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Effectiveness of the Passenger Services Organization in the Airport Terminal Complex




Nowadays, air transportation is vividly convenient and wide-spread way of travelling. With the increasing competitiveness between airports and passenger volumes it is important for airports to maintain the quality of passenger services and efficiency of the overall performance. Hence, the studying of organization of passenger services is substantial. This research aims at analyzing the passenger flows in case of different terminal organization schemes. For analysis and forecasting the passenger flows, the simulation modeling is often used. This research will focus on discrete-event simulation modeling and with the use of the software AnyLogic the models of departing passenger processing in the terminal will be built. As a result, two models with the different inputs will be compared. Finally, a conclusion about the factors influencing the performance of the terminal services will be made.

Key words: air terminal complex, airport capacity, airport passenger flows, simulation modeling, discrete-event simulation

Table of Contents


Literature Review


Anticipated results




Background of the Study. With the increasing connections between people around the world, air transportation is becoming more significant and demanded. Airports have to continuously develop, modernize and grow to maintain the increasing passenger flows with the appropriate service level. Apart from that, there’s increasing competitiveness on the market which make the airports to come up with the features that make them stand out from others: connections with other modes of transport, wide variety of destinations or comfortable conditions for layover.

At present, passengers are inclined to choose air transport for long-distance travel. While the load on the airport increases, a risk of reducing the quality and speed of passenger service appears. This may affect the financial performance of the airport and the level of airport service. Thus, airports are continuously being improved and complicated in their structure and functionality. Passengers are the main users of airports and, particularly, of passenger terminals.

Problem Statement. For managing passenger flows in the terminal and sustaining sufficient financial results, airports should consider, analyze and forecast the numerous aspects related to the passenger processing. There is always a need for further research of problem areas, for identification of the weak points of the terminal, for the reorganization of processes within the airport terminal complex. As a result of this analysis, the measures on modernization and implementation of the innovations are taken. The issue deriving from this is the choice of a method to analyze the performance and the parameters to consider.

Research aim and objectives. The main aim of this research is to study the effectiveness of the organization of passenger service in the airport complex using the discrete event simulation modeling. The aim leads to the statement of the following research question: “What is the effect of the partial restructuring of the passenger terminal services?”

To achieve the stated aim, the following objectives were set:

–                     To analyze development processes at airports in the world; identify the factors affecting the system;

–                     To explore the fundamentals of modeling and mathematical methods proposed for solving the problems of the efficiency of organizing passenger service;

–                     To determine the optimal method for modeling the system under study and conduct a simulation of the service system for air passengers.

Delimitations of the Study. In this research I am going to focus one the processes related to the flows of departing passengers excluding baggage handling processes. The baggage handling processes may be taken into account after further development and upgrade of this research. Furthermore, the data is empirical, and no real data set is collected for this research.

Professional Significance. The relevance and significance of this research lies in the fact that the issues of optimizing passenger service processes at the airport nowadays are more important than ever. For the passenger transportation industry, it is crucial to remain competitive and maintain passenger traffic levels. It is necessary to take into consideration the changing requirements of passengers to the system, their desires and lifestyle. Modern airports are striving to win passengers’ attention by offering the best transit conditions, level of service, availability of modern amenities and infrastructure. In addition, there are no systematic and generally accepted methods for solving the aforementioned issues.

Structure of the proposal. The structure of the proposal is defined by the stated objectives. The first part is the review of the studies related to the field and topic of the research. The second one focuses on methodology used for the achieving the aim and the results, which will be described in the final part.

Literature Review

For deeper understanding the topic of the research it is vital to observe the modern studies and theoretical materials. For the analysis of the processes in the passenger terminal the researcher should learn about the airport system in general and about the peculiarities of passenger processing modeling.

The distinctive features of modern airports. Airports have evolved and transformed from the stations serving the planes to the large, complicated, multifunctional systems. Modernization of air terminal complexes comes down to redevelopment of buildings and increase in the areas of inspection sites, automation of baggage handling, communication systems, transition to greater passenger autonomy (Redondi, 2011). In addition to that, Edwards (2004) mentions the three key points that distinguishes modern airports from the airports of twentieth century. These three factors are:

– land-use diversity;

– intermodal transport availability;

– environmental sensitivity.

Land-use diversity is a feature that means that, as airports have developed, they started to provide people with the number of services, including non-aviation ones. Modern airports have business lounges and conference rooms that operate independently of travel; they offer the hotel services used by local people and travelers; they have shopping malls and they perform as significant warehousing centers. Intermodal transport availability implies that airports provide the ability to switch between aircraft, car, metro, bus, rail. The facilities for changing transport are built in the airport. Without a doubt, effective integration of various types of transport distinguishes the modern airport. This helps passengers to arrive to the airport with reduced stress and it encourages the airport to grow as a business or leisure center. The environmental consciousness feature can be expressed by five distinct ways:

– Airports are designed to respond to, rather than resist, climate, ecology and nature.

– Terminals are designed to reduce the use of energy.

– Terminals employ materials of low toxicity and maximize natural sources of light and ventilation.

– Planting forms an important air purification and spiritual function in and around terminals.

