Pythagorean Fuzzy Decision-Making Framework for Sustainability Assessment of Intercity Bus Terminals

Abstract

Sustainability assessments of intercity bus terminals are increasingly important because of their environmental, social, and infrastructural impacts on urban systems. However, existing studies mainly focus on urban transport and often overlook tangible infrastructure aspects. This study addresses this gap by proposing an integrated sustainability assessment methodology tailored for intercity bus terminals, incorporating the novel “Tangibles” dimension alongside economic, environmental, and social pillars. A hybrid multi-criteria decision-making (MCDM) framework is developed, combining Pythagorean Fuzzy Stepwise Weight Assessment Ratio Analysis (PF–SWARA) and Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF–TOPSIS) to handle uncertainty and subjectivity in expert evaluations. The methodology is applied to seven intercity bus terminals in İstanbul, Türkiye. The results reveal that safety and security-related criteria received the highest importance weights, highlighting the critical role of secure infrastructure in sustainable terminal evaluation. Sensitivity and comparative analyses confirm the robustness and adaptability of the proposed model. This study contributes a novel decision support tool for sustainable transportation infrastructure planning and offers practical guidance for terminal development policies. Future research may explore applying the model in different geographic contexts and extending the analysis with alternative distance metrics.

Keywords

environmental impact sustainability assessment intercity bus terminals pollution reduction Pythagorean fuzzy numbers decision-making

In today’s world, where technology develops and transforms rapidly, the relationships between all systems and the environment must be defined correctly, and sustainable models must be put forward. Sustainability evaluation is an important step in assuring the long-term sustainability and resilience of a system ( 1 ), and it has become more relevant in many fields, such as transportation, infrastructure, and urban planning. Sustainability evaluation may be used to drive decision-making processes, prioritize investments, and discover innovation and improvement possibilities. In this context, transportation systems, like many other systems, are an important pillar of sustainability and require accurate assessment and planning ( 2 ). Sustainability assessment is especially significant in intercity bus terminals because it gives insight into the environmental and social implications of terminal operations, as well as the economic benefits to the local community. Sustainability can be accomplished by increasing public transportation ridership ( 3 ).

Intercity passenger transport plays an important role in transportation systems ( 4 ) for the distance traveled and the potential, and it should be evaluated for sustainability. Intercity bus terminals play a significant role in sustainable transportation by linking cities and regions and offering important mobility services to millions of people every day. However, the construction, operation, and maintenance of these terminals have substantial environmental, social, and economic implications; therefore, it is essential to evaluate their sustainability performance to ensure that they operate responsibly and sustainably. The sustainability assessment is crucial for intercity bus terminals, which are major transportation and mobility hubs. Intercity bus stations need to evaluate sustainability for numerous reasons. First, intercity bus terminals often have a considerable effect on the environment because of the large amounts of energy and resources required by buildings and infrastructure, as well as the emissions from buses and other vehicles using the terminal ( 5 ). Second, intercity bus terminals are often located in metropolitan areas, and their operations can have substantial social consequences, including noise and air pollution, congestion, and increased traffic. Third, intercity bus terminals are significant economic assets, as they provide employment, stimulate economic activity, and generate taxes for the local community. To ensure that intercity bus terminals contribute to sustainable development and satisfy the needs of the communities they serve, it is vital to analyze their sustainability performance.

Sustainability assessment has traditionally focused on three pillars: economic, environmental, and social sustainability ( 6 ). However, it is increasingly recognized that additional factors that are relevant to the sustainability of intercity bus terminals are required. One such factor is “Tangibles” which encompasses the design, functioning, and maintenance of the physical features of the bus terminal. These characteristics are sometimes overlooked in conventional sustainability studies; however, they are vital when assessing the long-term profitability and resilience of intercity bus terminals. Because intercity bus terminals are public assets that play a vital role in the communities they serve, it is crucial to include tangible aspects in the assessment of their sustainability. They should be conceived, developed, and maintained so that they align with the needs of the communities they serve and promote sustainable mobility. The design and operation of intercity bus terminals must be user-friendly and accessible, with clear signs, sufficient seating and waiting spaces, and easy access to public transport. Regular cleaning and repairs should be performed as necessary to ensure the continued safety and functionality of the buildings. Incorporating tangibles into the examination of the sustainability of intercity bus terminals is particularly crucial because it enables a more thorough assessment of the sustainability of these facilities. By analyzing the physical characteristics of a bus terminal, its economic, environmental, and social sustainability, as well as its overall usefulness and long-term viability, can be determined.

The sustainability assessment of intercity bus terminals should include tangibles as a fourth pillar to ensure that they are planned, built, and operated to promote sustainable transportation and satisfy the requirements of the communities they serve. By evaluating the physical characteristics of a bus terminal, it is possible to evaluate its overall sustainability and make informed design, building, and operating decisions. This can contribute to the sustainability, accessibility, and long-term viability and resilience of intercity bus terminals and the transportation system overall. By ensuring that intercity bus terminals are designed, constructed, and operated to support sustainable mobility and meet the needs of the communities they serve, it is possible to create more livable and resilient cities, which contribute to the long-term viability and resilience of the transportation system. Therefore, the location of intercity bus terminals within the urban structure should be well-defined and systematically determined, considering the relationships between all components of sustainability ( 7 ). The location of an intercity bus terminal is a multidimensional decision problem, and it should be planned in an integrated manner with long-term plans, considering many factors, such as the environment, financial management, social structure, and physical conditions necessary for sustainability ( 8 – 10 ).

Sustainability and the selection of a bus terminal are closely related. Th location of a bus terminal can have a significant impact on its sustainability and environmental impact. Conducting a sustainability assessment can help identify areas for improvement, such as reducing carbon emissions, increasing accessibility, and promoting social inclusion. By evaluating the pros and cons of various design and operational options, sustainability assessment can also prioritize actions that will lead to a more sustainable future. By incorporating sustainability evaluation into intercity bus terminals, it is possible to build more livable and resilient communities while simultaneously lowering the environmental impact of transportation networks. When determining the location of the bus terminal, several sustainability aspects must be considered. Accessibility is essential ( 11 ) because the terminal must be easily accessible to bus passengers and public transit to reduce the dependency on private automobiles and promote the use of sustainable transportation choices. In addition, it is essential to place the station near key destinations, such as retail areas, schools, and businesses, to reduce the need for additional transportation and maximize passenger convenience. In addition, the terminal design should promote energy efficiency by incorporating natural ventilation, solar panels, and energy-efficient lighting. To reduce the environmental impact of garbage, effective waste management systems, such as recycling bins and composting facilities, must be in place. The terminal should include sustainable mobility choices, such as car and bike sharing, to encourage environmentally beneficial means of transportation. The results of a sustainability assessment can be used to identify ways to improve various aspects, such as reducing carbon emissions, enhancing accessibility, and promoting social inclusion.

This study addresses the following research questions: (1) How can a comprehensive sustainability assessment framework be developed for intercity bus terminals that integrates the tangible aspects of terminal infrastructure?; and (2) How effective is the proposed methodology when practically applied to real-world cases, specifically intercity bus terminals in İstanbul?

The primary objective of this study is to develop a comprehensive sustainability assessment methodology for intercity bus terminals, incorporating the novel fourth pillar, Tangibles. The addition of Tangibles integrates terminal design, operational effectiveness, and infrastructure maintenance into sustainability assessments, which enriches traditional frameworks. By evaluating economic, environmental, social, and tangible dimensions collectively, the proposed methodology enables more informed and holistic decision-making. The goal is to foster the development of sustainable and accessible intercity bus terminals that effectively address community needs and enhance the resilience of transportation infrastructure.

A secondary objective is the practical application of this methodology to evaluate İstanbul’s intercity bus terminals. İstanbul represents an ideal setting because of its significant intercity transportation demands and rapid urban growth. By assessing İstanbul’s terminals, this study identifies the sustainability strengths and weaknesses of each terminal, highlighting areas for improvement. The findings from this study offer a practical framework that other urban centers can adopt to evaluate and enhance the sustainability of their bus terminal infrastructures.

Several sustainability assessment methodologies, including life cycle assessment ( 12 ), Eco-indicator 99 ( 13 ), the environmental assessment method ( 14 ), and the sustainable design assessment system ( 15 ), are widely recognized and applied across various sectors. However, when assessing intercity bus terminals, traditional methodologies often fail to capture sector-specific sustainability factors, such as terminal accessibility, operational efficiency, and the provision of sustainable transport alternatives. In addition, existing methods frequently overlook the complex, dynamic interactions influenced by transportation demand, urban development, and policy changes.

To overcome these limitations, this study proposes an innovative methodology explicitly tailored for intercity bus terminals. By integrating fuzzy logic and multi-criteria decision-making (MCDM), the proposed approach effectively captures and manages uncertainty, ambiguity, and subjective expert opinions inherent in sustainability assessments. Fuzzy logic enables sustainability factors to be articulated using qualitative linguistic terms, which accurately reflect real-world complexities. The MCDM provides a structured framework for systematically analyzing trade-offs between conflicting sustainability objectives. Combining these approaches ensures a robust and comprehensive evaluation of the sustainability of intercity bus terminals, addressing quantitative data and qualitative expert assessments.

