessment, second to identify capabilities and limitations, and third to identify issues for future research.
The remainder of the paper consists of five sections. Section 2 presents the methodology used, section 3 presents terminal performance assessment models based on objectively measured performance assessment metrics, section 4 discusses terminal performance assessment based on both objective and subjective evaluation of performance metrics, while section 5 provides information on techniques using subjectively assessed terminal performance metrcis. Finally, Section 6 summarizes the research findings and provides directions for future research.
The objective of this section is to introduce the methodology used to classify airport passenger terminal assessment models. Three main streams of research were identified: i) models using objective measures/estimations, ii) models combining objective and subjective data, and iii) models using subjective data (see Figure 1).
The first category of models evaluates airport passenger terminal facilities using objectively measured metrics (e.g., available space and waiting time). Depending on the types of models used to estimate the parameters expressing LOS, this category is further divided in two subcategories: i) 'analytical models' and ii) 'simulation models'.
The second category of models develops scales for measuring the LOS of airport passenger terminal facilities. These models compare the actual performance with the perceived (by the users) quality of services offered by airport passenger terminal facilities for various types of facilities and/or service providers. Data incorporated in the models of this category are both objective (e.g., waiting time, processing time, and available space) and subjective (e.g., perceived waiting time, ambience of terminal, and courtesy of staff). Based on the method used to develop the scales, this category is further divided into three subcategories: 'psychometric scaling models', 'fuzzy set theory models, and 'Perception-Response (P-R) curve method'.
The third category of models measures the performance of the entire airport passenger terminal based on passenger perception. These models incorporate subjective data. Models under this category are divided into two subcategories: 'models assessing the relative importance of factors affecting the LOS' and 'models assessing the performance of the entire terminal'.
In what follows, we provide an overview of the models, methods, and techniques used in each one of the above mentioned categories.
MODELS USING OBJECTIVE MEASURES/ ESTIMATIONS TO ASSESSING TERMINAL PERFORMANCE
Level of Service (LOS) standards provide airport planners, designers, and operations managers with vital information regarding the performance of various types of facilities. For design purposes the space needed for terminal facilities is estimated based on required space per passenger and forecasted/measured traffic levels. For operational analysis observed or estimated values of parameters expressing the LOS are compared against tabulated values determining the corresponding LOS. The variables required to assess terminal LOS (i.e., space per passenger, waiting time, etc.) can be obtained through: i) field measurements (for terminals in operation), ii) the use of analytical models, and iii) the use of simulation models.
Analytical models provide an aggregate representation of airport operations using a set of mathematical expressions. Analytical models are simpler, less data intensive, faster, and less accurate as compared to simulation models, and they are more suitable to support strategic decisions (6,7).
Simulation models provide opportunity for detailed analysis and cover a wider range of operational issues. Simulation models are more suitable to support operational decisions. Simulation models require more effort for their development and more detailed data for validating and using them (6,7). For a detailed review of existing analytical and simulation models, the reader is referred to (6, 8, 9, 10, 11).
Analytical and simulation models are used to estimate capacity, delays, and LOS of airport terminal facilities. Incorporating terminal specific characteristics (e.g., terminal area, number of service channels, passenger behaviour characteristics, and service characteristics) and established LOS standards, the performance of airport terminal facilities can be assessed through analytical and simulation models.
Terminal Level of Service (LOS) Standards
Level of Service (LOS) standards measure the performance of airport passenger terminal facilities based on objectively measured indicators (see Table 1). Transport Canada (12) defined the level of service as the area provided per passenger (i.e., measured in m2 per passenger). This approach uses a six-point scale (i.e., LOS is measured ranging from A "excellent" to F "system breakdown"). The Transport Canada level of service concept supports the assessment of five types of facilities in the airport terminal: check-in, waiting/circulation facilities, hold rooms, baggage claim area, and pre-Primary Inspection Line (PIL).
