Metaverse is defined as a fictional universe that could serve as a simulation environment of reality. Beginning in the past with games, it becomes increasingly integrated into human life as time passes. Metaverse usage is inevitable in every aspect of life. One of its potential application areas could be urban transportation. A novel fuzzy trigonometric based on the combination of the Full Consistency Method (FUCOM) and Combined Compromise Solution (CoCoSo) is proposed to rank three alternatives with twelve criteria under four major aspects: managerial, safety, user, and urban mobility. In the first stage, fuzzy FUCOM methods are used to calculate the weights of the criteria. In the second stage, the fuzzy trigonometric based CoCoSo method is applied to evaluate and rank the alternatives. The proposed model enables the nonlinear processing of complex and uncertain information using fuzzy trigonometric functions. The findings demonstrate focusing on a particular age group can make it easier to integrate the metaverse with urban transportation. The findings of this study have the potential to serve as a guide for decision-makers. The metaverse-based applications could be started by policymakers, which is a promising opportunity with potential boundaries beyond human comprehension making this statement weaker.

As the climate change enforces decision-makers (DMs) to change established policies and strategies, investors are motivated to finance the development of green projects. Because of their environmental effects, land-use transport projects are of special importance in this process. Different types of environment-friendly land-use transportation projects are financed by the private sector or regulatory authorities and institutions. However, the choice of the correct project is a complex issue. In the decision-making process, social concerns are crucial as well as financial, technical and environmental ones. In order to solve the multi-criteria decision-making problem, we propose a novel group decision support model using the Logarithm Methodology of Additive Weights (LMAW) and Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) based on the fuzzy Z extension (ZE)-numbers. The proposed group decision support model has a unique capability for decision. The opinions of two groups of the DMs and experts are used to consider the decision reliability in two different stages to reach an optimal decision. To illustrate the use of model, we create a scenario that considers four small-scale green finance planning alternatives that are evaluated using twelve criteria that reflect the choice problem's economic, environmental, technical, and political aspects. According to the findings, the optimum plan should be both inclusive and equitable, as well as economically efficient. Selection of the best green finance planning involves consideration of socioeconomic variables.

The concept of the Metaverse, an immersive simulated world with parallels to reality, has gained significant prominence in recent times. Initially popularized through gaming, the Metaverse is now poised to infiltrate various aspects of human life. Intelligent transportation systems represent a promising yet challenging domain for Metaverse integration. Alternative implementations can create challenges in different dimensions. A comprehensive evaluation that takes challenges and opportunities for the different dimensions into account is required in decision making process of choosing the best implementation method. This study presents the development of a novel evaluation model, the Fermatean Fuzzy Operational Competitiveness Rating (OCRA) model, which incorporates the Fermatean Fuzzy Distance Measure (FF-DM) and Relative Closeness Coefficient (FF-RCC) techniques. The model is tested in a case to rank three alternative approaches, considering criteria of four key dimensions: managerial, safety, user, and urban mobility. In the first stage, the FF-DM and FF-RCC-based tool is employed to determine the criteria weights. In the second stage, an enhanced version of the Fermatean Fuzzy OCRA model, utilizing FF-DM and FF-RCC, is employed to rank the alternatives. The findings indicate that policymakers' decisions in traffic management hold the potential to shape the trajectory of the Metaverse movement, representing an unparalleled opportunity with implications that extend beyond our current comprehension.

The mining sector is vital for the world's economy and provides an important source of wealth for various countries. The mines of the future must adhere to sustainable principles, which rely on applying the right technologies. This study evaluates various alternatives for choosing the best process technology, for sustainable mining, by using an interval rough decision-making model, considering four main criteria (cost, efficiency, environmental, and social) and fifteen sub-criteria. The interval rough approach was used to treat uncertainty and imprecision in information, which enabled objective processing of uncertainty in information. A novel approach that makes use of hybrid Heronian and Power Averaging (HPA) functions based on interval rough number is developed to assess different process technologies for sustainable mining. Nonlinear interval rough HPA functions were used to see the criteria's mutual influence and eliminate the impact of extreme and unreasonable arguments in the initial decision matrix. A case study is used to illustrate the feasibility of the proposed model and a sensitivity analysis is conducted to examine the effects of criteria weights in ranking. The results show that the proposed methodology is a powerful tool for objective decision-making.

