ABSTRACT Our study delves into the crucial issue of road safety by examining the intricate dynamics of driver behaviour, often resulting in tragic accidents. The importance of comprehending these behaviours is acknowledged, leading us to propose an innovative decision‐making support model that integrates the analytic hierarchy process (AHP) with the best worst method (BWM) in a fuzzy context. Our objective is to effectively evaluate the overall influence of driver behaviour on road safety while reducing ambiguity in assessments. In a practical case study involving skilled drivers in Budapest, Hungary, a thorough survey was conducted to prioritise key driving behaviour factors that impact road safety. Our findings reveal ‘errors’ as the most vital aspect, followed by specific behaviours like ‘colliding when reversing without observation’ and ‘driving under the influence of alcohol’. By simplifying the survey procedure and offering practical insights, our unified model improves decision‐making for policymakers striving to tackle road safety issues efficiently. To conclude, our research showcases the effectiveness of merging AHP and BWM methodologies in a fuzzy setting to obtain valuable perspectives on road safety concerns, ultimately aiding in the advancement of sustainable transportation systems.

The coronavirus pandemic significantly increased the use of essential medical supplies, resulting in a surge in medical waste generation. This surge has spurred extensive research into sustainable disposal methods for safe and environmentally responsible medical equipment management. Addressing this multifaceted issue falls within the domain of multi-criteria decision-making. This study presents a comprehensive framework for selecting optimal medical waste treatment methods, considering economic, technological, environmental, and social factors. This is the first study to address the problem of selecting a medical waste disposal technology using the Fuzzy Dombi Bonferroni. The mean operator to combine expert opinions, the fuzzy preference selection index method to evaluate the criteria and the fuzzy compromise ranking of alternatives from distance to ideal solution method to rank the alternatives. According to the weightings, the social dimension holds the highest significance at 0.3217. Disinfection efficiency ranks as the most critical criterion, weighing in at 0.0823. The autoclave is rated as the top disposal technique, with a utility function value of 5.4579. Sensitivity analyses ensured the stability and reliability of the models. The adaptability of the applied model to sustainable practices such as energy conversion, material recycling, and resource recovery represents an essential aspect of policymaking in waste management. This assessment can guide policy formulation or improvement processes for waste disposal.

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.

Sustainable urban transport is the key factor for surviving the cities and developing the supply quality of the urban transport system has been esteemed in sustainable improvement for the cities. This work attempts to provide a sustainable and efficient solutions for ameliorating public bus transport system in Dublin city, Ireland. The developed system will attract private car users, which interns will achieve detract CO2 emissions, minimize traffic congestions and maximize commuter satisfaction. To evaluate this complex problem, the novel Parsimonious Analytic Hierarchy Process (P-AHP) is structured in a spherical fuzzy environment. The parsimonious spherical fuzzy analytic hierarchy process (P– SF-AHP) model considers as an efficient solution not only for evaluating a large number of alternatives or criteria when using AHP, however, it esteems the hesitant scoring of the decision maker. The results are demonstrated and analyzed in detail and the step-by-step description of the procedure might foment other applications of the model. The unique process for evaluating the supply quality of urban transport system consumes less time and effort during estimating the survey, moreover, it provides more consistent and reliable outcomes through avoiding the uncertainty and ambiguity of decision makers during evaluation process.

Transport systems are pivotal in sustainable development, profoundly impacting social, environmental, and economic sustainability in urban settings. Integrating decision support systems becomes imperative to underscore the necessity for change and facilitate informed policy decisions based on current conditions. This study evaluates Budapest, Hungary's urban public transport system to foster sustainability. Experts in the transportation field in Budapest were engaged as evaluators. The study employs the newly developed Magnitude Based Fuzzy Analytic Hierarchy Process, chosen for its accuracy and computational efficiency compared to existing methods. The obtained results align with those from the Modified Fuzzy Logarithmic Least Squares method, affirming its reliability. Comparative and sensitivity analyses validate, ensure consistency, and test the robustness of the proposed model findings. The study concludes that introducing new buses emerges as the most viable solution for enhancing the service quality of the existing transport system. The adopted results on a real dataset, its application in transportation evaluation, and the recommendation of new buses as an optimal solution offer insights into sustainable urban transport development.

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.

This paper introduces a novel Integrated Interval Rough Pivot Pairwise Relative Criteria Importance Assessment (IRN PIPRECIA) model combined with Interval Rough Combined Compromise Solution (IRN CoCoSo), marking a significant advancement in sustainable traffic flow management for commercial vehicles. This innovative merger is a first in literature, methodologically enhancing the evaluation of road sections based on critical parameters including passenger car equivalent (PCE) 85%, AADT, road conditions, and accident data. Our model systematically fills the research gap in holistic traffic performance analysis, providing a unique tool for prioritizing road safety and efficiency. The key scientific contribution is the model’s ability to integrate causal and consequential traffic factors into a single framework, offering a novel multi-criteria decision-making (MCDM) approach. Results, validated through various verification models, show the integrated model’s effectiveness in real-world scenarios, confirming its robustness and stability. With strong engineering application potential, our work supports urban planners and traffic managers in making informed, sustainable decisions. The model extends beyond traditional traffic analysis, promising a shift towards more adaptive, data-driven infrastructure management. Future research will aim to refine the criteria basis and explore real-time decision-making through advanced MCDM applications.

Developing a comprehensive data-driven strategy for evaluating the organisational culture in companies to foster digital innovation involves a multi-criteria decision-making (MCDM) problem. This needs to consider various organisational culture characteristics that influence digital innovation success, assign significance weights to each characteristic, and recognise that distinct organisational cultures may excel in different aspects necessitates the proper handling of data variations. Hence, to provide organisations seeking to align cultural practises with digital innovation objectives with valuable insights, this study aims to develop an MCDM model for evaluating and benchmarking organisational culture in companies to foster digital innovation. The benchmarking decision matrix is formulated based on the intersection of evaluation characteristics and a list of organisational culture aspects in companies. The MCDM model is developed in two phases. Firstly, a new weighting model, q-rung picture fuzzy-weighted zero-inconsistency (q-RPFWZIC), is formulated for assessing the evaluation characteristics under the q-rung picture fuzzy sets environment. Secondly, the simple additive weighting (SAW) model is formulated for benchmarking the organisational culture in companies using the extracted weights of the evaluation characteristics. The results indicate that characteristic C6 (corporate entrepreneurship) has the highest weight, with a value of 0.161, while characteristic C3 (employee participation, agility and organizational structures) and C7 (digital awareness and necessity of innovations) has the lowest weight of 0.088. Company A2 secures the top rank with a score of 0.911, satisfying eight evaluation characteristics, whereas company A7 holds the last rank order, satisfying only one evaluation characteristic, obtaining a score of 0.101. In model evaluation, several scenarios were considered in a sensitivity analysis test based on a 100% increment in weight values for each characteristic to validate the reliability of the model results.