Evaluation of Sustainable and Intelligent Transportation Processes Considering Environmental, Social, and Risk Assessment Pillars Employing an Integrated Intuitionistic Fuzzy-Embedded Decision-Making Methodology

Abstract

The intelligent urban transportation system is a significant component of the economic development process of countries. However, the transportation system is one of the largest contributors to greenhouse gas emissions, and transportation accidents may cause environmental damage due to the transport of hazardous materials. Hence, a sustainable transportation system is significant in providing safe, environmentally friendly, and intelligent urban transport modes for economies when achieving sustainable development goals and evaluating environmental, social, and risk assessment pillars. This paper aims to evaluate the sustainable and intelligent urban transportation systems of fifty global economies by using nine main and fifty-six sub-criteria. In this paper, nine main and fifty-six sub-criteria are defined to evaluate the sustainable and intelligent urban transportation systems of fifty global economies. The nine main criteria and their sub-criteria have never been used before for assessing the transportation systems of fifty global economies. The experts’ opinions are asked to deal with uncertainty when generating pairwise comparison matrices for specified criteria. Then, a novel integrated intuitionistic fuzzy-based AHP and VIKOR framework is proposed to assess the sustainable and intelligent urban transportation systems of fifty global economies. Economic (C1), safety (C2), and hazards (C3) are the top three weighted criteria from the results of the framework for evaluating the sustainable and intelligent urban transportation systems of fifty different economies. Also, the environmental impact and utilization (C5) and sustainability (C8) criteria are notable, and they constitute 21.6% of the total weight for the evaluation of sustainable and intelligent transportation processes. Then, several different scenarios and comparison studies are also presented for the fifty global economies. Sweden, the United States, and Denmark are the top three choices for sustainable and intelligent urban transportation systems based on the results. Moreover, managerial recommendations of the application are drawn for sustainable and intelligent transportation processes. Finally, the safe, reliable, sustainable, and intelligent transportation process may positively impact economic, environmental, and social aspects of the development process of global economies when minimizing potential disruptions and risks.

1. Introduction

Transport plays a vital role in the economic development and growth of countries. Transportation is critical for bringing together different inputs used in producing goods and services needed by people in economies to create added value and distribute the outputs from the production process to various markets. Developments in transportation have now become a part of the development process. It is not surprising that economic development is strongly related to transportation opportunities. Moreover, goods transportation is the most essential process for competition in the global economy. The transportation sector creates job opportunities and affects the labor market’s efficiency. Other sector benefits include increasing the country’s trade volume, contributing to production efficiency, balancing consumer prices, and preventing price fluctuations.

The entire transportation sector creates an enormous environmental negative impact on energy consumption and greenhouse gas (GHG) emissions [1]. The International Energy Agency (IEA) reported that cars and vans used in 2022 will account for 48% of global transportation carbon dioxide emissions. The transportation sector is one of the sectors responsible for GHG emissions. In 2022, the transportation sector will come second after the energy sector worldwide [2,3]. The European Union (EU) estimates that the sector volume will increase in the near future due to the increase in freight transportation and people’s demand for transportation. In this case, the sector will increasingly continue to be a source of GHGs. Increasing environmental concerns and developing vehicle technologies have led people to use and research more environmentally friendly alternatives. However, decision-makers are taking steps to reduce these emissions in the medium and long term by taking a series of measures, establishing an efficient transportation infrastructure, and encouraging the use of more environmentally friendly vehicles [4]. The EU aims for zero carbon emissions, as stated in its 2050 targets [1]. Therefore, this goal can be achieved with safe, clean, affordable, reliable, and more active transportation modes. In other words, a sustainable mobility system is indispensable for societies. Furthermore, due to the increasing need for transportation in societies and its resulting various impacts, transportation sector operations must reflect the sustainable development and climate action goals. Sustainability in transportation is directly and indirectly related to more than half of the United Nations’ (UN) Sustainable Development Goals (SDGs) from the 2030 agenda [5].

Moreover, increasing sustainable transportation alternatives in vulnerable communities is one of the best ways for countries to support human development and social inclusion. Globally emerging climate risks, the COVID-19 pandemic, and regional wars have deeply affected the transportation sector. Increasing the resilience of the global transportation sector will make it easier for countries to adapt to changing conditions in international competition. Efficiency and resilience in logistics are crucial to driving sustainable economic growth and improving food security [6].

Increasing the efficiency of the processes, different transportation modes, and energy conservation and shifting to renewable energies both have the potential to reduce approximately 30% of the carbon emissions resulting from the transportation sector [7]. Along the same lines, the World Bank Logistics Performance Index 2023 report stated that the green logistics demand is increasing day by day. It has been noted that almost 75% of carriers demand environmentally friendly logistics options when exporting to high-income countries [8].

To reduce GHG emissions globally, governments have begun to take a series of steps to encourage the use and adoption of environmentally friendly vehicles. The regulations that have already come into force and will be put into force regarding these targets promote the use of alternative fuel vehicles, such as solar energy, electricity, biodiesel, LNG, and CNG vehicles, and limit the sale and use of conventional vehicles. Therefore, in parallel with the increasing environmental concerns in societies, developments in environmentally friendly vehicles, especially electric vehicle (EV) technology, have also increased rapidly. Furthermore, electric vehicles have an approximately 13% lower total cost of ownership per mile than internal combustion engine vehicles and also provide net current savings of approximately USD 200,000 over an average 15-year lifespan [9].

The research motivation concepts and gaps are summarized as follows: Safe, reliable, sustainable, and intelligent urban transportation systems contribute to the economic, environmental, and social aspects of societies. Also, the expert’s evaluation may include uncertainty and vagueness. Thus, the assessment of sustainable and intelligent urban transportation systems is a significant concern for global economies when considering uncertainty and risk pillars. In this paper, the criteria weights are obtained for evaluating the sustainable and intelligent transportation system, and then the fifty global economies are ranked. An integrated intuitionistic fuzzy-based AHP and VIKOR technique is proposed to prioritize the weights of the nine criteria and fifty-six sub-criteria and rank the fifty global economies in terms of their sustainable and intelligent transportation systems when dealing with uncertainty. This paper addresses this research gap to evaluate the sustainable and intelligent transportation processes of the fifty global economies using an integrated intuitionistic fuzzy-embedded decision-making methodology. To the best of our knowledge, the presented fuzzy-embedded method and the defined criteria and sub-criteria in this paper have not been studied in the literature concerning the fifty global economies.

The research contributions of this paper are provided as follows: First, this research study is addressed to show the implementation of environmental impact and utilization and sustainability metrics with safety and hazard metrics, including new metrics, like, for example, epidemics. Thus, the integrated intuitionistic fuzzy-based AHP and VIKOR technique is developed to determine the weights of these metrics for sustainable and intelligent urban transportation systems and rank the fifty global economies. Second, traditional multi-criteria decision-making (MCDM) methods may not be effective in considering experts’ instability under uncertainty and vagueness. On the contrary, the integrated technique presented in this paper is suitable for overcoming experts’ instability in a fuzzy environment. In addition, the integrated MCDM technique, considering uncertainty, is used for the first time to evaluate the sustainable and intelligent urban transportation systems of fifty global economies when examining the specified criteria involving environmental impact and utilization, sustainability, safety, and hazards. Third, the three experts’ opinions are surveyed to generate pairwise comparison matrices (PCMs) associated with the linguistic scale under uncertainty. In addition, the criteria and sub-criteria are specified and associated with the experts’ opinions, published reports, and the literature, filling the research gap for sustainable and intelligent urban transportation systems. Fourth, comparison and verification studies are also conducted to evaluate the sustainable and intelligent urban transportation systems of fifty global economies when considering uncertainty and risk pillars. Furthermore, a number of scenario analyses are carried out to assess the performances of fifty global economies related to environmental impact and utilization, sustainability, safety, and hazard exposures when presenting managerial recommendations of the presented application of the transportation process in this paper. Finally, Figure 1 shows the flowchart of the presented sustainable transportation framework.

The remainder of the paper is structured as follows: Section 2 provides brief literature reviews of sustainable transportation performance indicators and fuzzy-based decision-making techniques under uncertainty. Then, Section 3 describes the problem description and proposed methodology. Next, Section 4 presents the application of the proposed methodology under uncertainty, including the determination of criteria and alternatives, prioritizing the criteria, and ranking economies. Further, Section 5 provides managerial insights into the presented application of the sustainable and intelligent transportation process. Finally, Section 6 draws the conclusions of this study.

2. Literature Review

This section first introduces a brief review of evaluative articles on sustainable transportation. Then, intuitionistic fuzzy-based MCDM studies are reviewed in order to evaluate the alternatives when dealing with uncertainty.

