Planning the Airport Terminal Facilities Based on Traffic Demand Forecast and Dominant Share of Airline Business Model: Case Study of Pula Airport
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University of Zagreb, Faculty of Transport and Traffic Sciences, 10000 Zagreb, Croatia
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Pula Airport, 52100 Ližnjan, Croatia
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Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(5), 2547; https://doi.org/10.3390/app15052547
Submission received: 22 January 2025
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Revised: 21 February 2025
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Accepted: 24 February 2025
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Published: 27 February 2025
(This article belongs to the Section Transportation and Future Mobility)
Abstract
:Today’s airport passenger terminals are required to be planned and designed to ensure flexibility for future adjustments at minimal cost, but also to respond to changes in demand and/or needs of passengers, airlines, and aircraft. To achieve these goals for airports and their operators, planning must be flexible and balanced. Recent data show that the airline business model of low-cost carriers continues to grow, especially after the pandemic. The analysis of the passenger traffic demand and shares of airline business models against the capacity of the existing airport terminal facilities can indicate whether certain adjustments are needed to meet the future conditions. In this research, forecasting of traffic demand and shares of airline business models was made. The forecasting tools of Python and MS Excel were used. Based on traffic demand forecasts and the dominant airline business model, guidelines for future airport terminal planning were proposed for the case-study airport. An example of the adjustment of airport terminal facilities at Pula Airport passenger terminal is provided using AutoCAD, according to forecasted traffic demand and the dominant share of low-cost carriers.
1. Introduction
There are three basic airline business models, namely the traditional or “network” (Full Service Carrier—FSC or Full Service Network Carrier—FSNC), low-cost (Low-Cost Carrier—LCC), and charter (Charter Carrier—CC) business models. Each of these business models has its own features and strategies to attract passengers. The business model of low-cost carriers was based on different principles than traditional full-service network carriers, with an emphasis on reducing costs to enable the lowest prices in target markets.
The evolution of the low-cost model in Europe took at least a decade longer than in the USA, as the first low-cost carriers only emerged after the completion of a liberalization process that took place in several stages, starting in the late 1980s and lasting until the mid-1990s [1]. The low-cost business model typically uses newer, more homogeneous, mid-sized fleets, dense seating configurations, point-to-point flights from smaller, less congested airports, direct ticket sales via the internet (without intermediaries or agencies), basic transportation services, etc. [2,3,4].
According to data in 2023, low-cost carriers account for a significant share of the air transport market, often ranging from 30% to 35% of the global market. This market segmentation continues to grow, especially after the recovery from the COVID-19 pandemic. The low-cost carriers’ market is expected to grow to more than USD 254 billion by 2027. These estimates have held up despite the challenges caused by the pandemic, and many analysts expect further growth due to the recovery in passenger traffic and changes in demand for air travel [5]. Recent data also show that the low-cost carriers market continues to grow strongly, especially after the pandemic. The global LCC market is forecasted to reach USD 440.5 billion by 2030, with a compound annual growth rate (CAGR) of 15.2% from 2023 to 2030. The growth is primarily driven by technological advances, changing passenger preferences towards more affordable options, and the expansion of secondary airports that reduce carriers’ operating costs [6].
The low-cost terminal (LCT) design concept is based on cost reduction with a simultaneous emphasis on terminal operational efficiency. Key elements include reducing the size and space of the terminal, providing basic services, and ensuring an adequate quality of service that meets the needs of airlines and passengers. The provision of basic airport terminal facilities depends on the type of airport operations, the structure of the fleet, and the volume of passenger traffic [7,8]. A significant number of LCTs were built as a result of the growth of the share of LCCs and the need to adapt passenger terminals to their simplified requirements.
In this research, forecasting of traffic demand and the shares of airline business models were made. The forecasting techniques of Python and MS Excel were used. Based on traffic demand forecasts and shares of airline business models, guidelines were proposed for future planning of airport terminal facilities. An example of layout adjustment of airport terminal facilities at Pula Airport passenger terminal is presented using AutoCAD, according to forecasted traffic demand and the shares of airline business models.
2. Theoretical Background
As the aviation industry continue to develop passenger terminals are required to be designed to ensure the adaptability to future changes at minimal costs, posed by passengers, airlines and/or aircraft. The most important elements of designing a passenger terminal are the assessment of demand (capacity) and level of service (LoS) standards [9,10]. It is also important to define parameters such as waiting times and space per passenger for each airport terminal facility [11]. Further benefit to the operational efficiency of a passenger terminal is the additional classification of spatial and temporal parameters of passenger processing (space per passenger, maximum waiting times, seat occupancy rate, etc.) according to the IATA LoS standards [10], and the specific features of each airline business model.
The aviation industry is still undergoing changes in the form of new service concepts and uncertainties regarding various issues, such as the level of service in relation to traditional or “network” (FSC), low-cost (LCC), and charter (CC) airline business models. Many airports do not have the possibility of selecting the service users, which places different business demands on the airport regarding services and capital investments.
Each of the airline business models (full-service, low-cost, charter) has its own strategies to attract customers. Although full-service carriers provide the largest range of services, i.e., a better quality of service, their prices are significantly higher. Hence, passengers more often choose LCCs, whose prices are more affordable and whose growth impacts the business of other airlines. Due to the increase of LCCs, their impact on airports is also increasing, forcing the airports to adjust their terminals for LCCs, i.e., low-cost terminals (LCTs) [12,13].
Barrett [14] was one of the first to predict the growth of LCCs and predicted the necessity for adaptation of passenger terminals to them in 2004. However, a potential problem with such terminals lies precisely in the different expectations from the perspective of airlines, passengers, and airport operators. LCCs offer a reduced volume of services, which is changing the traditional layout of the passenger terminal, e.g., it requires short aircraft turnaround times and limited “dwell” times for passengers [9]. LoS targets of LCCs are generally below the LoS optimum proposed by IATA [10], i.e., the interior design of low-cost terminals reflects the low-cost approach. Therefore, in conclusion, passenger profile forecasting is very important for establishing an adequate airport terminal design, which can ensure a satisfactory level of service to passengers and make sure that the terminal is capable of generating sufficient commercial revenue from LCC users and ultimately accomplish maximum operational efficiency [15].
A significant number of low-cost terminals (LCTs) have been built as a result of the worldwide growth of LCCs. This has led to the airport terminal concept with fewer airport terminal facilities offered to users (passengers) and reduced costs associated with the terminal development and operation [16].
The majority of the professional and scientific literature encourages selection of a simulation method to develop a new conceptual project and assess the future capacities of a passenger terminal. Many authors who deal with the analysis of the necessary capacities cite recommendations for maximum waiting times or sufficient available space per passenger in order to achieve a higher level of service at every airport terminal facility [17,18,19]. Furthermore, the ICAO Airport Planning Manual—Masterplanning [20] does not suggest a specific method that is most appropriate for capacity assessment but only states that the system must be capable of meeting the requirements that arise in terms of satisfactory quality and sufficient capacity for the time of the highest traffic load.
In 1992, Odoni and De Neufville [8] were two of the first researchers to raise methodological questions in the passenger terminal design. Later models were improved with technological development and are constantly adapted to passengers. However, even though there are various models aiming at passenger flow improvement [21], there is still no unified model for the optimization of the entire airport passenger terminal system, and various researchers use different optimization approaches.
Consequently, there is an evident need for additional optimization of airport terminal facilities, in relation to airline business models, in order to maximize the capacity and operational efficiency of the passenger terminal. An overview of relevant studies with highlighted contributions is provided in the following Table 1.