IATA LOS standards (15, 16) provide support for terminal design and operational assessment. These standards are based on available space per passenger and implicitly incorporate dwell time (i.e., total time spent at a terminal facility) suggesting that a decrease of dwell time results in decrease of space required. IATA standards adopted and slightly modified the six-point scale (i.e., from A "excellent" LOS to F "unacceptable" LOS) initially developed by Transport Canada. The metric incorporated in IATA standards is available space per passenger (see Table 1). The 2004 version of the IATA's manual provides a more detailed analysis incorporating additional parameters (e.g., required space for passengers using baggage carts) (de Nef).
Standards measuring LOS of airport passenger terminal facilities (i.e., Transport Canada 1979, FAA 1988, IATA 1995, and IATA 2004) are summarized in Table 1. The FAA standards suggest a single value for planning and designing three types of terminal facilities (i.e., waiting and circulation, holding, and Government Inspection services). IATA standards support more in depth analysis measuring LOS of terminal facilities on a five-point scale (i.e., check-in, waiting and circulation, holdroom, bag claim, and Government Inspection services). Comparing both standards, FAA and IATA 1995, it can be inferred that the proposed FAA values correspond to IATA service level "C". The updated version of IATA standards, 2004, takes into account additional parameters, such as, proportion of passengers using baggage carts. IATA 2004 are more demanding as they propose higher threshold values for space requirements as compared to IATA 1995.
In summary, LOS standards are applied to measure the performance of individual passenger terminal facilities based primarily on passenger density. The metrics used to evaluate the Level of Service offered by the different types of terminal facilities can be obtained through field measurements and/ or through the use of analytical or simualtion models. A limitation of these standards is that they provide an assessment for individual facilities, as opposed to an assessment of the entire terminal. Furthermore, the approach taken to assess the Level of Service (LOS) does not consider the interaction among the different types of facilities. In addition, the passenger perception is not considered in setting up the threshold values defining the cut-off points for the different levels of service, while other important attributes contributing to passenger comfort are not explicitly considered by these standards.
MODELS COMBINING OBJECTIVE AND SUBJECTIVE DATA TO ASSESS TERMINAL PERFORMANCE
This category involves models assessing airport terminal performance by incorporating passenger perception. Models belonging to this category aim at identifying threshold values for defining LOS based on passenger perception. These models are based on passenger surveys and use a variety of techniques to develop scales comparing actual with perceived LOS. Research related to terminal performance is divided in three groups based on the technique applied: i) psychometric scaling, ii) fuzzy concept, and iii) Perception-Response (P-R) curves model. Table 2 presents an overview of models discussed in this section.
Psychometric Scaling Models
Models under this category aim at developing LOS scales through the association of objectively measured and perceived values of parameters defining LOS for passenger terminal facilities. In order to perform the analysis, data related to actual and perceived values of performance indicators are collected. The objectively measured and perceived values are associated through the use of different types of methods, such as psychometric scaling (17,18,19), fuzzy set analysis (20,21), and Perception-Response curves (22).
A framework evaluating check-in facilities using psychometric scaling was developed and tested at the San Francisco International Airport (17). Psychometric scaling is used to transform qualitative responses into a quantitative scale. A single quantitative index measuring LOS was developed in (17). This index includes waiting time and space availability. The relative importance of the two parameters combined to form a single LOS index is expressed by the fact that an increase of one minute in waiting time is equivalent to an increase of 6.4 ft2 in space availability.
A scale measuring the perception of check-in performance at the São Paulo International Airport was developed using the psychometric scaling approach (18). Passengers were asked to rate their experience at check-in counters on a five-point scale (i.e., from 1 "unacceptable" to 5 "excellent"). Parallel to these data, waiting time, processing time, and available space at the check-in counter were also measured. Three criteria for measuring the performance of check-in counters were used: i) waiting time, ii) processing time, and iii) available space. Waiting and processing time were measured based on the number of minutes that passengers wait at check-in, while available space was evaluated based on passenger density. A separate LOS index for each of the three criteria was developed. Through a weighted average approach, a combined index, which measures the overall LOS considering simultaneously the waiting time, processing time, and available space of check-in facilities, was developed. According to this research, waiting and processing time considered together have almost the same influence on the combined LOS index as the space availability alone.