2.1. Literature Review on Sustainable Transportation Performance Indicators

The concept of transportation sustainability was put forward almost three decades ago. The first studies in the literature are studies on how philosophical concepts related to how to measure this concept will find their counterpart in practice. Hence, Ref. [10] carried out a survey covering both research in the literature and transportation trends or policies by researchers or experts. The author evaluated various performance indicators related to sustainability currently considered in transportation. The author also analyzed the practices related to the measurement and monitoring of these indicators and discussed the results and experiences. Later, Ref. [11] proposed a framework that depended on the social, economic, and environmental dimensions of sustainability in the transportation sector. In this framework, cost-effectiveness, net effects, quality, comprehensiveness, comparability, functionality, transparency, and understandability/clarity pillars are considered. Later, Ref. [12] applied the technique for order of preference by similarity to ideal solution (TOPSIS) and Choquet integral methods to evaluate the transportation network sustainability of European countries. The main criteria consist of 17 environmental, economic, and social indicators. The results obtained from both methods stated that Germany had the best value among the 15 selected countries. The main reason why Germany is sustainable compared to other countries is its high environmental and social scores. Along the same lines, Ref. [13] surveyed indicators related to sustainability by analyzing 17 studies available in the literature. Cities around the world were compared by creating a composite index by selecting three indicators: social, environmental, and economic. In a similar way, Ref. [14] conducted literature research and stated that traffic safety depends on driver and user behaviors, road conditions, vehicle factors, traffic opportunities, and socio-economic factors. The authors then proposed a road safety performance index method considering transportation infrastructure characteristics. This risk index is calculated based on six main vital indicators. These are the number of accidents, the density of road junctions, surface anomalies, problems with road signs, and deficiencies in safety barriers.

Road safety performance is an important issue in transportation. In particular, Ref. [15] classified local municipalities as homogeneous and compared the road safety performance of these classes with the current physical structure on a local basis. The determined structure and culture factors to road safety performance include climate, relief, road network, population density, demographics, economic condition, the modal split of transport, and land use. Next, Ref. [16] addressed the evaluation of rail transit line systems. The authors developed a hierarchical multi-criteria decision-making satisfaction framework for a case study of the Istanbul rail network. The proposed method starts with statistical analysis. Then, it is evaluated by applying the fuzzy analytic hierarchy process and fuzzy Choquet integral. The main criteria are train and station comfort, time, information systems, accessibility, security, welcoming, fare, and ticketing. Furthermore, the results indicated that safety, time, and accessibility are the most important criteria. Then, Ref. [17] proposed an entropy TOPSIS-RSR (rank-sum ratio) method to assess road safety risk based on the composite Road Safety Risk Index (RSRI). The index consists of human, vehicle, road, environmental, and management factors, as well as traffic and personal risks. First, thirty-one provinces of China were evaluated and then grouped into five classes considering the risk index.

Public transportation accessibility provides efficient ways to reach destinations with suitable costs and times. Thus, Ref. [18] studied an evaluation of public transport accessibility with three criteria at a strategic level. These criteria are transit coverage, transit supply, and route diversity. TOPSIS was applied for evaluation, and then the various regions in Abu Dhabi were classified by K-means cluster analysis. Later, Ref. [19] conducted a literature review on public transportation services and analyzed the studies in terms of objectives, methods, data type, and results. The research perspective includes decisions mainly at the operational level and partly at the tactical level. These studies especially consider minimizing operational costs, idle time, and waiting time. The reviewed studies utilized vehicles with internal combustion engines; hence, the assumptions of all the studies ignored environmental concerns related to transportation. Next, Ref. [20] applied a multi-criteria analysis to evaluate the quality of public transport services in Florianópolis, Brazil. To compute the evaluation index, the main components related to comfort, vehicles, services, information, reliability, security, payment, entertainment, accessibility, and infrastructure have been weighted and included in the function.

Regarding the transportation system and institutions, we initially identified sustainable transportation indicators. Then, the determined indicators were analyzed, and the impact of these indicators on the transportation system performance of different states in the United States was evaluated. Along the same lines, Ref. [21] initially surveyed the literature relating to the Urban Mobility Index. The authors then chose the most popular Sustainable Urban Mobility Index and applied it to the public transport of the Metropolitan Region of the Great Vitoria, Brazil. The results were discussed, considering the indicators with the best and worst scores. The indicators/main criteria were defined as social, economic, and environmental and were left without necessary explanations. Hence, all criteria should require detailed explanations. Ref. [22] examined the supply chain sustainability in developing economies from the perspective of Industry 4.0. By conducting a case study in the Indian manufacturing industry, they have identified the potential challenges that may arise. The challenges were then grouped into organizational, legal, strategic, and technological categories using Explanatory Factor Analysis and weighted using AHP. Organizational challenges were found to be of the highest importance, while legal issues were found to be the least important. Similarly, Ref. [23] evaluated transportation service providers using economic, environmental, social, and operational criteria. The criteria were weighted with the best worst method (BWM) and evaluated by applying VIKOR. While the operational pillar ranks first, the social is the least important one. In terms of the sub-criteria, the service cost ranks first, and employee welfare comes last. Later, Ref. [24] applied the IV-VIKOR technique with the FNBC method and evaluated the OECD member countries with road safety performance indicators using the Human Development Index (HDI). Ref. [25] proposed an integrated approach involving the principal component analysis/factor analysis (PCA/FA) and fuzzy logic (FL) methodologies to assess the transportation sustainability of Indian states. The proposed integrated sustainable transportation index covers more than one hundred economic, social, and environmental indicators.

Recently, Ref. [26] determined a series of transport-related indicators for the UN SDGs so as to evaluate the progress of the SDGs in China’s transport sector and to analyze the interactions between the selected indicators. The analysis results stated that infrastructure construction, transportation volume, safety, and land use indicators received high scores; conversely, the results indicated that there are still great difficulties in using clean energy in the transportation sector. Similarly, Ref. [27] evaluated the 14-year road transportation sustainability performance of 29 Chinese provinces with socioeconomic and demographic variables. The several selected variables are GDP, labor, capital, energy, CO₂, SO₂, population density, innovation index, etc. Additionally, a hybrid approach integrated with data envelopment analysis and TOPSIS is proposed. The results show that there is a high level of variability regarding sustainability among different provinces in China. Using both literature research and European Commission (EC) directives, Ref. [28] developed a framework for the Sustainable Urban Mobility Plan that can be implemented to increase the welfare of the municipal population. Some of the indicators determined for this plan are affordability, accessibility, air pollution, mobility, noise, road deaths, accessibility, congestion, satisfaction, safety, etc. To evaluate these, Cross Impact Matrix Multiplication Applied to Classification (MICMAC) was adapted to the study. The results indicate traffic congestion as the strongest urban mobility indicator. This is followed by affordability, efficiency, mobility, and integration. Ref. [29] proposed a simulation-based framework to analyze plastic waste assessment processes. The methodology comprises 27 sub-criteria considering economic, social, security, energy, and environmental dimensions. Only three alternative processes were weighted with the BWM and ranked with the vector-based method. More recently, Ref. [30] studied the existing logistics performance index (LPI) and Environmental Performance Index (EPI) and combined these indices with simple mathematical expressions to define the Green Logistics Performance Index (GLPI). Later, in their study, they compared members of the European Union according to these three indices. Ref. [31] proposed an interval-valued Pythagorean fuzzy BWM and TOPSIS to rank sustainable energy systems in smart cities. The study involves five criteria and alternatives. The study defined the concept of environmentalism and sustainability through carbon emissions and environmental impact. Risks, safety, etc., are crucial pillars related to smart cities that were ignored within the limited criteria and alternatives.

Table 1. Brief literature review on sustainable transportation.

Study	Economic(al)	Risk	Safety	Energy	Environmetal	Infrastructure	Social	Sustainable	Method	Location
[12]	+				+		+		TOPSIS, Choquet integral	Fifteen EU countries
[11]	+				+		+			Sustainable Framework
[13]	+				+		+			Global cities
[14]			+						Risk index	Crotone (Calabria, south Italy)
[15]	+					+	+		Cluster analysis	Municipals in Netherlands
[16]	+		+			+			FAHP- fuzzy Choquet integral	İstanbul rail network
[17]			+		+	+	+		Entropy TOPSIS–RSR	Thirty-one provinces of China
[24]	+		+			+	+		IV-VIKOR with FNBC	Thirty-six OECD countries
[18]						+	+		TOPSIS, K-means	Abu Dhabi
[21]	+					+	+			The Metropolitan Region of the Great Vitoria, Brazil
[20]	+					+	+		Evaluation index	Florianópolis, Brazil
[22]	+					+	+		EFA, AHP	Manufacturing industry, India
[23]	+				+		+		BWM, VIKOR	Transport service providers
[26]								+	Regression	China
[27]	+			+	+		+		DEA-TOPSIS	Twenty-nine provinces of China
[30]								+		Members of EU
[28]	+		+	+					MICMAC	Members of EU
[29]	+		+	+	+		+		BWM, vector-based ranking	Chemical processes
[25]	+				+		+		PCA/FA-FL	Twenty-seven states of India
[31]	+				+	+	+		BWM-TOPSIS	Five cities
This study	+	+	+	+	+	+	+	+	Intuitionistic fuzzy-based AHP and VIKOR	Fifty global economies

provides a brief summary of the literature review on the stream of sustainable transportation. A wide variety of performance indicators related to the transportation sector have been discussed in the literature, and the bottom line of the table summarizes the differences between this proposed study and existing studies. In terms of indicators, unlike current studies, the proposed study evaluates sustainable transportation with a holistic approach, considering expert opinions. From an application perspective, the study offers a more comprehensive comparison with up-to-date data, considering different global income levels and fifty economies on the continent. In terms of method, this is the first study to use the integrated intuitionistic fuzzy-based AHP and VIKOR technique in this evaluation.