Considering the relevant literature, this paper proposes a methodology for planning (optimizing) the capacity of airport terminal facilities at passenger terminals where there is a visible trend of change in the share of airline business models from FSC to LCC, based on forecasts of traffic demand and the shares of airline business models.
As per the review of the previous literature (Table 1), it can be concluded that there is no similar methodology used, which combines forecasts of traffic demand and the share of airline business models as a basis for proposing modifications to airport infrastructure, specifically the passenger terminal. Thus, this paper contributes with new insight/approach in the field of airport planning, as well as in optimizing passenger terminal capacity with regard to future changes in the air transport business environment.
In accordance with the obtained estimates of traffic demand and the share of airline business models, it is possible to plan in detail the airport terminal facilities, in this case Pula Airport, which allows for airport terminal capacity optimization and improvement of the level of service.
3. Materials and Methods
In general, forecasts represent informed assumptions of future activity. They depend on detailed analysis and precise assessment of historical traffic demand trends, projection of economic growth and other relevant factors that might impact the local air transport market growth. A detailed and accurate analysis ensures more precise forecasting, particularly for short-term periods. Forecasts developed for medium or long-term periods provide airport decision makers with important guidance, for example, the requirements of new or expanded infrastructure, etc.
Insights into future air traffic activities (forecasts) serve as the basis for effective terminal design decisions, i.e., they are useful in determining the necessity for new or expanded terminal facilities.
The traffic demand forecast should include passenger traffic as well as aircraft operations. Forecasts of passenger activity and aircraft operations are usually developed on an annual and peak basis to ensure that future facilities are planned to meet demand levels even at peak times.
Examples of forecasting methods being used can be found in various aviation sectors such as air navigation services, airline operations, and airport operations. Available examples of forecasting methods used in various aviation sectors were examined in [43,44,45,46,47,48,49], and a detailed mathematical elaboration of each forecasting method is available in [50,51].
Forecasting methods used in airport operations were examined and tested to determine the optimal forecasting method for this research. The most common forecasting methods used in airport operations are the Naive Forecasting Method, Simple Average Method, Simple Moving Average Method, and Exponential Smoothing Methods (including Simple Exponential Smoothing Method, Holt’s Exponential Smoothing Method, and Holt–Winters Exponential Smoothing Method). These methods were considered for selection. Other forecasting methods (ARIMA modeling, machine learning (ML) forecasting models, deep learning (DL) forecasting models, artificial neuron network (ANN) forecasting models, etc.) were not considered due to some key shortcomings, such as limited ability to capture nonlinear patterns, problems with complex seasonal trends, necessity for large amounts of high-quality data, potential loss of valuable information due to necessary data transformations, necessity for significant computer processing power and data training time, etc.
Statistical data on the passenger traffic at a sample airport (Franjo Tuđman Airport) in the period from January 2006 to December 2021 were used as a dataset for all forecasting examples, applying various forecasting methods. Python forecasting tools and MS Excel tools were utilized for analysis and comparison of the results. The most suitable forecasting method was selected and applied in this research. The forecasting method applied in this research was selected according to its applicability to the case at hand, i.e., Pula Airport, which records show seasonality (airport is the busiest during the tourist season).
Franjo Tuđman Airport was taken for the analysis of different forecasting methods as an example of one airport in Croatia. Croatia is known as a tourist destination, so the demand for air traffic increases significantly during the summer months. In Croatia, all airports, from the aspect of traffic demand, have similar characteristics—strong seasonality, dominance of international traffic, dominance of passenger traffic over cargo traffic, dependence on charter and seasonal lines, etc. Considering that Franjo Tuđman Airport is the largest air transport hub in the country and a key point for international and domestic flights, it records the largest number of passengers and flights and has significant seasonality; it was chosen as an example for the analysis and selection of an appropriate (optimal) method of traffic demand forecasting at airports. This method was applied to forecast traffic demand and airline business model shares at Pula Airport.
3.1. Analysis of the Forecasting Methods
For the analysis and the selection of an applicable forecasting method, the statistical monthly data of passenger traffic at Franjo Tuđman Airport were used [52] from 2006 to 2021 (as per Table 2).
The dataset shown in Table 2 is divided into two sets, i.e., training data (data from 2006–2019) and test data (actual data for 2020 and 2021) to check the correctness/success of each forecasting model.
Figure 1a shows an example of a graphical display of data, and Figure 1b shows a display of trends and seasonality of data. The Python 3.6 programming language within the PyCharm tool was used to create the following examples to enable the selection of the most appropriate forecasting method among the Naive Forecasting Method, Simple Average Method, Simple Moving Average Method, Simple Exponential Smoothing Method, Holt’s Exponential Smoothing Method, and Holt–Winters Exponential Smoothing Method. Other forecasting methods, such as ARIMA modeling or machine learning (ML) models, were not considered due to several key limitations, including challenges in capturing nonlinear patterns, difficulties in handling complex seasonal trends, the necessity for large volumes of high-quality data, possible loss of valuable information during necessary data transformations, and the requirement for substantial computational power and long training times.
The data shown above are divided into two sets: training data (data from 2006–2019)—shown in blue on the graph—and test data (actual data for 2020 and 2021)—shown in orange on the graph. Based on the training data, values for the next 10 years are forecasted, and the forecasts are shown in green on the graphs. The quality of each forecasting model was evaluated using the default criterion—root mean square error (RMSE). A lower RMSE indicates that the model’s predictions closely match the actual values, which implies a strong performance.
In the following text, using the above-mentioned data, some of the applicable forecasting methods are elaborated in more detail.
3.1.1. Naive Forecasting Method
The Naive Forecasting Method, or naive models, assume that the occurrence level for the forecast period is equal to the occurrence level from the last available period, i.e., forecast = last month’s data. Based on the last data set (training data), a forecast was made (Figure 2). All values are the same because the prediction was made relative to the values of the last data set. The evaluation criterion for this model could not be applied, as this model repeats the last month’s data.
3.1.2. Simple Average Method
The Simple Average Method is a forecasting method that uses the mean value of data for the n most recent time periods to forecast demand/traffic for the next time period (Figure 3), i.e., forecast = average of all data for the past months. The evaluation criteria RMSE for this model is equal to 42,956.
3.1.3. Simple Moving Average Method
The Simple Moving Average Method, having in mind that the last observation in a time series has a greater influence on the future than the first observation, takes an average of solely the last observations for predicting the future. The Simple Moving Average Method takes the average of the last 12 months and forecasts this average value for the following months (Figure 4). The result is better than the Simple Average Method or Naive Forecasting Method. The seasonal effect cannot be captured by this method, but there is a trend line in the forecast that increases linearly. The evaluation criterion RMSE for this model is equal to 31,157.
3.1.4. Exponential Smoothing Methods
Exponential smoothing methods are the Simple Exponential Smoothing Method (records only level), Holt’s Exponential Smoothing Method (records level and trend), and Holt–Winters Exponential Smoothing Method (records level, trend, and seasonality).
The Simple Exponential Smoothing Method uses historical averages of a variable over a period to attempt to predict its future behavior. The forecast is based on a weighted average of the previous forecast and the latest demand point, but there is no seasonality. This method is very similar to the previous one, except that it is a weighted average of all past points, where more recent points are given more weight (Figure 5). The evaluation criterion RMSE for this model is equal to 32,123.
Holt’s Exponential Smoothing Method is an extended form of the simple exponential smoothing model. In addition to the level influence, this model also takes into account the influence of trend when forecasting future demand and captures both level and trend. It is possible to capture the trend of the data, but it is not possible to capture seasonality (Figure 6). The evaluation criteria RMSE for this model is equal to 30,289.
