1. Introduction

As any traveller can attest to, airline terminal operations have changed drastically over the last decade. The most visible of these changes has been the tightened security measures adopted after September 11th and the subsequent changes in security-checking procedures. However, many other less visible changes have also had drastic effects on the management and planning of terminals. Most notably is the adoption of new ticketing technology, such as the addition of e-ticketing capabilities, new ticketing rules, and remote check-in options. Each of these changes has had a major impact on passenger flows, space allocation, processing times, waiting lines, and waiting times. In turn, it impacted the terminal's performance, levels of service (LOS), and the overall passenger experience. For example, the deployment of e-ticketing machines has significantly decreased ticketing times and pushed passengers more quickly to security, which accordingly increased security checkpoint lines and waiting times. Also, remote check-in has affected the arrival patterns of passengers where passengers arrive much closer to flight time than before, therefore changing queuing patterns across the terminal functional areas.

With all these changes, it is difficult for terminal operators to gauge the effects of these changes on the flow of passengers through the terminal. In fact, it might be virtually impossible for operators to predict how the system will be affected when these changes occur simultaneously and at a highly dynamic pace. As a result, terminal operators have been placed in a reactive position that led to lower performance, LOS, and physical space availability at a given time. The situation will be worse if the many other changes on the horizon are considered, such as the common-use ticketing counters, known traveller programmes, and partnerships with hotels and resorts that provide remote check-in and baggage-handling services for passengers.

The need for predictive capabilities has become vital to the decision-making process of airport terminal planners and operators. In this paper an airport terminal decision support system methodology is presented: a simulation environment that will provide airport terminal operators with a decision support tool and the capability to analyse the end-to-end airport terminal operations. The tool will enable them to quantitatively predict and compare the impact of the new procedures, regulations, and their counterpart decisions on the terminal's performance, LOS, and the overall passenger experience. In particular, the decision support tool and its underlying methodology presented in this paper are intended to provide terminal operators with the capability to assess the terminal's LOS performance measures such as the space allocation per passenger at different functional areas, the various passenger waiting times at different processing points, and the availability of resources to handle various services for passengers and their luggage.

In addition, the decision support tool can be used to evaluate the impact of several factors on the terminal operational performance. Factors can include passenger arrival/departure patterns and trends, capacities, and availability of resources, and also, new technologies, new regulatory and security requirements, natural disasters such as hurricanes or ice storms, public transportation factors that impact the terminal, key equipment failures, routine maintenance schedules, and variability and randomness inherent in the whole system.

2. Background

The intellectual focus presented in this paper was to develop and apply scientifically sound methodologies to quantify and assess flow and related LOS in airport terminal functional areas. Terminal capacity and space allocation in passenger terminals are among the critical aspects of airport design as well as operation. Its importance also increases as we consider the LOS targets and ever-increasing passenger volumes of the nation's airports. LOS is a commonly accepted metric for assessing the performance of airport terminal functional areas under different scenarios (Davis and Braaksama, 1987; Correia and Wirasinghe, 2004). It has been developed for functional areas in which (or through which) passengers will wait (or flow). Current LOS standards use several levels; each level dictates a target customer service level starting from 'excellent' to 'unacceptable.' Levels of service standards developed in the 1970s did not consider the total time that the passenger requires or utilizes a given space and were always designed for peak hour operations (Correia and Wirasinghe, 2004). Additionally, the standards did not address the subjective nature of the passenger experience (eg how satisfied or comfortable did the passenger feel during a particular segment of the terminal experience). In general, airport planners specify two or three target LOS, based on the period where higher LOS is expected during normal periods, and lower LOS during congested and peak hours. Airports utilize general guidelines to determine capacities, allocate space, assign resources, and decide on other aspects of terminal design towards target LOS. These guidelines have been used for years, and improvements in specific airport LOS were primarily achieved through the diligent efforts of innovative and forward-thinking airport planners and operations managers.

Although the majority of airports share a common objective regarding the safe and efficient movement of passengers via aviation services, airports are generally different and in some instances unique in their passenger terminal design. Airports can be generally classified according to their size, measured in Million Annual Passengers, number of terminals, and number of gates. This classification generally indicates the airport's capacity for handling passengers, baggage, aircrafts, and other supporting elements such as inter-modal transportation systems and miscellaneous services offered. Airports can also be classified according to the type of passengers the airport handles, including origin and destination travellers or connecting travellers. Airports generally have different layouts and appearance, different settings for ticketing and security checkpoints, different passenger processing and information processing technologies deployed with varying levels of maturity, and differing passenger 'in-terminal' circulation systems such as escalators and elevators.