2.2. Literature Review of the Intuitionistic Fuzzy-Based MCDM Studies

In the literature, many MCDM techniques, such as the analytic hierarchy process (AHP), TOPSIS, etc., have been presented for decision problems. In this paper, intuitionistic fuzzy-associated MCDM techniques are summarized as follows: Ref. [32] proposed an intuitionistic fuzzy analytic hierarchy process (IFAHP) with intuitionistic fuzzy values. Also, the presented IFAHP by [32] enhanced the inconsistent preferences without the decision-maker participation when considering more complex decision-making problems. Along the same lines, Ref. [33] used the IFAHP technique to evaluate the four main human capital indicators. Next, Ref. [34] offered a preference programming related to determined weights from the IFAHP technique, where decision makers state their judgments of pair-wise comparisons with generalized triangular intuitionistic fuzzy numbers for several examples.

Moreover, Ref. [35] used a SWOT analysis to identify the criteria and sub-criteria of a reverse logistics outsourcing decision-making problem. Further, Ref. [35] applied the IFAHP technique to evaluate the weights among the relevant criteria and sub-criteria of reverse logistics outsourcing. Then, Ref. [36] used the fuzzy set theory for a decision-making problem when evaluating the outsource manufacturers under uncertainty. For this particular purpose, Ref. [36] presented an interval-valued intuitionistic fuzzy AHP- and TOPSIS-embedded technique for the evaluation of the outsource manufacturers. In a similar way, Ref. [37] developed an interval type-2 fuzzy AHP technique originating from an intuitionistic fuzzy set for the technology selection of a damless hydroelectric power plant. Next, Ref. [38] applied an integrated interval-valued intuitionistic fuzzy AHP and TOPSIS technique for the prioritization of production strategies. In the same way, Ref. [39] applied the IFAHP technique for decision problems with imprecise information. Also, Ref. [39] introduced a distance-focused priority technique from intuitionistic fuzzy sets for the supplier selection problem. The recent intuitionistic fuzzy-based MCDM studies are presented as follows: Significantly, Ref. [40] integrated intuitionistic fuzzy sets and AHP to obtain better performance. In addition, Ref. [40] investigated the formulated differences between the AHP weight and the normalized defuzzified IFAHP weight and conducted two case studies associated with supplier selection scenarios. In a similar way, Ref. [41] presented a circular intuitionistic fuzzy analytic hierarchy process (CIFAHP) and a circular intuitionistic fuzzy VIKOR (CIF-VIKOR) technique for a multi-expert supplier selection problem. Further, Ref. [42] presented interval-valued circular intuitionistic fuzzy (IVCIF) sets and their mathematical operations to integrate the uncertainty. Next, Ref. [42] introduced an IVCIF-based AHP method for the application of digital transformation project selection problems.

3. Problem Description and Proposed Methodology

In this section, the problem description is provided. Next, the proposed intuitionistic fuzzy-based integrated methodology is presented to obtain criteria weights and rank the alternatives, including benchmarking analysis associated with the income levels of fifty global economies.

3.1. Problem Description

Practical problems in the real world often consist of a number of non-proportionate and conflicting criteria, and often, no solution can be found to satisfy all criteria at the same time. For this particular situation, MCDM methods are able to identify the ranking list and the solution acquired with specified weights being associated with the opinions of the experts. Moreover, in the real world, the performance values and the weights of the specified criteria are not known precisely. Also, the knowledge of experts is not so precise. Thus, an imprecise and uncertain environment is considered for the criteria to be evaluated. In these particular situations in decision-making problems, fuzzy approaches are used to find the weights of the criteria. Then, overall performance scores associated with exploring the research gap from the literature, published reports, and the weights of the criteria from the opinion of the experts are obtained.

3.2. Integrated Intuitionistic Fuzzy-Based AHP and VIKOR

An integrated intuitionistic fuzzy-based AHP and VIKOR approach is developed in this sub-section. In addition, the equations are provided to obtain the weights and rank the alternatives using the MCDM techniques by the researchers [32,36,43]. In this paper, the equations are formulated based on the fuzzy framework for transportation processes. The proposed methodology consists of three main parts. The first part consists of stages 1–10 and is based on the intuitionistic fuzzy-based AHP technique in order to find criteria weights based on the judgments of the experts. The second part is related to stages 11–16, and it is associated with the VIKOR technique to rank the alternatives based on the weights acquired from the first part. The third part consists of stage 17, including a conclusion for an MCDM problem, benchmarking analyses of the income levels of fifty global economies, and managerial recommendations. The details of each step are provided as follows:

Stage 1: Start the procedure of the intuitionistic fuzzy-based AHP method, and then go to stage 2.

Stage 2: Pairwise comparison matrices (PCMs) are built based on the linguistic scale, and decision makers (DMs) make choices in order to fill the PCMs with the linguistic terms in

Table 2. Linguistic scale and its associated intuitionistic fuzzy set.

Linguistic Scale	Membership Value	Non-Membership Value
Absolutely low linguistic term (ALLT)	[0.10, 0.25]	[0.65, 0.75]
Very low linguistic term (VLLT)	[0.15, 0.30]	[0.60, 0.70]
Low linguistic term (LLT)	[0.20, 0.35]	[0.55, 0.65]
Medium low linguistic term (MLLT)	[0.25, 0.40]	[0.50, 0.60]
Approximately equal linguistic term (AELT)	[0.45, 0.55]	[0.30, 0.45]
Medium high linguistic term (MHLT)	[0.50, 0.60]	[0.25, 0.40]
High linguistic term (HLT)	[0.55, 0.65]	[0.20, 0.35]
Very high linguistic term (VHLT)	[0.60, 0.70]	[0.15, 0.30]
Absolutely high linguistic term (AHLT)	[0.65, 0.75]	[0.10, 0.25]
Exactly equal linguistic term (EELT)	[0.50, 0.50]	[0.50, 0.50]

.

Stage 3: The linguistic scale-based pairwise matrices are transformed into their associated intuitionistic fuzzy sets in Table 2 to acquire intuitionistic PCMs and an aggregated pairwise comparison matrix (${\widetilde{APCM}}_g$). The ${\widetilde{APCM}}_g$ is denoted as follows:

$${\widetilde{APCM}}_g=\left(\begin{array}{ccc}\left(\left[{\mu }_{g_{11}}^{-},{\mu }_{g_{11}}^{+}\right],\left[{\pi }_{g_{11}}^{-},{\pi }_{g_{11}}^{+}\right]\right)& \dots & \left(\left[{\mu }_{g_{1n}}^{-},{\mu }_{g_{1n}}^{+}\right],\left[{\pi }_{g_{1n}}^{-},{\pi }_{g_{1n}}^{+}\right]\right)\\ ⋮& \ddots & ⋮\\ \left(\left[{\mu }_{g_{n1}}^{-},{\mu }_{g_{n1}}^{+}\right],\left[{\pi }_{g_{n1}}^{-},{\pi }_{g_{n1}}^{+}\right]\right)& \cdots & \left(\left[{\mu }_{g_{nn}}^{-},{\mu }_{g_{nn}}^{+}\right],\left[{\pi }_{g_{nn}}^{-},{\pi }_{g_{nn}}^{+}\right]\right)\end{array}\right)$$

(1)

where ${\mu }_{ij}$ and ${\pi }_{ij}$ represent the membership and non-membership functions of the fuzzy set, respectively.

Stage 4: The score judgment matrix ($\widetilde{SJM}$) is denoted with the associated score function of an interval-valued intuitionistic fuzzy number as follows:

$$\widetilde{SJM}=\left(\begin{array}{ccc}\left[{\mu }_{g_{11}}^{-}-{\pi }_{g_{11}}^{+},{\mu }_{g_{11}}^{+}-{\pi }_{g_{11}}^{-}\right]& \dots & \left[{\mu }_{g_{1n}}^{-}-{\pi }_{g_{1n}}^{+},{\mu }_{g_{1n}}^{+}-{\pi }_{g_{1n}}^{-}\right]\\ ⋮& \ddots & ⋮\\ \left[{\mu }_{g_{n1}}^{-}-{\pi }_{g_{n1}}^{+},{\mu }_{g_{n1}}^{+}-{\pi }_{g_{n1}}^{-}\right]& \cdots & \left[{\mu }_{g_{nn}}^{-}-{\pi }_{g_{nn}}^{+},{\mu }_{g_{nn}}^{+}-{\pi }_{g_{nn}}^{-}\right]\end{array}\right)$$

(2)

Stage 5: The interval exponential matrix, ($\widetilde{IEM}$), is obtained in the following way:

$$\widetilde{IEM}=\left(\begin{array}{ccc}\left[e^{\left({\mu }_{g_{11}}^{-}-{\pi }_{g_{11}}^{+}\right)},e^{\left({\mu }_{g_{11}}^{+}-{\pi }_{g_{11}}^{-}\right)}\right]& \dots & \left[e^{\left({\mu }_{g_{1j}}^{-}-{\pi }_{g_{1j}}^{+}\right)},e^{\left({\mu }_{g_{1j}}^{+}-{\pi }_{g_{1j}}^{-}\right)}\right]\\ ⋮& \ddots & ⋮\\ \left[e^{\left({\mu }_{g_{n1}}^{-}-{\pi }_{g_{n1}}^{+}\right)},e^{\left({\mu }_{g_{n1}}^{+}-{\pi }_{g_{n1}}^{-}\right)}\right]& \cdots & \left[e^{\left({\mu }_{g_{nn}}^{-}-{\pi }_{g_{nn}}^{+}\right)},e^{\left({\mu }_{g_{nn}}^{+}-{\pi }_{g_{nn}}^{-}\right)}\right]\end{array}\right)$$

(3)