Holt–Winters Exponential Smoothing Method, or Winters model, also known as the Holt–Winters model or the “trend and seasonally adjusted” exponential smoothing model, is an extension of Holt’s model that is used in situations where it is desired to apply the influence of seasonality when calculating demand forecasts. It records the level, trend, and seasonality, as per Figure 7. The evaluation criterion RMSE for this model is equal to 19,027, which shows the best fitness of the model (compared to the previously shown models).
3.2. Comparison and Selection of the Appropriate Forecasting Method
In conclusion, by comparing the above methods, the difference in forecasts can be observed (in relation to the principles of each method). Given that targeted demand consists of three basic factors: level, trend, and seasonal factor, the Holt–Winters Exponential Smoothing Method constitutes the optimal method. For example, based on training data (2006–2019) for the period 2020–2032, Figure 7 shows a linear increase—forecast (green), while the actual data for 2020 and 2021 (orange) show a difference in relation to the obtained forecast. A decrease in demand in 2020 and 2021 was caused by the COVID-19 pandemic.
Exponential smoothing methods (especially the Holt–Winters method) are widely used in airport traffic demand forecasting due to their specific advantages. They can adapt quickly to changes because they respond effectively to seasonal and short-term fluctuations in demand. They are simple to implement and do not require complex data or modeling. A major advantage is that these methods give more weight to recent data, which allows for more accurate traffic demand forecasts, giving current trends more influence. These methods do not require the collection of large amounts of data and can provide good results even with limited historical data.
Other forecasting methods, such as ARIMA models, have certain disadvantages such as the limited ability to capture nonlinear patterns because ARIMA is based on linear dependencies and can have problems with complex seasonal and nonlinear trends; data often need to be transformed for the model to be effective, which can lead to loss of information. Also, forecasting with machine learning models requires a large amount of high-quality data for reliable forecasts, significant computer processing power, and training time. Due to these key shortcomings, such methods were not considered for use in this case of traffic demand forecasting at the case-study airport.
Considering the analysis of forecasting methods, the Holt–Winters Exponential Smoothing Method was selected as optimal for this research, as it performs better than the other analyzed methods. However, the Holt–Winters Exponential Smoothing Method also comes with several limitations. It assumes that trends will persist linearly, making it less suitable for datasets with nonlinear or shifting trends. Additionally, it struggles with multiple or irregular seasonal patterns and is highly sensitive to sudden changes, anomalies, or outliers, which can distort forecasts. Furthermore, the model adapts slowly to abrupt shifts in data patterns, such as those caused by financial crises or the COVID-19 pandemic. For these reasons, a historical data period was selected that excluded such anomalies, focusing on the post-financial crisis and pre-COVID-19 timeframe.
3.3. Data Collection
Pula Airport is one of nine international airports in Croatia. It is located in the southwestern part of Istria in the municipality of Ližnjan, i.e., 8.6 km from the city center of Pula. Pula Airport has a rich history that is closely connected with military and civil aviation in the Istrian region. In the 1970s, Pula Airport experienced intensive development with the construction of new facilities and an increase in the number of passengers, especially during the summer tourist season, which was followed by an increase in the number of flights and the arrival of larger aircraft, which led to further modernization and expansion of the airport. In the late 1990s, there was a significant increase in traffic, and the structure of guests changed. In the more recent period, from 2000 onwards, investments in infrastructure, terminal modernization, and security systems have become more frequent, as well as an increase in the number of destinations across Europe [53,54].
Pula Airport has plans for expansion and modernization, including adaptation to accommodate the increasing number of international passengers. At the same time, the airport is becoming an increasingly popular destination for LCCs, which is one of the reasons why this particular airport was chosen for the case study.
All historical traffic data were collected at Pula Airport for the period 2009 to 2019 for the purpose of obtaining traffic demand forecasts (passengers and aircraft operations). Data from 2020 onwards were not used because the selected period 2009–2019 represents a period of stability in terms of business, i.e., the forecast of demand can be more accurate and realistic for this selected period. Figure 8 shows historical traffic data collected from Pula Airport as well as all crisis periods marked with light blue bars. The year 2008 was marked by a financial crisis, while the year 2020 was marked by the COVID-19 pandemic (see Figure 8); therefore, the aforementioned crisis periods were not included in the historical data set for the forecast. The tools used to create forecasts are Python and MS Excel, and the forecasts are made for the future period, 2020–2026.
Figure 9 shows an example of the Pula Airport original data on the number of passengers carried in 2019. The total number of passengers is divided among airlines that transported them, and the ICAO three-letter designators, or 3LDs [55], are used instead of full names, i.e., BAW is British Airways, EZY is easyJet, NAX is Norwegian Air Shuttle, RYR is Ryanair, TOM is TUI Airways, TRA is Transavia, etc.
4. Results
Forecasts of traffic demand in terms of the number of passengers and aircraft operations were made by using the historical traffic data collected at Pula Airport for the period 2009 to 2019. The forecasting tools of Python and MS Excel were used. For the evaluation of the model quality, the default criteria were used, i.e., root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE). A lower RMSE suggests that the model’s predictions closely align with the actual values, indicating strong performance, as well as a lower value of SMAPE, indicating better model performance. The results are presented in the following subchapters.
4.1. Forecasts of Traffic Demand at Pula Airport
Table 3 shows historical traffic demand data (passengers and aircraft operations) for Pula Airport in the period 2009–2019.
Table 4 shows the forecasted traffic demand values (in the number of passengers and the number of aircraft operations) for Pula Airport in the period 2020–2026.
Figure 10a shows graphically the forecasted values (with its confidence intervals—upper/lower limit marked with dashed lines) of the number of passengers at Pula Airport for the period 2020–2026, using the historical data from 2009 to 2019. In the figure, this is called the pre-COVID-19 scenario, which was chosen as more reliable. The evaluation criteria RMSE is 97,448, and SMAPE is 0.14 (or 14%) which indicates good fitness of the model.
For the purpose of presenting the robustness of this approach, Figure 10b shows an alternative forecast for the period 2023–2026 if the historical data from 2009 to 2022 were used, i.e., if post-COVID-19 data were used. This is called the post-COVID-19 scenario. The evaluation criteria RMSE is 296,977, and SMAPE is 0.60 (or 60%), which indicates poor fitness of the model.
Figure 10c shows the comparison of these two scenarios. The yellow dashed line shows the trend of historical data of the pre-COVID-19 period, which lines up with forecasted values of the future period, 2020–2026, while the green dashed line represents the trend of historical data of the post-COVID-19 period (starting at the point where the pattern begins to form again—after the COVID-19 lockdown). This green trendline is increasing much faster than before the COVID-19 lockdown, as can be observed; it lines up with the upper limit forecast values of the future period, 2023–2026.
Figure 11 shows graphically the forecasted values (with their confidence intervals—upper/lower limit marked with dashed lines) of the number of aircraft operations at Pula Airport for the period 2020–2026. The evaluation criteria RMSE is 897, and SMAPE is 0.08 (or 8%), which indicates very good fitness of the model.
4.2. Forecasting the Share of Airline Business Models at Pula Airport
This part analyzes and forecasts the share of business models at Pula Airport. All historical data were collected from Pula Airport for the period from 2009 to 2019, for the purpose of creating forecasts of the share of business models. Data from 2020 onwards were not used because the selected period 2009–2019 represents a period of stability in terms of business, and obtained forecasts can be more precise and realistic for this selected period. As previously explained, the crisis periods (financial crisis, COVID-19 pandemic) were not included in the historical dataset. The forecasts were created by using Python and MS Excel forecasting tools for the future period, 2020–2026.
4.2.1. Analysis of the Share of Airline Business Models at Pula Airport
The following Table 5 shows the shares of airline business models by year at Pula Airport in the period 2009–2019.