These differences greatly contribute to the dynamic nature of the interaction and relationship between passengers and functional areas and between inter-dependent functional areas. These differences will also greatly impact the LOS and the allocation of capacity and passenger space. Therefore, standards that were designed and targeted to fit every airport were at worst impractical and at best questionable due to uncertainty arising out of site-specific levels of variability. Because these guidelines were static, relied heavily on averages, and did not take into consideration the stochastic or the dynamic factors previously discussed, they could not be adapted for every situation or airport. Therefore, targeted overall LOS might not have been consistently achieved or at best achieved within certain functional areas but not achieved on others. The traditional way of one solution fits all might be hampering airports from achieving their unique 'best possible' level of services economically.

3. Literature review

To provide a point of reference for the presented methodology, it will be useful to briefly discuss prior attempts published in the literature to advance the practice regarding terminal space allocation based on scientific methods. Some of these practices take into consideration airport differences, unique settings, and features, including the fluctuation and pattern in airport passenger flows through the terminal functional areas. Some of the related research will be briefly discussed and contrasted to the approach we are adopting in this paper.

The active research in this area started in the early 1960s (Cox and Smith, 1961; Lee, 1966), when queuing models were developed for enplanement passenger check-in resulting in the determination of average service and wait times. This study was useful in the design/redesign of efficient ticketing functional areas, including optimum capacity and space allocation. Subsequently other queuing models were developed and used to analyse not only ticketing areas but the entire departure lounge in an airport terminal taking into consideration economic implications (Mckelvey, 1988; Odoni and Neufville, 1992a, 1992b). Other similar studies considered stochastic analysis and included other functional areas such as corridors, seating, immigration, well wishers, greeters, etc (De Neufville and Grillot, 1982). However, no record was found for successful attempts to consider all the functional areas or the end-to-end passenger experience, that is, from airport entry to departure or from flight arrival to exiting the airport.

In the 1990s other models were developed, considering passenger socialization and 'next-logical' movement patterns to determine optimal space requirements. As discussed, queuing theory is currently one of the most frequently used analytical methods for space programming in passenger terminals (see Gilliam, 1979). Queuing theory is, in essence, the mathematical study of waiting lines (or queues). There are several related processes, such as arriving at the back of the queue, seizing physical space within the queue, waiting in the queue, and being served by the server, for example ticketing agent, at the front of the queue. One method to solve queuing problems involves analytical methods which make simplifying assumptions such as the following: arrival times are random and the time between arrivals is distributed exponentially, service times are also distributed exponentially, the queue is of first come first served type, and there are no significant interdependencies, among other assumptions. These assumptions compromise the accuracy of the results because the real-world situation is altered to accommodate these assumptions. When decisions and/or passenger space allocations are based solely on queuing theory, there is a high probability of either lowering the customer service levels due to a smaller space allocated per passenger or incurring unnecessary cost due to the extra space allocated for queuing areas. Hence, our approach while not abandoning queuing theory in its totality will instead utilize more advanced and appropriate methods. In particular, we will use simulation modelling and analysis, which will be discussed in more detail later in this section.

Another noteworthy approach previously utilized in passenger terminal analysis is the time–space concept (Parizi and Braaksma, 1992). Here the airport terminal functional areas were considered as time–space zones where passengers flowed or waited and required different spaces, than they currently occupy for a certain period of time. However, once again time–space does not consider several critical variables, and certain highly complex variables are totally ignored, hindering the achievement of accurate results and thus the real or full benefits of the analysis undertaken (Benz, 1987).