– The airport authorities and local communities cooperate on environmental action.

These five points are expressions of the new environmental awareness. Macro level environmental action involve partnerships between airport authorities, local councils, schools and wildlife groups.

The variety of approaches to study passenger flows. As the main aim of the research is to examine the passenger flows, it is necessary to observe the studies related to the different approaches to this topic. In modern studies the issue of modeling passenger flows is often approached as a study of the queuing systems. In his study, Wang (2017) points out that the passengers, who can be considered as the requests for service, go through some procedures which can be considered as servers. This approach helps to spot the bottlenecks of the system and to discover the ways of reducing the waiting time. The algorithm is the following: first of all, the estimation of the passenger flow waiting for security check is performed using a Poisson process. After that, the Poisson distribution is mixed with a multiple M/M/s model. The next step is the description of the model of arriving for security check passengers. The model applies to Gumbel extreme value estimation and predicts the busiest time in the airport. This study is carried out with the use of real data collected from some big airports. The data is used for building a hybrid Poisson model and to perform the simulation of passenger volume. Finally, with the application of Markov Chain theory the passenger flow is randomly simulated and the results of two models are analyzed.

The queuing systems method often implies the simulation modeling. It is represented by many articles offering the different types of models and parameters.

For instance, Xie (2015) proposed a simulation-based approach for simulation of departing passengers at the airport terminal.  First of all, the number of passengers for each flight is modeled based on load factors and the number of seats in the aircraft. Following that, the passenger behavior for each flight is modeled by randomly generated periods of time between the services. Thus, the time at each stage is calculated based on the scheduled time. The number of passengers is calculated for each time interval. It should be noted that this approach is based on a probability distribution gathered from a real data set. At last, the effectiveness of the proposed approach is confirmed by real data.

The similar approach was proposed by Ju (2007). The aim of passenger flow model is to assess the performance of a modernized infrastructure and to discover some merits that after making changes. It concentrates on the processes in the terminal waiting room and its facilities. The task is to find out if the room and the amenities would be capable of meeting the requirements regarding the passenger flow volumes. Once the simulation is carried out it is also possible to analyze the other performance indicators and to spot the bottlenecks and their impact.  Besides, the simulation also helps to identify the optimal resource placement for passenger terminal waiting rooms, thus allowing to improve their capacities and service level.

Thus, a lot of simulation models are performed to analyze the particular process or category of passengers. This research will also focus on one category of passengers, which is the departing passengers.

The factors influencing the passenger processing in airports. Some studies refer to the role of technologies used for passenger processing. The modern technologies indeed positively affect the performance since they improve the speed of the services. To assess and to identify the optimal number of equipment used in the terminal, Romanenko (2017) offers to use feasibility study in order to figure out the optimal combination of equipment. This requires a set of indicators to compare the effectiveness of the options considered. Inasmuch as we need the simulation modeling for assessing the feasibility of implementation of the system, we need to define the characteristics of the system which can perform as indicators of efficiency. The study observes the issue of forming the set of performance indicators for the processing of departing passengers and their baggage in the regional airport complex with approximately 1 to 5 million passengers. The system is subdivided into the subsystem of passengers and baggage processing on the stage of check-in and baggage check-in, and the subsystem of baggage handling.

Despite the fact that baggage handling is a very important process in passenger flow analysis, this research will not take it into account.

For creating a sufficient simulation, it is crucial to define the parameters which will be used to build a model and their values. According to Mayorov (2014) there are several ways to analyze the system. He points out that using simulation modeling you can conduct experiments to evaluate the system, changing such parameters as the schedule, schedule of passengers’ arrival, their service requirements, routes in the terminal, the amount of equipment in the check-in, customs and baggage claim areas, the location of shops, random occurrences, etc.

Mayorov (2014) also claims that questions regarding the airport complex that can be answered by simulation modeling can be divided into three main groups:

1) the definition of quantitative indicators: the number of staff; the size of areas; the number of equipment, desks, transport, etc.;

2) optimization of layouts: the best mutual allocation of various zones, resources; conveyor systems topology;

3) optimization of operation logic and work rules: the possibility of increasing work efficiency without additional investments in equipment by optimizing management.

Taken all of these studies into consideration, I will develop and compare two models of departing passenger flows using discrete-event simulation modeling. One of the models will contain the improvements of quantitative indicators. Such approach is not presented in the studies. Thus, it will possibly help to look at the problem from a different angle.


The discrete-event simulation model which will be the main method of this research is a discrete, dynamic and stochastic model. This method was chosen for research as it focuses on the process in the system, not on its participants (Kelton & Law, 1991). The models will be constructed using the special software and will represent the passenger terminal with the number of servers which are check-in, security checks, passport controls, boarding pass control.

First of all, the models of the services chain in the airport complex will be described. I will describe the two options of organizing the services. In order to identify possible throughput with different system behavior, I will make the two different models, one of them with the extra processing server. The server would be the baggage handling desk for the certain categories of passengers. By implementing this desk, it is possible to unload other desks which may result in better overall performance. The suggestion is that the second model with the additional server will give the best results. Passengers will move through the servers and form the flows and waiting lines. The final point of simulation is the boarding desk server.