Figure 12 shows graphically (via pie charts) the shares of airline business models by year at Pula Airport in the period 2009–2019, according to Pula Airport data [56].
Figure 13 shows an analysis of the shares of all airline business models with linear trendlines by year at Pula Airport in the period 2009–2019. The figure clearly shows that the share of low-cost carriers (LCC) has been continuously increasing in the observed period, while the business models of traditional (FSC) and charter (CC) carriers have been declining. The dominance of low-cost carriers (LCC) is evident at Pula Airport.
4.2.2. Forecasts of the Shares of Airline Business Models at Pula Airport
Based on the collected data and analysis of the shares of airline business models at Pula Airport, forecasts of the shares of airline business models for the period 2020–2026 have been made, as shown in Table 6. Python and MS Excel forecasting tools (which contain algorithms for moving average, exponential smoothing, and linear regression methods) were used to create forecasts of the shares of airline business models at Pula Airport.
Figure 14 shows graphically the forecasted values (with their confidence intervals—upper/lower limit marked with dashed lines) of the share of traditional airlines (FSC) at Pula Airport for the period 2020–2026. The evaluation criteria RMSE is 1.76, and SMAPE is 0.08 (or 8%), which indicates very good fitness of the model. The forecast shows a decline in the share of traditional air carriers at Pula Airport.
Figure 15 shows graphically the forecasted values (with their confidence intervals—upper/lower limit marked with dashed lines) of the share of LCCs at Pula Airport for the period 2020–2026. The evaluation criteria RMSE is 1.92, and SMAPE is 0.03 (or 3%), which indicates very good fitness of the model. The forecast shows a convincing increase in the share of LCCs at Pula Airport.
Figure 16 shows graphically the forecasted values (with their confidence intervals—upper/lower limit marked with dashed lines) of the share of charter carriers (CC) at Pula Airport for the period 2020–2026. The evaluation criteria RMSE is 2.43, and SMAPE is 0.10 (or 10%), which indicates good fitness of the model. The forecast shows a decline in the share of charter carriers at Pula Airport.
Figure 17 shows graphically the forecasted values (with their confidence intervals—upper/lower limit marked with dashed lines) of the share of other airline business models at Pula Airport for the period 2020–2026. The evaluation criteria RMSE is 0.46, and SMAPE is 0.25 (or 25%), which indicates acceptable fitness of the model. The forecast shows an equal share of other airline business models at Pula Airport.
Figure 18 shows all forecasts of the shares of airline business models with the corresponding trends at Pula Airport for the period 2020–2026, where the dominance of LCCs at Pula Airport and a progressive growth in share, in the future period, can be observed.
5. Guidelines for Future Planning of Airport Terminal Facilities Based on Traffic Demand Forecast and Share of Airline Business Models: Example of Airport Terminal Facilities Adjustment at Pula Airport
In order to provide realistic guidelines for future planning of airport terminal facilities at Pula Airport, a comparison of forecasted traffic demand and the share of airline business models has been made. Table 7 shows the forecasted shares of each airline business model and the corresponding forecasted values of the number of passengers for each year of the forecast period, 2020–2026.
Figure 19 shows a comparison of the forecasted shares of each airline business model and the forecasted number of passengers for each year of the forecast period 2020–2026. For example, according to Figure 19, for the year 2025 (and thus for others), on the left side, it can be seen that the share of LCCs at Pula Airport is expected to increase, i.e., it amounts to 68%; while on the right side, the corresponding division of the forecasted number of carried passengers as per the predicted shares, i.e., the predicted total number of passengers at Pula Airport in 2025, amounts to 1,029,892 passengers, of which 68% are expected to travel with LCCs, i.e., 700,326 passengers. In accordance with the obtained estimates of traffic demand and the share of airline business models, it is possible to plan the airport terminal facilities of Pula Airport and thus optimize the capacity and level of service of the passenger terminal at Pula Airport.
In accordance with the forecasts obtained of traffic demand and the shares of airline business models, it is possible to plan the airport terminal facilities of Pula Airport and thus optimize the capacity and level of service of the passenger terminal.
According to Figure 19g, for the year 2026, it is visible that an increase in the share of the LCC business model at Pula Airport is expected. For example, for the year 2026, the increase amounts to 70%. It is expected that 70% of passengers will travel with LCCs, i.e., 700,522 passengers. Below, using the example of the check-in area, the segmentation of passengers while waiting in line by type of airline business model is proposed, based on the estimate of the share of airline business models and the forecasted number of passengers.
The check-in area at Pula Airport passenger terminal contains 16 counters. As previously mentioned, according to the forecasts made, it was obtained that in 5 years 70% of passengers will use the services of LCCs. According to this percentage, the airport terminal facilities, i.e., the check-in area, were redistributed in the AutoCAD software tool (version 2024). Based on the above, it is necessary to foresee four counters for full-service/charter carriers (FSC/CC) and the remaining 12 for low-cost carriers (LCCs), as per Figure 20 (marked in red squares).
The previous figure provides an example of a modification of the check-in area as one of the most important airport terminal facilities, but the modification proposal can also be applied to other facilities. The most important data that was sought within the study was the estimate of the percentage of LCC share, i.e., 70%. Knowing this data, changes to the passenger terminal design can be proposed for other airport terminal facilities. In order to increase the relevance of the study, recommendations have been added for the security control area and gate holdroom area. It is also necessary to note that each airport, in accordance with the obtained projection of their future trend in the share of airline business models, could approach the analysis of the modification in relation to the spatial capabilities (areas) of each airport terminal facility. Likewise, each airport could determine the desired level of service (LOS) that it wants to provide within its passenger terminal, which will have an impact on the spatial and temporal requirements, i.e., the number of passengers and possible congestion.
For example, at Pula Airport, due to a lack of static capacity, there is a delay in the security control of passengers and hand baggage. The passenger and hand baggage security control area consists of four lines equipped with two metal detectors and four X-ray machines. Hence, the lines can be separated, and passengers using LCC services can be separated, who are also willing to wait longer and use a smaller area than passengers using FSC services. Furthermore, analyzing the facilities at Pula Airport, it can be concluded that a very small area is planned for immigration/emigration control at departure. Conversely, the area planned for passenger check-in is very large (545.5 m2), especially considering that today most passengers using LCC services check-in via mobile phone or self-service kiosks. Therefore, modifications for another two airport terminal facilities are presented in Figure 21 and Figure 22 in order to provide more examples of applying this concept (redistribution shown in orange/blue areas for LCC and FSC/CC carriers).
6. Discussion
The focus of this study was to optimize future planning of airport terminal facilities based on traffic demand forecasts and the dominant share of airline business models. A case study was conducted at Pula Airport that provided all necessary historical data on passenger traffic demand and shares of airline business models for the period from 2009 to 2019. An analysis of the data on passenger traffic demand and shares of airline business models was conducted, and the results showed that in the period from 2009 to 2019, Pula Airport had a visible trend of change in the shares of airline business models from full-service carriers (FSC) to low-cost carriers (LCC). Based on historical data, forecasts of passenger traffic demand (plus, additionally, the number of aircraft operations) and the shares of airline business models were made using the forecasting tools of Python and MS Excel. The expected number of passengers and expected shares of airline business models were obtained, i.e., the dominant airline business model—LCC—was identified at Pula Airport.
According to the obtained results, Pula Airport can expect the rise of low-cost airline flights, i.e., the share of the LCC airline business model. Results show that in the future period of 7 years, a rise is expected to go from 61% in the first year up to 70% in the last forecasted year.
