Using SWARA for the Evaluation Criteria of Connecting Airports with Railway Networks

Abstract

The optimisation of airport infrastructure capacities lacks adequate tools that would enable airport owners and managers to make strategic decisions related to sustainable development and strengthening multimodal connectivity. Assessing the sustainability of the transport system is one of the important issues in creating transport policies worldwide. In this research, the methodology of multi-criteria decision making (MCDM) was used, which can be applied to decision making and the evaluation of transport projects, considering more than one criterion in the selection process. The Stepwise Weight Assessment Ratio Analysis (SWARA) method is one of the new MCDM methods. The SWARA method will assess the weights of the selected main criteria and sub-criteria for the multimodal connection of airports to the railway transport infrastructure. In this method, the expert plays an important role in the evaluation and calculation of the criteria weights. This research also aims to respond to the need to define a framework for objective and transparent decision-making based on the assessment of the weighting factors of the selected main criteria and sub-criteria. To assess the justification for the choice of railway transport for connecting airports, financial, traffic, environmental, and availability criteria were used.

1. Introduction

Transport is one of the most basic needs of every society and plays a key role in running countries’ economies and industries. Therefore, considering the projected growth of air transport, the air industry will need to innovate to keep up with the development of the transport system. New concepts will increasingly be required to optimise the use of new technologies, processes, and designs for the entire journey. Despite the uncertainty these turbulent times and transformational developments bring, IATA estimates [1] that global passenger travel will return to 2019 activity levels by 2024 and grow considerably over the next two decades. Between 2019 and 2040, air passenger numbers are expected to grow at an average annual rate of 3.3%. IATA strives to ensure that major airport expansion projects develop cost-effective facilities that balance capacity and demand, while providing the functionality, service levels, and operational efficiency needed to justify the future investment.

Given the projected air traffic growth, strategic European Union (EU) documents point to potential problems related to the lack of airport infrastructure and superstructure. It is emphasised that without appropriate infrastructure, significant development of the airport and its surroundings is not possible, and that it should be planned in a modular manner regarding traffic and economic growth projections. One of the ten objectives of the White Paper on Transport (EU) for a competitive and efficient transport system refers to the connection of all airports to the core railway network, preferably high-speed, by 2050, and to the fully functional multimodal Trans-European Transport Network (TEN-T) [2]. The challenge for policymakers is, therefore, to provide a policy framework for optimal integration between transport modes. In its Communication on an Aviation Strategy for Europe [3], the European Commission stressed that European airports should promote efficient airport services that encourage the integration of different transport modes, leading to a more efficient transport network and improved passenger mobility [4].

The integration of major airports into the European high-speed rail network has made significant progress in recent years [5]. Airport availability is an important criterion for airport selection, and therefore, for its development and sustainability. When air passengers choose a destination, they consider the door-to-door transport chain. Therefore, the factors influencing passengers’ decisions to choose a particular option go beyond the price and quality of air services from airport to airport. The decision for or against a particular air service and airport depends, to some extent, on the transport connectivity between the city and the airport. The demand for public transport is growing globally, and the need for a standardised, integrated public transport network is now more important than ever.

The authors of [6] point out that despite the existence of multimodality, coordination between air and land transport modes and their systems is largely lacking, indicating a lack of true multimodality. The study [7] on the interconnection of urban mobility with air transport infrastructure, developed by the EU to provide further evidence on the connections between airports, the TEN-T network, and the city centres they serve, as well as between these airports and passenger hubs, which enable the connection of air transport with rail hubs, contributes to the understanding of this issue.

However, car availability still plays an important role, as this mode remains predominant at most airports. Moreover, parking spaces represent a significant source of non-aeronautical revenue in airport operations.

2. Materials and Methods

To solve decision-making problems in the field of transport, we have reviewed the current applications of MCDM methods based on the available scientific literature, which uses different approaches to research decision-making problems related to transport (AHP, TOPSIS, PROMETHEE, and MAMCA).