Psychometric scaling has been also used to develop indices for measuring total service time, walking distance, and orientation at the São Paulo International Airport (19). Total service time was measured as the total of curbside waiting time, check-in waiting and processing time, security screening waiting and processing time, and departure waiting time. Two orientation indices were introduced in this study (19). The first orientation index (orientation I) was measured as the ratio between actual and minimum walking distance, while orientation II was measured as the difference in walking time between novices and experts, divided by the route length. Passengers were asked to evaluate the walking distance, orientation, and total service time. In addition, data regarding the actual walking distance was collected. Level of service categories expressing the perceived terminal performance according to total service time, total walking distance, orientation I, and orientation II were developed. According to this study, a LOS "C" is provided when the total service time is between 88 and 242 minutes, the walking distance is between 415 and 922 meters, orientation I is between 2.1 and 3.4, and orientation II is between 1.08 and 1.73.
Fuzzy Set Theory Models
Fuzzy set theory also has been used to develop scales for assessing airport passenger terminal performance. A model for measuring the actual and perceived, by arriving and departing passengers, performance of a terminal using fuzzy set analysis was developed and validated at the Chiang Kai-Shek International Airport (20). Passengers were asked to state their perceived waiting and processing time for either check-in (i.e., in case of departing passengers) or baggage claim facilities (i.e., in case of arriving passengers) and rate the performance of these facilities. Furthermore, actual waiting and processing times at check-in and baggage claim facilities were video recorded. A five-point scale assessing actual and perceived LOS based on objective and subjective assessment of waiting time was developed using fuzzy set theory. The analysis revealed that the time perceived by passengers was greater than the actual time. For instance, passengers assigned to check-in facilities a LOS "A" when the actual processing time was on average less than 2.1 minutes, while LOS "A" was assigned to check-in when the perceived processing time was on average 3.6 minutes.
A similar type of research was reported (21). This research compared passenger perception of congestion level with the actual passenger density. Data were collected from departing and arriving passengers at Taipei international and domestic airports. A five-point scale based on the perception of available space for peak and non-peak periods were developed to assess the LOS offered. This research provides comparisons of results for: i) the same facilities in the same airport at different periods (i.e., peak and off-peak), ii) different facilities at the same airport, and iii) similar and different facilities at both international and domestic airports. A conclusion emerging from this research is that passengers are less tolerable to congestion at baggage claim than at check-in areas. Also, it was found that passengers travelling during peak hours usually expect terminal congestion and therefore they have lower standards and are more tolerable to crowding-conditions.
Perception-Response (P-R) Curve Method
The performance of processing facilities (e.g., check-in, baggage claim, and security check) was assessed through the Perception-Response (P-R) model (22). The model was applied at the Birmingham, Manchester, and East Midlands airports in the United Kingdom. This P-R model measured perceived service quality of waiting facilities based on the passenger perception of time spent in those facilities. The model assigns a service level to processing facilities according to the time passengers spend in these facilities. A three-point scale was developed measuring the perceived service quality as "good", "tolerable", or "bad. For instance, LOS "tolerable" was assigned to security check when processing time was between 6.5 and 10.5 minutes.
Models defining LOS based on passenger perception have been developed to support assessment of terminal facilities (i.e., check-in, baggage claim, and circulation facilities) (see Table 2). The main advantage of these models is that they use passenger perception to determine the threshold values defining the different classes of LOS. These models assess the performance of individual facilities or groups of facilities and not the terminal as a whole. Another issue that merits attention is that the applicability and transferability of these models to other airport, besides those where the specific studies have been performed is not straightforward.
MODELS USING SUBJECTIVE DATA TO ASSESS TERMINAL PERFORMANCE
Two subcategories of models using subjectively measured data to assess terminal performance are identified. The first subcategory aims to assess the relative importance of various facility attributes used to determine the performance of terminal facilities. The second category assesses the performance of the terminal as a whole. Table 3 presents an overview of the models presented in this section.