Results of the forecasted number of passengers (i.e., traffic demand) show that Pula Airport can also expect a rise in passenger traffic, going from 863,116 passengers in the first year of forecasting up to 1,100,747 passengers in the last year of forecasting.
If an expected dominant share of the airline business models (in the case of Pula Airport, the LCC model) is obtained, it is possible to apply it to the expected number of passengers in the same year. Hence, the calculations are made for each year of the forecasting period, i.e., in the first year of the forecasting period, the expected total passenger traffic is 863,116 passengers, while the expected dominant share of airline business models is LCC with the share of 61%. Applying that share to expected total passenger traffic of 863,116 passengers, it can be calculated that 61% of 863,116 passengers, i.e., 526,500 passengers will use the services of low-cost carriers (LCCs). Analogously, in the last year of the forecasting period, the expected total passenger traffic is 1,100,747 passengers, while the expected dominant share of airline business models is LCC with the share of 70%. Applying that share to the expected total passenger traffic of 1,100,747 passengers, it can be calculated that 70% of 1,100,747 passengers, i.e., 770,522 passengers, will use the services of low-cost carriers (LCCs).
In accordance with the obtained forecasts of traffic demand and the shares of airline business models, it is possible to plan the airport terminal facilities of Pula Airport and thus optimize the capacity and level of service of the passenger terminal. The IATA Airport Development Reference Manual—ADRM—was applied in this research, as it is the main document for the development of airport terminal facilities and the optimization of level of service (LOS). IATA created it for dimensioning space and facilities, determining the processing time of passengers and baggage by segments in the traffic flow, and calculating airport capacity. These IATA standards can be compared to standard capacity measures. The capacity of a passenger terminal is not uniquely determined; it depends on the desired level of service (LOS), i.e., what is the acceptable level of the number of passengers per unit of space and the acceptable waiting time of passengers in line for a particular airport. Each airport determines this individually. For example, for 100 m2, the capacity is 50 passengers, i.e., 2 m2 per passenger, but if 1 m2 per passenger is acceptable, the capacity will be 100 passengers. Therefore, it is primarily a matter of the preferences of the airport management. It is also important to note that over the years, the IATA LOS concept has undergone a series of additional improvements with the aim of making it more accurate and applicable to airports. Today, IATA defines four different LOS recommendations in its ADRM, i.e., over-design, optimum, sub-optimum, and under-provided. The recommendation is certainly to use the optimum level, i.e., to ensure optimal terminal space that will meet passenger requirements. A large number of airports use this concept to help them determine the requirements for facilities to accommodate the planned throughput. The optimum LOS was also used in this study. It is advisable to use the IATA LOS standards, bearing in mind that passengers have different requirements/needs, and especially taking into account the diversity of airline business models.
Examples of check-in area, security control area, and gate holdrooms’ area modification/segmentation according to estimated traffic demand and shares of airline business models at Pula Airport passenger terminal are provided. The examples show an adjustment of the airport terminal facilities; for example, for check-in area modification, it can be made from the current 16 counters to a breakdown into two areas, where one contains four counters for full-service/charter carriers (FSC/CC), and the second contains 12 for low-cost carriers (LCC).
Pula Airport was chosen for the case study due to the fact that it is becoming an increasingly popular destination for low-cost carriers. Hence, Pula Airport has plans for further expansion and modernization and adjustment to LCC airlines, including adaptation to accommodate the increasing number of passengers while preserving an optimal level of service.
Traffic demand and the share of airline business forecasting models were primarily used to demonstrate the possibility of modifying the airport terminal facilities, taking into account the trend of airline business models, using the example of a smaller airport that is striving towards the dominance of the low-cost business model. In this way, other airports (primarily, for example, Croatian smaller coastal airports) that have a strong seasonality and are increasingly experiencing a change in the share of airline business models from FSC dominance to LCC, can apply this methodology to their airport terminal facilities.
7. Conclusions
This paper proposes a methodology for planning/optimizing the capacity of airport terminal facilities at passenger terminals where there is a visible trend of change in the shares of airline business models, based on forecasts of traffic demand and the shares of airline business models.
In this research, forecasting of traffic demand and the shares of airline business models was made for a case-study airport, i.e., Pula Airport. The forecasting techniques of Python and MS Excel were used. Based on traffic demand forecasts and shares of airline business models, guidelines were proposed for future planning of airport terminal facilities. An example of an adjusted airport terminal facilities’ layout at Pula Airport passenger terminal is demonstrated using AutoCAD, according to forecasted traffic demand and the shares of airline business models.
The importance of the study lies in the fact that the structure of the airline business models at an airport is changing, where low-cost carriers become more dominant over traditional or charter business models. Hence, airports need to adjust to these changes accordingly to maintain profitability and keep passenger satisfaction at an acceptable level of service.
The method of forecasting the traffic demand and shares of airline business models at an airport provides an insight into the future conditions and gives the opportunity to adjust airport terminal facilities at an airport passenger terminal to future scenarios. One example is provided for an adjustment of the check-in area of the airport passenger terminal at Pula Airport.
A key limitation of the research that can be identified lies in the fact that forecasts are not entirely accurate and should always be taken with a degree of caution. Although they are based on the analysis of historical data and various factors, the future is always subject to changes that cannot be fully predicted. This includes unforeseen events such as economic crises, pandemics like the recent COVID-19, political changes, technological innovations, or natural disasters, which can significantly impact the market. Therefore, forecasts are useful as guidelines, but there is always a certain degree of uncertainty. It is also important to mention potential external factors such as possible regulatory changes, airline competition, or economic changes that may affect this trend (of LCC growth).
For example, when considering the regulatory aspect, it is important to consider the possibility of introducing stricter regulations that may increase costs for low-cost carriers, which could potentially lead to higher ticket prices and reduced competitiveness. Also, if regulations regarding safety and health become stricter (such as stronger requirements for baggage screening, health care, or testing), this could increase operating costs for LCCs, which could negatively affect their low ticket prices (and reduce competitiveness). It is known that LCCs mostly operate on the principle of low prices and high flight frequencies, and increased competition, especially from full-service carriers introducing low-cost options, may reduce the market share of LCCs, as competitors may offer similar services with better connectivity or additional services. Other factors, such as economic recessions and their impact on the LCC growth trend, can also be taken into account. In fact, low-cost carriers usually profit in economic crises because passengers are looking to reduce the cost of travel. In times of recession, low-cost options become more attractive to the general public. By comparison, during periods of economic growth, demand for travel increases, but competition in the sector can become more intense; thus, LCCs face the challenge of maintaining low prices while increasing passenger numbers.
In addition to the above, various other factors, such as geopolitical factors or technological innovations, can be taken into account. Geopolitical uncertainty may increase travel uncertainty, which could impact LCCs and reduce flight demand. On the other hand, LCCs can react to changes in the market by rerouting routes or offering promotions to restore demand. At the same time, LCCs have the possibility of faster implementation of new technologies that reduce costs, such as digitization and automation of the application process or increasing the efficiency of the fleet, which positively affects this trend.
Hence, external factors such as regulatory changes, competition, economic trends, and global crisis situations can significantly shape the growth trend of LCCs. Given that low-cost carriers often have the advantage of flexibility and lower operating costs, they may be in a better position to adapt than large full-service carriers. However, increased competition, greater economic pressures, and external uncertainties (such as pandemics and geopolitical risks) may pose challenges, and LCCs will need to continuously innovate and optimize their operations to maintain their growth trend.
In future work, the plan is to develop scenarios for other airport terminal facilities, such as security control, immigration/emigration control, gate holdrooms, baggage reclaim, etc. Developing future scenarios provides valuable guidelines for future planning of the airport terminal facilities at an airport passenger terminal. Also, one of the future studies focuses on the further optimization of the passenger terminal capacity in accordance with forecasts of shares and other airline business models, such as charter carriers, on all main terminal components with the aim of maximizing the capacity and operational efficiency of the passenger terminal.