The wide range of areas of application of multi-criteria decision-making models has conditioned the rapid and continuous development of methods in this area. The multi-criteria decision-making method (MCDM) is used in a wide range of research. The multi-criteria decision-making method is a mathematical procedure that is an integral part of multi-criteria decision-making. The main characteristic of multi-criteria decision-making is that it is used to solve very complex and intricate decision-making problems.

Most often, the initial stages of traffic planning involve civil, architectural, mechanical, traffic, geodesy, and other experts. An objective and authoritative assessment of planned systems is not possible without understanding the influence and importance of numerous factors and criteria. One of the prerequisites for the application of multi-criteria analysis procedures is the determination of combinations of selected criteria, determination of weighting coefficients, and ranking of criteria using the selected MCDM method, as shown in Figure 1 [8]. Determining the importance of criteria or preferences considers the assessments of selected experts.

Multi-criteria evaluations have been applied to various aspects of transport projects, including project cost estimation, route selection, and project prioritisation. In this paper, the authors analysed a total of 58 scientific articles on the application of MCDM/MCDA methods to selected transport decision-making problems between 2000 and 2019 worldwide [9]. During the analysis, six areas of action related to transport infrastructure were identified: quality and safety of public transport, scenarios for the development of public transport systems, selection of investment locations, road, air, rail and maritime transport, electric vehicles, and other areas of action. Using multi-criteria decision analysis (MCDA) for the evaluation of transport projects, the authors provide an overview of the increasing use of MCDA methods in the evaluation of transport projects and explore which types of transport decisions they are applied to [10].

The authors of [11,12] provide a comprehensive overview of various multi-criteria decision-making methods and their applications, as well as techniques that are useful to decision-makers in comparing alternatives that have different scales of measurement. Weighting is a critical step in multi-criteria decision-making processes, and subjective methods play a significant role in this area, where experts play an important role in assessing and calculating weights. The ability to assess experts’ opinions on the relative importance of criteria is a key element of this method. Subjective weighting is a critical component in multi-criteria decision-making methods, as it directly influences the final decision by prioritising criteria based on expert judgment [13,14]. For this reason, there is a need to use the SWARA method, which will efficiently and quickly process the collected data and ultimately make the optimal decision.

2.1. SWARA Method

Stepwise Weight Assessment Ratio Analysis (SWARA) was developed by Keršuliene et al. 2010 [15]. It is a multi-criteria decision-making (MCDM) method designed to assign weights to different criteria based on expert opinions using a sequential approach [16,17]. Each expert determines the priority of each criterion based on his/her implicit knowledge, information, and experience. It involves two important steps: (1) prioritising criteria with expert consultation and (2) a weighting process [18]. Current research is applying this method as a new framework for evaluating and prioritising indicators for assessing the sustainability of the transport system. The SWARA technique is also useful in the decision-making process and for creating transport policies at the highest level of decision-making on important topics [19]. The method has attracted significant attention when it comes to its application and when it comes to solving various problems such as product design, sustainability assessment of green buildings, corporate social responsibility, and sustainability [20,21,22,23,24].

The SWARA method allows decision makers (experts) to freely express their expertise, since fixed measures or scales do not define it. This method can be used in any setting as a decision support system to resolve practical and scientific discussions among conflicting objectives. The process of determining the weights of the criteria is shown in Figure 2 [25].

Despite significant research in the field of multi-criteria models, the aspect of transport connectivity of airports with other modes of transport, and especially airports with railway infrastructure, has not been investigated and analysed using the SWARA method. Therefore, this research will contribute to a new approach to using this method in the field of transport planning, with the aim of improving the multimodal connectivity of airports.

2.2. SWARA Technique in Steps

This sequential process allows decision makers to incorporate expert judgment in a structured manner, making SWARA a useful tool for prioritising criteria in a variety of applications. Here is a quick overview of how the method works.