Literature Review

Although there is no direct MCDM study on the sustainability of intercity bus terminals in the literature, to the best of the authors’ knowledge, some studies have mentioned them using various methods for different types of transportation. For instance, Alp et al. ( 16 ) deal with the choice of a garage location for buses used in urban transportation using a fuzzy analytical hierarchy process (AHP) and the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) methods. In their study, six main criteria, such as cost, infrastructure, accessibility, social and economic structures, macro and environmental factors, and their subcriteria, are taken into consideration. The proximity to the service area was determined as the subcriterion of the highest importance. A new terminal is proposed in the district of Silivri, İstanbul. Zečević et al. ( 8 ) address the problem of choosing a location for an intermodal transport terminal that contributes to sustainability by saving energy, time, and costs. To support decision makers, a new hybrid MCDM model was developed that combines fuzzy Delphi, fuzzy Delphi-based fuzzy analytic network process (ANP), and fuzzy Delphi-based fuzzy Višekriterijumska Optimizacija I Kompromisno Rešenje (VIKOR) methods. To address sustainability, they determined six main criteria, including land use, connectivity, environmental impact, economic and social criteria, technical criteria and utilities, and 29 subcriteria related to these. In line with these criteria, a sustainable intermodal terminal location was selected in Belgrade. Seker and Aydin ( 17 ) use Interval Valued Intuitionistic Fuzzy Analytical Hierarchy Process and Common Distance Based Assessment (IVIF–AHP and CODAS) approaches to evaluate the sustainability of a university’s public transportation network in a large metropolitan city. Gülhan and Ceylan ( 18 ) conducted a study that organized the criteria to be considered in the selection of a bus station location in their study, evaluating the adaptation of bus stations to cities and their synchronized operation. Therefore, factors such as the expansion possibilities of bus terminal facilities, traffic, connections to other transportation types, economy, and infrastructure features come to the fore. Rao ( 19 ) defines a new sustainability scale based on The Decision Making Trial and Evaluation (DEMATEL) and ANP. Among the criteria determined, the quality of the service is of the highest importance, and the least important is the price. Therefore, it is important to provide quality service rather than price for a sustainable approach. Daneshgar et. al. ( 20 ) assess the sustainability dimensions for sustainable urban transport network needs in developing countries. It presents a framework based on a goal programming model for the Best Worst Method (BWM) to prioritize the evaluation criteria. The framework is applied to a real case study in Yazd, Iran, to evaluate, prioritize, and select transportation projects. Solanki and Agarwal ( 21 ) studied a comprehensive transportation evaluation index with five indicators that can influence and shape the performance of urban public transport. To identify the urban transport system’s key performance indicator (KPI), a hybrid Complex Proportional Assessment - TOPSIS (COPRAS-TOPSIS) method is used to analyze the service quality. This model was validated using various MCDM methods, including AHP and Fuzzy AHP.

Arevalo and Gerike ( 22 ) review the sustainability evaluation method for public transportation. Their study aims to identify gaps in this field, focusing on existing frameworks for assessing the sustainability of public transport systems in Latin America, and to identify region-specific research needs and recommendations. Their study analyzes three types of articles, including scientific papers, international guidelines, and local and national Latin American public transport evaluation guidelines. In total, 69 sources were analyzed, and MCDM methods and sequential indicator models are the most preferred evaluation frameworks. Stojcic et al. ( 23 ) review the MCDM literature in sustainable engineering during 2008–2018. Kumar and Anbanandam ( 24 ) proposed an MCDM framework to evaluate the sustainability of multimodal freight terminals by considering the social, technical, economic, environmental, and political dimensions (STEEP) using intuitionistic fuzzy AHP. Dushenko et al. ( 25 ) analyze the sustainability of the port development project in the urban area of Scandinavia using AHP. Perrera et al. ( 26 ) define KPIs for the environmental sustainability of container terminals in Sri Lanka by AHP. Tadic et al. ( 27 ) use AHP and CODAS to determine the best site for a dry port terminal based on the three pillars of sustainability. The results of their study demonstrate that MCDM is a suitable method for sustainable engineering studies, with most of the methods employed based on uncertainty theory, including fuzzy, gray, and neutrosophic theories.

Más-López et al. ( 28 ) propose a simple, practical method for assessing the sustainability of infrastructure projects based on the three main pillars: environmental, economic, and social. A scoring system (0–5) is used to evaluate predefined criteria. These scores are aggregated into a Total Influence Factor, which classifies the project’s sustainability level and identifies areas for corrective action. The methodology offers a flexible, objective framework aligned with ISO standards and current sustainability rating tools, such as Envision, CEEQUAL, and INVEST. Ladi et al. ( 29 ) integrate the Driving Forces–Pressure–State–Impact–Response (DPSIR) framework with X-Matrix (DPSIR-X) analysis to examine the environmental impacts of Tehran’s transportation sector. It identifies key driving forces, such as urbanization and vehicle growth, linking them to air pollution, carbon dioxide emissions, and ecological degradation. The DPSIR-X model reveals weak connections between states and impacts, suggesting a need for systemic policy changes to achieve sustainable urban mobility. Kumari et al. ( 30 ) reviewed 23 years of global studies on noise pollution at bus terminals. It identified widespread violations of permissible noise levels and highlights inconsistencies in monitoring practices, such as a lack of meteorological data and a standardized methodology. Their paper calls for globally unified noise monitoring protocols and improved noise mapping to support better terminal design and mitigation strategies for sustainable urban transportation.

The existing literature has explored sustainability in various transportation contexts; however, most studies focus on urban transit systems, with limited attention paid to intercity bus terminals. Recent research has started to address sustainability in terminal planning; however, a gap remains in comprehensive, criteria-based evaluations specifically tailored to intercity terminals. In addition, the application of advanced fuzzy MCDM techniques in this domain is notably lacking. To address this, this study proposes a novel integrated methodology using PF–SWARA and PF–TOPSIS for sustainability assessment. To the best of our knowledge, this is the first study to combine these two methods within a Pythagorean fuzzy (PF) environment to evaluate intercity bus terminals.

This approach extends the existing frameworks by incorporating a fourth pillar, Tangibles, alongside the traditional economic, environmental, and social dimensions. This inclusion reflects structural and operational considerations that are often overlooked in previous assessments. From a methodological perspective, the proposed model captures quantitative data and expert-driven qualitative judgments under uncertainty. From an application standpoint, it is demonstrated through a case study of İstanbul’s intercity terminals. Therefore, this study fills a notable research gap by offering a structured and adaptable decision support tool for sustainable terminal planning.

Evaluation Criteria System

Combining the ideas of PF theory with the SWARA–TOPSIS methods is the proposed framework for assessing sustainability. The PF theory is utilized to deal with uncertainty during the assessment process, and the SWARA–TOPSIS approach is used to establish the weights of the sustainability criteria and evaluate the performance of intercity bus terminals. The framework evaluates sustainability in four key dimensions: environmental, financial, social, and tangible, taking into account tangible and intangible issues. The proposed methodology offers a complete assessment of the sustainability of intercity bus terminals, taking into account the complexity and multiplicity of sustainability.

Environmental, financial, and social are the three main pillars of sustainability. These interrelated pillars attempt to establish a sustainable future. The environmental pillar focuses on minimizing the negative environmental effects of bus terminals, including air and water pollution, waste production, and energy use. The financial pillar attempts to construct a bus terminal that is cost-effective, economically feasible, and lucrative. The social pillar emphasizes the social effect of the bus terminal, including accessibility, safety, and inclusion for all passengers, regardless of age, gender, or ability. These three pillars are vital for a sustainable intercity bus terminal because they guarantee the terminal’s economic, social, and environmental responsibility.

There are numerous reasons for including the Tangibles element as the fourth dimension in examining the sustainability of bus terminals. First, it acknowledges the importance of infrastructure and amenities, as well as other physical assets, to the long-term viability of bus terminals. The functioning, efficiency, and efficacy of a bus terminal are significantly influenced by its physical assets. A sustainability study on bus terminals may better evaluate their total performance, including physical and operational elements, when tangible factors are considered. Incorporating tangibles into the examination of sustainability can help overcome the limits of conventional sustainability assessment approaches, which often only analyze the environmental and social components. By incorporating tangibles, the sustainability analysis of bus terminals can provide a more detailed evaluation of their sustainability performance, leading to better decision-making and more effective interventions. Including tangibles in the sustainability examination can offer decision makers a more comprehensive perspective on the intercity bus terminal and its effect on the surrounding community. This can lead to a more balanced examination of the trade-offs between various sustainability objectives and help to guarantee that the bus terminal is sustainable in every way, not just ecologically or socially.

In this study, different bus terminals in İstanbul are evaluated for the three pillars of sustainability factors. Therefore, the most commonly used and important criteria in the literature were included. Specifically, the criteria system is established with a multistage approach. First, the sustainability literature on transportation is reviewed, and the most commonly used criteria are determined. Then, three experts are asked to approve the most suitable criteria for the urban situation. The most approved ones are chosen as criteria. Then, the evaluation system is constructed with four dimensions as main criteria, 12 Level-2 criteria and 53 Level-3 criteria are determined as subcriteria. The hierarchical criteria structure is constructed, as given in Table 1.

Table 1.

Criteria System for Bus Terminal Sustainability Evaluation

Criteria	Level-2 criteria	Level-3 criteria
Social (S)	Safety and security (S1)	S11: Terrorism
		S12: Vandalism and/or civil disorder
		S13: Theft of data and/or infrastructures
		S14: Cargo damage risk
	Responsibility (S2)	S21: Effects of the neighborhood area
		S22: Service quality
		S23: Stress-free travel and travel activities
		S24: Social acceptability
		S25: Compliance with the politics
		S26: Corporate governance
Environmental (E)	Adaptation (E1)	E11: Parks and other green spaces
		E12: Harmony with urban master plan
		E13: Land availability for facility expansion
		E14: Natural resources and protected areas
	Environmentally friendly (E2)	E21: Air pollution
		E22: Soil and water pollution
		E23: Energy consumption
		E24: Noise pollution
	Land usage (E3)	E31: Government policy
		E32: Reconstruction and building plans
		E33: Density of residential areas
	Disasters (E4)	E41: Earthquakes
		E42: Floods
		E43: Fires
		E44: Dust and pollution
Financial (F)	Cost (F1)	F11: Project cost
		F12: Operation cost
		F13: Travel cost
	Benefit (F2)	F21: Job access and employment
		F22: Accessibility to the workforce
		F23: Accessibility to education
		F24: Number of ticketing booths
	Obstacles (F3)	F31: Traffic load
		F32: Congestion
		F33: Staff/bus ratio
Tangibles (T)	Infrastructure (T1)	T11: Sewage
		T12: Water supply, gas, electricity
		T13: Technological infrastructure
		T14: Municipality services
	Features (T2)	T21: Capacity of transportation
		T22: Capacity of passenger evacuation
		T23: Expansion space of station
		T24: Parking and waiting area
		T25: Maintenance and repair area
		T26: Warehouse area
		T27: Barrier-free transportation
	Accessibility (T3)	T31: Proximity downtown
		T32: Proximity to adjacent station
		T33: Proximity to public transport
		T34: Proximity to suppliers
		T35: Intermodal transportation availability
		T36: Proximity to complementary facilities
		T37: Proximity to the main highway artery

Social Criteria

The social dimension has an important role in long-term sustainability, as bus terminals contain a lot of human activity. It is divided into two main Level-2 criteria to evaluate the passengers and employees better, and the cargoes carried. In Safety and security (S1), the time spent by people and cargo was evaluated. For passengers, it is important to provide security against criminal activities in the terminal, prevention from theft and vandalism, and the protection of personal belongings. For employees, possible data theft must be addressed along with the same precautions ( 8 , 31 , 32 ). In the Level-2 responsibility criteria (S2), dimensions such as the comfort and stress-freeness of the passengers and the service quality, which the terminal is responsible for, are evaluated. In addition, it is important for sustainability if the people in the region are uncomfortable with the terminal ( 8 , 33 ).