In conclusion, the functionality of the passenger terminal can be improved in accordance with their requirements, ultimately achieving greater operational efficiency.
Author Contributions
Conceptualization, J.P.; methodology, J.P. and D.B.; software, J.P., D.B. and I.L.; validation, J.P., I.Š. and I.L.; formal analysis, J.P.; investigation, J.P.; resources, I.Š. and I.L.; data curation, J.P., I.Š. and I.L.; writing—original draft preparation, J.P. and D.B.; writing—review and editing, J.P. and D.B.; visualization, J.P.; supervision, J.P. and I.Š. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data supporting reported results can be found in this paper. Data were gathered and analyzed via means and methods described in this paper.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| AHP | Analytic hierarchy process |
| AM | Analytical model |
| AMSS | Airport Management Strategy Software |
| CAGR | Compound annual growth rate |
| CC | Charter carrier |
| FAHP | Fuzzy Analytic Hierarchy Process |
| FSC | Full-service carrier |
| FSNC | Full-service network carrier |
| IATA | International Air Transport Association |
| ICAO | International Civil Aviation Organization |
| LCC | Low-cost carrier |
| LCT | Low-cost terminal |
| LoS | Level of service |
| SA | Simulated annealing |
| SM | Simulation model |
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Figure 1.
Examples of (a) graphical display of data; (b) graphical display of data trends and seasonality.
Figure 1.
Examples of (a) graphical display of data; (b) graphical display of data trends and seasonality.
Figure 2.
Example of forecasting using the Naive Forecasting Method.
Figure 3.
Example of forecasting using the Simple Average Method.
Figure 4.
Example of forecasting using the Simple Moving Average Method.
Figure 5.
Example of forecasting using the Simple Exponential Smoothing Method.
Figure 6.
Example of forecasting using Holt’s Exponential Smoothing Method.
Figure 7.
Example of forecasting using the Holt–Winters Exponential Smoothing Method.
Figure 8.
The impact of the COVID-19 pandemic on (a) passenger traffic at Pula Airport; (b) aircraft operations at Pula Airport [54].
Figure 8.
The impact of the COVID-19 pandemic on (a) passenger traffic at Pula Airport; (b) aircraft operations at Pula Airport [54].
Figure 9.
Example of the original passenger traffic data from Pula Airport [56].
Figure 9.
Example of the original passenger traffic data from Pula Airport [56].
Figure 10.
Forecasted number of passengers at Pula Airport (a) in the period 2020–2026 (pre-COVID-19 version); (b) in the period 2023–2026 (post-COVID-19 version); (c) comparison of pre-COVID-19 and post-COVID-19 trends.
Figure 10.
Forecasted number of passengers at Pula Airport (a) in the period 2020–2026 (pre-COVID-19 version); (b) in the period 2023–2026 (post-COVID-19 version); (c) comparison of pre-COVID-19 and post-COVID-19 trends.
Figure 11.
Forecasted number of aircraft operations at Pula Airport in the period 2020–2026.
Figure 12.
Structure of business models shares at Pula Airport (a) in 2009; (b) in 2010; (c) in 2011; (d) in 2012; (e) in 2013; (f) in 2014; (g) in 2015; (h) in 2016; (i) in 2017; (j) in 2018; (k) in 2019 [56].
Figure 12.
Structure of business models shares at Pula Airport (a) in 2009; (b) in 2010; (c) in 2011; (d) in 2012; (e) in 2013; (f) in 2014; (g) in 2015; (h) in 2016; (i) in 2017; (j) in 2018; (k) in 2019 [56].
Figure 13.
Analysis of the shares of airline business models in the period 2009–2019.
Figure 14.
Forecast of the share of the traditional carriers (FSC) at Pula Airport.
Figure 15.
Forecast of the share of the LCCs at Pula Airport.
Figure 16.
Forecast of the share of the charter carriers (CC) at Pula Airport.
Figure 17.
Forecast of the share of the other airline business models at Pula Airport.
Figure 18.
Forecast of the future structure by share of the airline business models at Pula Airport.
Figure 18.
Forecast of the future structure by share of the airline business models at Pula Airport.
Figure 19.
Comparison of forecasted shares of business models and forecasted number of passengers for 2020–2026: (a) Comparison for 2020; (b) Comparison for 2021; (c) Comparison for 2022; (d) Comparison for 2023; (e) Comparison for 2024; (f) Comparison for 2025; (g) Comparison for 2026.
Figure 19.
Comparison of forecasted shares of business models and forecasted number of passengers for 2020–2026: (a) Comparison for 2020; (b) Comparison for 2021; (c) Comparison for 2022; (d) Comparison for 2023; (e) Comparison for 2024; (f) Comparison for 2025; (g) Comparison for 2026.
Figure 20.
Example of check-in area modification/segmentation according to estimated traffic demand and share of airline business models at the Pula Airport passenger terminal.
Figure 20.
Example of check-in area modification/segmentation according to estimated traffic demand and share of airline business models at the Pula Airport passenger terminal.
Figure 21.
Example of security control area modification/segmentation according to estimated traffic demand and share of airline business models at the Pula Airport passenger terminal.
Figure 21.
Example of security control area modification/segmentation according to estimated traffic demand and share of airline business models at the Pula Airport passenger terminal.
Figure 22.
Example of gate holdrooms’ area modification/segmentation according to estimated traffic demand and share of airline business models at the Pula Airport passenger terminal.
Figure 22.
Example of gate holdrooms’ area modification/segmentation according to estimated traffic demand and share of airline business models at the Pula Airport passenger terminal.
Table 1.
The methodologies used in the optimization of the passenger terminal—literature overview.
