Public transportation frameworks assume a critical role in the metropolitan region, especially in huge urban communities, where they offer a feasible answer for easing gridlock, moderating commotion contamination, and diminishing CO2 discharges. This paper presents some novel Fermatean fuzzy Heronian mean operators based on Archimedean t-norms, specifically the generalized Fermatean fuzzy Archimedean Heronian mean (GFFAHM) and the Fermatean fuzzy Archimedean geometric Heronian mean (FFAGHM). The study investigates different unique instances of these operators while exploring their essential properties. Besides, a powerful multiattribute decision-making (MADM) method is developed using the proposed operators. This approach offers a key asset for handling complex decision-making problems. Overcoming the capabilities of conventional BM operators, the GFFAHM and FFAGHM operators effectively minimize the potential redundancy in interrelationships during the decision-making process. The inclusion of the flexible parameters ρ and ϕ, which have a big impact on the decision-making process’ outcomes, increases the adaptability and resilience of the Archimedean t-based operators. To depict the feasibility of the proposed MADM method, a thorough quantitative model is introduced, outlining its useful execution. Through an exhaustive case study, the predominance of the proposed approach over existing strategies is experimentally established. Remarkably, the study uncovers that reducing fares is the most compelling factor in expanding the public transport framework, subsequently advancing sustainable urban transport. This study’s findings provide useful insights into decision-making processes and practical implications for policymakers, allowing them to make informed and impactful changes to the public transportation system. The proposed MADM method can play a vital role in upgrading the public transport framework, making way for remarkable strategy interventions in urban transport sustainability.

This paper proposes a rectified complex spherical fuzzy set (rCTSFS) model that enables the phase term of a complex number to function truthfully to its inherent meaning of representing directions, phases, or color hues. In addition, this paper proposes the score and accuracy functions for rectified complex spherical fuzzy numbers (rCTSFn), which allows the three types of membership degrees of an rCTSFn to fulfill human judgment/intuition. The proposed rCTSFS model proves to be a productive extension of the complex spherical fuzzy set (CSFS), complex fuzzy set (CFS), and spherical fuzzy set (SFS) models. On the other hand, Dombi t-norms prove more flexible and comprehensive than some of the other families of triangular norms, such as the algebraic t-norms and the Einstein t-norms, due to the presence of a parameter γ. The parameter γ determines the amount of aggressiveness at estimating the maximum and the minimum of a population based on a sample obtained. Therefore, this paper proposes two Dombi-Choquet integral operators, namely, the rectified complex t-spherical fuzzy arithmetic Dombi-Choquet integral (rCTSFAγ,wλ) operator and the rectified complex t-spherical fuzzy geometric Dombi-Choquet integral (rCTSFGγ,wλ) operator. A multi-criteria decision making (abbr. MCDM) algorithm utilizing the two Dombi-Choquet integral operators is innovated. The proposed Dombi-Choquet MCDM algorithm for the rCTSFS model is applied to an MCDM problem related to diabetic retinopathy detection on five real-life fundus images of different severity levels taken from the Messidor2 dataset. Our newly proposed algorithm proves to be the only algorithm that yields the correct diagnostic results that match the hard truth. On the other hand, none of the 50 algorithms observed among recent works in literature can produce the correct diagnostic results, even after lending the fuzzification procedure innovated in this work to them.

Enhancing road safety through a more effective understanding of drivers' behavior is a viable approach to curbing traffic collisions. When evaluating driving behavior, the selection of methodologies is diverse, often facing scrutiny. This study aims to detect, compare and quantify critical drivers' behavior factors concerning road safety in Budapest, Hungary. Employing the Analytic Hierarchy Process (AHP) within a spherical fuzzy framework, based on Spherical Fuzzy Sets (SFS), we assess driver preferences. Kendall's test gauges’ agreement levels among hierarchical driver groups. At Level 1, our Spherical Fuzzy AHP (SFAHP) identifies 'Lapses' as crucial, followed by 'Errors' for experienced and young drivers. However, foreign drivers prioritize 'Errors' and 'Violations.' At Level 2, “Aggressive violations” prevails across all groups, contrasting with “Ordinary violations.” At Level 3, “Driving with alcohol use” reigns supreme. Kendall's concordance demonstrates low similarity at Level 1, while strong agreement surfaces for Levels 2 and 3. Our insights can empower transportation authorities to bolster road safety strategies by addressing these pivotal behavior factors.