Environmental Criteria

Environmental sustainability pays attention to the environment and the neighborhood. It is divided into four main Level-2 criteria: Adaptation (E1); environmental awareness— Environmentally friendly (E2); Land usage (E3); and prevention against natural or humanitarian catastrophes—Disasters (E4). The location of the terminal and its effect on the environment are important for sustainability. It should be in harmony with living spaces, such as natural resources in the environment and protected areas. For a sustainable terminal, a choice should be made in line with the development and growth plans for the city in the coming decades, as well as contributing to urban development ( 18 ). Here, the structural plans of the states and the policies implemented are the main factors to be considered for sustainable terminals ( 8 , 34 ). The density of residential areas close to the terminal is especially important for the people living in those areas. In the event of a possible expansion, the land is expected to have an additional area to prevent damage to the structures surrounding the terminal ( 8 , 16 ). Whether a possible enlargement will negatively affect protected areas negatively is an issue that needs to be carefully considered ( 10 , 19 ).

Another Level-2 criterion is that the terminal is compatible with the green environment. The use of environmentally incompatible technology and the consumption of fossil fuel in vehicles at the terminal are the most damaging situations to the environment and the atmosphere. Every business with a green environmental understanding should take measures to reduce carbon emissions and other harmful gases. Although it may seem that only the number of vehicles used for transportation and the type of fuel used are important to the green environment, the facilities within the terminal should also utilize materials that will not harm the soil and water resources. Tracking the amount of energy used or providing this energy naturally is necessary for sustainable transportation. In addition, noise pollution measures should be taken to ensure that terminals, which are crowded environments, are compatible with the environment socially and environmentally ( 8 , 32 , 33 , 35 ).

Financial Criteria

The financial dimension is another important dimension for sustainability, which should meet the financial expectations of stakeholders. The financial dimension is evaluated using three Level-2 criteria: Cost (F1), Benefit (F2), and Obstacles (F3). Therefore, transportation costs, travel costs between stations, and project costs (infrastructure costs, technical problems, and loading–unloading costs) should be low ( 33 ). The higher the value of the Benefit Level-2 criterion, the more sustainable the transport system. The increase in the labor force employment rate and accessibility to work provide the greatest financial support. The ease of access to the workforce, education, market, and shopping center is also an important criterion to consider for evaluation ( 8 , 32 , 35 ). In addition, the number and variety of ticketing booths in the terminal should be considered for both financial benefit and social interaction ( 36 ). The last Level-2 criterion, Obstacles, would cause financial difficulties for sustainability. Although this criterion does not directly interact with the financial situation, its consequences can be challenging for managers. For example, the negative effects of weather on the transportation route, the negative effects of the loading and unloading process, the traffic load, or congestion in the city because of the terminal ( 8 , 33 ).

Tangibles Criteria

Tangibles are added to the three main sustainability dimensions to evaluate intercity bus terminals. To facilitate a more detailed assessment, this criterion is divided into three subcriteria: Infrastructure, Facility features, and Accessibility. For a sustainable terminal, an evaluation is mandatory for urbanization trends, population densities, and infrastructure and superstructure services offered to the region. In addition to the main infrastructure services, such as water, electricity, natural gas, and sewerage, telecommunication and technological infrastructure services, and municipal services should also be taken into account ( 8 , 10 , 16 ). The Level-2 Infrastructure criterion and its associated Level-3 criteria have been selected to represent these requirements. One of the most important things for a terminal is the facility’s features and capacity limits. Therefore, another of the Level-2 criteria has been chosen as Facility features. Criteria, such as passenger and bus capacity, the availability of additional areas for potential developments, parking, waiting, maintenance, storage, and barrier-free access areas, are critical for a sustainable terminal ( 10 ). Another Level-2 criterion is defined as Accessibility. Sustainable management is essential for a sustainable terminal, and the proximity and availability of an alternative terminal to potential customers, as well as the estimated income–expense balance that these will affect, should be planned in advance ( 8 , 18 ). The terminal should be located close to the city center, which increases its proximity to potential customers. In addition, several subcriteria must be taken into account, such as compliance with public transportation methods, connection to the main artery of the highway, proximity to rival terminals, and the availability of necessary suppliers, which are defined at the third level to ensure comfortable and safe access for passengers.

Proposed Methodology

When evaluating the sustainability of intercity bus terminals, fuzzy logic effectively addresses the ambiguity and subjectivity inherent in decision-making processes. Unlike traditional methods that rely strictly on precise numerical values, fuzzy logic incorporates partial truths and subjective expert opinions, which manage uncertainties often present in real-world scenarios ( 37 ). Specifically, PF logic, as used in this study, enhances traditional fuzzy approaches by allowing experts to express preferences more flexibly and accurately through a broader representation of uncertainty. Integrating PF logic with MCDM techniques, such as SWARA and TOPSIS, allows the comprehensive consideration of multiple conflicting criteria while effectively balancing their trade-offs ( 38 ). The SWARA method determines criteria weights systematically and transparently, and TOPSIS efficiently ranks alternatives according to these weighted criteria. Combining these methods within a PF environment addresses the limitations encountered when each method is applied independently, significantly enhancing the robustness and accuracy of sustainability assessments. This integrated approach explicitly accounts for both qualitative and quantitative information, providing decision makers with a more realistic and nuanced evaluation of the sustainability performance of intercity bus terminals.

The proposed methodology for evaluating the sustainability of intercity bus terminals is shown in Figure 1 and consists of three main stages. Expert opinion and a review of the relevant literature are used to develop the hierarchy according to the three sustainability pillars in the first stage. In the second stage, the PF–SWARA approach is employed to determine the relative weights of the main and subcriteria based on the experts’ opinions. In the final stage, the PF–TOPSIS methodology is used to analyze bus terminals and select the most sustainable one.

Figure 1.

Proposed sustainability assessment methodology.

Yoon and Hwang ( 39 ) introduced TOPSIS to the literature in 1983 as a fairly simple decision-making method ( 40 ), and it has since become one of the most widely used MCDM methodologies ( 41 , 42 ). TOPSIS determines the best alternative by considering the distances from the positive and negative ideal solutions. This technique is the most traditional method for solving group choice problems by computing positive and negative ideal solutions ( 43 ). Because the distances are bilateral, the most appropriate one is determined by considering the cost and benefit criteria ( 44 ). TOPSIS can be integrated with different weighting methods, such as AHP ( 45 ), entropy ( 46 ), and BWM ( 47 ), to obtain more realistic results for different problems. In this study, TOPSIS is integrated with SWARA to determine the weights of the criteria. SWARA is one of the MCDM methods used to determine the weights of the criteria and rank them in importance order ( 37 ). SWARA was developed by Keršulienė et al. ( 48 ) as a decision maker-oriented subjective decision-making method. The SWARA gives decision makers the opportunity to choose their own priorities, considering the current conditions. Furthermore, the role of experts determined as decision makers is even more important in this method ( 49 ). The main feature of this method is the use of expert opinions when determining the weights of the criteria. However, sometimes crisp (numerical) values cannot reflect real-life situations, because human opinions can include fuzziness and uncertainty by nature ( 50 ). To handle these negativities, most MCDM methodologies are extended in different fuzzy environments to handle uncertainty ( 51 ). In this study, PF numbers are used in SWARA and TOPSIS to tackle uncertainty and fuzziness better.

A PF number $\tilde{P}$ in a ﬁxed set X, can be defined as given in Equation 2 ( 52 ). Let X be ﬁxed and µ _SELVA (x): X $\to [0, 1]$ , $v_{\tilde{p}} (x) : X \to [0, 1]$ , and $π_{\tilde{p}} (x) : X \to [0, 1]$ represent the degree of membership, non-membership, and hesitancy of the element $x \in X$ to $\tilde{P}$ (45),

\tilde{P} \approx {x, μ_{\tilde{p}} (x), v_{\tilde{p}} (x); x \in X}

(1)

0 \leq μ_{\tilde{p}} {(x)}^{2} + v_{\tilde{p}} {(x)}^{2} \leq 1

(2)

π_{\tilde{p}} (x) = \sqrt{1 - μ_{\tilde{p}} {(x)}^{2} - v_{\tilde{p}} {(x)}^{2}}

(3)

First, PF–SWARA was employed to determine the importance weights of the main and subcriteria for each level of hierarchy. Then, PF–TOPSIS was used to perform a sustainability assessment for the weights of the criteria determined. The steps of the proposed hybrid approach are given as follows.

Step 1: Experts are evaluated, and weights for each expert are determined. Experts are evaluated using the linguistic terms given in Table 2.