2.2.1. Step One

Determine the requirements for the criteria that must be ranked according to their importance. In this phase, specialists (selected experts) rank the defined criteria according to their importance; for example, the most important criteria are ranked first, and the least important are ranked last, while the criteria in between are ranked in importance.

2.2.2. Step Two

Determine scientific criteria (S_j), which is the comparative importance of the average value. Starting with the second-ranked criteria, it is necessary to find their importance, that is, how much more important criterion (C_j) is than criterion (C_j+1).

$$S_j \text{↔}+1={\sum }_{k=1}^{r}Cj\leftrightarrow j+1 /r$$

(1)

2.2.3. Step Three—The Coefficient (K)

Calculate the coefficient (K_j) as follows:

$$K_j=\left\{\begin{array}{c}1, j=1\\ S_{j+1},\hspace{1em} j>1\end{array}\right.$$

(2)

2.2.4. Step Four—The Initial Weight

Determine the recalculated weight q_j as follows:

$$q_j=\left\{\begin{array}{c}1, j=1\\ q_{j-1}/K_j \hspace{1em}j>1\end{array}\right.$$

(3)

2.2.5. Step Five—Relative Weight

Calculating the weight value of criteria with a sum equal to one:

$$W_j=q_j /{\sum }_{j=1}^n{q}_j$$

(4)

where W_j represents the value of the relative weight of the criteria.

3. Research Methodology

The research methodology can be briefly illustrated by the following steps:

• Collecting the necessary information (criteria) during theoretical study and fieldwork on the subject matter, based on available literature, to find the main criteria and sub-criteria when selecting the optimal land transport infrastructure.
• Communication with relevant subjects for the purpose of selecting a sufficient number of experts from different areas of transport policies who are competent in issues relevant to the research topic.
• Using an open questionnaire for selected experts who work on projects of strategy, management, and the design of airport infrastructure and land transport infrastructure.
• Using the SWARA technique to determine the weight of the main criteria and sub-criteria and ranking them for the purpose of planning in the process of selecting the optimal land transport infrastructure for connecting airports.

4. Criteria for Evaluating Railway Transport Infrastructure for Connecting Airports

To conduct a multi-criteria analysis, the key criteria and their sub-criteria were first identified. These criteria are relevant to the airport’s multimodal connectivity in terms of connection to the railway transport infrastructure (

Table 1. Criteria and sub-criteria for multimodal airport connectivity.

Main Criteria	Sub-Criteria
F (Financial)	F1	Investment price
	F2	Financing method
	F3	Justification
	F4	Subvention
T (Traffic)	T1	Number of transported passengers at the airport
	T2	Integration with the existing transport network
	T3	Adequate choice of rail route
	T4	Reducing bottlenecks
E (Environmental)	E1	Air pollution
	E2	Noise emissions
	E3	Increased landscape fragmentation
	E4	Impact on landscape
A (Availability)	A1	Orientation towards the city centre
	A2	Connecting the wider urban area
	A3	Connecting regions
	A4	Transport service without transfers
	A5	Comfort, safety, and speed of travel

). Financial, traffic, environmental, and availability criteria are considered the most important criteria.

One main criterion is finance (F) because the basic logic of public service provision suggests they should be financed from public sources. However, practice has shown that relying exclusively on public sources is generally insufficient. Four basic financing models are used to finance new and maintain existing infrastructure facilities and systems: (1) public, (2) private, (3) mixed (public–private partnership) and (4) financing from EU funds. Achieving the objectives of the TEN-T network within the planned timeframe requires a significant amount of financial resources. Given the limited availability of public funds, increasing investments from a combination of the above financing models in strategic transport infrastructure is considered essential. The finance criterion has the following sub-criteria: (F1) Investment price, (F2) Financing method, (F3) Justification, and (F4) Subvention.