Another approach, and the methodology we are embracing, is the use of simulation modelling and analysis, which provides a significantly higher measure of accuracy for studying passenger flow in an airport terminal. Contrasting simulation modelling and other analytical methods, simulation can be considered as one of the best-fit solutions for modelling and analysing a passenger terminal for capacity analysis under varying situations, taking into consideration LOS and several cost factors. Since simulation modelling can, by nature, model deterministic as well as stochastic systems, it takes into consideration the variability and the dynamic features of the system modelled, allowing analysis of the end-to-end passenger experience within the airport terminal at a varying level of details. However, our early results of using simulation were not as successful as one might have expected. In retrospect it was determined that since simulation is a highly advanced analytical method, requiring special skills, early practitioners were not fully schooled in the basics of the underlying sciences and therefore the results reflected the lack of understanding of the rigour required. The input and output analysis requires a significant knowledge of statistics and experimental design.

From our perspective, and agreed upon by practitioners within the domain, simulation modelling and analysis is a very powerful tool, which when correctly used can be the most practical, accurate, and useful alternative. Our simulation methodology described in this paper provides a model that generically mimics airport operations, including all the functional areas. The model can be tailored to any airport and can be populated with data for any period of time (ie peak or non-peak). The simulation can be executed (run) for any period from 1 h of operation to 1 year of operation, selected days of the week, and selected periods of the year, reporting, numerically and graphically, performance metrics such as various LOS and space occupied. For example, a certain functional area performance or LOS can be reported for a given time of day, day of week, month, etc.

4. Airport terminal operations simulation using AirSim

The simulation methodology we are presenting in this paper was implemented in an easy-to-use end-to-end airport terminal simulation tool called AirSim. AirSim was developed by our team at Productivity Apex, Inc. to meet the emerging needs of airport planners and operations managers. AirSim is a generic airport simulation modelling and analysis tool that can be used to analyse the current state of the airports functional areas, key processing elements, and their performance or LOS. It enables assessing and predicting the LOS at different functional areas under operational parameters and conditions such as changing flight schedules, higher passenger traffic, routine maintenance of functional areas, and variable resource schedules. It can also consider atypical changes, such as the event-driven implementation of new regulations, or new technologies that drastically alter functional areas processing scheme or passenger flow patterns.

Because of the highly variable nature of airport operations, it was determined that a flexible and extensible discrete event simulation model would be required to provide meaningful information in a timely and efficient manner.

This understanding of the challenges facing airport operators and the processes underlying this system acted as the catalyst for the development of the tool AirSim. The simulation logic modules and templates were the product of extensive on-site observation and brainstorming sessions with airport subject matter experts. First, conceptual flow diagrams for the overall passenger flow and each of the major processes were created and the key passenger attributes for each process were identified. Then simulation logic modules were developed to represent these processes and to collect critical attributes and data from the user. Modules were developed to model the items shown in Table 1. (These modules were tested and validated in real airport projects.) The modules described in Table 1 provide the set of essential building blocks that can be used to quickly build simulation models for a wide range of airport configurations. Also, these modules provide the required flexibility to conduct what-if analysis for evaluating the changes in regulations and passenger patterns, and the actions taken to preserve and improve the level of service at the airport terminals.

Table 1 - Module and templates needed for constructing airport models.

Full table

AirSim also includes modules and templates that model the airport of the future, that is, remote checking from hotel, home, etc, which result in a different passenger's arrival patterns to the airport. These modules have been tested and validated in Orlando International Airport (OIA). OIA is one of the first to implement remote checking in conjunction with Disney.

Creating these reusable, generic templates and modules has greatly reduced the modelling effort, allowing the modellers to rapidly construct discrete event simulation models for any airport with any design or layout; a 2-week project is possible. Existing CAD drawings of the focus airport's layout can be imported as a background and the modules are placed in the appropriate areas to create an accurate representation of the system. However, this is an optional feature that might not be used unless it is necessary. The second component of AirSim is a generic graphical user interface (GUI). This GUI is designed to allow the user to design what-if scenarios, populate and run the model, and view results. The screenshot shown in Figure 1 provides a representative version of the GUI.

Figure 1.

Screenshot of AirSim GUI.

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The GUI is composed of two frames: the left frame is the model browser and the right frame is the attributes and the corresponding data of each operational scenario. Once the different operational scenarios have been developed, the GUI allows users to populate and run multiple scenarios with a single press of a button.