Then, Equation 4 is used to determine the weight of each expert. Let $d$ be the number of experts, and $E_{k} = (μ_{k}, v_{k})$ be the PF number for the evaluation of the expert $k .$

Let X be ﬁxed and µ _SELVA (x): X $\to [0, 1]$ . represents the degree of membership of the element $x \in X$ to $\tilde{P}$ . Then, $v_{\tilde{p}} (x) : X \to [0, 1]$ represents the degree of non-membership of the element $x \in X$ to $\tilde{P}$ ( 50 ).

w_{k} = \frac{(μ_{k}^{2} + π_{k}^{2} (\frac{μ_{k}^{2}}{μ_{k}^{2} + v_{k}^{2}}))}{\sum_{k = 1}^{d} (μ_{k}^{2} + π_{k}^{2} (\frac{μ_{k}^{2}}{μ_{k}^{2} + v_{k}^{2}}))}

(4)

Step 2: Construct a decision matrix taking opinions from experts about each criterion using the linguistic terms given in Table 3. Let $A_{ik} = (μ_{ik}, v_{ik})$ be the evaluation of criterion $i$ by expert $k$ .

Step 3: Expert opinions about criteria are aggregated ( 53 ) to construct a PF decision matrix.

z_{i} = Y (μ_{i}, v_{i}) = Y (\sqrt{1 - \prod_{k = 1}^{d} {(1 - μ_{i k}^{2})}^{w_{k}}}, \prod_{k = 1}^{d} {(v_{i k})}^{w_{k}})

(5)

where $Y (μ_{i}, v_{i})$ represents the aggregated weight for criterion $i$ .

Step 4: Normalized crisp score values of each criterion $S^{*} (i)$ are calculated.

S^{*} (i) = \frac{(S (i) + 1)}{2}

(6)

S (i) = {(μ_{i})}^{2} - {(v_{i})}^{2}

(7)

where $S (i)$ is a score function for criterion $i$ .

Step 5: Criteria are ranked in descending order according to normalized score values.

Step 6: The comparative significance $(p_{i})$ of the normalized score value for each criterion is determined from the second preferred criterion by differencing the score values of criterion $(i)$ and $(i - 1) .$

Step 7: The comparative coefficient $(k_{i})$ for each criterion is calculated.

k_{i} = {\begin{matrix} 1, i = 1 \\ s_{i} + 1, i > 1 \end{matrix}

(8)

Step 8: Recalculated weights $(q_{i})$ are estimated.

q_{i} = {\begin{matrix} 1, i = 1 \\ \frac{q_{(i - 1)}}{k_{i}}, i > 1 \end{matrix}

(9)

Step 9: Criteria weights are determined. Where $n$ represents the number of criteria.

w_{i} = \frac{q_{i}}{\sum_{i = 1}^{n} q_{i}}

(10)

Step 10. Repeat Steps 1–9 for the inner levels of the criteria hierarchy.

Step 11: The alternative evaluation matrix is constructed using the linguistic terms in Table 4. Then, the linguistic terms are converted into their PF values. Let $A = (C_{i} (x_{j}))_{mxn}$ be an alternative evaluation matrix and $C_{i} (i = 1, 2, . . ., n$ ) and $x_{j} (j = 1, 2, . . ., m$ ) be the values of the criteria and alternatives, respectively.

Step 12: Positive ( $x^{+}$ ) and negative ideal solutions ( $x^{-}$ ) are determined for each criterion.

\begin{matrix} x^{+} = {C_{i}, max_{j} 〈 s (C_{i} (x_{j})) 〉 | i = 1, 2, . . ., n} \\ x^{+} = {〈 C_{1}, \tilde{P} (u_{1}^{+}, v_{1}^{+}) 〉, 〈 C_{1}, \tilde{P} (u_{2}^{+}, v_{2}^{+}) 〉, \dots, 〈 C_{n}, \tilde{P} (u_{n}^{+}, v_{n}^{+}) 〉} \end{matrix}

(11)

\begin{matrix} x^{-} = {C_{i}, min_{j} 〈 s (C_{i} (x_{j})) 〉 | j = 1, 2, . . ., n} \\ x^{-} = {〈 C_{1}, \tilde{P} (u_{1}^{-}, v_{1}^{-}) 〉, 〈 C_{1}, \tilde{P} (u_{2}^{-}, v_{2}^{-}) 〉, \dots, 〈 C_{n}, \tilde{P} (u_{n}^{-}, v_{n}^{-}) 〉} \end{matrix}

(12)

Step 13: Distances from the ideal solutions are calculated for each alternative. Where $w_{i}$ represents the criteria weights.

\begin{matrix} PDIS (x_{j}, x^{+}) = \sum_{j = 1}^{n} w_{i} d (C_{i} (x_{j}), C_{i} (x^{+})) \\ D (x_{i}, x^{+}) = \frac{1}{2} \sum_{i = 1}^{n} w_{i} (| {(u_{ij})}^{2} - {(u_{i}^{+})}^{2} | + | {(v_{ij})}^{2} - {(v_{i}^{+})}^{2} | + | {(π_{ij})}^{2} - {(π_{i}^{+})}^{2} |) \end{matrix}

(13)

\begin{matrix} NDIS (x_{j}, x^{-}) = \sum_{j = 1}^{n} w_{i} d (C_{i} (x_{j}), C_{i} (x^{-})) \\ D (x_{i}, x^{+}) = \frac{1}{2} \sum_{i = 1}^{n} w_{i} (| {(u_{ij})}^{2} - {(u_{i}^{+})}^{2} | + | {(v_{ij})}^{2} - {(v_{i}^{+})}^{2} | + | {(π_{ij})}^{2} - {(π_{i}^{+})}^{2} |) \end{matrix}

(14)

Step 14: Revised closeness of each alternative is computed.

ϵ (x_{j}) = \frac{D (x_{j}, x^{-})}{D (x_{j}, x^{-}) + D (x_{j}, x^{+})}

(15)

Step 15: Alternatives are ordered in descending order according to the revised closeness. The most suitable alternative is determined with the highest value ( 54 ).

Table 2.

Linguistic Terms for Evaluating Experts

Linguistic term	µ	$v$
Absolutely skilled	0.95	0.1
Very skilled	0.8	0.25
More skilled	0.7	0.35
Skilled	0.5	0.55
Less skilled	0.45	0.6
Very less skilled	0.15	0.9

Note: µ = membership degree; $v$ = non-membership degree.

Table 3.

Linguistic Terms for Evaluating Sustainability Criteria

Linguistic term	$μ$	$v$
Extremely low important	0.15	0.95
Very low important	0.25	0.9
Low important	0.3	0.85
Medium low important	0.35	0.75
Medium important	0.45	0.65
Medium high important	0.6	0.5
High important	0.7	0.35
Very high important	0.8	0.3
Extremely high important	0.85	0.25

Note: µ = membership degree; $v$ = non-membership degree.

Table 4.

Linguistic Terms for Evaluating Alternatives

Linguistic term	$μ$	$v$
Extremely bad	0.10	0.99
Very bad	0.10	0.97
Bad	0.25	0.92
Medium bad	0.40	0.87
Fair	0.50	0.80
Medium good	0.60	0.71
Good	0.70	0.60
Very good	0.80	0.44
Extremely good	1.00	0.00

Note: µ = membership degree; $v$ = non-membership degree.

Real Case Application for İstanbul

İstanbul, one of the most crowded cities in Türkiye, is also one of the most important economic, cultural, and tourism centers. To provide better intercity transportation, there is one large and many small-scale intercity bus terminals on each side of the city. The positions of the terminals are shown in Figure 2. Because İstanbul is the center for intercity, regional, and international transport connections, it is expected that the bus terminals have certain characteristics. These characteristics are especially important for the sustainability of the bus terminal. The proposed PF–SWARA integrated PF–TOPSIS framework is implemented for the problem of bus terminal sustainability assessment to demonstrate the applicability and usefulness of the proposed approach. Therefore, the proposed approach evaluates seven bus terminals in İstanbul.

Figure 2.

Bus terminals in İstanbul.

First, an expert group consisting of three experts was formed to obtain their opinions about the aforementioned criteria weights and alternative evaluation. The expert group included two academics from different departments of the university and one representative from the transportation sector. Both academics study public transportation and sustainability. The manager works on the transportation operations at the public bus terminal in İstanbul. The interviews were conducted with experts to obtain their opinions. The application process of the proposed sustainability assessment method is explained step by step as follows.

Step 1: Experts were evaluated according to their experience using Table 2. Three experts were evaluated as MS, VS, and S, respectively. Then, the weights of the experts (E-1, E-2, and E-3) were determined using Equation 4, which yielded values of 0.376, 0.417, and 0.207, respectively.

Step 2: Each expert first evaluated the main criteria and decided on the importance of each main criterion. Experts defined their opinions using linguistic terms. The linguistic terms were converted into PF numbers using Table 3. Table 5 presents the main evaluation criteria for each expert.

Step 3: Evaluations from the experts were aggregated using Equation 5, as given in Table 6.

Step 4: Normalized score values of the main criteria were calculated by Equations 6 and 7. The normalized score values for Social, Environmental, Financial, and Tangibles were 0.592, 0.661, 0.5, and 0.611, respectively.

Step 5: The main criteria were ordered according to their normalized score values in descending order. The ranking of the main criteria was determined as Environmental, Tangibles, Social, and Financial.

Step 6: The comparative significance values for the main criteria were determined by differencing the normalized score values. For example, the comparative significance of the second ranking criterion (Tangibles) was calculated by differencing its normalized score value from the first ranking criterion, Environmental (0.661–0.611), as presented in Table 7.

Step 7: The comparative coefficients were calculated. At this step, $k_{i}$ for the first ranked criterion was determined as one, and the others were calculated based on the comparative significance values presented in Table 7.

Step 8: The recalculated weights of the main criteria were determined by Equation 9, as presented in Table 7.

Step 9: The weights of the main criteria were calculated by normalizing the weights recalculated by Equation 10. Figure 3 shows the main weights of the criteria for the sustainability assessment of the problem of intercity bus terminals.

