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This study proposes novel operational laws that extend the Frank t-norm and t-conorm to develop a new class of aggregation operators (AOs), namely the quasirung orthopair fuzzy Frank weighted average, weighted geometric, ordered weighted average, and ordered weighted geometric operators. These operators are specifically designed to manage uncertain and imprecise information within multi-attribute group decision-making (MGADM) environments. The proposed operators exhibit desirable mathematical properties such as flexibility, robustness, and compatibility, making them highly suitable for complex fuzzy decision contexts. Flexibility is notably enhanced through the independent tuning of the parameters , , and , allowing for more refined control over membership (MD), non-membership (NMD), and interaction behaviors. An entropy-based approach is employed to objectively determine unknown attribute weights, minimizing subjective bias. A real-world case study on the selection of an optimal investment location demonstrates the practical applicability of the proposed method. The results show an improvement in decision-making accuracy by approximately 7.5% compared to traditional approaches. Sensitivity analysis confirms the stability and reliability of the proposed operators under varying conditions. Comparative results further highlight the method’s superiority in terms of accuracy, interpretability, and adaptability to input variations. The paper concludes by outlining special cases and acknowledging certain limitations, offering directions for future research.

Short-term, automated car rental services, i.e., car sharing, are a solution that has been improving in urban transportation systems over the past few years. Due to the intensive expansion of the systems, service providers face increasing challenges in their competitiveness. One of them is to meet the customer expectations for the fleet of vehicles offered in the system. Although this aspect is noted primarily in the literature review on fleet optimization and management, there is a gap in research on the appropriate selection of vehicle models. In response, the article aimed to identify the vehicles best suited for car-sharing systems from the customer’s point of view. The selection of suitable vehicles was treated as a multi-criteria decision-making issue; therefore, the study used ELECTRE III—one of the multi-criteria decision-making methods. The work focuses on researching the opinions of users who rarely use car-sharing services in Poland. The most popular car models in 2021, equipped with internal combustion, hybrid, and electric engines, were selected for the analysis. The results indicate that the best suited cars are relatively large, spacious, and equipped with electric drive and represent the D segment of vehicles in Europe. In addition, these vehicles are to be equipped with a powerful engine, a spacious boot, and a fast battery charging time. Interestingly, small city cars, so far associated with car sharing, ranked the worst in the classification method. In addition, factors such as the warranty period associated with the quality of the vehicles, or the number of car doors, are not very important to users. The results support car-sharing operators in the process of selecting or modernizing a fleet of vehicles.

Short-term car rental services, i.e., carsharing, is a solution that has been developing better and better in urban transport systems in recent years. Along with intensive expansion, service providers have to face an increasing number of challenges to compete with each other. One of them is meeting the expectations of customers about the fleet of vehicles offered in the system. While this aspect is noticed in the literature review mainly in terms of fleet optimization and management, there is a research gap regarding the appropriate selection of vehicle models. In response, the article was dedicated to identifying the vehicles that were best suited to carsharing systems from the point of view of frequent customers. The selection of appropriate vehicles was treated as a multi-criteria decision issue, therefore the study used one of the multi-criteria decision support methods—ELECTRE III. The work focuses on researching the opinions of users (experts) who often use carsharing services in Poland. The study included a list of the most popular vehicles in Europe in 2021, including classic, electric, and hybrid cars, and a list of 11 evaluation criteria. The research results indicate for frequent users the advantage of conventional drive vehicles over electric and hydrogen vehicles. Moreover, they indicate that the best vehicles are relatively large cars (European car segments C and D) with the greatest possible length, boot capacity, engine power, number of safety systems, and quality. On the other hand, the least important issues are the number of seats in the vehicle and the number of doors. Interestingly, the vehicles selected by frequent users questioned the concept of small city cars, which occupied a small public space on which carsharing was supposed to focus. The results obtained support the operators of carsharing services in making fleet decisions.

Many algorithms related to vehicular applications, such as enhanced perception of the environment, benefit from frequent updates and the use of data from multiple vehicles. Federated learning is a promising method to improve the accuracy of algorithms in the context of vehicular networks. However, limited communication bandwidth, varying wireless channel quality, and potential latency requirements may impact the number of vehicles selected for training per communication round and their assigned radio resources. In this work, we characterize the vehicles participating in federated learning based on their importance to the learning process and their use of wireless resources. We then address the joint vehicle selection and resource allocation problem, considering multi-cell networks with multi-user multiple-input multiple-output (MU-MIMO)-capable base stations and vehicles. We propose a “vehicle-beam-iterative” algorithm to approximate the solution to the resulting optimization problem. We then evaluate its performance through extensive simulations, using realistic road and mobility models, for the task of object classification of European traffic signs. Our results indicate that MU-MIMO improves the convergence time of the global model. Moreover, the application-specific accuracy targets are reached faster in scenarios where the vehicles have the same training data set sizes than in scenarios where the data set sizes differ.

This paper presents a novel integrated multi-criteria decision-making model consists of the CRITIC (CRiteria Importance Through Intercriteria Correlation) technique and the EDAS (The Evaluation based on Distance from Average Solution) method to evaluate the selection of the urban rail vehicles operated in the public transport systems. In order to determine the selection criteria in a more realistic perspective, a board of experts consists of seven members, who perform as senior executives in the public transport institutions of the country, was constructed and many round tables meetings were organized with together the members of the board for determining the procedure to follow to reach meaningful and applicable results. At the beginning of the research, approximately fifty selection criteria were determined by the research team and some of them were eliminated during the preparation process of this study. Finally, 22 factors were determined as the selection criteria, which will be used in this research. These factors are the selection criteria directly or indirectly affecting the selection process and results. Also, rail tram types currently operated in the various cities of Turkey were determined as decision alternatives. These options were evaluated by using the proposed integrated MCDM approach. When the obtained results are evaluated, it can be seen that the proposed model has the potential to give very successful results for evaluating the selection process of the urban rail systems. Moreover, it can be implemented as a decision support system by decision-makers, who perform in the public transport authorities. Finally, it can also be applied for decision-making problems faced in the various fields.