Stage 6: When establishing the priorities, the ith priority vector, $\widetilde{p{w}_i}$, is found using the following formula:

$$\begin{array}{c}{\widetilde{pw}}_i=\left[{\displaystyle \frac{{\displaystyle \sum _{j=1}^n}{e}^{\left({\mu }_{g_{ij}}^{-}-{\pi }_{g_{ij}}^{+}\right)}}{{\displaystyle \sum _{i=1}^n}{\displaystyle \sum _{j=1}^n}{e}^{\left({\mu }_{g_{11}}^{+}-{\pi }_{g_{11}}^{-}\right)}}},{\displaystyle \frac{{\displaystyle \sum _{j=1}^n}{e}^{\left({\mu }_{g_{11}}^{+}-{\pi }_{g_{11}}^{-}\right)}}{{\displaystyle \sum _{i=1}^n}{\displaystyle \sum _{j=1}^n}{e}^{\left({\mu }_{g_{ij}}^{-}-{\pi }_{g_{ij}}^{+}\right)}}}\right]\hfill \\ \Rightarrow {\widetilde{pw}}_i=\left[p{w}_i^{-},p{w}_i^{+}\right]\left(i=1,2,\dots ,n\right)\hfill \end{array}$$

(4)

Stage 7: The possibility degree, $Ps\left({\widetilde{pw}}_i\ge {\widetilde{pw}}_j\right)$, denotes the probability for the random event $\left\{{\widetilde{pw}}_i\ge {\widetilde{pw}}_j\right\}$. Possibility degrees are calculated as follows:

$$\begin{array}{c}Ps\left({\widetilde{pw}}_i\ge {\widetilde{pw}}_j\right)=p{s}_{ij}={\displaystyle \frac{max\left(p{w}_i^{+}-p{w}_j^{-},0\right)-max\left(p{w}_i^{-}-p{w}_j^{+},0\right)}{\left(p{w}_i^{+}-p{w}_i^{-}\right)+\left(p{w}_j^{+}-p{w}_j^{-}\right)}}\hfill \end{array}$$

(5)

Similarly, the possibility degrees are found for the random event $\left\{{\widetilde{pw}}_j\ge {\widetilde{pw}}_i\right\}$ as follows:

$$\begin{array}{c}Ps\left({\widetilde{pw}}_j\ge {\widetilde{pw}}_i\right)=p{s}_{ji}={\displaystyle \frac{max\left(p{w}_j^{+}-p{w}_i^{-},0\right)-max\left(p{w}_j^{-}-p{w}_i^{+},0\right)}{\left(p{w}_i^{+}-p{w}_i^{-}\right)+\left(p{w}_j^{+}-p{w}_j^{-}\right)}}\hfill \end{array}$$

(6)

Stage 8: The prioritized possibility degrees are obtained as follows:

$$\begin{array}{c}p{w}_i=0.5+{\displaystyle \sum _{j=1}^n}p{s}_{ij}-1/\phantom{\sum _{j=1}^{n}p{s}_{ij}-1n}n\hfill \end{array}$$

(7)

Stage 9: The ith normalized weight, $w_i$, is found as follows:

$$\begin{array}{c}n{w}_i={\displaystyle \frac{p{w}_i}{{\displaystyle \sum _{i=1}^n}p{w}_i}}\hfill \end{array}$$

(8)

Stage 10: The main criteria and sub-criteria weights are found when repeating stages 2–9. Next, go to stage 11 to evaluate the alternatives.

Stage 11: Start the procedure of the VIKOR method, and then go to stage 12.

Stage 12: A multi-attribute decision-making matrix is generated as follows:

$$\begin{array}{c}{\left[f_{ij}\right]}_{n\times m}={\left(\begin{array}{cccc}{f}_{11}& f_{12}& \dots & f_{1m}\\ f_{21}& f_{22}& \dots & f_{2m}\\ \dots & \dots & \dots & \dots \\ f_{n1}& f_{n2}& \dots & f_{nm}\end{array}\right)}_{n\times m}\hfill \end{array}$$

(9)

where $f_{ij}$ represents the value of the ith alternative on the jth criterion.

Stage 13: The positive ideal solution (PIS) and negative ideal solution (NIS) are determined as follows:

$$\begin{array}{c}{PIS}_i=max{f}_{ij}\hfill \\ {NIS}_i=min{f}_{ij}\hfill \end{array}$$

(10)

where $i=1,2,\dots ,n\mathrm{and}j=1,2,\dots ,m$.

Stage 14: The values, $S_j\mathrm{and}{R}_j$, are computed as follows:

$$\begin{array}{c}{S}_j={\displaystyle \sum _{i=1}^n}n{w}_i\left({PIS}_i-f_{ij}\right)/\phantom{n{w}_i\left({PIS}_i-f_{ij}\right)\left({PIS}_i-{NIS}_i\right)}\left({PIS}_i-{NIS}_i\right)\hfill \\ R_j=\underset{i}{max}n{w}_i\left({PIS}_i-f_{ij}\right)/\phantom{n{w}_i\left({PIS}_i-f_{ij}\right)\left({PIS}_i-{NIS}_i\right)}\left({PIS}_i-{NIS}_i\right)\hfill \end{array}$$

(11)

Stage 15: The values, $Q_j$, are calculated using the following equation:

$$\begin{array}{c}{Q}_j=0.5\left(S_j-S^{+}\right)/\phantom{0.5\left(S_j-S^{+}\right)\left(S^{-}+S^{+}\right)}\left(S^{-}+S^{+}\right)+0.5\left(R_j-R^{+}\right)/\phantom{0.5\left(R_j-R^{+}\right)\left(R^{-}+R^{+}\right)}\left(R^{-}+R^{+}\right)\hfill \\ \mathrm{where}{S}^{+}=\underset{j}{min}{S}_j,S^{-}=\underset{j}{max}{S}_j,R^{+}=\underset{j}{min}{R}_j,\mathrm{and}{R}^{-}=\underset{j}{max}{R}_j.\hfill \end{array}$$

(12)

Stage 16: The alternatives are sorted depending on the ascending order of the $S,R,\mathrm{and}Q$ values. Then, go to stage 17 in order to conclude the results.

Stage 17: Draw a conclusion for the multi-attribute decision-making problem with the different scenario analyses, benchmark the income levels of fifty global economies, and provide managerial insights.

4. Application of the Proposed Methodology Under Uncertainty

This section consists of four sub-sections as follows: First, the specified criteria and alternatives are explained. Second, the criteria are prioritized using stages 1–10 in the proposed methodology. Third, the fifty global economies are ranked based on stages 11–16 in the proposed methodology. Four, the fifty global economies are ranked as how they relate to the business region and income level when applying stage 17 in the proposed methodology.

4.1. Criteria and Alternatives

Evaluations of sustainable and intelligent urban transportation systems are associated with several factors that affect the performances of global economies. For this reason, the assessments of sustainable and intelligent transportation systems are critical to achieving more efficient and resilient logistics systems for the economic growth of each country. In this study, nine main criteria and fifty-six sub-criteria are selected based on surveying the experts, papers from scholarly databases, and published reports. The main criteria are listed as follows: economic, safety, hazards, energy, environmental impact and utilization, infrastructure, social, sustainability, and logistic performance criterion (see Figure 2).

Table 3. Description of criteria.