In uncertain information environment, bi-polar preferences can be elicited from experts and processed to be exerted over some weights determination for multiple-agents evaluation. Recently, some weighting methodologies and models in uncertain and preference involved environment with multiple opinions from multiple experts are proposed in some literature. However, in that existing method, when collecting different types of preferences from a single expert, sometimes some subtle cognitive inconsistency may occur. To eliminate such inconsistency, this work elaborately analyzes the possible reasons and proposes some amendment together with a new distinguishable set of formulations for modeling. In addition, we further consider two situations of the weighting models for the problem, with one only considering the situation of single expert with no risk of cognitive inconsistency and the other considering the case of multiple experts wherein some inconsistency might occur. Numerical example and comparison are also presented accordingly.

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.

Uncertainties are pervasive in ever-increasing more practical evaluation and decision making environments. Numerical information with uncertainty losses more or less credibility, which makes it possible to use bi-polar preference based weights allocation method to attach differing importance to different information granules in evaluation. However, there lacks effective methodologies and techniques to simultaneously consider various categories of involved bi-polar preferences, not merely the magnitude of main data which ordered weighted averaging aggregation can well handle. This work proposes some types and categories of bi-polar preference possibly involved in preference and uncertain evaluation environment, discusses some methods and techniques to elicit the preference strengths from practical backgrounds, and suggests several techniques to generate corresponding weight vectors for performing bi-polar preference based information fusion. Detailed decision making procedure and numerical example with management background are also presented. This work also presents some practical approaches to apply preferences and uncertainties involved aggregation techniques in decision making.

For the first time, the critical worldwide problem of prioritizing zero-emission last-mile delivery (LMD) solutions for sustainable city logistics is addressed and solved in this article. It not only aims to help city logistics companies sustainably decarbonize urban freight distribution but also provide valuable practical guidelines. To evaluate zero-emission LMD solutions, this article presents a novel multicriteria group decision-making methodology with dual hesitant fuzzy (DHF) sets. First, we propose some improved operations on DHF elements and investigate their vital properties. Second, based on these operations, we develop DHF improved weighted averaging operator to overcome the drawbacks of the existing operators on DHF sets. Third, for measuring the weights of criteria, a new model called the cross-entropy-based optimization model (CEBOM) is developed. Fourth, for the rational aggregation of the preferences, we formulate a new method namely score-based double normalized measurement alternatives and ranking according to the compromise solution (SDNMARCOS). The proposed DNMARCOS method couples the linear and vector normalization techniques. It is composed of the complete compensatory model and the incomplete compensatory model. Thus, SDNMARCOS is more robust compared to the available state-of-the-art approaches. To exhibit the applicability of the proposed DHF-CEBOM-SDNMARCOS methodology in real-world settings, a case study for one of the largest Austrian logistics companies in Serbia is provided. The research findings show that electric light commercial vehicles are the best LMD solution. Also, it is recommended to consider electric cargo bikes as a viable mid-term solution. The superiority of the introduced methodology is demonstrated through the comparative investigation.