Models Assessing the Relative Importance of Factors Affecting Terminal Performance
The relative importance of various terminal service attributes and facilities in determining the overall passenger terminal performance has been studied at a number of airports (23, 24, 25, 26, 27). These studies were conducted at different airports and incorporate the perception of different passenger categories and expert opinions. The perception of the relative importance of the various factors affecting passenger terminal performance was studied at the Bandaranaike International Airport in Sri Lanka was researched (23). A survey was performed to elicit the opinion of transfer passengers regarding the performance of the following terminal facilities used by them: transit, rest rooms, restaurants and bars, duty free shops, security, other facilities (e.g., toilet and Internet facilities), and overall airport. Regression Analysis was used to determine the factors influencing the perceived LOS. Listed in order of importance these factors are: courtesy/ helpfulness of security staff, quality of Flight Information Displays, availability of drinking water, quality of guidance/ signage/ directions, availability of seats in transfer area, and quality of audio information/ information staff.
The perception of departing passengers regarding the relative importance of factors affecting the performance of various terminal facilities was studied at the Montreal International Airport (24). The findings of this study suggested that the passengers assign different degree of importance to the attributes of the various types of facilities. For circulation facilities provision of information was identified as the most important determinant of the quality of service. For waiting facilities seat availability received the highest ranking, while for processing facilities waiting time was identified as the most important factor. It is important to stress the fact that the importance of the various service quality determinants differs also among passenger groups (i.e., passengers are grouped according to their purpose of trip, sex, and age).
Another study researching the perception of terminal quality of departing passengers was conducted at the Montreal International Airport. This study (25) identified that the following six variables exert the highest influence on airport passenger terminal performance: i) provision of information, ii) waiting time, iii) convenience, iv) availability of seats, v) concessions, and vi) internal environment. On the basis of these six variables, the authors proposed four indices to measure the performance of the terminal: information provision, waiting time, availability of seats, and concessions.
In addition to passengers, experts (e.g., airport directors and consultants) have been used to assess the relative importance of passenger terminal service attributes (26). This study identified the following four factors, which influence airport passenger terminal service quality: passenger service (i.e., retail and duty free having the highest importance), airport access (i.e., parking having the highest importance), airline-airport interface (i.e., gate boarding areas having the highest importance), intra-terminal transportation (i.e., intra-terminal transportation).
A model considering the performance of the service personnel in addition to the performance of facilities and services offered was introduced in (27). The performance of the service personnel was assessed through the use of the following indicators: immediate response to complaints, individualized attention, and prompt response to requests.
Models Assessing the Performance of the Entire Terminal
In the previous sections, methods for assessing the LOS of individual airport passenger facilities were presented. However, besides assessing the performance of the individual facilities, it is useful for airport terminal planners and operators to be able to assess the overall performance of the entire terminal. Since the terminal consists of a series of facilities and their performance may be measured with different indicators, the assessment of the relative importance of the various indicators should be determined. Different methodologies have been used to determine the relative importance of the facilities affecting the passenger perception of the Level of Service offered by a terminal.
The fuzzy Multi-Attribute Decision Making approach was used to develop a composite index for assessing the overall performance for airport passenger terminals (28). Five travel experts took part in a survey, which evaluated the performance of 14 Asia-Pacific international airports. This study identified the following six attributes (listed in order of importance) influencing the performance of the entire terminal: courtesy of staff, security, convenience, comfort, processing time, and information visibility.
The overall performance of the passenger terminal of São Paulo International Airport was assessed using Analytical Hierarchy Process (AHP) (29). This study was based on data collected from 100 randomly selected passengers. The following types of facilities and their attributes were used in this study: parking (courtesy, security, and availability of parking spots), departure hall (security, orientation, information, comfort, and services), check-in (processing and waiting time, and courtesy), departure lounge (courtesy and comfort), and concessions (courtesy and variety of stores). This study identified that the most important facility in determining terminal service quality is check-in having a weight 33%, while concessions were identified as having the lowest importance with a weight of 10%. Facility attributes with the highest influence on perceived service quality include security for parking facilities, orientation for the terminal hall, processing time for check-in facilities, comfort for departure lounge, and variety for concessions.
An index for determining overall terminal performance was developed using Regression Analysis in (30). The following facilities were included in the analysis: enplaning curbside, check-in, security screening, departure lounge, circulation areas, concessions, walking distance, and orientation. Passengers at the São Paulo International Airport were asked to evaluate the performance of each type of facility. Through the use of Regression Analysis, it was determined that curbside has the greatest impact on perceived service quality, followed by orientation, gate lounges, and check-in facilities.