Step 10: The same experts were consulted to construct the assessment matrixes of Level-2 criteria. Expert evaluations and aggregated weights for each Level-2 criterion are given in Table 7. The weight of each Level-2 criterion was then calculated by reapplying Steps 1–9 of the proposed methodology. Table 8 gives the local and global weights of Level-2 criteria. The global weights were calculated by multiplying the local weights by the relevant main criteria weights.

Finally, the same weight determination procedure was repeated to determine the weights of the Level-3 criteria. Expert evaluations for Level-3 criteria are given in Table 9. After obtaining expert evaluations for Level-3 criteria, the local weights of each criterion were calculated. Then, the global weights for Level-3 criteria were determined by multiplying the local weights of the related Level-2 and main criteria weights.

Figure 3.

Main criteria weights.

Table 5.

Main Criteria Evaluation by the Experts

> > >

Criteria	E-1	E-2	E-3
Social	MH	H	M
Environmental	H	H	M
Financial	M	M	MH
Tangibles	VH	M	M

Note: E-1, 2, and 3 = experts 1, 2, and 3; M = Medium important; MH = Medium high important; H High important; VH = Very high important; EH = Extremely high important.

Table 6.

Main Criteria Evaluation by the Experts

Criteria	Aggregated evaluation
Criteria	µ	$v$	π
Social	0.625	0.455	0.634
Environmental	0.682	0.377	0.627
Financial	0.552	0.552	0.625
Tangibles	0.659	0.460	0.594

Note: µ = membership degree; $v$ = non-membership degree; π = hesitancy degree.

Table 7.

Pythagorean Fuzzy Stepwise Weight Assessment Ratio Analysis for the Main Criteria

Criteria	Normalized score	$p_{i}$	$k_{i}$	$q_{i}$
Environmental	0.661	-	1	1
Tangibles	0.611	0.050	1.050	0.952
Social	0.592	0.020	1.020	0.934
Financial	0.500	0.092	1.092	0.855

Note: p = comparative importance; $k$ = comparative coefficient; q = recalculated weight.

Table 8.

Level-2 Criteria Evaluation by the Experts and Criteria Weights

/td> > > /td> > > /td> > /td> > >

Main criteria	Level-2 criteria	E-1	E-2	E-3	Local weight	Global weight
Social (S)	Safety and security (S1)	VH	H	H	0	0.141
Social (S)	Responsibility (S2)	M	M	M	0.436	0.109
Environmental (E)	Adaptation (E1)	L	M	MH	0.294	0.079
	Environmentally friendly (E2)	VH	VH	MH	0.262	0.070
	Land usage (E3)	MH	M	L	0	0.060
	Disasters (E4)	EH	M	M	0.220	0.059
Financial (F)	Cost (F1)	L	M	M	0	0.091
	Benefit (F2)	MH	H	M	0.329	0.075
	Obstacles (F3)	VL	ML	L	0	0.063
Tangibles (T)	Infrastructure (T1)	EH	VH	VH	0.378	0.096
	Features (T2)	H	M	M	0.320	0.081
	Accessibility (T3)	MH	H	M	0.302	0.077

Note: E-1, 2, and 3 = expert 1, 2, and 3. Extemely low important: EL, Very low important: VL, Low important: L, Medium low important: ML, Medium important: M, Medium high important: MH, High important: H, Very high important: VH, Extremely high important: EH.

Table 9.

Level-3 Criteria Evaluation by the Experts

> > > > > > > /td> > > > > /td> /td> > /td> /td> > /td> /td> > /td> > /td> /td> > > /td> /td> /td> > /td> /td> > /td> /td> /td> /td> > > > /td> /td> > /td> > /td> > >

	E-1	E-2	E-3	Weightt		E-1	E-2	E-3	Weightt		E-1	E-2	E-3	Weightt
S11	MH	VH	VH	0.040	E11	MH	M	M	0.013	T11	H	V	VH	0.025
S12	M	H	M	0.035	E12	EH	H	V	0.016	T12	EH	VH	VH	0.026
S13	ML	H	V	0.036	E13	H	M	MH	0.014	T13	H	M	H	0
S14	L	M	ML	0.030	E14	VH	EH	VH	0.017	T14	H	M	MH	0.022
S21	ML	M	M	0.015	E21	MH	EH	VH	0.021	T21	H	M	MH	0.011
S22	MH	VH	H	0	E22	MH	EH	H	0	T22	MH	MH	MH	0.011
S23	M	H	M	0.018	E23	ML	VH	M	0	T23	VH	M	H	0
S24	MH	MH	MH	0.018	E24	L	V	M	0	T24	M	H	M	0
S25	H	M	MH	0.019	E31	VH	MH	MH	0.022	T25	MH	MH	M	0
S26	H	M	M	0	E32	MH	M	H	0	T26	VL	M	M	0.008
F11	MH	H	M	0.026	E33	MH	ML	M	0	T27	VH	EH	H	0
F12	H	H	H	0	E41	H	M	H	0	T31	VH	MH	H	0
F13	M	M	M	0.021	E42	MH	MH	M	0	T32	ML	M	M	0
F21	M	H	H	0	E43	M	H	M	0	T33	EH	VH	VH	0.014
F22	MH	H	M	0.027	E44	M	H	M	0.018	T34	L	M	M	0.009
F23	L	M	L	0						T35	VH	H	H	0
F24	L	L	M	0.018						T36	M	M	M	0
F31	M	V	MH	0.024						T37	H	H	H	0
F32	ML	M	M	0.018
F33	MH	M	M	0.021

Note: S1 = safety and security; S2 = responsibility; F1 = cost; F2 = benefit; F3 = obstacles; E1 = adapation E2 = envoronmentally friendly; E3 = land usage; E4 = disasters; T1 = infrastructure; T2 = features; T3 = accessibilty; EL = Extemely low important; VL = Very low important; L = Low important; ML = Medium low important; M = Medium important; MH = Medium high important; H = High important; VH = Very high important; EH = Extremely high important.

Step 11: Seven different bus terminals were evaluated using the importance weights calculated by PF–SWARA. The same experts evaluated the terminals for each criterion using the linguistic terms given in Table 4. The alternative evaluation matrix was constructed, as given in Table 10, by consolidating the expert opinions. When evaluating the alternatives, the higher value is the more desirable situation.

Step 12: The alternative evaluation matrix was converted to Pythagorean numbers. Ideal solutions were determined using Equations 11 and 12 for each criterion. Positive and negative ideal solutions are given in Tables 11 and 12, respectively.

Step 13: The distances from the ideal solutions were calculated for each bus station. In this step, the criteria weights determined by PF–SWARA are utilized. Table 13 presents the positive and negative distances.

Step 14: The final scores for the terminals were determined by Equation 15, as shown in Figure 4.

Step 15: Terminals were ordered based on the final scores, and Sultanbeyli was the most sustainable intercity bus terminal in İstanbul, Türkiye.

The terminal, which ranks first for sustainability, is Sultanbeyli, and it differs from other alternatives with a final score of 0.676. As previously shown, the most important Level-3 subcriteria were Terrorism and Data and/or Infrastructure theft. Therefore, Sultanbeyli is a safe district with a low crime rate, and its advantageous position for the location of the bus terminal draws attention. In addition, the terminal operates in harmony with its environment and is integrated with the opportunities offered by the region for future plans and infrastructure opportunities. This places this terminal ahead of the others for the Environmental criterion, which is the most important main criterion. Because there is no vegetation or protected area in the region, which carries a risk of destruction, this supports the results. The Harem Terminal ranks second for sustainability. This terminal, which draws attention for ease of transportation, can be reached quickly by metro, metrobus, or bus. In addition to this feature, which is very important for metropolitan cities, the Harem Terminal is advantageous for passenger capacity and potential development volume.

Table 10.

Alternative Evaluations by the Experts

	S11	S12	S13	S14	S21	S22	S23	S24	S25	S26	E11	E12	E13	E14
Esenler	B	VB	VB	B<	EB	G<	EG	VB	VB	G<	EB	B<	EB	F<
Alibeyköy	F	MG	F<	MB	MB	G<	EG	F<	F	VG	VB	G<	B	MB
Avcılar	B	B	VB	MG	MB	MG	MG	F<	F	F	B	MG	MB	F<
Harem	F	MG	F<	MB	MB	G<	VG	MG	MG	G<	MB	G<	MB	F<
Dudullu	F	F	MG	F<	F	MG	G<	MG	MG	MG	MB	MG	B<	F
Samandıra	G	G	MG	F<	MG	MG	B<	F	G	F	F	F	F	MG
Sultanbeylii	VG	VG	G<	G	G	G	B	MG	G<	F	F	MG	MG	G<
	E21	E22	E23	E24	E31	E32	E33	E41	E42	E43	E44	F11	F12
Esenler	EB	B<	VB	EB	B<	EB	VB	MB	F<	B	EB	B<	B
Alibeyköy	B	B	MB	MB	F<	MB	MB	MB	MB	MB	VB	F<	F
Avcılar	B	MB	MB	MB	F<	MB	MB	EB	VB	VB	VB	F<	F
Harem	EB	B<	VB	VB	MG	VB	B<	MB	B<	MB	VB	B<	B
Dudullu	MB	B<	B	B	MG	MB	MB	VB	F<	F	VB	F<	F
Samandıra	MB	F<	F	MB	G<	F	MG	B<	MG	MG	B<	F	F
Sultanbeyli	F	MB	F<	MB	G<	F	G	B	MG	G<	MB	G<	G
	F13	F21	F22	F23	F24	F31	F32	F33	T11	T12	T13	T14	T21
Esenler	G	VG	VG	G<	EG	EB	EB	VG	F<	G	MG	MG	EG
Alibeyköy	MG	G<	G	MG	G<	MB	MB	MG	F<	MG	MG	VG	VG
Avcılar	MG	VG	VG	MG	G<	MB	MB	MG	F<	MB	F<	MG	G<
Harem	G	VG	VG	G<	EG	F<	F	VG	F<	G	MG	MG	VG
Dudullu	MG	VG	VG	G<	VG	F<	F	G	F	G	MG	G<	G
Samandıra	MG	MG	MG	MB	F<	MG	MG	F<	F	MG	MG	MG	F<
Sultanbeyli	MG	MG	MG	F<	F	MG	MG	F<	F	MG	MG	MG	F<
	T22	T23	T24	T25	T26	T27	T31	T32	T33	T34	T35	T36	T37
Esenler	EG	B<	VB	B<	B	VG	EG	MG	VG	EG	VG	EG	G<
Alibeyköy	VG	MB	MG	MB	MB	MG	EG	MG	G<	EG	G<	EG	G<
Avcılar	G	VB	MB	VB	VB	MB	G<	MG	G<	G	G	G	MG
Harem	VG	MB	F<	MB	MB	VG	VG	MG	VG	VG	VG	VG	G<
Dudullu	G	MB	MB	MB	MB	MG	F<	MG	F<	F	F	F	MG
Samandıra	F	F	F	F	F	F	VG	B<	MG	VG	MG	VG	MB
Sultanbeyli	F	F	F	F	F	F	G	B	MG	G<	MG	G<	MG