The second main criterion is Traffic (T). Efficient transport services and infrastructure are essential to exploiting the economic advantages of all EU regions, supporting the internal market and growth, and promoting economic, territorial, and social cohesion. Given its central role, transport is closely linked to policy areas such as environmental protection, developing an integrated multimodal network, and efficiently managing resources. The traffic criterion has the following sub-criteria: (T1) Number of passengers transported at the airport, (T2) Integration with the existing transport network, (T3) Adequate choice of rail route, and (T4) Reducing bottlenecks.

In today’s society, the environmental (E) criterion is in the spotlight because achieving the emission reduction targets will require a complete shift towards reduced energy use and the use of cleaner energy, as well as more efficient use of transport infrastructure. An energy-efficient and decarbonised transport system is a key area that contributes to reducing greenhouse gas emissions, as most of the transport projects selected for funding relate to non-road transport modes. The environmental criterion has the following sub-criteria: (E1) Air pollution, (E2) Noise emission, (E3) Increased landscape fragmentation, and (E4) Impact on the landscape.

From the different stakeholders’ views, another main criterion is availability (A). The ongoing congestion of airports, both on the air side and land side, requires innovative measures and alternative methods to improve major airports’ connectivity with different land transport modes. For this purpose, optimal ways of transporting passengers and cargo must be found, using as many modes of transport as possible, while minimising travel time and cost. The availability criterion has the following sub-criteria: (A1) Orientation towards the city centre, (A2) Connecting the wider urban area, (A3) Connecting regions, (A4) Transport service without transfers, and (A5) Comfort, safety, and speed of travel.

The criteria were defined using sources [8,9,10,15,16,17,18,26,27]. Each of the identified criteria was further broken down into less complex components, or sub-criteria. This is to enable a more qualitative approach to the multi-criteria ranking of variants, as well as the possibility of analysing the results and drawing conclusions about the importance of each criterion for selecting rail transport for connecting airports.

5. Selection of Experts

The SWARA method, which relies on expert opinion, effectively prioritises criteria based on their subjective importance. To determine the weight of the evaluation criteria, a team of nine experts was formed to make decisions on the selection of airport rail connections. For this purpose, a survey was conducted in February 2025 among selected experts whose knowledge, experience, and activities are related to management, spatial planning, and design of land transport infrastructure, as well as decision-making. The survey is shown in Supplementary materials. The survey results are presented without the personal data (anonymous) of the selected experts. The list and references of the experts are presented in

Table 2. Selection of experts.

Expert No.	Education Level [EQF]	Specialisations	Experience [Years]	Company
1.	MBA [7]	Airport management	40	Freelancer consultant, Dubrovnik, Croatia
2.	Ph.D. [8]	Airport planning	20	Zagreb Airport Ltd., Zagreb, Croatia
3.	M.Eng. [7]	Railway transport	13	Croatian Regulatory Authority for Network Industries, Zagreb, Croatia
4.	M.Eng. [7]	Public transport-integrated	17	Zagreb Area Integrated Transport Ltd., Zagreb, Croatia
5.	MBA [7]	Environmental	20	Vita Projekt Ltd., Zagreb, Croatia
6.	Ph.D. [8]	Railway operator	28	Croatian Railway Passenger Transport Ltd. (HŽ Putnički prijevoz d.o.o.), Zagreb, Croatia
7.	MBA [7]	Air transport	35	Zagreb International Airport Jsc (Međunarodna zračna luka Zagreb-MZLZ), Zagreb, Croatia
8.	LLM [7]	Urban planning	26	Ministry of the Sea, Transport and Infrastructure, Zagreb, Croatia
9.	MBA [7]	Finance	21	Ernst & Young Consulting Ltd., Zagreb, Croatia

.

The team of experts conducted an open survey in which they evaluated 4 criteria and 17 sub-criteria. Saaty’s scale of relative importance was used to assess the relative importance of the criteria and sub-criteria, i.e., preference (

Table 3. Saaty’s scale for determining relative importance, i.e., preference [28].