C1	Economic	Description
C1.1	CO 2 emission relative to GDP	It measures CO2 produced from transport relative to GDP
C1.2	Energy consumption	It evaluates the energy consumption of transportation in relation to GDP
C1.3	Growth rate of GDP	It measures the annual growth rate of real GDP per employed person
C1.4	Domestic material consumption	It measures the total quantity of materials that an economy utilizes directly
C1.5	Investment (GFCF)	It refers to the purchase of produced assets
C1.6	Gross domestic product (GDP)	It measures the added value created by the production of goods and services in the country during a particular period
C1.7	Investment by sector	It refers to sector-based investments in household, corporate, and general government
C1.8	Investment by asset	There are six categories of assets: residences, other buildings and structures, transport equipment, cultivated biological resources, and intellectual property products
C2	Safety
C2.1	Deaths by road user category— 2 or 3 wheeler	Deaths among 2 or 3 wheeler road users
C2.2	Deaths by road user category—4 wheeler	Deaths among 4 wheeler road users
C2.3	Deaths by road user category—cyclist	Deaths among cyclist road users
C2.4	Deaths by road user category—others	Deaths among 2 or 3 wheeler road users
C2.5	Deaths by road user category—pedestrian	Deaths among 2 or 3 wheeler road users
C2.6	Mortality caused by road traffic injury	Road traffic injuries cause mortality
C2.7	Road accidents	Number of road accidents
C3	Hazards
C3.1	Earthquake	The indicator measures absolute and relative exposure to earthquakes with Modified Mercalli Intensity in MMI categories VI and VIII
C3.2	Flood	The indicator measures the absolute and relative exposure to floods in one year
C3.3	Tsunamis	The indicator measures the absolute and relative exposure to tsunamis in one year
C3.4	Epidemic	The indicator determines the total population affected by epidemics per country every year
C3.5	Cyclone	The indicator evaluates the total and proportional exposure to tropical cyclones and storm surges
C4	Energy
C4.1	Renewable energy consumption	The indicator measures the share of renewable energy in total final consumption
C4.2	Renewable energy production	The indicator measures the contribution of renewable energy sources to the total primary energy supply
C4.3	Primary energy supply	The indicator measures the amount of primary energy supply
C4.4	Crude oil production	The indicator measures the amount of crude oil production
C4.5	Electricity generation	The indicator measures the amount of electricity produced from fossil fuels, nuclear power plants, hydroelectric power plants, geothermal systems, solar panels, etc.
C4.6	Nuclear power plants	The indicator measures the number of units in nuclear power plants in operation as of 2019
C5	Environmetal impact & utilization
C5.1	Air pollution (micrograms per cubic meter)	It measures a population’s average exposure to concentrations of suspended particles with an aerodynamic diameter of less than 2.5 microns
C5.2	Air pollution, population exposure	It measures the percentage of the population exposed to ambient PM2.5 concentrations that exceed the reference value established by the World Health Organization
C5.3	Total GHG emissions	The indicator measures total greenhouse gas emissions (carbon dioxide, methane, nitrous oxide and hydrofluorocarbons (HFCs), and sulfur hexafluoride)
C5.4	Biodiversity covered by protected areas	It measures the proportion of essential areas for terrestrial and freshwater biodiversity covered by protected areas according to ecosystem type
C6	Infrastructure
C6.1	Air transport, freight	It measures cargo, express and diplomatic bags carried during each flight stage in metric tonnes multiplied by kilometers traveled
C6.2	Air transport, passengers carried	It measures passengers of air carriers registered in the country
C6.3	Container port traffic	It measures the flow of containers from land to sea modes of transport and vice versa in units of a standard-size container twenty-foot equivalent
C6.4	Quality of air transport infrastructure	The indicator measures the quality of air transport infrastructure on a scale of 1 to 7
C6.5	Quality of port infrastructure	The indicator measures the quality of port infrastructure on a scale of 1 to 7
C6.6	Quality of railroad infrastructure	The indicator measures the quality of railroad infrastructure on a scale of 1 to 7
C6.7	Investment in transport with private participation	This indicator measures, in US dollars, commitments to infrastructure projects in the field of transportation that have reached financial closure and serve the public directly or indirectly
C8	Sustainability
C8.1	Sustainable Mobility	The indicator evaluates achieving the Sustainable Development Goals under the main headings of universal access, efficiency, safety, and green mobility
C8.2	The Urban Mobility Readiness Index	The indicator measures the maturity of a city’s innovation ecosystem in urban mobility This index includes technologies such as artificial intelligence
C8.3	Sustainable cities mobility index	The indicator measures cities according to the three pillars of sustainability: planet, people and profit
C8.4	EPI	The indicator evaluates countries on climate change performance, environmental health, and ecosystem vitality
C8.5	Human Development Index	The indicator evaluates average achievement in key dimensions of human development, based on the criteria of living a long and healthy life, being knowledgeable, and having a reasonable standard of living
C9	Logistic performance criterion
C9.1	Good governance index	The indicator measures countries’ economic growth, human capital creation, and social cohesion strength
C9.2	Liner Shipping Connectivity Index	The indicator measures how well countries are connected to global maritime networks
C9.3	Logistics performance index	The indicator measures the trade logistics performance of countries

summarizes the description of the specified criteria. Moreover, fifty global economies were selected in America, Asia, Europe, the Middle East, and Africa when asking the opinions of the decision-making team, considering the GDP and published reports about global economies. In addition, the chosen economies cover 90.86% of the world GDP (currency: USD). Thus, income levels worldwide are considered when specifying countries.

C1 Economic: Global interest in the transportation sector has continued to increase in recent years due to the fact that decision makers state that the sector is at the center of sustainable development. The sustainability of the sector increases economic growth and improves accessibility for its members. It also raises integration in economies by taking into account increasing environmental concerns.

C2 Safety: This safety indicator includes indicators such as deaths, injuries, number of accidents, etc. The WHO states that more than 1 million people die and 20–50 million people are injured in road traffic accidents worldwide every year [45]. In addition to the pain caused by these accidents, the treatment costs of injured people and the loss of the working ability of those who are disabled bring about a financial economic burden. Road traffic accidents impose an economic burden on economies, costing almost 3% of their annual gross domestic product [45]. Moreover, taking precautions against accidents in road traffic can reduce the risk of injury and death. In the Sustainable Development 2030 agenda, the aim to reduce deaths and injuries in road traffic by 50% is stated [45].

C3 Hazards: The global economy thrives on the flow of products, and human needs have become dependent on the products and services offered by this transportation sector. In this regard, the country’s logistics system must operate efficiently and productively. However, assets in this system are still exposed to a range of potential disruptions and threats, such as earthquakes, floods, tsunamis, hurricanes, epidemics, etc. These disasters can significantly disrupt production in economies or affect the flow of international input supplies [43].

C4 Energy: Energy is one of the most critical components of sustainable transportation. The use of zero-emission electric or alternative fuel vehicles, as well as low-emission internal combustion engine vehicles, can lead to energy-saving and affordable transportation.

C5 Environmetal impact and utilization: The main indicator of environmental dimensions includes the negative effects on the economy related to transportation. Further, sustainable transportation aims to reduce/stop increasing environmental impacts and thus increase economic benefit. Therefore, sustainable transportation affects many dimensions, such as social equality, public health, resilient cities, urban–rural networks, and the productivity of rural areas.

C6 Infrastructure: Infrastructure investments stimulate economic growth, increase employment, and improve livelihoods. Infrastructure investments increase the country’s capacity, create opportunities for society, provide economic competitiveness, and help global integration. Increasing sustainable transportation options, especially in low-income communities, is one of the most essential ways for countries to achieve human development and social inclusion. Investments in transportation infrastructure support economic growth, create jobs, and connect the community to critical services such as health or education [46].

C7 Social: This indicator incorporates community statistics regarding transportation and easy access to public transport. Regarding the social dimension of the transport system, it measures the challenges and opportunities in terms of affordability, reliability, and accessibility for different users of the system’s modernization [47]. Additionally, in some countries, informal employment constitutes a significant part of the economy and labor market. Although informal employment is vital in sectors and income generation, it leaves people with a higher level of vulnerability and insecurity. The ILO reports state that indicators such as unemployment rate and time-related underemployment in these countries are incomplete and unsuccessful in expressing the labor market [48]. From the perspective of transport equality and social inclusion, indicators such as accessibility to public transport for people who do not have a car or are impaired, connectivity for low-income or deprived geographical areas, etc., are important in this regard [23,49].

C8 Sustainability: This main criterion consists of internationally known and popular indices that express the level and status of countries regarding sustainability and sustainable mobility. Sustainable mobility involves reducing emissions from transportation and minimizing fuel consumption, taking into account the regenerative capacity of the environment. This term, also known as green driving, aims to contribute to society by consuming fewer resources in sustainable transportation, supporting climate protection, and facilitating the growth of more green areas [50]. The Environmental Performance Index (EPI) evaluates countries worldwide from the perspective of climate change performance, environmental health, and ecosystem vitality, with 40 performance metrics classified into 11 main sustainability-related categories [51]. Furthermore, the HDI measures human development by taking into account the dimensions of living a long and healthy life, being knowledgeable, and having a reasonable standard of living [52,53].

C9 Logistic performance criterion: Logistics performance is a measure that indicates the trade logistics performance of countries. It includes ease of connection within the supply chain of partners, logistics service quality, and a number of structural components related to infrastructure and controls in international trade and transportation [8]. While the good governance index measures countries’ economic growth, human capital creation, and social cohesion strength, the liner shipping connectivity index represents how well countries are connected to global maritime networks

4.2. Prioritizing the Criteria

The criteria weights are obtained using stages 1–10 in the intuitionistic fuzzy-based AHP method. A decision-making team has experience with sustainable and intelligent urban transportation systems. In addition, the decision-making team consists of three people with doctoral degrees in industrial engineering and civil engineering, with a concentration on transportation and more than ten years of work experience. Then, PCMs are generated based on the opinions of the decision-making team. In the Appendix A,

Table A1. Pairwise comparison matrices for DM1, DM2, and DM3.

DM1	C1	C2	C3	C4	C5	C6	C7	C8	C9
C1	EELT	MLLT	MHLT	EELT	LLT	MLLT	MLLT	HLT	VHLT
C2		EELT	HLT	MHLT	MLLT	EELT	HLT	HLT	HLT
C3			EELT	MLT	MLLT	LLT	EELT	MLLT	HLT
C4				EELT	EELT	EELT	MLLT	ELLT	LLT
C5					EELT	MLLT	LLT	LLT	MHLT
C6						EELT	MLLT	EELT	MHLT
C7							EELT	EELT	HLT
C8								EELT	EELT
C9									EELT
DM2	C1	C2	C3	C4	C5	C6	C7	C8	C9
C1	EELT	MLLT	MHLT	AELT	LLT	LLT	MLLT	HLT	VHLT
C2		EELT	HLT	HLT	MLLT	AELT	HLT	HLT	HLT
C3			EELT	MLLT	MLLT	LLT	AELT	MLLT	AELT
C4				EELT	AELT	AELT	AELT	EELR	LLT
C5					EELT	MLLT	LLT	LLT	AELT
C6						EELT	MLLT	AELT	MHLT
C7							EELT	AELT	HLT
C8								EELT	AELT
C9									EELT
DM3	C1	C2	C3	C4	C5	C6	C7	C8	C9
C1	EELT	LLT	MHLT	AELT	MLLT	MLLT	MLLT	HLT	VHLT
C2		EELT	HLT	HLT	MLLT	MHLT	HLT	MHLT	HLT
C3			EELT	AELT	MLLT	LLT	HLT	MLLT	HLT
C4				EELT	MHLT	MHLT	MLLT	MHLT	LLT
C5					EELT	MLLT	LLT	LLT	MHLT
C6						EELT	MLLT	MHLT	MHLT
C7							EELT	MHLT	MHLT
C8								EELT	MHLT
C9									EELT

shows the PCMs of the decision-making team. The three experts individually make their choices to fill the PCMs in Table A1 using Table 2. Indeed, PCMs are important in analyzing the preferences of the three experts and proceeding to the other stages for obtaining weights. After applying the presented intuitionistic fuzzy-based AHP method,

Table 4. Weights for criteria and sub-criteria.