Output reporting is enhanced as the output reports can be customized to only show the LOS for a particular functional area or the whole airport, eliminating the time-consuming task of searching through the myriad of available statistics provided. An example of the graphical output is shown in Figures 2 and 3. Figure 2 shows the number of passengers waiting in the line of an airline ticketing over a full operational day. On the other hand, Figure 3 shows the sq. ft. per passenger in this particular ticketing area, where the total area is 1000 sq. ft. Figures 4 and 5 display similar information during the peak periods. Airport terminal operators can predict each functional area LOS and compare it to their target levels. Figure 5 shows the target LOS lines pertaining to space allocation. This comparison might result in questioning the total space allocated for this particular area to sustain a target LOS; other alternative decisions might be the increasing number of servers to process passengers, which in turn will decrease the number of passengers waiting at any given time. However, by using the simulation environment, the decision makers can design a set of experiments to evaluate several decisions pertaining to these different scenarios and implement the decision that brings the most operationally feasible solution.

Figure 2.

Number of passengers waiting in an airline ticketing line over a full operational day.

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Figure 3.

The sq. ft. per passenger in an airline ticketing line over a full operational day.

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Figure 4.

Average number of passengers waiting in an airline ticketing line during the peak period.

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Figure 5.

The sq. ft. per passenger in an airline ticketing line during the peak period with 1000 sq. ft. total area.

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As shown in Figures 3 and 5, the sq. ft. occupied per passenger changes over time, which results in varying LOS for passengers based on the time of day. AirSim can aid decision makers to assess changes in passenger LOS as space allocated to each functional area is changed. For instance, one scenario could allocate more space for the check-in period, for example 1500 sq. ft. instead of 1000 sq. ft. as shown in Figure 6. In this scenario, the space per passenger with the 1500 sq. ft. is higher and the lowest space LOS achieved is LOS B (20 sq. ft./passenger) versus the lowest achieved with 1000 sq. ft. is LOS C (15 sq. ft./passenger). AirSim will quantify that change per passenger and will compare it to the LOS targets; also the total area can be defined differently based on the time of the day to maintain a target space LOS at any time of the day, that is, peak or non-peak.

Figure 6.

The sq. ft. per passenger in an airline ticketing line during the peak period with 1500 sq. ft. total area.

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Parameters other than total area allocated can be changed, for example by changing the ticketing agents schedule during the peak period, that is, increasing the number of agents will also enable processing more passengers per hour, thus decreasing the number of passengers waiting, and consequently reducing space requirement or increasing the space LOS if space remained fixed. The decision maker can run many scenarios and generate the output side by side for comparison.

Simulation modelling will require data in order to be able to populate and run the simulation model and analyse the airport performance and LOS. Data should be collected to sufficient depth to provide statistically valid sampling for input to the simulation model. Based on our experience, the data have to be collected during a busy time of the year during a peak period at the terminal. At a minimum the data should cover a full operational day for a sufficient number of hours before the first peak period through number of hours after the last peak period. The peak periods should be selected based on normal flight arrival and departure schedules. However, it was proven that collecting data for the full week renders more accurate results and analysis. From a simulation and statistical perspective, it is more technically correct to collect over a week period, which means a larger sample size. The data collected should capture passenger flow (enplanement and deplanement), functional areas processing times (ticketing, security screening, facility usage, etc), waiting times, passenger transfer times between functional areas, capacities, resources schedules (ticketing agents, number of functioning security checkpoints, number of elevators, etc), functional areas physical space (dimensions, layout, etc), horizontal transfer, vertical transfer, and passenger behaviours (time passengers arrive in advance of the flight, % passengers who check in bags, % passengers who use e-ticketing, etc). Also, the data should include feedback from passengers on their perception of the time spent in a specific functional area and their comfort level as related to the number of people in the specific functional area.

The data should consider the different airport functional areas for enplanement and deplanement passengers. For enplanement, it should include curbside ticketing/curbside check-in, ticketing, well wishers, security screening checkpoints, horizontal and vertical circulation, and gate areas/holding rooms. For deplanement, it should include hold room exit, horizontal and vertical circulation, exit from secured to non-secured areas, meeters and greeters, baggage claim/carousel, and inner curb.