Intensity of Importance	Definition
1	Equal priority
2	Low importance
3	Moderate priority
4	Moderately increased importance
5	High priority
6	Highly increased importance
7	Very high priority
8	Very, very high importance
9	Absolute priority

), with intensity of importance in the interval from 1 to 9, where 1 point indicates the lowest decision difficulty and 9 points the highest [28].

6. Descending Order of the Main Criteria and Sub-Criteria by Expert’s Opinion

The ranking of the main criteria in descending order according to the scores of nine experts is shown in

Table 4. Main criteria in descending order according to expert assessment.

Expert	F	T	E	A
Expert 1	7	8	8	8
Expert 2	8	8	7	8
Expert 3	9	9	8	8
Expert 4	8	7	6	8
Expert 5	7	8	8	8
Expert 6	9	9	8	9
Expert 7	3	8	8	8
Expert 8	9	8	6	6
Expert 9	8	8	6	8
Total = 9	Sum = 68	Sum = 73	Sum = 65	Sum = 71

. Each expert defines the professional level of scores for each selected main criterion by applying scores in the interval 1–9.

After ranking the main criteria in descending order, criterion T is in the first position, A is in the second position, F is in the third position, and E is in the fourth position.

After that, the sub-criteria were assessed by experts in such a way that the order of assessment of the sub-criteria was from the highest score to the lowest.

Table 5. Financial sub-criteria in descending order according to expert assessment (F).

Expert	F1	F2	F3	F4
Expert 1	3	3	5	7
Expert 2	7	6	8	7
Expert 3	8	7	8	8
Expert 4	7	7	6	6
Expert 5	7	2	8	6
Expert 6	5	6	6	9
Expert 7	6	7	8	3
Expert 8	8	6	5	3
Expert 9	9	8	9	4
Total = 9	Sum = 60	Sum = 52	Sum = 63	Sum = 53

, Table 6, Table 7 and Table 8 present the sub-criteria in descending order according to the assessment of nine experts for each selected sub-criterion using scores in the interval (1–9).

After ranking the financial sub-criteria in descending order, the nine experts put sub-criterion (F3) in the first position, (F1) in the second position, (F4) in the third position, and (F2) in the fourth position.

Table 6. Traffic sub-criteria in descending order according to expert rating (T).

Expert	T1	T2	T3	T4
Expert 1	8	9	5	7
Expert 2	8	7	7	8
Expert 3	9	9	8	8
Expert 4	7	8	8	7
Expert 5	7	6	8	8
Expert 6	5	5	6	8
Expert 7	7	9	6	8
Expert 8	6	8	7	6
Expert 9	9	8	6	4
Total = 9	Sum = 66	Sum = 69	Sum = 61	Sum = 64

Table 6. Traffic sub-criteria in descending order according to expert rating (T).

Expert	T1	T2	T3	T4
Expert 1	8	9	5	7
Expert 2	8	7	7	8
Expert 3	9	9	8	8
Expert 4	7	8	8	7
Expert 5	7	6	8	8
Expert 6	5	5	6	8
Expert 7	7	9	6	8
Expert 8	6	8	7	6
Expert 9	9	8	6	4
Total = 9	Sum = 66	Sum = 69	Sum = 61	Sum = 64

After ranking the traffic criteria in descending order, the nine experts put sub-criterion (T2) in the first position, (T1) in the second position, (T4) in the third position, and (T3) in the fourth position.

Table 7. Environmental sub-criteria in descending order according to expert assessment (E).

Expert	E1	E2	E3	E4
Expert 1	8	4	5	8
Expert 2	8	8	7	7
Expert 3	8	8	6	6
Expert 4	7	7	6	6
Expert 5	7	5	6	6
Expert 6	6	6	6	6
Expert 7	7	7	6	5
Expert 8	6	5	5	4
Expert 9	8	6	9	9
Total = 9	Sum = 65	Sum = 54	Sum = 56	Sum = 51

Table 7. Environmental sub-criteria in descending order according to expert assessment (E).