| Research | Methodology | Contribution | Airports |
|---|---|---|---|
| Andreatta et al., 2001 [22] | Analytical method—SLAM Simulation method—AIRLAB | Analysis and capacity assessment of airport terminal facilities | Milan Airport Venice Airport |
| Brunetta and Romanin-Jacur, 2001 [23] | Simulation method—AIRLAB | Creation of a flexible model for defining and applying alternative operational policies in the terminal; modeling of passenger behavior; animation of operations inside the terminal | Milan Airport |
| Takakuwa and Oyama, 2003 [24] | Simulation method—ARENA | Traffic flow optimization and queue reduction | Kansai International Airport |
| Caot et al., 2003 [25] | Linear programming method Sensitivity analysis | Reduced total working hours of check-in; improved passenger terminal performance | Ottawa International Airport |
| Ahyudanari and Vandebona, 2005 [26] | Time block method | Achieved minimization of total system costs | Birmingham Airport, Brisbane Airport, Hong Kong International Airport, Melbourne Airport, Orlando International Airport |
| Gozzi et al., 2009 [27] | Simulation method—ARENA | Optimized check-in counters and security control units; optimized costs and proposed scenarios of development | Naples Airport |
| Adacher et al., 2016 [28] | Optimization algorithm based on a surrogate method that integrates a simulation module | The optimal number of check-in counters and security control units has been determined | Naples Airport |
| Nõmmik and Antov 2017 [29] | Direct observation of operations Capacity equations | Proposed measures to increase terminal capacity at regional airports | Ülenurme Airport |
| Sismanidou and Tarradellas, 2017 [30] | Traffic demand forecasting methods | Traffic demand forecasting with flexible planning to achieve more economic efficiency in airport capacity expansions | Madrid-Barajas Airport |
| Adeke, 2018 [31] | Queueing theory Simulation method—SimEvents (MATLAB) | Modeling of the waiting process at the airport check-in facilities | Manchester Airport Leeds Bradford Airport |
| Kim et al., 2018 [32] | Queueing theory Simulation method—ARENA | Optimized operation of the self-service check-in (self-service kiosks and automatic document control) | n/a |
| Štimac et al., 2020 [33] | Optimization method—Airport Management Strategy Software (AMSS) | Developed an application for optimizing airport capacity and negotiating with airlines | Franjo Tuđman Airport |
| Orhan and Orhan 2020 [34] | Discrete event simulation model | Analysis of the processes and potential bottleneck points in the airport’s international passenger operations; assessment of the waiting time, queue lengths, and optimal resource allocation | Hasan Polatkan Airport |
| Verma et al., 2020 [35] | Agent-based simulation model | Proposal of the various policies for system throughput improvements at the airport | Bangalore International Airport |
| Alodhaibi et al., 2020 [36] | Optimization method—Simulated Annealing (SA) model | Defining allocation of additional resources to reduce passenger waiting times | n/a |
| Rodríguez-Sanz et al., 2021 [37] | Random Forest algorithms Feature analysis | New approach in predicting behavioral patterns in queues at airport terminal facilities; optimization of the airport’s capacity via resource allocation | Palma de Mallorca Airport |
| Mangortey et al., 2022 [38] | Machine learning (ML) | Development of an ML technique to analyze and assess the effectiveness of management decisions and daily airport operations | Newark International Airport |
| Memika and Korkusuz Polat, 2023 [39] | Optimization method—Boarding 4.0 model Fuzzy AHP (FAHP) | Redesigned classical boarding process using IoT technology—Boarding 4.0 to reduce boarding times | Istanbul Airport |
| Wang et al., 2023 [40] | Queueing theory Simulation method—Monte Carlo | Reducing the average waiting times by optimizing gate setup procedures | Chicago O’Hare International Airport |
| Oprea et al., 2024 [41] | Simulation method—ARENA | Development of the assessment and analysis tool to handle crowd dynamics and improve overall terminal efficiency, safety, and sustainability | Henri Coanda International Airport |
| AlKheder et al., 2024 [42] | Optimization method Simulation method—CAST model | Analyzing the passenger movement from the terminal entrance to the gate of the aircraft to optimize the design of the facilities | Kuwait International Airport |
| Pivac et al., 2025 [12] | Optimization method Expert Choice (AHP) | Modifying the existing LoS concept to align with the business characteristics of low-cost carriers, enhancing the efficiency of airport terminal facilities | n/a |
Table 2.
Passenger traffic at Franjo Tuđman Airport for the period from 2006 to 2021 [52].
Table 2.
Passenger traffic at Franjo Tuđman Airport for the period from 2006 to 2021 [52].
| Year | January | February | March | April | May | June | July | August | September | October | November | December | TOTAL |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2006 | 100,548 | 95,264 | 119,347 | 135,606 | 153,562 | 171,954 | 182,129 | 176,079 | 167,850 | 16,100 | 135,621 | 128,653 | 1,582,713 |
| 2007 | 119,887 | 113,609 | 140,759 | 159,628 | 176,917 | 188,918 | 206,541 | 205,528 | 191,354 | 180,827 | 161,155 | 147,332 | 1,992,455 |
| 2008 | 137,658 | 130,181 | 164,899 | 165,785 | 196,963 | 209,911 | 233,507 | 230,077 | 213,975 | 195,610 | 161,650 | 152,237 | 2,192,453 |
| 2009 | 139,850 | 122,726 | 149,675 | 168,555 | 182,591 | 198,048 | 226,135 | 221,762 | 198,593 | 174,738 | 145,369 | 134,200 | 2,062,242 |
| 2010 | 125,014 | 113,570 | 144,481 | 141,125 | 185,360 | 201,096 | 234,261 | 231,444 | 213,917 | 187,579 | 152,316 | 141,398 | 2,071,561 |
| 2011 | 138,370 | 125,978 | 160,113 | 191,032 | 218,411 | 230,174 | 256,759 | 249,774 | 241,575 | 208,569 | 157,282 | 141,061 | 2,319,098 |
| 2012 | 132,155 | 118,113 | 155,672 | 189,877 | 214,960 | 237,212 | 259,596 | 260,809 | 244,506 | 216,544 | 165,277 | 145,761 | 2,342,309 |
| 2013 | 139,397 | 126,729 | 175,384 | 186,663 | 205,559 | 228,448 | 249,066 | 243,954 | 234,887 | 208,632 | 151,760 | 149,752 | 2,300,231 |
| 2014 | 135,758 | 128,410 | 165,336 | 192,443 | 217,775 | 243,984 | 260,777 | 269,500 | 251,284 | 226,182 | 175,692 | 163,830 | 2,430,971 |
| 2015 | 150,667 | 141,833 | 185,693 | 197,726 | 235,133 | 248,438 | 278,438 | 284,419 | 271,647 | 239,147 | 184,255 | 170,402 | 2,587,798 |
| 2016 | 157,111 | 153,075 | 199,201 | 202,282 | 246,961 | 267,259 | 307,218 | 306,887 | 287,664 | 253,498 | 197,880 | 187,051 | 2,766,097 |
| 2017 | 168,788 | 154,679 | 192,533 | 240,168 | 268,847 | 310,921 | 351,532 | 347,663 | 330,855 | 297,682 | 222,697 | 205,682 | 3,092,047 |
| 2018 | 191,276 | 170,658 | 223,642 | 253,843 | 300,676 | 331,533 | 379,308 | 372,590 | 345,770 | 318,074 | 234,075 | 214,865 | 3,336,310 |
| 2019 | 191,197 | 181,154 | 232,978 | 280,790 | 311,368 | 336,618 | 366,242 | 376,026 | 350,138 | 330,598 | 247,277 | 231,145 | 3,435,531 |
| 2020 | 203,035 | 184,236 | 97,063 | 5118 | 13,881 | 44,402 | 78,070 | 93,553 | 65,963 | 55,289 | 42,715 | 41,498 | 924,823 |
| 2021 | 38,063 | 31,534 | 43,731 | 54,092 | 69,019 | 100,933 | 154,323 | 194,993 | 191,092 | 182,838 | 164,278 | 179,582 | 1,404,478 |
Table 3.
Number of passengers and aircraft operations at Pula Airport in the period 2009–2019 [56].
Table 3.
Number of passengers and aircraft operations at Pula Airport in the period 2009–2019 [56].
| Year | Number of Passengers | Number of Aircraft Operations |
|---|---|---|
| 2009 | 318,838 | 9126 |
| 2010 | 332,399 | 6830 |
| 2011 | 355,920 | 6984 |
| 2012 | 367,455 | 7132 |
| 2013 | 351,196 | 7292 |
| 2014 | 382,992 | 7106 |
| 2015 | 359,426 | 6954 |
| 2016 | 436,121 | 7692 |
| 2017 | 595,812 | 9288 |
| 2018 | 718,187 | 10,076 |
| 2019 | 777,568 | 10,428 |
Table 4.
Forecasted number of passengers and aircraft operations at Pula Airport in the period 2020–2026.
Table 4.