Methodologies for managing traffic in real-time are evolved in a different direction with the development of Connected Autonomous Vehicles (CAVs). Most of the research uses a specific penetration rate of CAVs. The contribution of this study is to fill that gap by testing a new real-time traffic management method, named SWSCAV (utilization of shockwave-speed information and connected autonomous vehicles for traffic management in case of congestion or incident) in case of a traffic incident in an uninterrupted traffic flow environment with a very limited number of CAVs to improve conditions of the traffic flow conditions. By changing the lane and duration of the simulated incident and adjusting the arrival frequency of the CAVs and the position where the CAVs reduce their speeds, 189 different scenarios are tested. Mean flow, density, and speed values are selected as measures of effectiveness. These metrics are inspected both network-wide and locally using a Simulation of the Urban Mobility (SUMO) environment. A maximum of 12 CAVs are used. The results show that introducing SWSCAV with a limited number of CAVs means speed and flow values can increase up to 20.35% and 2.45%, respectively. Additionally, a decrease in mean density up to 32.54% can also be observed with this management approach. Density heat maps are used to inspect the influence of the incident.

Traffic incident management is combining the assets of authorities to identify, deal with, and manage traffic problems as rapidly as possible while providing the safety of on-scene responders and the traveling public. The advancement of autonomous vehicles is an opportunity for enhancing incident management implementations. This study aims to provide policymakers with four main alternatives to control freeway incidents using autonomous vehicles in mixed traffic. The presented alternatives are namely autonomous vehicles behaving as human-driven vehicles, ones connected, ones using an algorithm for incident management, and ones used in the traditional incident management methodology. The study also aims to introduce an integrated decision-making tool that is comprehendible for policymakers and mobility experts. It is based on the integration of an Entropy-based approach and the complex proportional assessment (COPRAS) method under the type-2 neutrosophic number (T2NN) environment. T2NN can represent uncertainties such as uncertainty, inconsistency, and inconsistency in real-world problems. T2NN-Entropy is presented to reveal the objective importance of evaluation criteria for freeway incident management. T2NN-COPRAS is proposed to order alternatives when deciding on the behavior of autonomous vehicles. The comparative investigation shows the superiority of the T2NN-Entropy-COPRAS model. Its major advantages are high robustness in making real-world multi-criteria decisions due to the triple-normalization backbone, and high flexibility in solving complex decision-making problems. The research findings show that using an algorithm for incident management is the best alternative to solve problems during and after an incident in mixed traffic, while autonomous vehicles that act like human-driven vehicles are the least advantageous.

Interval-valued Fermatean fuzzy sets play a significant role in modelling decision-making problems with incomplete information more accurately than intuitionistic fuzzy sets. Various decision-making methods have been introduced for the different classes IFSs. In this study, we aim to introduce a novel two-phase interval-valued Fermatean fuzzy dominance method which suits the decision-making problems modelled under the IVFFS environment well and study its applications in the adoption of energy consumption in Urban mobility considering digital carbon footprint. The proposed method considers the importance and performance of one alternative with respect to all others, which is not the case with many available decision-making algorithms introduced in the literature. Transportation is one of the most significant sources of global greenhouse gas (GHG) emissions. Numerous potential remedies are proposed to reduce the quantity of GHG generated by transportation activities, including regulatory measures and public transit digitalization initiatives. Decision-makers, however, should consider the digital carbon footprint of such projects. This study proposes three alternatives for reducing GHG emissions from transportation activities: incremental adoption of digital technologies to reduce energy consumption and greenhouse gases, disruptive digitalization technologies in urban mobility, and redesign of urban mobility using regulatory approaches and economic instruments. The proposed novel two-phase interval-valued Fermatean fuzzy dominance method will be utilized to rank these alternative projects in order of advantage. First, the problem is converted into a multi-criterion group decision-making problem. Then a novel two-phase interval-valued Fermatean fuzzy dominance method is designed and developed to rank the alternatives. The importance and advantage of the proposed two-phase method over other existing methods are discussed by using sensitivity and comparative analysis. The results indicate that rethinking urban mobility through governmental policies and economic tools is the least advantageous choice, while incremental adoption of digital technologies is the most advantageous.