Secondly, I will set input figures necessary for simulation. The figures would be defined based on the standards of passengers processing set by IATA and empirically. IATA has established rules for tariffing and fees, rules for checking-in and screening passengers, requirements for passengers and baggage, conditions for returning tickets and filing claims (Humphries, 2002). The processing of departing passengers of the terminal is characterized by such features as the average, maximum and minimum numbers of passengers waiting in lines before each server, the average, maximum and minimum time spent in the waiting line, the total numbers of passengers successfully left the system. The capacity of the terminal is the number of passengers that the terminal serves for a certain period of time. This indicator will be analyzed as the most vivid parameter to change from the changes within the system.

Finally, the models will be constructed with the use of the software AnyLogic. Using Pedestrian library of the program I will construct and run the simulation of the standard algorithm of services. The Pedestrian library allows to set the figures for each server and to build a scheme similar to real-life situation (Curcio, 2007).

Anticipated results

The results of the research are expected to answer the stated research question. With the two optional ways to organize the passenger services being simulated, it would be possible to define which one is more efficient. The modeling will help to estimate the performance of the system, to find out the bottlenecks and to make conclusions about the necessary changes to implement. The results will be presented in figures showing the average waiting time, average time in the system, average time spent on each step of the processing, the maximum and minimum sizes of queues on each step. Besides, the research will present the observation and the comparison of the methods used to approach the issue.

The new observations that will be made are the significance of each stage of processing for the overall performance and the impact of the different parameters on the system. The importance and need for more parameters to consider will also be estimated.

The presented research will be useful for the airport executives, organizations which are involved into this industry like ICAO, IATA, and also for airlines representatives. This information can introduce the new way to look at the problem and to evaluate the prospects of the further development.


At present, due to the development of the aviation industry, modern airports must meet ever higher requirements. The terminals expand the range of services provided and are transformed into complex systems with a large number of internal services, carriers, passengers. Under these conditions, due to the growing passenger traffic in the terminals, there is a risk of reducing the quality and speed of passenger service, which affect the financial performance and level of service. That is why the study of terminal capacity is important and relevant in the framework of this industry of logistics. The solution to this problem is possible with the use of modeling, which allows to track the behavior of the system, identify bottlenecks, predict the performance of the complex.

Thus, this research aims at the study of passenger flows in airports and offers the solution to the problem by using simulation modeling to assess changes in the state of the system, to identify problem areas, to predict the situation with a particular external influence. The discrete-event simulation model is built in AnyLogic using the Pedestrian library. Two models with the different quantitative parameters will be compared and analyzed. The benefit of the research is that it is based on calculations and modeling which give precise results.

As for the further research prospects, there are aspects to be improved. First and foremost, the model can consider both passenger and baggage flows. Secondly, it is preferable to take into account more features of a real object. In addition, it is better to perform modeling using real databases of the terminal operation. With the real data set the research will be useful for airport executives.


  • Curcio, D., Longo, F., Mirabelli, G., & Pappoff, E. (2007, June). Passengers’ flow analysis and security issues in airport terminals using modeling & simulation. In European Conference on Modeling & Simulation, Praga-Repubblica Ceca (pp. 4-6).
  • Edwards, B. (2005). The modern airport terminal: new approaches to airport architecture. New York: Spon Press.
  • Humphreys, I., & Francis, G. (2002). Performance measurement: a review of airports. International journal of transport management1(2), 79-85. doi: 10.1177/1087724X0264004
  • Ju, Y., Wang, A., & Che, H. (2007, September). Simulation and optimization for the airport passenger flow. In 2007 International Conference on Wireless Communications, Networking and Mobile Computing (pp. 6605-6608). doi: 10.1109/WICOM.2007.1621
  • Kol’tsov, I. V., & Romanenko, V. A. (2017). Otsenka funktsional’noy effektivnosti sistemy obsluzhivaniya perevozok v aerovokzal’nom aeroportu [Evaluation of effectiveness of the transportation service system at the airport terminal]. Vestnik Samarskogo universiteta. Aerokosmicheskaya tekhnika, tekhnologii i mashinostroyeniye, 16 (3).
  • Law, A. K., & Kelton, W. W. 1991. Simulation Modeling and Analysis. New York, USA: McGraw-Hill
  • Mayorov, N. N., & Fetisov, V. A. (2014). Metod otsenki propusknoy sposobnosti aerovokzal’nogo kompleksa s pomoshch’yu imitatsionnogo modelirovaniya [Airport Capacity Simulation Assessment]. Informatsionno-upravlyayushchiye sistemy, (6 (73)).
  • Redondi, R., Malighetti, P., & Paleari, S. (2011). Hub competition and travel times in the world-wide airport network. Journal of Transport Geography19(6), 1260-1271. doi: 10.1016/j.jtrangeo.2010.11.010
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  • Xie, H., & Gao, Y. (2015, December). Simulation Based Estimation Approach for Departure Passenger Flow at Airport Terminal. In 2015 8th International Symposium on Computational Intelligence and Design (ISCID) (Vol. 2, pp. 543-546). doi: 10.1109/WICOM.2007.1621

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