Forecasted number of passengers and aircraft operations at Pula Airport in the period 2020–2026.
| Year | Lower Limit | Forecasted Number of Passengers | Upper Limit | Lower Limit | Forecasted Number of Aircraft Operations | Upper Limit |
|---|---|---|---|---|---|---|
| 2020 | 721,898 | 863,116 | 1,004,334 | 9331 | 11,247 | 13,164 |
| 2021 | 673,500 | 863,584 | 1,053,668 | 8920 | 11,499 | 14,079 |
| 2022 | 705,622 | 934,439 | 1,163,256 | 8323 | 11,429 | 14,534 |
| 2023 | 681,868 | 943,820 | 1,205,772 | 8002 | 11,556 | 15,111 |
| 2024 | 731,630 | 1,029,423 | 1,327,217 | 8335 | 12,376 | 16,417 |
| 2025 | 705,832 | 1,029,892 | 1,353,951 | 8230 | 12,628 | 17,026 |
| 2026 | 752,345 | 1,100,747 | 1,449,149 | 7829 | 12,557 | 17,285 |
Table 5.
The shares of airline business models by year at Pula Airport in the period 2009–2019 [56].
Table 5.
The shares of airline business models by year at Pula Airport in the period 2009–2019 [56].
| Year | FSC (%) | LCC (%) | CC (%) | Other (%) |
|---|---|---|---|---|
| 2009 | 34 | 44 | 21 | 1 |
| 2010 | 27 | 40 | 32 | 1 |
| 2011 | 32 | 44 | 23 | 1 |
| 2012 | 29 | 45 | 25 | 1 |
| 2013 | 29 | 45 | 25 | 1 |
| 2014 | 23 | 49 | 27 | 1 |
| 2015 | 26 | 45 | 28 | 1 |
| 2016 | 20 | 54 | 24 | 2 |
| 2017 | 22 | 56 | 21 | 1 |
| 2018 | 22 | 57 | 20 | 1 |
| 2019 | 20 | 59 | 20 | 1 |
Table 6.
Forecasts of the shares of airline business models at Pula Airport.
| Year | Lower Limit | Share of FSC—Forecast (%) | Upper Limit | Lower Limit | Share of LCC—Forecast (%) | Upper Limit | Lower Limit | Share of CC—Forecast (%) | Upper Limit | Lower Limit | Share of Other—Forecast (%) | Upper Limit |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2020 | 15 | 17 | 25 | 55 | 61 | 68 | 13 | 21 | 28 | 0.65 | 1 | 1.75 |
| 2021 | 13 | 18 | 23 | 55 | 61 | 68 | 12 | 20 | 27 | 0.67 | 1 | 1.77 |
| 2022 | 10 | 14 | 19 | 58 | 65 | 71 | 12 | 20 | 28 | 0.68 | 1 | 1.79 |
| 2023 | 12 | 15 | 21 | 60 | 66 | 74 | 10 | 18 | 27 | 0.70 | 1 | 1.81 |
| 2024 | 9 | 13 | 19 | 59 | 67 | 74 | 10 | 19 | 28 | 0.72 | 1 | 1.83 |
| 2025 | 6 | 13 | 16 | 62 | 68 | 77 | 9 | 18 | 27 | 0.74 | 1 | 1.84 |
| 2026 | 8 | 12 | 18 | 64 | 70 | 80 | 8 | 17 | 27 | 0.76 | 1 | 1.86 |
Table 7.
Forecasted shares of business models and forecasted values of the number of passengers at Pula Airport.
Table 7.
Forecasted shares of business models and forecasted values of the number of passengers at Pula Airport.
| (FSC) Year | Lower Limit | Forecasted Share in % | Upper Limit | Lower Limit | Forecasted Passengers | Upper Limit |
|---|---|---|---|---|---|---|
| 2020 | 15 | 17 | 25 | 133,397 | 146,730 | 214,730 |
| 2021 | 13 | 18 | 23 | 114,442 | 155,445 | 196,428 |
| 2022 | 10 | 14 | 19 | 83,903 | 130,821 | 166,547 |
| 2023 | 12 | 15 | 21 | 100,062 | 141,573 | 183,380 |
| 2024 | 9 | 13 | 19 | 81,099 | 133,825 | 165,086 |
| 2025 | 6 | 13 | 16 | 50,553 | 133,886 | 135,212 |
| 2026 | 8 | 12 | 18 | 66,705 | 132,090 | 152,052 |
| (LCC) YEAR | Lower Limit | Forecasted Share in % | Upper Limit | Lower Limit | Forecasted Passengers | Upper Limit |
| 2020 | 55 | 61 | 68 | 473,106 | 526,500 | 584,357 |
| 2021 | 55 | 61 | 68 | 472,666 | 526,786 | 583,918 |
| 2022 | 58 | 65 | 71 | 497,922 | 607,385 | 609,175 |
| 2023 | 60 | 66 | 74 | 513,871 | 622,921 | 638,504 |
| 2024 | 59 | 67 | 74 | 513,430 | 689,713 | 638,066 |
| 2025 | 62 | 68 | 77 | 538,686 | 700,326 | 663,323 |
| 2026 | 64 | 70 | 80 | 555,217 | 770,522 | 692,069 |
| (CC) Year | Lower Limit | Forecasted Share in % | Upper Limit | Lower Limit | Forecasted Passengers | Upper Limit |
| 2020 | 13 | 21 | 28 | 116,267 | 181,254 | 241,648 |
| 2021 | 12 | 20 | 27 | 107,073 | 172,717 | 232,454 |
| 2022 | 12 | 20 | 28 | 99,842 | 186,888 | 240,247 |
| 2023 | 10 | 18 | 27 | 90,647 | 169,887 | 231,054 |
| 2024 | 10 | 19 | 28 | 84,094 | 195,590 | 238,169 |
| 2025 | 9 | 18 | 27 | 74,899 | 185,380 | 228,976 |
| 2026 | 8 | 17 | 27 | 68,857 | 187,127 | 235,580 |
| (Other) Year | Lower Limit | Forecasted Share in % | Upper Limit | Lower Limit | Forecasted Passengers | Upper Limit |
| 2020 | 0.65 | 1 | 1.75 | 5584 | 8631 | 15,131 |
| 2021 | 0.67 | 1 | 1.77 | 5741 | 8636 | 15,288 |
| 2022 | 0.68 | 1 | 1.79 | 5898 | 9344 | 15,445 |
| 2023 | 0.70 | 1 | 1.81 | 6055 | 9438 | 15,602 |
| 2024 | 0.72 | 1 | 1.83 | 6212 | 10,294 | 15,759 |
| 2025 | 0.74 | 1 | 1.84 | 6369 | 10,299 | 15,916 |
| 2026 | 0.76 | 1 | 1.86 | 6525 | 11,007 | 16,073 |
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Pivac, J.; Štimac, I.; Bartulović, D.; Lonjak, I. Planning the Airport Terminal Facilities Based on Traffic Demand Forecast and Dominant Share of Airline Business Model: Case Study of Pula Airport. Appl. Sci. 2025, 15, 2547. https://doi.org/10.3390/app15052547
AMA Style
Pivac J, Štimac I, Bartulović D, Lonjak I. Planning the Airport Terminal Facilities Based on Traffic Demand Forecast and Dominant Share of Airline Business Model: Case Study of Pula Airport. Applied Sciences. 2025; 15(5):2547. https://doi.org/10.3390/app15052547
Chicago/Turabian StylePivac, Jelena, Igor Štimac, Dajana Bartulović, and Ivan Lonjak. 2025. "Planning the Airport Terminal Facilities Based on Traffic Demand Forecast and Dominant Share of Airline Business Model: Case Study of Pula Airport" Applied Sciences 15, no. 5: 2547. https://doi.org/10.3390/app15052547
APA StylePivac, J., Štimac, I., Bartulović, D., & Lonjak, I. (2025). Planning the Airport Terminal Facilities Based on Traffic Demand Forecast and Dominant Share of Airline Business Model: Case Study of Pula Airport. Applied Sciences, 15(5), 2547. https://doi.org/10.3390/app15052547
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