Climate change affects the world. Due to excessive GHG emissions, urban transportation contributes to this threat. Policymakers and authorities want to reduce transportation-related GHG emissions. An imaginary urban area with high transportation-related greenhouse gas emissions, dense, interconnected transportation modes, and a high population density is considered. Istanbul, Turkey meets the criteria of this imaginary place, so the case analysis considers this city. Istanbul’s decision-makers are looking for effective strategies to prioritize urban climate change policy alternatives for transportation activities. Four alternative strategies and 13 criteria are presented in this context. Innovative multi-criteria decision-making (MCDM) method with the interval-valued Fermatean fuzzy sets (IVFFSs) strategies is proposed for advantage-prioritization so decision-makers can select the most effective strategies for policies. Utilizing the IVFFSs, the proposed method effectively tackles the qualitative/quantitative data and uncertain information that occurs in realistic applications. In this study, firstly IVFF-heronian mean operators with their desirable characteristics are presented to aggregate the IVFF information. The proposed operators can overcome the drawbacks of existing IVFF information-based operators by considering the relationships between IVFF numbers. Based on IVFF heronian mean operators, a hybrid decision-making framework is proposed by integrating criteria importance through inter-criteria correlation (CRITIC), rank sum (RS), and the double normalization-based multi-aggregation (DNMA) methods with IVFF information. In this method, the CRITIC and RS methods are implemented to derive the objective and subjective weights of the considered evaluation criteria and DNMA is applied to prioritize urban climate change policy alternatives for transportation activities. Sensitivity and comparative analyses with existing studies confirm the proposed framework. The evaluation results show that the integration of transportation sectors, strategies, and innovations across different urban areas in all regions option has the highest overall utility degree (0.731) among a set of four urban climate change policy alternatives for transportation activities.

Using self-powered sensors, traffic data may be collected continuously, efficiently, and sustainably once connected autonomous vehicles (CAVs) are a part of metaverse technology. Metaverse self-powered sensors can capture uninterrupted data that allow for activities such as the management of the traffic network, the optimization of transportation facilities, and the management of urban and intercity journeys to be performed. In addition, metaverse technology creates a new field of study. Evaluating the systems involved in current transportation activities together with the metaverse can increase the efficiency and sustainability of transportation. The main purpose of this study is to prioritize four alternatives of CAVs in metaverse with self-powered sensors using a novel decision making model. The proposed hybrid decision making framework includes two stages. In the first stage the fuzzy full consistency method (fuzzy FUCOM) is applied to find the weighting coefficients of criteria. In the second stage, a fuzzy non-linear model based on fuzzy Aczel-Alsina functions (fuzzy Aczel-Alsina weighted assessment - ALWAS method) is defined to rank the alternatives. Four alternatives are defined and evaluated using twelve different criteria under four headings, namely, technical advancement, environmental, implementation, and financial aspects. A case study has been created for the experts to evaluate the alternatives most effectively. The results of the study indicate that using self-powered sensors for integrating real-time traffic management in the metaverse is the most advantageous alternative.