Models assessing the perceived performance of airport passenger terminal provide useful input for: i) developing a combined index measuring the quality of service for the entire passenger terminal as opposed to individual facilities and services, and ii) incorporating the preferences of the terminal users in determining the overall level of service offered. Although these models provide useful information regarding the relative importance of the various types of terminal facilities and identify the relative importance of the variables used to assess the level of service offered, their applicability is limited to the airports where these studies have been conducted.
Passenger satisfaction surveys have been used to assess the performance of passenger terminals (31, 32, 33). Airports Council International (ACI) performs the Airport Service Quality (ASQ) survey (31). In this survey participate over 100 airports and over 200.000 passengers are interviewed. ASQ measures the performance of individual terminal facilities as well as the performance of the entire terminal. The performance of the following five service areas (i.e., access, check-in, passport/personal ID control, security, and finding your way) is rated through the qualitative assessment of the performance of the following attributes (31): ground transportation to/from airport, availability of parking facilities, value for money of parking facilities, availability of baggage carts/trolleys, waiting time at check-in queue/line, efficiency of check-in staff, courtesy and helpfulness of check-in staff, waiting time at passport/personal ID inspection, courtesy and helpfulness of inspection staff, courtesy and helpfulness of security staff, thoroughness of security inspection, waiting time at security inspection, safety and security feeling, ease of navigating through the airport terminal, flight information screens, and walking distance inside the terminal.
Passenger satisfaction surveys were reported in (32, 33). The survey reported in (32) measures passenger satisfaction on the basis of 27 items, which are divided into five categories: terminal comfort and amenities, security and immigration, shops, food, and beverages, getting around, and general items, while the survey reported in (33) measures passenger satisfaction based on: airport accessibility, baggage claim, check-in, terminal facilities, security check, and food and retail services.
The results of passenger satisfaction surveys can be used to rank airport terminals according to their performance and provide useful input for identifying facilities and services requiring further improvements.
CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH
A state-of-the-art and state-of-practice review of models assessing airport passenger terminal performance was presented. Relevant research was classified into three categories according to the type of data used to perform terminal performance assessment.
The field of airport passenger terminal performance assessment has evolved over time. Early approaches included only objective measures of individual facilities (e.g., check-in, baggage claim, and security screening) comprising the airport passenger terminal. These objective measurements (e.g., waiting time, processing time, and available space) are used in order to design and/or assess the Level of Service for a given terminal. Subsequent developments led to the introduction of the point of view of passengers in determining terminal performance. Another important development in the modelling of airport passenger terminal performance assessment is the effort to assess not only individual facilities, but also to provide an index measuring the overall terminal performance.
A conclusion emerging from this literature review is that there is a convergence on the types of facilities used in assessing terminal performance and the Key Performance Indicators (KPIs) used to assess them. The relative importance of the factors affecting the performance of different types of facilities varies according to the type of facility. Also, the perception of the LOS differs among the different types of passenger groups (e.g., frequent vs. less frequent flyers, elderly vs. younger passengers). Furthermore, it should be stressed that there is complementarity among the different approaches. For instance, models developed for individual facilities can be used in conjunction to models assessing the relative importance of each facility in order to assess the performance for the entire terminal. Finally, the impact of recent developments associated with the use of terminal facilities, like increased security standards, introduction of larger aircraft, e.g. A380, and remote check-in, on airport level of service standards should be further investigated.
Due to the increasing importance of passenger perception in assessing the quality of service of airport passenger terminals, a need for models considering the overall passenger experience at the terminal is emerging. Future research for developing these models should: i) consider both tangible and intangible measures of performance (e.g., comfort of terminal and quality of information), as well as measures associated with the performance of the service personnel, ii) provide the capability of measuring both the relative importance of each service determinant and the degree of satisfaction perceived by the passengers in relation to the service provided to them, and iii) recognize the effect of passenger characteristics on the perceived quality of services. These models will provide useful support for making decisions that will improve the passenger experience at airport terminals and consequently will increase airport passenger terminal attractiveness.