Expert	E1	E2	E3	E4
Expert 1	8	4	5	8
Expert 2	8	8	7	7
Expert 3	8	8	6	6
Expert 4	7	7	6	6
Expert 5	7	5	6	6
Expert 6	6	6	6	6
Expert 7	7	7	6	5
Expert 8	6	5	5	4
Expert 9	8	6	9	9
Total = 9	Sum = 65	Sum = 54	Sum = 56	Sum = 51

After the ranking of environmental sub-criteria in descending order, the nine experts put sub-criterion (E1) in the first position, (E3) in the second position, (E2) in the third position, and (E4) in the fourth position.

Table 8. Availability sub-criteria in descending order according to expert rating (A).

Expert	A1	A2	A3	A4	A5
Expert 1	8	8	8	7	8
Expert 2	8	8	7	6	8
Expert 3	8	8	8	8	8
Expert 4	7	7	7	7	8
Expert 5	9	7	7	6	7
Expert 6	6	6	6	6	9
Expert 7	8	8	8	6	8
Expert 8	5	7	6	5	6
Expert 9	8	7	7	6	8
Total = 9	Sum = 67	Sum = 66	Sum = 64	Sum = 57	Sum = 70

Table 8. Availability sub-criteria in descending order according to expert rating (A).

Expert	A1	A2	A3	A4	A5
Expert 1	8	8	8	7	8
Expert 2	8	8	7	6	8
Expert 3	8	8	8	8	8
Expert 4	7	7	7	7	8
Expert 5	9	7	7	6	7
Expert 6	6	6	6	6	9
Expert 7	8	8	8	6	8
Expert 8	5	7	6	5	6
Expert 9	8	7	7	6	8
Total = 9	Sum = 67	Sum = 66	Sum = 64	Sum = 57	Sum = 70

After ranking the availability sub-criteria in descending order, the nine experts put sub-criterion (A5) in the first position, (A1) in the second position, (A2) in the third position, (A3) in the fourth position, and (A4) in the fifth position.

6.1. Determination (S_j) of the Comparative Significance of the Average Value of the Main Criteria

The results of applying Equation (1) from Section 2.2 to the main criteria for obtaining the average value (S_j) from the responses of the nine experts are shown in

Table 9. Weights of the main SWARA criteria using the relative importance technique.

Criteria	sj	kj	wj	qj	qj
T		1	1	0.2630	0.26
A	0.0274	1.0274	0.9733	0.2560	0.26
F	0.0423	1.0423	0.9339	0.2457	0.25
E	0.0441	1.0441	0.8944	0.2353	0.24

.

6.2. Determination (S_j) of the Comparative Significance of the Average Value of the Sub-Criteria

The comparative significance of the average values of the sub-criteria was obtained.

Table 10. Weights of SWARA financial sub-criteria using the relative importance technique.

Criteria	sj	kj	wj	qj	qj
F3		1	1	0.2741	0.27
F1	0.0476	1.0476	0.9545	0.2616	0.26
F4	0.1167	1.1167	0.8548	0.2343	0.23
F2	0.0189	1.0189	0.8390	0.2300	0.23

,

Table 11. Weights of SWARA traffic sub-criteria using the relative importance technique.

Criteria	sj	kj	wj	qj	qj
T2		1	1	0.2666	0.27
T1	0.0435	1.0435	0.9583	0.2555	0.26
T3	0.0303	1.0303	0.9301	0.2480	0.25
T4	0.0781	1.0781	0.8627	0.2300	0.23

,

Table 12. Weights of SWARA environmental sub-criteria using the relative importance technique.

Criteria	sj	kj	wj	qj	qj
E1		1	1	0.2758	0.28
E3	0.1231	1.1231	0.8904	0.2456	0.25
E2	0.0175	1.0175	0.8751	0.2414	0.24
E4	0.0179	1.0179	0.8597	0.2371	0.24

and

Table 13. Weights of SWARA availability sub-criteria using the relative importance technique.