These data have to be collected according to a data collection plan. The data collection plan will provide a procedural approach for collecting the data, validating the data, analysing the data, and transforming the data to a suitable format for the simulation. The following steps can be used to accomplish the data collection process:

1. Interview airport operators and subject matter experts, and collect preliminary data on airport design and layout, busiest time of the year, peak periods, and other information related to functional areas, circulation, etc.
2. Develop a list of data required and submit to airport operators or subject matter experts.
3. If data are available from airport operators or subject matter experts, go to step 5.
4. If data are not available, a facility visit is crucial, develop data collection forms, assemble a data collection team, and collect the data. The data will be collected using the following techniques:
5. Time and motion study.
6. Questionnaires targeting passengers, airline managers, security personnel, other key airport personnel.
7. Analyse the data, check for outliers, consistency check, etc.
8. Model the data for simulation model input.

Data in dynamic systems, such as airports, are stochastic and contain numerous sources of randomness. During data collection, the project team should not only be concerned with what data to collect, but how to collect it to capture the randomness and to ensure statistical validity. Rigorous statistical methods should be applied to the data to ensure the selection of valid input models—that is, probability distributions that accurately mimic the behaviour of the random input processes driving the airport under study. Examples of sources of randomness are the inter-arrival rate of passengers to the airport, check-in method selected, ticketing times at various counters, security check time, and walking times between functional areas. Once the input data are collected, analysed, and validated from a statistical point of view, it is used to populate AirSim through the GUI and will run simulation models for various scenarios for a given airport. The output from the simulation model will be analysed to draw conclusions for each functional area in the airport under study. This analysis will produce tables and graphs related to the airport performance and LOS, similar to the graph shown in Figure 5 and 6. Several simulation scenarios will be developed to provide more in-depth analysis and insights on airport performance and the different functional areas LOS under several settings. The next section will describe a detailed case study of implementing the methodology in an airport.

5. Customizing AirSim for an airport

The primary objective of this effort was to provide airport officials with a tool that would allow them to predict the effect that changes such as the increased passenger volume, the implementation of new programmes such as those discussed in the previous section, and changing security processes will have on passenger flow within the terminal. In order to customize AirSim to meet this objective, the first step was to determine the appropriate level of details and scope of the model. Based on information solicited from the customer, the following airport areas were identified as critical:

• Security Checkpoints—Due to the continuously changing nature of the TSA's (Transportation Security Administration) security policies, it was critical that the tool provide enough detail to allow for the sub-processes within security, the number of lanes, the schedules for each lane, and the types of passengers served by each lane to be dynamic.
• Ticket Stations and Lobby—This was an area of great interest to OIA because of the effect that technological advances such as remote check-in and policy changes such as common use ticketing counters have had and will continue to have on the processes associated with checking in passengers. Because the airlines make the determination to adopt new initiatives or technologies, it was decided that the model must provide an adequate level of detail and enough flexibility to specify these parameters for each airline.
• Baggage Screening—It was determined that the model had to provide the capability to model not only the typical baggage handling flow for each airline but must be able to accommodate the newly introduced EDS (Explosives Detection System) machines as well as atypical baggage flows because of special programmes like the Disney Magical Express.
• Landside Terminal Curbs—In order to provide insight into and allow for better management of the ever increasing traffic flows surrounding the airport, the model was designed to include the curbside pickup and dropoff of passengers by both private and commercial vehicles. Users can manipulate parameters in the curb such as arrival patterns, pickup/dropoff times and curbside resources.
• Based on these requirements, a model was developed utilizing the AirSim simulation logic modules along with a minimal number of standard Arena logic modules to accurately model the flow of passengers through the system. Furthermore, model animation was developed (see Figure 7 for an example) to reflect the level of detail in the simulation model. The animation was developed to accurately represent the terminal design, including ticketing station, security checkpoints, elevators, people movers, facilities, etc, and allow airport authorities to visualize the passenger flow through the terminal as well as evaluate it numerically and graphically based on the model outputs.

Figure 7.

Model animation for security checkpoint lanes.

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Once completed, the customized model was verified and validated by comparing the results for specific dates to video footage of various queues throughout the airport on those dates as well as detailed scrutiny by subject matter experts.

The model is being used to support day-to-day operations to identify potential bottlenecks based on scheduled passengers and resources, and is also being used to provide information for larger strategic decisions. OIA recently went through renegotiations with Disney regarding the future of the Disney Magical Express, and the model was used to evaluate the effect that the increased passenger loads would have on queues and congestion of the terminal side where Disney operates. The results of this study helped OIA and Disney in developing a timeline that Disney would need to move its operations from the A-side to the B-side of the terminal in order to accommodate the projected passenger increases as well as moving one of the sub-processes that acted as a bottleneck from the first floor to the second in order to reduce congestion and increase passenger flow.