Note: B = bad; VB = very bad; EB = extremely bad; G = good; EG = extremely good; F = fair; MB = medium bad; S11 = terrorism; S12 = vandalism and/or civil disorder; S13 = theft of data and/or infrastructure; S14 = cargo damage risk; S21 = effects of the neighborhood area; S22 = service quality; S23 = stress-free travel and travel activities; S24 = social acceptability; S25 = compliance with politics; S26 = corporate governanace; daptation; E2 = environmentally friendly; E11 = parks and other green spaces; E12 = harmony with urban master plan; E13 = land availability for facility expansion; E14 = natural resources and protected areas; E22 = reconstruction and building plans; E23 = energy consumption; E24 = noise pollution; E31 = government polic; E32 = reconstruction and building plans; E33 = density of residential areas; E41 = earthquakes; E42 = floods; E43 = fires; E44 = dust and pollution; F11 = project cost; F12 = operation cost F13 = travel cost; F21 = job access and employment; F21 = job access and employment; F22 = accessibility to the workforce; F23 = accessibility to education; F24 = number of ticketing booths; F31 = traffic load; F32 = congestion; F33 = staff/bus ratio; T11 = sewage; T2 = features; T12 = water, gas, electricity supply; T13 = technological infrastructure; T21 = capacity of transportation; T22 = capacity of passenger evacuation; T23 = expansion space of station; T24 = parking and waiting area; T25 = maintenance and repair area; T26 = warehouse area; T27 = barrier-free transportation; T31 = proximity to downtown; T32 = proximity to adjacent station; T33 = proximity to public transport; T34 = proximity to suppliers; T35 = intermodal transportation availability; T36 = proximity to complementary facilities; T37 = proximity to the main highway artery.

Table 11.

Positive Ideal Solutions

Cr.	µ	$v$		µ	$v$		µ	$v$		µ	$v$		µ	$v$		µ	$v$
S11	0.8	0.44	S26	0.8	0.44	E31	0.7	0.6	F13	0.7	0.6	T12	0.7	0.6	T27	0.8	0.44
S12	0.8	0.44	E11	0.5	0.8	E32	0.5	0.8	F21	0.8	0.44	T13	0.6	0.71	T31	1	0
S13	0.7	0.6	E12	0.7	0.6	E33	0.7	0.6	F22	0.8	0.44	T14	0.8	0.44	T32	0.6	0.71
S14	0.7	0.6	E13	0.6	0.71	E41	0.4	0.87	F23	0.7	0.6	T21	1	0	T33	0.8	0.44
S21	0.7	0.6	E14	0.7	0.6	E42	0.6	0.71	F24	1	0	T22	1	0	T34	1	0
S22	0.7	0.6	E21	0.5	0.8	E43	0.7	0.6	F31	0.6	0.71	T23	0.5	0.8	T35	0.8	0.44
S23	1	0	E22	0.5	0.8	E44	0.4	0.87	F32	0.6	0.71	T24	0.6	0.71	T36	1	0
S24	0.6	0.71	E23	0.5	0.8	F11	0.7	0.6	F33	0.8	0.44	T25	0.5	0.8	T37	0.7	0.6
S25	0.7	0.6	E24	0.4	0.87	F12	0.7	0.6	T11	0.5	0.8	T26	0.5	0.8	-	-	-

Note:µ = membership degree; $v$ = non-membership degree; S11 = terrorism; S12 = vandalism and/or civil disorder; S13 = theft of data and/or infrastructure; S14 = cargo damage risk; S21 = effect of neighborhood area; S22 = service quality; S23 = stress-free travel and travel activities; S24 = social acceptability; S25 = compliance with politics; S26 = corporate governance; E11 = parks and other green spaces; E12 = harmony with the urban master plan; E13 = land availability for facility expansion; E14 = natural resources and protected area; E21 = air pollution; E22 = soil and water pollution; E23 = energy consumption; E24 = noise pollution; E31 = government policy; E32 = reconstruction and building plans; E33 = density of residential areas; E41 = earthquakes; E42 = floods; E43 = fires; F11 = project cost; F12 = operation cost; F13 = travel cost; F21 = job access and employment; F22 = accessibility to the workforce; F23 = accessibility to education; F24 = number of ticketing booths; F31 = traffic load; F32 = congestion; F33 = staff/bus ratio; T11 = sewage; T12 = water, gas, electricity supply; T13 = technological infrastructure; T14 = municipality services; T21 = capacity of transportation; T22 = capacity of passenger evacuation; T23 = expansion space of station; T24 = parking and waiting area; T25 = maintenance and repair areas; T26 = warehouse area; T27 = barrier-free transportation; T31 = proximity downtown; T32 = proximity to adjacent station; T33 = proximity to public transport; T34 = proximity to suppliers; T35 = intermodal transportation availability; T36 = proximity to complementary facilities; T37 = proximity to the main highway artery.

Table 12.

Negative Ideal Solutions

Cr.	µ	$v$		µ	$v$		µ	$v$		µ	$v$		µ	$v$		µ	$v$
S11	0.25	0.92	S26	0.5	0.8	E31	0.25	0.92	F13	0.6	0.71	T12	0.4	0.87	T27	0.4	0.87
S12	0.1	0.97	E11	0.1	0.99	E32	0.1	0.99	F21	0.6	0.71	T13	0.5	0.8	T31	0.5	0.8
S13	0.1	0.97	E12	0.25	0.92	E33	0.1	0.97	F22	0.6	0.71	T14	0.6	0.71	T32	0.25	0.92
S14	0.25	0.92	E13	0.1	0.99	E41	0.1	0.99	F23	0.4	0.87	T21	0.5	0.8	T33	0.5	0.8
S21	0.1	0.99	E14	0.4	0.87	E42	0.1	0.97	F24	0.5	0.8	T22	0.5	0.8	T34	0.5	0.8
S22	0.6	0.71	E21	0.1	0.99	E43	0.1	0.97	F31	0.1	0.99	T23	0.1	0.97	T35	0.5	0.8
S23	0.25	0.92	E22	0.25	0.92	E44	0.1	0.99	F32	0.1	0.99	T24	0.1	0.97	T36	0.5	0.8
S24	0.1	0.97	E23	0.1	0.97	F11	0.25	0.92	F33	0.5	0.8	T25	0.1	0.97	T37	0.4	0.87
S25	0.1	0.97	E24	0.1	0.99	F12	0.25	0.92	T11	0.5	0.8	T26	0.1	0.97	-	-	-

Note:µ = membership degree; $v$ = non-membership degree; S11 = terrorism; S12 = vandalism and/or civil disorder; S13 = theft of data and/or infrastructure; S14 = cargo damage risk; S21 = effects of the neighborhood area; S22 = service quality; S23 = stress-free travel and travel activities; S24 = social acceptability; S25 = compliance with politics; S26 = corporate governance; E11 = parks and other green spaces; E12 = harmony with the urban master plan; E13 = land availability for facility expansion; E14 = natural resources and protected area; E21 = air pollution; E22 = soil and water pollution; E23 = energy consumption; E24 = noise pollution; E31 = government policy; E32 = reconstruction and building plans; E33 = density of residential areas; E41 = earthquakes; E42 = floods; E43 = fires; F11 = project cost; F12 = operation cost F13 = travel cost; F21 = job access and employment; F21 = job access and employment; F22 = accessibility to the workforce; F23 = accessibility to education; F24 = number of ticketing booths; F31 = traffic load; F32 = congestion; F33 = staff/bus ratio; T11 = sewage; T12 = water, gas, electricity supply; T13 = technological infrastructure; T14 = municipality services; T21 = capacity of transportation; T22 = capacity of passenger evacuation; T23 = expansion space of station; T24 = parking and waiting area; T25 = maintenance and repair areas; T26 = warehouse area; T22 = capacity of passenger evacuation; T23 = expansion space of station; T24 = parking and waiting area; T25 = maintenance and repair areas; T26 = warehouse area; T27 = barrier-free transportation; T31 = proximity downtown; T32 = proximity to adjacent station; T33 = proximity to public transport; T34 = proximity to suppliers; T35 = intermodal transportation availability; T36 = proximity to complementary facilities; T37 = proximity to the main highway artery.

Table 13.

Distances from Ideal Solutions

Station	PDIS	NDIS
Esenler	0.269	0.172
Alibeyköy	0.200	0.244
Avcılar	0.293	0.152
Harem	0.195	0.251
Dudullu	0.220	0.224
Samandıra	0.206	0.238
Sultanbeyli	0.144	0.300

Note: PDIS = Distance to positive Ideal Solution; NDIS = Distance to negative Ideal Solution.

Figure 4.

Final scores of bus terminals.