Criteria	sj	kj	wj	qj	qj
A5		1	1	0.2152	0.22
A1	0.0429	1.0429	0.9589	0.2063	0.21
A2	0.0149	1.0149	0.9448	0.2033	0.20
A3	0.0303	1.0303	0.9170	0.1973	0.20
A4	0.1094	1.1094	0.8266	0.1779	0.18

show the result of applying Equation (1) from Section 2.2 to the financial, traffic, environmental, and availability sub-criteria, to obtain the average value (S_j) according to the ratings of the nine experts.

6.3. Final Weights for Main Criteria Using the SWARA Technique

The final weights for the main criteria for the selection of railway transport infrastructure for connecting airports using the SWARA method are shown in

Table 14. Final weights for the main criteria for selecting rail transport infrastructure for connecting airports.

Main Criteria	Final Weights (FW)
Traffic	26.3%
Availability	25.6%
Financial	24.6%
Environmental	23.5%

.

6.4. Final Weights for Sub-Criteria Using the SWARA Technique

The final weights for financial, traffic, environmental, and availability sub-criteria for the selection of railway transport infrastructure for connecting airports using the SWARA method are presented in

Table 15. Final weights for financial sub-criteria for the selection of rail transport infrastructure for connecting airports.

Financial Sub-Criteria	Final Weights (FW)
Justification (F3)	27.4%
Investment price (F1)	26.2%
Subvention (F4)	23.4%
Financing method (F2)	23.0%

,

Table 16. Final weights for transport sub-criteria for the selection of rail transport infrastructure for connecting airports.

Traffic Sub-Criteria	Final Weights (FW)
Integration with the existing transport network (T2)	26.7%
Number of transported passengers at the airport (T1)	25.5%
Adequate choice of rail route (T3)	24.8%
Reducing bottlenecks (T4)	23.0%

,

Table 17. Final weights for environmental sub-criteria for the selection of rail transport infrastructure for connecting airports.

Environmental Sub-Criteria	Final Weights (FW)
Air pollution (E1)	27.6%
Increased landscape fragmentation (E3)	24.6%
Noise emissions (E2)	24.1%
Impact on landscape (E4)	23.7%

and

Table 18. Final weights for availability sub-criteria for the selection of rail transport infrastructure for connecting airports.

Availability Sub-Criteria	Final Weights (FW)
Comfort, safety, and speed of travel (A5)	21.5%
Orientation towards the city centre (A1)	20.6%
Connection of the wider urban area (A2)	20.3%
Connecting regions (A3)	19.7%
Transport service without transfers (A4)	17.8%

.

6.5. Final Weights for Aggregation Using the SWARA Technique

Once we have obtained the weights for the main criteria and sub-criteria, we can combine these weights. For example, if a sub-criterion under a particular main criterion is weighted (w_i) (relative to the main criterion) and the main criterion has a weight (W), the aggregated weight for that sub-criterion can be calculated as:

$$Aggregate weight=W·w_i$$

(5)

This step ensures that the influence of each sub-criterion is proportional to the importance of its parent criterion. By following this hierarchical approach, SWARA can effectively accommodate a multi-level decision structure. The relative importance estimates for the sub-criteria according to the experts’ ratings are shown in

Table 19. Assessment of relative importance for sub-criteria according to expert ratings.

Sub-Criteria	Value	Value%	Sub-Criteria	Value	Value%
T2	0.070123	7.01%	F4	0.057557	5.76%
F3	0.067332	6.73%	E2	0.056791	5.68%
T1	0.067201	6.72%	F2	0.056491	5.65%
T3	0.065224	6.52%	E4	0.055794	5.58%
E1	0.064899	6.49%	A5	0.055092	5.51%
F1	0.064272	6.43%	A1	0.052828	5.28%
T4	0.060498	6.05%	A2	0.052051	5.21%
E3	0.057787	5.78%	A3	0.050520	5.05%
			A4	0.045539	4.55%
Total Value		1.000000
Total Value %				100.00%

.