Blockchain technology and metaverse advancements allow people to create virtual personalities and spend time online. Integrating public transportation into the metaverse could improve services and collect user data. This article introduces a hybrid decision-making framework for prioritizing sustainable public transportation in Metaverse under q-rung orthopair fuzzy set (q-ROFS) context. In this regard, first, q-rung orthopair fuzzy (q-ROF) generalized Dombi weighted aggregation operators and their characteristics are developed to aggregate the q-ROF information. Second, a q-ROF information-based method using the removal effects of criteria and stepwise weight assessment ratio analysis models are proposed to find the objective and subjective weights of criteria, respectively. Then, a combined weighting model is taken to determine the final weights of the criteria. Third, the weighted sum product method is extended to q-ROFS context by considering the double normalization procedures, the proposed operators and integrated weighting model. This method has taken the advantages of two normalization processes and four utility measures that approve the effect of benefit and cost criteria by using weighted sum and weighted product models. Next, to demonstrate the practicality and effectiveness of the presented method, a case study of sustainable public transportation in metaverse is presented in the context of q-ROFSs. The findings of this article confirms that the proposed model can recommend more feasible performance while facing numerous influencing factors and input uncertainties, and thus, provides a wider range of applications.

The term metaverse, which shows a 3D-designed virtual medium where people can connect through their avatars to spend time, telecommute, and socialize, has entered our lives fast. There are limitless implementations that can take place in the metaverse. Integration of another technological innovation, which is autonomous vehicles to the metaverse, is at hand. There are numerous alternative uses of autonomous vehicles in the metaverse. In this study, three alternative implementation options for autonomous vehicles in the metaverse are investigated. These alternatives are evaluated using the proposed multi-criteria decision-making (MCDM) method under twelve different criteria, which are grouped under four main aspects, namely technological, societal, legal and ethical, and transportation. A novel hybrid model based on q-rung orthopair fuzzy sets (q-ROFSs) which consists of three stages is presented to express the framework definition, calculate the weight coefficients of the criteria, and rank various alternatives. In the first stage, the structure of the problem is created. In the second stage, q-ROFSs based OPA algorithm is used to calculate the weights of the criteria. In the last stage, q-ROFSs based RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval) is applied to choose the best alternative among the three alternatives. Finally, we present a case study to verify our proposed method. The results of this study have the potential to be used as a guide by decision-makers of the metaverse while integrating autonomous vehicles into the transportation system.

Advances in V2V, V2I, and autonomous vehicle technologies have made autonomous buses possible in cities. Soon, autonomous bus operations will be common in urban areas, which will improve sustainability, safety, and the city’s technology. These buses have different operation types. Each operation has advantages and disadvantages. Therefore, the goal of this study is to serve as a guide for decision-makers during the transition to autonomous buses. Four alternatives are evaluated based on eleven criteria organized under four main aspects, namely autonomous buses for special uses, autonomous buses for last-mile uses, autonomous cars in mixed traffic, and autonomous buses in closed systems. We propose an Ordinal Priority Approach (OPA) method for determining the criteria weights and application of fuzzy Dombi Bonferroni (DOBI) methodology for the evaluation of alternatives. When compared to the other three alternatives in this study, the results show that deploying autonomous buses in mixed traffic is the most advantageous option.

Advances in IoT and IoV technology have made connected autonomous vehicles (CAVs) data sources. Using CAVs as data sources and in incident management algorithms can create faster, more reliable, and more effective algorithms. This paper proposes a modified standard normal deviation (MSND) incident detection algorithm that uses CAVs as data sources and considers multiple traffic parameters. MSND is utilized in conjunction with two other incident detection algorithms, Standard Normal Deviation (SNS) and California (CAL), in a method of incident management known as Variable Speed Limits (VSL). SUMO Traffic Simulation Software is used to evaluate the effectiveness of the proposed method. A 10.4-kilometer road network is developed. Numerous scenarios are simulated on this road network, with variables including traffic demand, autonomous vehicle penetration rate, incident location, incident length, and incident lane. On the effectiveness metrics of detection rate, false alarm rate, and mean time to detect, simulation results demonstrate that the proposed method outperforms the SND and California methods. In terms of detection rate, the MSND algorithm performs the best, with a 12.27% improvement over the SND algorithm and a 21.99% improvement over the California method. After integrating all incident detection algorithms with the VSL traffic management method and simulating each combination, it was determined that the MSND-VSL integration reduced average density in the critical region by 19.73 percent, followed by SND-VSL with a 13.94 percent reduction and CAL-VSL with a 9.9 percent reduction.