Some points draw attention if the terminals Esenler and Avcılar, which are in the last two places with scores of 0.390 and 0.342, are examined in more detail. First, although Esenler Bus Station is advantageous because of its proximity to the city center, it is disadvantaged in planning and infrastructure services. However, there is an imbalance between passenger volume and service delivery capacity. A potential future expansion is likely to pose significant infrastructure and compliance challenges. However, the compatibility of the Esenler Terminal with its surroundings and the level of adoption by locals are other disadvantages.

Avcılar is in last place. The disadvantages created by its location, which is a smaller bus station than its competitors, draw attention. Avcılar is one of the districts with the highest risk, and this situation is directly or indirectly related to many parameters affecting the sustainability of the terminal. However, the region’s crime rate and weak infrastructure security provide evidence to support this ranking.

Sensitivity Analysis

A sensitivity analysis was performed to emphasize the effectiveness and robustness of the alternative evaluation methodology because of changes in distance measures. The distance measures were changed to the Euclidean distance used in PF–TOPSIS. The Euclidean distance between two PF numbers $\tilde{P}$ and $\tilde{Q}$ , is calculated as follows ( 55 ):

\hat{e_{p}} (\tilde{P}, \tilde{Q}) = \sqrt{\frac{1}{2} \sum_{i = 1}^{n} {{(μ_{\tilde{P}}^{2} (x_{i}) ‒ μ_{\tilde{Q}}^{2} (x_{i}))}^{2} + {(v_{\tilde{P}}^{2} (x_{i}) ‒ v_{\tilde{Q}}^{2} (x_{i}))}^{2} + {(π_{\tilde{P}}^{2} (x_{i}) ‒ π_{\bar{Q}}^{2} (x_{i}))}^{2}}}

(16)

The Euclidean distances of the alternatives from ideal solutions are calculated by Equation 14. Table 14 shows the distance from the ideal solutions for each terminal. Then, the final score of each alternative was determined based on the Euclidean distances and the alternatives were ordered, as given in Table 14.

Table 14.

Final Ranking of Alternatives

Station	PDIS	NDIS	Score	Ranking
Esenler	0.125	0.081	0.392	6
Alibeyköy	0.093	0.113	0.549	3
Avcılar	0.136	0.071	0.341	7
Harem	0.090	0.117	0.564	2
Dudullu	0.103	0.104	0.503	5
Samandıra	0.097	0.109	0.530	4
Sultanbeyli	0.067	0.138	0.673	1

Note: PDIS = Distance to positive Ideal Solution; NDIS = Distance to negative Ideal Solution.

According to Table 14, the alternative ranking was the same as the proposed methodology. Therefore, Sultanbeyli is the most sustainable intercity bus terminal in İstanbul among the seven terminals. Using different distance measures does not affect the results; therefore, the proposed methodology presents robust results.

A further sensitivity analysis was performed to evaluate the robustness of the proposed model when assessing the sustainability of intercity bus terminals. This analysis explored how variations in the weighting of the evaluation criteria might affect the final ranking of terminal alternatives. To reflect potential differences in expert opinions or shifting priority scenarios, five distinct sets of randomly generated weights were created, each normalized to sum to one (see Table 15).

Table 15.

Weight Sets for Sensitivity Analysis

Set	S11	S12	S13	S14	S21	S22	S23	S24	S25	S26	E11	E12	E13	E14
Set1	0.009	0.060	0.026	0.018	0.003	0.003	0.001	0.040	0.018	0.025	0.000	0.070	0.036	0.005
Set2	0.049	0.020	0.055	0.002	0.005	0.001	0.009	0.011	0.007	0.038	0.010	0.007	0.017	0.003
Set3	0.027	0.010	0.021	0.021	0.016	0.002	0.038	0.008	0.004	0.001	0.019	0.024	0.000	0.015
Set	E21	E22	E23	E24	E31	E32	E33	E41	E42	E43	E44	F11	F12
Set1	0.004	0.004	0.007	0.015	0.011	0.007	0.019	0.003	0.007	0.009	0.012	0.031	0.004
Set2	0.035	0.002	0.094	0.032	0.005	0.000	0.037	0.027	0.028	0.032	0.002	0.010	0.003
Set3	0.005	0.022	0.004	0.025	0.010	0.058	0.003	0.009	0.003	0.054	0.044	0.006	0.023
Set	F13	F21	F22	F23	F24	F31	F32	F33	T11	T12	T13	T14	T21
Set1	0.014	0.018	0.001	0.019	0.004	0.001	0.059	0.067	0.033	0.007	0.002	0.023	0.012
Set2	0.043	0.021	0.009	0.001	0.008	0.009	0.028	0.022	0.047	0.014	0.003	0.027	0.031
Set3	0.036	0.017	0.016	0.006	0.002	0.048	0.048	0.021	0.009	0.009	0.027	0.048	0.046
Set	T22	T23	T24	T25	T26	T27	T31	T32	T33	T34	T35	T36	T37
Set1	0.003	0.014	0.001	0.048	0.006	0.022	0.007	0.015	0.016	0.004	0.070	0.030	0.056
Set2	0.018	0.032	0.015	0.016	0.012	0.001	0.002	0.001	0.022	0.008	0.015	0.052	0.006
Set3	0.032	0.022	0.002	0.004	0.048	0.020	0.000	0.002	0.023	0.000	0.004	0.017	0.025

Note: S11 = terrorism; S12 = vandalism and/or civil disorder; S13 = theft of data and/or infrastructure; S14 = cargo damage risk; S21 = effects of the neighborhood area; S22 = service quality; S23 = stress-free travel and travel activities; S24 = social acceptability; S25 = compliance with politics; S26 = corporate governance; E11 = parks and other green spaces; E12 = harmony with the urban master plan; E13 = land availability for facility expansion; E14 = natural resources and protected area; E21 = air pollution; E22 = soil and water pollution; E23 = energy consumption; E24 = noise pollution; E31 = government policy; E32 = reconstruction and building plans; E33 = density of residential areas; E41 = earthquakes; E42 = floods; E43 = fires; F11 = project cost; F12 = operation cost F13 = travel cost; F21 = job access and employment; F21 = job access and employment; F22 = accessibility to the workforce; F23 = accessibility to education; F24 = number of ticketing booths; F31 = traffic load; F32 = congestion; F33 = staff/bus ratio; T11 = sewage; T12 = water, gas, electricity supply; T13 = technological infrastructure; T14 = municipality services; T21 = capacity of transportation; T22 = capacity of passenger evacuation; T23 = expansion space of station; T24 = parking and waiting area; T25 = maintenance and repair areas; T26 = warehouse area; T22 = capacity of passenger evacuation; T23 = expansion space of station; T24 = parking and waiting area; T25 = maintenance and repair areas; T26 = warehouse area; T27 = barrier-free transportation; T31 = proximity downtown; T32 = proximity to adjacent station; T33 = proximity to public transport; T34 = proximity to suppliers; T35 = intermodal transportation availability; T36 = proximity to complementary facilities; T37 = proximity to the main highway artery.

Utilizing these alternative sets of weights, the sensitivity analysis offers important insights into the consistency of the model. It reveals how adjustments to the relative importance of sustainability criteria may impact the ranking outcomes. The PF–TOPSIS method was reapplied for each generated weight set, allowing for an assessment of how terminal rankings shift under different priority assumptions. The calculated scores for each scenario are summarized in Table 16, and the resulting rankings of the terminals for each weighting scenario are presented in this table.

Table 16.

Results for Different Weight Sets

/td> /td> /td> /td> /td> /td> /td> /td> /td> /td> /td> /td> /td> /td> /td> /td> /td> /td> /td> /td> /td>

Station	Current		Set 1		Set 2		Set 3
Station	Score	Rank	Score	Rank	Score	Rank	Score	Rank
Esenler	0.390	6	0	6	0	6	0	6
Alibeyköy	0.549	3	0	3	0	2	0	2
Avcılar	0.342	7	0	7	0	7	0	7
Harem	0.563	2	0	1	0	4	0	3
Dudullu	0.505	5	0	5	0	5	0	5
Samandıra	0.535	4	0	4	0	3	0	4
Sultanbeyli	0.676	1	0	2	0	1	0	1

The sensitivity analysis results indicate that the overall ranking of the intercity bus terminals remains relatively stable across different weighting scenarios, demonstrating the robustness of the proposed model. Sultanbeyli consistently emerges as the top-ranked terminal in most scenarios (Sets 2 and 3), except in Set 1, where it ranks second, closely following Harem. Harem ranks first in Set 1 and maintains a strong performance in the other sets, typically placing in the top three. In contrast, Esenler and Avcılar persistently occupy the lower end of the rankings, showing little sensitivity to changes in the weighting of the criteria. These results suggest that slight fluctuations in the scores and rankings occur with changes in criteria weights; the relative positions of the terminals are largely unaffected, underscoring the reliability of the assessment framework under different expert perspectives or priority settings.

Comparative Analysis

A comparative analysis was conducted using an MCDM approach to further strengthen and broaden the proposed sustainability assessment methodology for intercity bus terminals. This analysis aims to address the inherent uncertainties in decision-making processes and offer valuable insights into the effectiveness of the chosen PF measurement approach. To validate this methodology, it was compared with another MCDM method. This comparison allowed the assessment of significant differences that exist between the rankings obtained. The Pythagorean Fuzzy Weighted Aggregated Sum Product Assessment (PF-WASPAS) technique was utilized (see [ 56 ] for details) to evaluate the alternatives based on the criteria weights derived from PF–WASPAS. The results of this analysis are presented in Table 17. Sultanbeyli is the terminal that stands out from the other alternatives with its final score value, ranking first for Sustainability in both methods. According to the results obtained with the PF–WASPAS method, Sultanbeyli is followed by Samandıra, Dudullu, Alibeyköy Harem, Avcılar, and Esenler. Sultanbeyli is in an advantageous location as a bus station, with its low crime rate and infrastructure open to development. Therefore, it may be a natural result that Sultanbeyli ranks first in both methods. Avcılar and Esenler bus stations ranked last in both methods, with different rankings. The main reason for this is the disadvantageous position of Esenler Bus Station for a possible expansion. For the Avcılar bus station, its location and being in a high-risk district are the main factors. These disadvantages negatively affect the sustainability of the terminal.