The paper studies a game theory model to ensure fairness and improve the communication efficiency in an unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (C-V2X) communication network using Markovian game theory in a federated learning (FL) environment. The UAV and each vehicle in a cluster utilized a strategy-based mechanism to maximize their model completion and transmission probability. We modeled a two-stage zero sum Markovian game with incomplete information to jointly study the utility maximization of the participating vehicles and the UAV in the FL environment. We modeled the aggregating process at the UAV as a mixed strategy game between the UAV and each vehicle. By employing Nash equilibrium, the UAV determined the probability of sufficient updates received from each vehicle. We analyzed and proposed decision-making strategies for several representative interactions involving gross data offloading and federated learning. When multiple vehicles enter a parameter transmission conflict, various strategy combinations are evaluated to decide which vehicles transmit their data to the UAV. The optimal payoff in a transmission window is derived using the Karush–Khun–Tucker (KKT) optimality conditions. We also studied the variation in optimal model parameter transmission probability, average packet delay, UAV transmit power, and the UAV–Vehicle optimal communication probabilities under different conditions.

In this paper, unmanned aerial vehicle (UAV)-assisted vehicular communications are investigated to minimize latency and maximize the utilization of available UAV battery power. As communication and cooperation among UAV and vehicles is frequently required, a viable approach is to reduce the transmission of redundant messages. However, when the sensor data captured by the varying number of vehicles is not independent and identically distributed (non-i.i.d.), this becomes challenging. Hence, in order to group the vehicles with similar data distributions in a cluster, we utilize federated learning (FL) based on an attention mechanism. We jointly maximize the UAV’s available battery power in each transmission window and minimize communication latency. The simulation experiments reveal that the proposed personalized FL approach achieves performance improvement compared with baseline FL approaches. Our model, trained on the V2X-Sim dataset, outperforms existing methods on key performance indicators. The proposed FL approach with an attention mechanism offers a reduction in communication latency by up to 35% and a significant reduction in computational complexity without degradation in performance. Specifically, we achieve an improvement of approximately 40% in UAV energy efficiency, 20% reduction in the communication overhead, and 15% minimization in sojourn time.

Roadside units (RSUs) play an important role in the request fulfillment of vehicles. In rural areas, RSUs are powered by renewable energy sources like solar energy. Hence, the request fulfillment of vehicles must be done in such a way that the energy consumption across RSUs is minimized. The requests are categorized into traditional application (low computation) requests and smart application (high computation) requests. To avoid excessive computation at RSUs, low computation requests are scheduled across RSUs while high computation requests are scheduled across fog servers for processing. In this paper, we propose an online energy-efficient Inter-RSU Scheduling Algorithm (ee-IRSA) and a distributed Ant Colony Optimization-based Load balancing technique (d-ACOL) for optimizing the request fulfillment of traditional and smart application requests, respectively. ee-IRSA ensures minimum and uniform energy consumption across RSUs while d-ACOL ensures minimum queue waiting time of requests across fog servers. In addition, a second-price auction game-based relay vehicle selection technique is proposed which further minimizes the energy consumption of RSUs. Simulation results show that ee-IRSA with relay vehicle selection reduces the energy consumption by 33%, and d-ACOL reduces the queue waiting time by an average of 48% as compared to other load balancing techniques.

The complexity of large-scale group decision-making (LSGDM) in the digital society is becoming increasingly prominent. How to achieve efficient consensus through social networks (SNs) has become a core challenge in improving the decision quality. First, conventional clustering methods often rely on a single-distance metric, neglecting both numerical assessments and preference rankings. Second, ensuring the decision authenticity requires considering diverse behaviors, such as trust propagations, risk preferences, and minority opinion expressions, for scientific decision-making in SNs. To address these challenges, a consensus-reaching process (CRP) method based on an enhanced K-means++ clustering is proposed. The above method not only focuses on minority opinion handling (MOH), but also incorporates decision indicator sets (DISs) to analyze the degree of opinion divergences within groups. First, the Hamacher aggregation operator with a decay factor completes trust matrices, improving the trust representation. Second, a personalized distance metric that combines cardinal distances with ordinal distances is incorporated into the enhanced K-means++ clustering, enabling more precise clustering. Third, weights for decision-makers (DMs) and subgroups are determined based on trust levels and degree centrality indices. Fourth, minority opinions are appropriately handled via considering the diverse backgrounds and expertise of DMs, leveraging a difference-oriented DIS to detect and adjust these opinions via weight modifications until a consensus is reached. Fifth, the alternative ranking is objectively generated via DIS scores derived from multigranulation rough approximations. Finally, the feasibility of the proposed method is validated via a case study on unmanned aerial vehicle (UAV) selection using online reviews, supported by a sensitivity analysis and comparative experiments demonstrating superior performances over existing methods. The result shows that the proposed model can enhance clustering accuracies with hybrid distances, objectively measure the consensus via DISs, handle minority opinions effectively, and improve LSGDM’s overall efficiencies.