Criterion	Calculated Weights for Each Criterion	Sub-Criterion	Calculated Local Weights	Calculted Global Weights
C1	0.127
		C1.1.	0.088	0.011
		C1.2.	0.097	0.012
		C1.3.	0.143	0.018
		C1.4.	0.143	0.018
		C1.5.	0.149	0.019
		C1.6.	0.134	0.017
		C1.7.	0.128	0.016
		C1.8.	0.118	0.015
C2	0.125
		C.2.1.	0.133	0.017
		C.2.2.	0.120	0.015
		C.2.3.	0.133	0.017
		C.2.4.	0.133	0.017
		C.2.5.	0.158	0.020
		C.2.6.	0.157	0.020
		C.2.7.	0.165	0.021
C3	0.116
		C.3.1.	0.265	0.031
		C.3.2.	0.205	0.024
		C.3.3.	0.180	0.021
		C.3.4.	0.178	0.021
		C.3.5.	0.172	0.020
C4	0.110
		C.4.1.	0.202	0.022
		C.4.2.	0.203	0.022
		C.4.3.	0.178	0.020
		C.4.4.	0.153	0.017
		C.4.5.	0.143	0.016
		C.4.6.	0.122	0.013
C5	0.113
		C.5.1.	0.276	0.031
		C.5.2.	0.241	0.027
		C.5.3.	0.295	0.034
		C.5.4.	0.188	0.021
C6	0.107
		C.6.1.	0.168	0.018
		C.6.2.	0.177	0.019
		C.6.3.	0.157	0.017
		C.6.4.	0.097	0.010
		C.6.5.	0.116	0.012
		C.6.6.	0.121	0.013
		C.6.7.	0.165	0.018
C7	0.106
		C.7.1.	0.098	0.010
		C.7.2.	0.088	0.009
		C.7.3.	0.108	0.011
		C.7.4.	0.100	0.011
		C.7.5.	0.108	0.011
		C.7.6.	0.088	0.009
		C.7.7.	0.089	0.009
		C.7.8.	0.086	0.009
		C.7.9.	0.069	0.007
		C.7.10.	0.078	0.008
		C.7.11.	0.088	0.009
C8	0.103
		C.8.1.	0.261	0.027
		C.8.2.	0.220	0.023
		C.8.3.	0.196	0.020
		C.8.4.	0.187	0.019
		C.8.5.	0.136	0.014
C9	0.093
		C.9.1.	0.362	0.034
		C.9.2.	0.312	0.029
		C.9.3.	0.326	0.030

shows the calculated weights of criteria and sub-criteria. As observed from Table 4, the highest weight among the nine criteria is economic (C1), which is 0.127. The finding of the economic criterion is in agreement with the previous paper by [54] in terms of economic development. The second-highest weight is safety (C2), and its weight is 0.125. As expected, the safety indicator is an important concern in analyzing deaths, injuries, etc. The finding of the safety criterion is consistent with the published report [45]. Next, the global economies are negatively affected by earthquakes, floods, tsunamis, hurricanes, epidemics, etc. Thus, the third-highest criterion is hazards (C3) based on this awareness, and its weight is 0.116. Then, the environmental concerns affect transportation operations. Based on this awareness, environmental impact and utilization (C5) is the fourth-highest weight, and its weight is 0.113. Next, energy (C4) is a significant part of sustainable and intelligent transportation systems and is the fifth-highest criterion based on the experts’ opinions. Further, infrastructure (C6) is the sixth-highest weight in Table 4, and its weight is 0.107. Then, the seventh criterion is social (C7) with a weight of 0.106. Next, sustainable mobility is a key concept to reduce emissions from transportation. Based on this awareness, the weight of the sustainability criterion (C8) is 0.103. Even though sustainability is the eighth-highest criterion, it contributes 10.30% to the evaluation process. Lastly, the logistic performance criterion is the lowest weight among the criteria associated with the decision-making team.

In Table 4, the local and global weights of the sub-criteria are presented. The most significant sub-criterion for each main criterion is summarized as follows. The investment (GFCF) (C1.5) is the most significant sub-criterion of economic. Then, road accidents (C2.7) is the most important sub-criterion of safety. Next, earthquake (C3.1) is the highest sub-criterion weight for hazards due to the experts’ experiences. Further, renewable energy production (C4.2) is important to contribute to sustainable transportation and intelligent systems. Based on this awareness, the maximum sub-criterion weight of energy is renewable energy production. Moreover, the total GHG emissions (C5.3) have the maximum sub-criterion weight for environmental impact and utilization. The findings are consistent with the previously published papers [54]. Then, air transport, passengers carried (C6.2) is the most significant sub-criterion of infrastructure. Also, air transport, freight (C6.1), and investment in transport with private participation (C6.7) are important sub-criteria as well. Next, passenger car registrations (C7.3) and the young population (C7.5) are the maximum sub-criteria of social. Further, sustainable mobility (C8.1) is the maximum sub-criterion weight for sustainability in order to achieve the sustainable development goals. Lastly, the good governance index (C9.1) is the highest weight of the logistic performance criterion.

4.3. Ranking Economies

The fifty global economies are ranked by applying stages 11–16 in the proposed methodology.

Table 5. Ranking economies considering the nine scenarios.