Table 17.

Final Scores of Alternative Locations by Pythagorean Fuzzy–WASPAS

Station	Final score	Ranking
Esenler	0.495	7
Alibeyköy	0.645	4
Avcılar	0.577	6
Harem	0.613	5
Dudullu	0.647	3
Samandıra	0.690	2
Sultanbeyli	0.748	1

Note: WASPAS = Weighted Aggregated Sum Product Assessment.

The objective of the comparative analysis was to assess the effectiveness of the integrated Pythagorean fuzzy-based MCDM approach proposed for the sustainability assessment methodology. Rankings were obtained for the same alternatives and sustainability assessment criteria using both methods, as shown in Figure 5. This evaluation demonstrates the performance of both methodologies when selecting an intercity bus terminal, considering the sustainability factors. Comparing these rankings helps to understand the advantages and disadvantages of each approach, identify the most effective methodology, and identify the criteria that have the greatest impact on the results. For example, the differences between the PF–WASPAS and PF–TOPSIS rankings reveal how specific criteria and their weights are addressed in each method. The similarity in the potential rankings between the two methods indicates the applicability of the selected criteria for sustainability.

Figure 5.

Results of comparative analysis.

Of note, the rankings of the mid-performing terminals show variation between the two methods. For example, Alibeyköy is ranked third by PF–TOPSIS but drops to fourth in PF–WASPAS, and Harem ranks second in PF–TOPSIS but fifth in PF–WASPAS. These discrepancies can be attributed to the inherent methodological differences between PF–TOPSIS and PF–WASPAS. PF–TOPSIS is based on the relative closeness to ideal and anti-ideal solutions; PF–WASPAS integrates additive and multiplicative utility functions, leading to varying sensitivities to changes in weights and performance scores. This difference becomes more pronounced in alternatives with moderate sustainability scores. This variation underscores the importance of method selection in MCDM problems and demonstrates the need for robust comparative analysis when validating sustainability assessments. In particular, the comparative interpretation of mid-tier terminals reinforces the analytical depth of this study and supports the reliability of the proposed framework.

Discussion

Environmental was determined as the most important criterion. Environmental sustainability is essential for intercity bus terminals because it has direct effects on the surrounding ecology and contributes to the preservation of the planet’s resources. Because of their size and activities, intercity bus terminals could create considerable volumes of trash and pollution. The negative effects of these actions on the environment include soil and water contamination, air pollution, and the depletion of natural resources, among others. Therefore, the design and operation of intercity bus stations must be ecologically responsible. This involves the use of environmentally friendly technology, the reduction of trash, and the implementation of sustainable practices, such as recycling and energy efficiency, to reduce the environmental effect of bus terminals. Therefore, intercity bus terminals can contribute to environmental protection and establish a more sustainable future for future generations.

The results revealed that the main criterion of highest importance for a sustainable bus terminal is Environmental. The Adaptation (E1), which represents the terminal’s relationships with its surroundings and its harmony with the environment, was the criterion of highest importance. Studies in the literature also confirm that environmental compatibility is the most important pillar of sustainability. For sustainability, environmental adaptation of intercity bus terminals is crucial for various reasons. Terminals must be designed and constructed to minimize their environmental impact and the consumption of natural resources. This can be accomplished using eco-friendly construction materials, incorporating energy-efficient systems and technologies, and implementing efficient waste management procedures. In addition, by adapting to the local climate and surroundings, intercity bus terminals can increase their resistance to extreme weather events, such as floods and heatwaves, which occur more frequently as a result of climate change. In addition, environmental adaptation can improve the overall user experience by providing passengers with safe and pleasant waiting areas and enhancing the visual appeal. Adaptation to the environment is, therefore, a significant part of sustainability and plays a vital role in guaranteeing the long-term survival and success of intercity bus terminals. Environmentally friendly practices at intercity bus terminals are important for several reasons of sustainability. First, intercity bus terminals are transportation hubs that serve many passengers every day. They contribute to environmental degradation through emissions, trash production, and energy use. Adopting eco-friendly techniques at intercity bus terminals reduces this impact and conserves natural resources. Second, a sustainable environment is necessary for the well-being of present and future generations. By introducing environmentally friendly methods into intercity bus terminals, the environmental sustainability of the surrounding region is improved, which is beneficial to local residents and animals. Finally, employing environmentally friendly methods at intercity bus terminals adds to the larger objective of preventing climate change and conserving the world for future generations.

Safety and security were the most important Level-2 criteria among the 12 criteria. Safety and security are crucial components of the long-term viability of intercity bus terminals, because they affect the well-being of passengers and employees. Ensuring the safety and security of intercity bus terminals creates a favorable atmosphere for people who use them and reduces the likelihood of accidents and incidents. This, in turn, helps to reduce the negative environmental impact of interstate bus travel and the danger of damage to the facilities themselves. Furthermore, by prioritizing safety and security in the design and operation of intercity bus terminals, operators can help to build trust and confidence in the public transport system, encouraging more people to use this mode of transport and reducing the number of cars on the road, which can have a substantial positive effect on the environment.

Taking into account the Level-3 criteria, the most important criterion was identified as Terrorism (S11) with an importance weight of 0.040. The safety and security of intercity bus terminals have a significant impact on the terminal’s operational sustainability. Terrorist threats have substantially impacted transportation infrastructure globally, highlighting the critical need for secure transit facilities. Implementing effective antiterrorism measures at intercity bus terminals directly reduces security risks, protecting passengers and staff. In addition, implementing adequate safety and security protocols contributes to passenger confidence and enhances the overall stability of transportation operations. Therefore, addressing terrorism through appropriate safety measures is integral to ensuring the sustainability and resilience of intercity bus terminals.

Theft of data and/or infrastructures (S13) was the second most important Level-3 criterion with an importance of 0.036. Theft of data and/or infrastructure at intercity bus terminals can have a substantial effect on the terminals’ viability. These incidents can result in monetary losses, reputational harm, and a decrease in consumer, supplier, and stakeholder confidence. In addition, these accidents may require greater investment in security measures, which can be costly and redirect resources from other crucial sustainability activities. Furthermore, data breaches can result in the loss of sensitive information and a breach of personal privacy, which can have far-reaching effects on individuals and companies. Consequently, ensuring the safety and security of data and infrastructure at intercity bus terminals is crucial for sustainability, as it protects the financial viability of these terminals, reduces the likelihood of incidents that can harm the environment, and maintains public confidence and trust. Security is very important for passenger safety and terminal systems.

In today’s world, where technology is developing day by day, sustainable management is only possible with the protection and correct use of data. When the Level-3 criteria were checked, which have relatively lower importance, the Warehouse areas (T26) and Proximity to adjacent station (T32) criteria drew attention. This shows that storage areas and the nearest station are less important for a sustainable terminal compared with other criteria. Therefore, it would be correct for decision makers to focus on the criteria of higher importance rather than these criteria to improve sustainability.

The results of this study demonstrate a strong alignment with prominent global and regional sustainability policies, enhancing the practical applicability of the proposed assessment framework. The emphasis on environmental sustainability corresponds with the objectives outlined in the European Green Deal, which aims to achieve climate neutrality by 2050 through significant pollution reduction, resource efficiency, and sustainable infrastructure development. Moreover, the integration of social and economic dimensions reflects the multidimensional approach of the United Nations Sustainable Development Goals, such as Sustainable Cities and Communities (Goal 11) and Climate Action (Goal 13). With an explicit focus on tangible standpoints of infrastructure and accessibility, this framework considers all the crucial aspects raised in these policy frameworks toward resilient and inclusive urban transport systems. Therefore, this study provides decision makers with an assessment orientation toward sustainability at intercity bus terminals and further advances broader policy agendas that promote sustainable urban mobility, environmental protection, and social welfare.

Conclusions

In this study, the sustainability of the intercity bus terminals in İstanbul was evaluated using the three-level criteria hierarchy model. In this model, there are four main criteria, which were formed by adding Tangibles to the three pillars of sustainability, 12 Level-2 criteria and 53 Level-3 criteria. Each level of criteria was evaluated by the experts, and the importance of each criterion was decided by PF–SWARA. After the expert evaluations, seven bus terminals in İstanbul were evaluated by PF–TOPSIS using the importance weights calculated by PF–SWARA.

According to the results, the Anatolian side terminals are more advantageous for sustainability than the European side terminals. It is effective here that the Anatolian side is less crowded, making it easier to reach the terminals despite the traffic jam. In addition, because urbanization in these regions progresses more slowly compared with the European side, the terminals are advantageous for criteria such as expansion and harmony with the environment. In addition, the European side terminals are located in old and risky areas for earthquakes. This situation seriously affects the infrastructure systems and other parameters.

This study has brought a different perspective to sustainable engineering by adding the Tangibles criterion and the basic sustainability criteria. In addition, this study presents the first integration of PF–SWARA and PF–TOPSIS, to the best of the authors’ knowledge. The subject of transportation sustainability for İstanbul is generally studied for urban public transportation. In this study, the intercity bus terminals were evaluated for sustainability. Therefore, this study has filled the gap in the literature for the sustainability evaluation of bus terminals in İstanbul.

Future studies could apply the proposed methodology to intercity bus terminals in different geographical contexts, including rural or less densely populated areas, to enhance the findings’ generalizability and applicability. In addition, it could be applied to select the locations of planned terminals in İstanbul. Future studies can adopt different MCDM approaches, and the results of future studies can be compared with the results of this study. Incorporating alternative distance metrics, such as the geometric mean, into the sensitivity analysis could offer further insights into the stability and robustness of the assessment framework.

Footnotes

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: E.A., B.Y.K.; analysis and interpretation of results: B.Y.K., A.T.; draft manuscript preparation: E.A., B.Y.K., A.T. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Bahar Yalcin Kavus

Alev Taskin

Data Accessibility Statement

The data are available on request to the corresponding author.

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