Optimal decision-making has become increasingly more difficult due to their inherent complexity exacerbated by uncertain and rapidly changing environmental conditions in which they are defined. Hence, with the aim of improving the uncertainty management and facilitating the weighting criteria, this paper introduces an improved fuzzy Einstein Combined Compromise Solution (CoCoSo) methodology. Such a CoCoSo model improves previous CoCoSo proposals by using nonlinear fuzzy weighted Einstein functions for defining weighted sequences. In addition, it proposes a novel algorithm for determining the criteria weights based on the fuzzy logarithmic function, therefore it allows decision-makers a better perception of the relationship between the criteria, as it considers the relationships between adjacent criteria; high consistency of expert comparisons; and enables the definition of weighting coefficients of a larger set of criteria, without the need to cluster (group) the criteria. Nonlinear fuzzy Einstein functions implemented in the fuzzy Einstein CoCoSo methodology enable the processing of complex and uncertain information. Such characteristics contribute to the rational definition of compromise strategies and enable objective reasoning when solving real-world decision problems. The efficiency, effectiveness, and robustness of the proposed fuzzy Einstein CoCoSo model are illustrated by a case study to create a conceptual framework to evaluate and rank the prioritization of public transportation management at the time of the COVID-19 pandemic. The results reveal its good performance in determining the transportation management systems strategy.

Traffic congestion affects urban areas negatively in many ways. Therefore, successful and efficient traffic management is a necessity to solve or at least alleviate traffic congestion. Hence, the usage of high-quality data is not only essential but also mandatory. Prediction of traffic behaviour over a certain period of time should be done using various characteristics and related data of traffic. In this study, the location, lane, and time data of each vehicle are obtained from cameras located in D100 Highway by computer vision. Besides, the event matrices are created manually to detect the circumstances such as shoulder violation, police stop, police control, traffic flow control, weather, vehicle on the shoulder, and police or ambulance on the shoulder. The effect of the different dynamics of these events in different lanes has been transformed into a single "Event Score" with the help of the weight coefficients obtained from Logistic Regression. Afterward, the traffic density and traffic event datasets are combined to predict the next frame of the traffic. Among the many prediction algorithms tested in this study, Support Vector Machine (SVM) and Recurrent Neural Networks (RNN) were able to predict the traffic density after 1, 3, and 5 minutes with the highest accuracy. As a result of this study, it has been observed that estimation algorithms using "Event Score" obtained with separate coefficients for each lane and historical traffic density data as independent variables give successful results in dynamic and/or static traffic density estimation.

In congested cities where commuting time doubles during peak hours, it is crucial to identify every network problem. Here, it is aimed to analyze the traffic behavior of the buses around the bus stops and to show the effects of the surrounding network to speedup the pattern of buses using the trajectory data of Global Positioning System (GPS)-equipped bus fleet. As an indicator of speed differences near bus stops, influence distance is suggested, wherein the speed of buses significantly changes while approaching and leaving the bus stops. Speed patterns of buses operating on 12 bus routes in Istanbul are analyzed. The data include more than 5000 daily trajectory log files and 25 million rows of location and time information during April 2016. The influence distance, measured using the fused lasso method, for the 438 bus stops varies from 36 to 174m, with an average value of 98m. In the second part of this paper, correlation of the influence distances of the bus stops with the surrounding interruptions is investigated. A distance matrix of surrounding interruptions to the nearest bus stop is generated. This matrix is used as parameters of M5 Prime, random forest, and extremely randomized trees models in order to predict the influence distances. The models show that the passenger demand plays a key role in the influence distances of the bus stops. It is also found that the changes in the number of lanes and the location of the traffic lights are quite effective on the influence distances.