	All Citeria		C1		C2		C3		C4		C5		C6		C7		C8		C9
Countries	Qj	Rank	Qj	Rank	Qj	Rank	Qj	Rank	Qj	Rank	Qj	Rank	Qj	Rank	Qj	Rank	Qj	Rank	Qj	Rank
Argentina	0.774	48	0.225	27	0.365	30	0.442	24	0.949	46	0.641	26	0.903	43	0.597	32	0.551	35	0.943	49
Australia	0.287	13	0.294	44	0.332	26	0.349	16	0.940	45	0.406	2	0.687	13	0.468	9	0.336	23	0.512	20
Austria	0.245	10	0.170	17	0.242	12	0.471	28	0.428	7	0.588	20	0.755	21	0.520	20	0.252	15	0.452	16
Belgium	0.283	12	0.186	19	0.348	27	0.356	17	0.757	25	0.626	24	0.736	19	0.467	8	0.249	13	0.226	5
Brazil	0.466	29	0.118	7	0.591	43	0.374	20	0.845	28	0.541	15	0.832	36	0.091	1	0.643	40	0.704	43
Bulgaria	0.707	44	0.001	1	0.433	36	0.619	38	0.625	16	0.573	35	0.914	48	0.507	17	0.398	27	1.000	50
Canada	0.158	7	0.126	10	0.360	29	0.451	26	0.463	9	0.400	9	0.650	10	0.489	14	0.315	21	0.424	15
Chile	0.695	42	0.124	8	0.390	34	0.849	48	0.925	42	0.684	30	0.852	39	0.638	36	0.574	36	0.577	31
China	0.758	47	0.307	46	0.402	35	0.677	40	0.875	31	1.000	50	0.144	1	0.676	39	0.587	38	0.287	9
Colombia	0.690	41	0.350	47	0.612	44	0.796	47	0.921	39	0.628	25	0.908	44	0.629	35	0.725	44	0.783	46
Croatia	0.484	30	0.125	9	0.320	22	0.641	39	0.446	8	0.595	34	0.858	40	0.534	23	0.323	22	0.691	41
Czechia	0.415	22	0.167	15	0.323	23	0.293	9	0.677	21	0.673	33	0.845	37	0.471	10	0.350	24	0.548	26
Denmark	0.059	3	0.095	4	0.265	15	0.000	1	0.510	11	0.506	18	0.763	23	0.512	19	0.042	2	0.393	14
Egypt, Arab Rep.	0.655	40	0.089	3	0.537	41	0.615	36	0.968	49	0.812	48	0.911	46	0.858	48	0.746	46	0.511	19
Estonia	0.364	21	0.188	20	0.282	17	0.333	13	0.411	5	0.000	1	0.849	38	0.511	18	0.257	16	0.650	35
Finland	0.264	11	0.174	18	0.370	32	0.337	15	0.351	4	0.390	10	0.728	18	0.561	27	0.054	3	0.520	23
France	0.127	6	0.215	25	0.301	20	0.377	21	0.627	17	0.522	19	0.649	9	0.594	29	0.144	8	0.284	8
Germany	0.215	9	0.235	31	0.260	13	0.371	19	0.601	14	0.610	21	0.565	4	0.533	22	0.111	7	0.149	3
Greece	0.506	33	0.140	12	0.223	10	0.749	46	0.603	15	0.645	37	0.792	31	0.538	25	0.442	30	0.689	39
Iceland	0.301	14	0.140	13	0.358	28	0.441	23	0.076	1	0.400	8	0.767	25	0.490	15	0.238	12	0.680	38
India	0.802	49	0.266	40	0.330	25	0.693	41	0.911	36	0.913	49	0.713	16	0.612	33	0.789	48	0.532	24
Indonesia	0.593	38	0.228	29	0.692	46	0.717	43	0.923	40	0.668	28	0.812	35	0.473	11	0.672	41	0.664	37
Ireland	0.332	18	0.133	11	0.330	24	0.188	3	0.760	26	0.388	7	0.701	14	0.483	12	0.352	25	0.567	30
Israel	0.431	24	0.169	16	0.218	9	0.571	34	0.882	32	0.677	39	0.802	32	0.328	3	0.304	19	0.564	29
Italy	0.436	25	0.238	34	0.234	11	0.571	35	0.557	12	0.647	27	0.778	28	0.625	34	0.408	28	0.613	34
Japan	0.488	31	0.265	39	0.262	14	0.947	49	0.895	34	0.545	13	0.566	5	0.748	43	0.173	10	0.256	7
Korea, Rep.	0.493	32	0.238	33	0.370	31	0.713	42	0.904	35	0.719	44	0.635	8	0.741	42	0.306	20	0.561	27
Malaysia	0.461	27	0.259	38	0.720	47	0.444	25	0.952	48	0.627	22	0.758	22	0.826	46	0.581	37	0.248	6
Mexico	0.560	37	0.076	2	0.175	7	0.618	37	0.927	43	0.648	32	0.782	29	0.595	30	0.675	42	0.691	40
Netherlands	0.305	16	0.193	22	0.589	42	0.482	29	0.741	24	0.677	31	0.654	11	0.487	13	0.097	5	0.104	2
New Zealand	0.337	20	0.232	30	0.441	37	0.512	30	0.889	33	0.383	6	0.755	20	0.466	7	0.288	17	0.546	25
Nigeria	0.703	43	0.247	36	0.642	45	0.520	33	0.766	27	0.780	46	0.996	50	0.880	49	0.917	50	0.739	44
Norway	0.305	17	0.153	14	0.382	33	0.320	11	0.226	2	0.397	12	0.790	30	0.538	24	0.145	9	0.582	32
Philippines	0.756	45	0.255	37	0.806	48	0.963	50	0.915	37	0.649	29	0.909	45	0.828	47	0.786	47	0.767	45
Poland	0.455	26	0.193	23	0.314	21	0.269	7	0.699	22	0.693	36	0.867	41	0.399	6	0.423	29	0.500	18
Portugal	0.199	8	0.100	5	0.295	19	0.285	8	0.494	10	0.405	5	0.766	24	0.642	37	0.374	26	0.514	21
Russian Federation	0.595	39	0.277	42	0.125	4	0.470	27	0.733	23	0.657	23	0.803	33	0.597	31	0.491	32	0.854	47
Saudi Arabia	0.549	36	0.279	43	0.961	50	0.309	10	0.846	29	0.804	47	0.771	27	0.580	28	0.881	49	0.475	17
Singapore	0.332	19	0.209	24	0.512	38	0.188	2	0.989	50	0.695	41	0.635	7	0.719	41	0.226	11	0.000	1
South Africa	0.518	34	0.357	48	0.285	18	0.249	6	0.923	41	0.714	42	0.809	34	0.645	38	0.725	45	0.518	22
Spain	0.304	15	0.246	35	0.065	2	0.357	18	0.587	13	0.436	3	0.660	12	0.545	26	0.250	14	0.590	33
Sweden	0.023	1	0.192	21	0.018	1	0.334	14	0.329	3	0.409	14	0.769	26	0.500	16	0.000	1	0.365	13
Switzerland	0.108	5	0.236	32	0.169	6	0.397	22	0.641	19	0.478	11	0.720	17	0.522	21	0.075	4	0.343	12
Tunisia	0.758	46	0.296	45	0.527	40	0.514	31	0.950	47	0.686	40	0.941	49	0.905	50	0.548	34	0.861	48
Turkiye	0.523	35	0.113	6	0.078	3	0.727	44	0.640	18	0.717	43	0.708	15	0.243	2	0.605	39	0.654	36
United Arab Emirates	0.421	23	0.273	41	0.280	16	0.326	12	0.920	38	0.755	45	0.565	3	0.690	40	0.467	31	0.306	10
United Kingdom	0.090	4	0.228	28	0.134	5	0.227	5	0.675	20	0.525	16	0.575	6	0.330	4	0.099	6	0.175	4
United States	0.043	2	0.219	26	0.213	8	0.516	32	0.413	6	0.458	4	0.166	2	0.341	5	0.296	18	0.329	11
Uruguay	0.463	28	0.366	49	0.525	39	0.221	4	0.863	30	0.451	17	0.914	47	0.780	44	0.522	33	0.564	28
Vietnam	1.000	50	1.000	50	0.907	49	0.729	45	0.938	44	0.636	38	0.893	42	0.806	45	0.692	43	0.699	42

presents the results of the VIKOR approach. While the first column refers to economies, the first row corresponds to the criteria-based results of the VIKOR approach. In other words, the all criteria column presents the results of all indicators; the remaining columns express each criterion’s results one by one.

According to the first scenario, which includes economic indicators, the highest-performing countries are Bulgaria, Mexico, Egypt, Arab Rep., Denmark, and Portugal. Bulgaria’s high score contributes to the sub-indicators of domestic material consumption, investment (GFCF), and gross domestic product (GDP). Countries with low scores in this scenario are Vietnam, Uruguay, South Africa, Colombia, and China. Vietnam has the lowest scores in the domestic material consumption, investment (GFCF), and investment by asset sub-indicators. Improvements in these economic indicators may also increase transportation performance.

According to the safety indicator, Sweden, Spain, Turkey, the Russian Federation, and the United Kingdom are the top five scoring countries. The main reason is that Sweden achieved the best results in the sub-indicators, such as deaths by road users, traffic injuries, and accidents. Sweden has adopted the Vision Zero approach, defined as a strategic safety policy against the risk of severe or fatal injury from the road transport perspective. The rate of wearing seat belts and helmets is high in society. In addition, this system is mandatory and is monitored and supported by a safety impact assessment and road inspections. The bottom five scorers are Saudi Arabia, Vietnam, the Philippines, Malaysia, and Nigeria. These countries have the lowest score in mortality caused by road traffic injury and road accidents.

Considering the risk profiles of the fifty economies, Denmark is the top scorer. Denmark ranks first because its location has a low risk of earthquakes, floods, tsunamis, and cyclones. Singapore, Ireland, Uruguay, and the United Kingdom are the rest of the five top-scoring countries. In the other case, the countries with the highest risk profile are the Philippines, Japan, Chile, Colombia, and Greece. The Philippines’ high probability of exposure to earthquakes, tsunamis, and cyclones causes it to be at the bottom of this category. Natural disasters can affect economies that are vulnerable to global supply chains and transportation.

Energy is one of the most essential inputs to the transportation sector. There are plans to either reduce or abandon the currently used resources on which the industry depends and transform them into clean resources in the future. Regarding energy as the main indicator, European countries, especially Scandinavian countries, dominate the list. Iceland, Norway, Sweden, Finland, and Estonia are the top five economies with the highest scores. Iceland owes its success to receiving the highest score in primary energy supply, renewable energy production, and consumption sub-pillars. The bottom scorers are countries that cannot benefit from clean energy sources and are dependent on conventional energy sources. These countries are, in order, Singapore, Egypt, Arab Rep., Malaysia, Tunisia, and Argentina.

Environmental sustainability is essential in meeting people’s needs and preserving/protecting the natural environment without compromising the availability of future resources. Air quality is directly linked to climate and ecosystem, and GHG emissions caused by burning fossil fuels reduce air quality. Regarding this pillar, the top five scorers are Estonia, Australia, Spain, the United States, and Portugal. Estonia has the best air quality compared to other economies. In the other spectrum, the lowest scorers are China, India, Egypt, Arab Rep., and Saudi Arabia. China has the highest air pollution and GHG emissions.

Infrastructure creates value in the economy by providing mobility in society, accessibility to services, and ease in the flow of products. It also contributes to social welfare by increasing social interaction. The top five economies in terms of transportation infrastructure are China, the United States, the United Arab Emirates, Germany, and Japan. The Chinese economy has achieved an increasing rise in world trade for decades by investing in the world’s largest and busiest ports. According to the infrastructure score, the economies at the bottom of the list are Nigeria, Tunisia, Bulgaria, Uruguay, as well as Egypt, Arab Rep. Nigeria has low scores in terms of investment in transport with private participation and quality of transportation modes.

Green mobility is critical for society to grasp. Transport must provide better connections to rural and remote areas, be accessible to people with limited mobility and disabilities, be affordable, and offer equal and good conditions to all segments of society. Regarding the social pillar, Brazil, Turkey, Israel, the United Kingdom, and the United States are the five top-scoring economies. At the other end of the spectrum are Tunisia, Nigeria, Egypt, Arab Rep., the Philippines, and Malaysia.

Sustainability briefly refers to low- and zero-emission environmentally friendly vehicles, energy-saving and affordable accessible transportation modes. The five top-scoring economies are Sweden, Denmark, Finland, Switzerland, the Netherlands, and the United Kingdom. European economies perform well in this pillar. Sweden has focused on sustainable transportation for many years and has allocated resources to environmentally friendly technologies. It also aims to increase the proportion of people traveling by public transport and improve the community’s quality of life. At the end of the list, the lowest scoring economies are Nigeria, Saudi Arabia, India, the Philippines, and Egypt, Arab Rep.