7. Discussion

The main feature of the SWARA method is the ability to assess experts’ opinions on the ratio of the significance of attributes in determining their weights. The method also allows experts to express the relative importance of criteria through comparisons, which can increase the relevance of the results. The method does not require complex calculations and can be implemented relatively quickly, even without software support.

The research results clearly show the obtained weighted results, but also how the opinions of experts influenced the final ratings. Using the SWARA multi-criteria decision-making method, a team of experts conducted an evaluation of four key criteria and 17 sub-criteria for connecting airports to railway transport infrastructure. Experts assigned the highest weight to the traffic criterion of 26.3%, followed by the availability criterion of 25.6% and the financial criterion of 24.6%, while the environmental criterion received 23.5%. The results show that the transport criterion is positioned in the first place, as the main factor, which should be a priority in the integration of transport systems in strengthening the multimodal connectivity of airports. The availability criterion is also highly rated by experts, while the financial and environmental criteria take smaller, but still significant, shares in the total weighted assessment.

The disadvantages of the SWARA method are found in the high degree of subjectivity, because the results depend heavily on the opinion of experts, which can lead to bias, and in the sensitivity to the order of the criteria, because the initial order of ranking the criteria significantly affects the final weights, which can be problematic if there is no consensus. In this paper, we applied only four criteria to avoid fatigue in the process and subjective errors that occur when choosing many criteria.

At the application level, the development of the model will serve as a guideline for transport planners and infrastructure owners in planning the optimal and sustainable ways of including railways in public urban transport, i.e., to eliminate erroneous assessments that would lead to project failure.

8. Conclusions

The findings from the desk research and interviews highlighted that the integration of multimodal air transport systems has a two-fold impact on the sustainability of airports and aviation. In many respects, the adoption of rail as an alternative mode of transport to reach the airport, instead of private cars, can result in reduced congestion and increased energy efficiency, and it can contribute positively to the environmental sustainability and resilience of the sector. As airports reach their capacity due to increased traffic growth, policymakers need to plan for greater integration and cooperation between different modes of transport so that new modes can partially replace road transport, as well as some short-haul flights, thereby freeing up capacity and reducing congestion.

The SWARA method was developed to assess the importance of the identified criteria and the relative weight of each criterion. As mentioned earlier, the SWARA method has some advantages, which are suitable for infrastructure owners and managers, that can improve the quality of decision-making in the planning and design process, and which are essential for making decisions on the type of ground transport infrastructure.

In this study, the application of this method in decision-making is shown in a numerical example, using four main criteria and seventeen sub-criteria. The financial, traffic, environmental, and availability criteria are considered the most important criteria used when selecting the type of land transport infrastructure to strengthen multimodal access to airports. Also, the opinion of experts was used to rank the main criteria and sub-criteria from highest to lowest. The opinion of experts was very important and respected. The experts who participated in the assessment of the weights of the criteria have experience and knowledge in planning, management, and organisation of transport systems.

This methodology can be used to make decisions on real-world issues of future research. It can play a key role in future decisions related to improving the integration and connectivity of different transport branches and ensuring sustainable and efficient transport systems in the long term. In this paper, the authors propose a new methodology for solving the problem of better connectivity and eliminating bottlenecks to establish a mutual relationship between urban logistics and airport-oriented development, as well as to support business and industrial activities. By applying such a model, we ensured the stability of the final values of the criteria and obtained optimal results based on the preferences of experts. Also, this methodology can be used to make decisions in real-world issues of future research in choosing the type of land transport infrastructure in different areas.

The established model can be applied to a specific example of choosing a solution for investment in transport infrastructure, given the state of the land transport route, using multi-criteria optimisation. Based on the SWARA analysis, it is recommended that further transport interventions focus on improving transport planning with a special emphasis on strengthening multimodality.