The model is also being used to evaluate what effect the addition of private baggage handlers will have on the security checkpoint queues. The addition of these baggage handlers could potentially free some TSA employees to assist with security checkpoints, thereby decreasing wait times. In short, AirSim has provided the airport authority at OIA with the ability for the first time to visualize and quantify the effects that changes to processes within the system will have on passenger flow.

6. Case study

In this section, the simulation methodology will be implemented for OIA. The implementation was developed in a multi-phased project over the course of 3 years. The OIA ranked 24th in the world and 14th in the US in 2004 in terms of passenger volume, with roughly 31 million passengers served. OIA is one of the fastest growing airports in the nation, with passenger volume expected to increase dramatically in the coming years. Unlike most of the other major airports, OIA is primarily an origin–destination airport with only about 7% of the total volume made up of transit passengers. OIA also has a number of special programmes in place that make its operations unique. It serves as the site for a pilot study of the 'known traveller programme,' in which biometric verification is used to identify frequent travellers in an effort to reduce security checkpoint processing times. OIA has also partnered with Disney to provide the Disney Magical Express Service, which provides free airport shuttle service, luggage delivery, and airline check-in for Disney hotel guests. Due to the success of this programme, a number of other resorts are considering the implementation of similar programmes. Because of this anticipated growth as well as the adoption of programmes and services like these, that OIA recognized the need for a decision support tool that would allow airport authorities to proactively plan for these changes.

As the national security measures progress, the security check procedures conducted by TSA change as well. Planners and operators find it very difficult to keep the LOS within their pre-set target levels. OIA planners used the simulation tool AirSim to re-plan and re-allocate resources in the security check areas to be able to maintain the LOS at the target levels. The airport authority believed that the waiting time in the queue and the number of passengers waiting in the queue will be significantly altered with the new security procedures implemented. AirSim was used to evaluate and quantify the two security procedures. The management at OIA defined several operational scenarios to be simulated for the security checkpoints. These scenarios are:

• The baseline scenario (current state).
• The new security procedures scenario.
• Opening one more security lane.
• Opening two more security lanes.

The varying parameters between the different scenarios and the output key performance indicators are shown in Table 2. The output includes: the average waiting time in the queue and the number waiting in the queue. The security check time data were collected and fitted into an empirical continuous probability distribution (designated by CONT(X)).

Table 2 - Simulation scenarios.

Full table

A graphical comparison for the waiting time in the security check queues and the number of passengers waiting in the queue over time of the four scenarios are shown in Figures 8 and 9.

Figure 8.

Waiting time in security under various operational scenarios.

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Figure 9.

Number of passengers in the security queue under various operational scenarios.

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As shown in Figures 8 and 9, the new security procedure significantly alters the waiting time in the queue and the number of passengers waiting. Adding an extra security checkpoint will position the LOS closer to the baseline; this scenario should be marked as the least acceptable scenario when scheduling security agents and planning for the queuing area. Adding two extra security checkpoints will even make the LOS levels better than baseline levels.

6. Conclusion

In this paper we presented a simulation methodology that assists airport planners and decision makers to analyse airport terminals. The simulation methodology is delivered in a software tool called AirSim that can be used by non-simulation experts. The tool is a one-stop interface, where the user can input data, create scenarios, design experiments, define required output, define output charts, and run the simulation experiments. The simulation methodology aims at enabling airport planners and managers to measure and predict key performance indicators related to airport terminal operations, in terms of efficiency and customer service level.

AirSim is a generic tool that can be used to model and simulate a wide range of airport configurations; it was designed to be easily customized and implemented to a particular airport. In AirSim, the simulation logic was defined in modules; each module represents a function at the airport. Using the methodology and modules presented in this paper, the development time to implement a simulation model for a given airport is significantly shortened.

The tool presented was implemented in OIA, and has been successfully used for several years by the airports planners. To demonstrate the value of the methodology, we presented several scenarios in this paper. These scenarios were built to evaluate the airport LOS: in particular, the waiting time of passengers in security lines, the number waiting in the queue, and the space allocated per passenger.

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