The set of indices covered in the logistic performance criterion expresses the most current perspective on the commercial logistics performance of economies. While Singapore, the Netherlands, Germany, the United Kingdom, and Belgium are in the first row, Bulgaria, Argentina, Tunisia, the Russian Federation, and Colombia are at the other end of the spectrum.

When all criteria are considered, the top five scorers, according to VIKOR’s results, are Sweden, the United States, Denmark, the United Kingdom, and Switzerland. European countries dominate the group. The overall results indicated that the top five countries have strong economic and investment performance, have determined safety policies in road safety, and have less exposure to possible risks. In addition, policies are in place in these countries to encourage and expand renewable energy. The high level of development in different modes of transportation and the support of social policies in the top five countries are also crucial. The main reason why these economies have higher performance is that they are dominant players in international trade and supply chains while considering environmental concerns. On the other spectrum, the lowest scorers are Vietnam, India, Argentina, China, and Tunisia, according to VIKOR’s results. The results showed that the countries at the bottom of the list perform less well regarding economic activities and road safety. The absence of a high amount of infrastructure investment in these economies, except for China, is one of the reasons for their low performance. These countries have the lowest environmental and social performances in terms of global economies.

We discussed the obtained criteria weights from the proposed methodology with the decision-making team, and they confirmed that the methodology steps were correctly applied. In addition, we validated the results of ranking economies considering the nine scenarios employing another MCDM technique for the specified problem. The ranking of economies is the same for both methods.

4.4. Ranking Economies Based on Business Region and Income Level

This section divides economies more homogeneously into categories based on their business regions and income levels [55].

Table 6. Top six scorers considering business region.

Europe & Central Asia	Qj	Rank	East Asia & Pacific	Qj	Rank	America	Qj	Rank	Middle East & Africa	Qj	Rank
Sweden	0.023	1	Australia	0.287	1	United States	0.043	1	United Arab Emirates	0.421	1
Denmark	0.059	2	Singapore	0.332	2	Canada	0.158	1	Israel	0.431	2
United Kingdom	0.090	3	New Zealand	0.337	3	Uruguay	0.463	3	South Africa	0.518	3
Switzerland	0.108	4	Malaysia	0.461	4	Brazil	0.466	4	Saudi Arabia	0.549	4
France	0.127	5	Japan	0.488	5	Mexico	0.560	5	Egypt, Arab Rep.	0.655	5
Portugal	0.199	6	Korea, Rep.	0.493	6	Colombia	0.690	6	Nigeria	0.703	6

provides the results for economies regarding their business regions. The first three columns represent Europe and Central Asia, East Asia and the Pacific are defined by the second three columns, the third three columns represent America, and finally, the Middle East and Africa are indicated by the last three columns, respectively. The economies with the highest sustainable transportation performance in their regions are Sweden, Australia, the United States, and the United Arab Emirates. Furthermore,

Table 7. Top six scorers considering income level.

High Income	Qj	Rank	Upper Middle Income	Qj	Rank	Lower Middle Income	Qj	Rank
Sweden	0.023	1	Malaysia	0.461	1	Egypt. Arab Rep.	0.655	1
United States	0.043	2	Brazil	0.466	2	Nigeria	0.703	2
Denmark	0.059	3	South Africa	0.518	3	Philippines	0.756	3
United Kingdom	0.090	4	Türkiye	0.523	4	Tunisia	0.758	4
Switzerland	0.108	5	Mexico	0.560	5	India	0.802	5
France	0.127	6	Indonesia	0.593	6	Vietnam	1.000	6

shows the results for economies in terms of their income levels. The countries with the highest sustainable transportation performance by income level are Sweden, Malaysia, and Egypt, Arab Rep., respectively.

5. Managerial Recommendations

European countries, especially Scandinavian countries, adopted the sustainability philosophy over two decades ago and set strategic and tactical steps and goals. To achieve sustainable goals, there are investments and support for these policies. For these reasons, European countries are the closest to achieving sustainable goals. In addition, these countries have slowly begun to receive investment returns. Therefore, sustainable development plays a significant role in meeting the needs of the present, including transportation. Moreover, efficient transportation systems contribute to economic, environmental, and social opportunities for global economies. On the other hand, a number of low-performing countries in Table 5 struggle with environmental concerns due to the exposure of air pollution to the population, total GHG emissions, and the lack of terrestrial and freshwater areas. It is also noted that high total GHG emissions can be measured because of the high population of countries such as China and India. Due to serious environmental concerns, sustainability should efficiently be implemented in transportation systems for the economic growth of global economies.

This study evaluates the sustainable and intelligent transportation processes of fifty global economies, examining nine criteria, such as economic, safety, hazards, energy, environmental impact and utilization, infrastructure, social, sustainability, and logistic performance when dealing with uncertainty. An integrated intuitionistic fuzzy-based AHP and VIKOR method was proposed for the assessment of sustainable and intelligent urban transportation systems. The novel integrated decision-making technique benefits from contemporary sustainability, safety, and hazards data to assess and benchmark sustainable and intelligent transportation processes for economies. New metrics, such as epidemics impacting transportation, are not dealt with by sustainable and intelligent urban transportation systems. Therefore, this study is the first research to comprehensively conduct and benchmark economic, environmental, safety, hazards, and sustainability-based metrics. The opinions of the decision-making team were considered when constructing PCMs under uncertainty. Based on intuitionistic fuzzy-based AHP results, economics, safety, and hazards are the three with the highest weights among the other six criteria. Also, sustainability has a vital weight where it provides 10.30% to the total weights of the evaluating criteria. Next, the VIKOR approach, which is the part of the proposed integrated decision-making technique, is applied to the fifty global economies located in Africa, America, Asia, Europe, and the Middle East. Hence, this research work comprehensively benchmarks the fifty global economies associated with their business regions and income levels. Sweden is the most suitable economy for the sustainable transportation system. It is followed by the United States, Denmark, the United Kingdom, and Switzerland.

Global economies may generate effective strategies in transportation systems by implementing economics, environmental impact and utilization, safety, hazard, and sustainability-based assessments when reducing operational costs and risks and minimizing adverse environmental impacts. In addition, the low-ranking economies in Table 5 could give priority to the implementation of strategies and policies to improve their transportation problems when considering sustainable philosophy, technological adaptations, economic factors, safety, and risk evaluations. So, the contribution of the economic growth is provided for each country. Also, global economies benefit from economics metrics when maximizing safety metrics and minimizing exposing hazards metrics, as shown in this paper. Finally, sustainable and intelligent transportation systems may take advantage of economics and sustainable benefits and reduce potential risks and negative impacts through involving environmental and operating abilities.

6. Conclusions

Developments in transportation systems provide an essential contribution to the development processes of global economies when considering holistic risk management and environmental protection-related concerns. In addition, the hazards and safety assessments are complicated problems for transportation systems. Hence, evaluating transportation systems is important to reduce risk and support sustainability. Further, developments of sustainable and intelligent urban transportation systems are prominent for economies, environmental protection, and societies. For this purpose, a decision-making method is needed to propose evaluating sustainable and intelligent transportation systems for global economies.

In this paper, an integrated intuitionistic fuzzy-based AHP and VIKOR technique is proposed to assess the sustainable and intelligent urban transportation systems of fifty global economies when dealing with uncertainty. The nine main criteria and the fifty-six sub-criteria are denoted for the fifty global economies. Economics has the highest weight, which is 0.127, among the eight main criteria from the results of the intuitionistic fuzzy-based AHP, which is a part of the integrated method. Investment (GFCF), domestic material consumption, and the growth rate of GDP are the top three weights for economics. Next, safety and hazards are other vital main criteria. In addition, sustainability provides 10.30% to the total weights of the main criteria. Hence, sustainability should not be ignored in order to benefit from economic growth. Moreover, the top five height global sub-criteria weights are found to be the total GHG emissions (C5.3), the good governance index (C9.1), air pollution (micrograms per cubic meter) (C5.1), earthquake (C3.1), and the logistic performance index (C9.3), respectively.

The VIKOR technique, which is part of the proposed methodology, is used in order to rank the fifty global economies. Also, different scenario analyses and benchmarking are conducted. Based on the results, Sweden is the most appropriate economy for a sustainable and intelligent transportation system. The United States and Denmark are in the second and third ranks, respectively. When benchmarking the business region, Sweden, Australia, the United States, and the United Arab Emirates are the most significant economies for sustainable and intelligent transportation systems. Sweden, Malaysia and Egypt, Arab Rep., are the top choices for sustainable and intelligent transportation systems associated with income levels.

The limitations of this study are summarized as follows: This study did not consider a couple of technologies, such as autonomous driving, vehicle–road collaboration, and 5G applications, because the urban mobility readiness index included these technologies. In this study, nine main criteria and fifty-six sub-criteria are considered. So, a number of indicators are not included in the criteria, such as the penetration rate of intelligent technology and the maturity of data sharing.

This research consists of nine main criteria and fifty-six sub-criteria to evaluate sustainable and intelligent transportation systems when ranking fifty global economies. An extension of this work could be conducted for future studies. First, additional main criteria could be included in one of the prospective studies in the context of sustainability. Also, a couple of indicators could be considered to evaluate the technology maturity model for the transportation process. Then, different fuzzy sets could be applied to specify the weights of the main criteria and sub-criteria and rank the economies. Lastly, the other decision-making techniques could be used to choose the most appropriate alternative.