In the creation of better multiple attribute decision-making (MADM) patterns to address the ambiguity in the expanding sophisticated of expert systems, the hypothesis of interval-valued intuitionistic fuzzy sets has proven to be an effective and advantageous technique. We employ Aczel–Alsina operations to remedy the MADM issue, wherein all data supplied by decision-makers is conveyed as interval-valued intuitionistic fuzzy (IVIF) decision matrices with all components described by an IVIF number (IVIFN). This allows us to satisfy much more demands from fuzzy decision-making concerns (IVIFN). In the framework of IVIFNs, we primarily describe several novel Aczel–Alsina operations. On the basis of these operations, we construct several novel IVIF aggregation operators, such as the IVIF Aczel–Alsina weighted averaging operator, the IVIF Aczel–Alsina order weighted averaging operator, and IVIF Aczel–Alsina hybrid averaging operator. We built up several features of such operators. We recommend an MADM technique dependent on the advanced IVIF aggregation operators. To demonstrate the effectiveness of the developed technique, we present an overview of research scientist selection. The experimental results show the viability and benefits of the created strategy by contrasting it with the different strategies. This paper reveals that some existing IVIF aggregation operators are particular instances of the operators induced in this paper.

This paper describes the new intuitionistic fuzzy aggregation operators in consequence of Aczel–Alsina operations that possess certain advantages in cases of solving real life problems. We first present some new operations of intuitionistic fuzzy sets (IFSs), for example, Aczel–Alsina sum, Aczel–Alsina product, and Aczel–Alsina scalar multiplication. At that point, we create some IF aggregation operators, for example, the IF Aczel–Alsina weighted averaging operator, the IF Aczel–Alsina ordered weighted averaging operator and IF Aczel–Alsina hybrid averaging operator. We set up different properties of these operators. It is demonstrated that suggested averaging operators have the properties of idempotency, boundary, monotonicity, and commutativity. Then, we design new techniques dependent on these operators to fix multiattribute decision making issues. We present an example of human resource selection to elaborate on the performance of our proposed approach. The outcome shows the practicality and viability of the new technique. Eventually, an organized comparison between the prevailing techniques and the suggested technique has been given.

The dual probabilistic linguistic (DPL) term sets are considered superior to probabilistic linguistic term sets. Further, the generalized Dombi (GD) operators are pretty flexible with the general parameters during the aggregation process. Besides, the Bonferroni mean (BM) operator has the advantage of considering interrelationships between criteria. In this study, we combine the merits of the GD operator, and BM operator for handling multicriteria group decision-making issues under a DPL setting. The existing research on DPL term sets do not focus on both the subjective and objective weights of decision-experts. As a result, the evaluation results are likely to be distorted. To tackle this situation, in this paper, we utilize the concepts of consistency and similarity between the decision-experts to determine the decision-experts subjective and objective weights, respectively. To calculate the weights of criteria, the grey correlation coefficient of the assessment value of criteria is used to reflect the similarity between the criteria and its reference value. Since the existing aggregation operators fail to capture the interrelations between criteria under DPL setting, so for aggregating criteria values, we propose DPL generalized Dombi BM weighted averaging and geometric aggregation operators. We provide a case study regarding biomass feedstock selection to focus on the applicability of these proposed operators. Furthermore, we investigate the effects of the parameters upon ranking order. We also perform a sensitivity assessment of criteria weights to test the stability of our method. Lastly, we provide a comparison between our approach with various extant methods.

The operations of -norm and -conorm, developed by Dombi, were generally known as Dombi operations, which may have a better expression of application if they are presented in a new form of flexibility within the general parameter. In this paper, we use Dombi operations to create a few Pythagorean fuzzy Dombi aggregation operators: Pythagorean fuzzy Dombi weighted average operator, Pythagorean fuzzy Dombi order weighted average operator, Pythagorean fuzzy Dombi hybrid weighted average operator, Pythagorean fuzzy Dombi weighted geometric operator, Pythagorean fuzzy Dombi order weighted geometric operator, and Pythagorean fuzzy Dombi hybrid weighted geometric operator. The distinguished feature of these proposed operators is examined. At that point, we have used these operators to build up a model to remedy the multiple attribute decision-making issues under Pythagorean fuzzy environment. Ultimately, a realistic instance is stated to substantiate the created model and to exhibit its applicability and viability.