In the realm of sustainable urban development, a paramount focus is placed on the amalgamation of environmental conservation, the integration of smart technology, and the promotion of social inclusivity. This approach advocates for transit-oriented development, the establishment of resilient infrastructure, and the active engagement of communities. A critical balance is sought between economic viability and adaptive governance, aiming to cultivate cities that are simultaneously environmentally conscious, economically vibrant, and socially equitable. Within this context, Multiple Attribute Decision Making (MADM) emerges as a pivotal tool, streamlining decision processes through the quantitative evaluation of alternatives against criteria such as environmental impact and social inclusivity. MADM plays an instrumental role in ensuring effective resource allocation, thereby fostering resilient infrastructure and optimizing the equilibrium between economic growth and sustainability in urban planning. This study delves into an advanced methodology for addressing uncertainties in decision-making, employing Picture Fuzzy Sets (PFSs), articulated through the meticulous application of the Measurement Alternatives and Ranking according to Compromise Solution (MARCOS). The utilization of the MARCOS strategy in decision-making is underscored by its proven robustness as a tool for pinpointing the optimal objective. This method integrates diverse aggregation strategies to adeptly navigate complex decision scenarios characterized by multiple criteria. To illustrate the adaptability and efficacy of the proposed methodology, a numerical case study is presented, offering a vivid demonstration of its practical application in the field of urban development.

Traffic accidents, a global concern, pose threats to lives and carry substantial economic and societal burdens. This paper explores the innovative integration of artificial intelligence (AI) into the ambiance of Multi Criteria Decision Making (MCDM) to address the challenge. We investigate AI's potential to enhance road safety globally, utilizing data analysis, predictive modeling, and intelligent traffic management systems. Our goal is to revolutionize accident prevention by optimizing decision-making processes and promoting safer and more efficient road environments. In the realm of information aggregation and fusion, growing interest among researchers has centered on probabilistic linguistic expression sets, which adeptly aggregate uncertain data. This article's primary aim is to explore methodologies for aggregating information within a probabilistic linguistic environment. To achieve this goal, we have introduced procedural laws based on the Sugeno-Weber (SW) framework for handling probabilistic linguistic term elements (PLTEs), rooted in both the product and sum of SW operations. Consequently, we have crafted a range of probabilistic linguistic aggregation techniques, encompassing the probabilistic linguistic SW Average (PLSWA) and Geometric (PLSWG), followed by weighted as well as ordered aggregation operators like PLSWWA and PLSWWG, PLSWOWA and PLSWOWG. By harnessing SW τ-norm and τ-conorm, we have developed versatile aggregation tools that facilitate information reinforcement. Through the utilization of proposed operators, we have presented strategies for effectively integrating probabilistic linguistic term sets (PLTs) in the context of MCDM. We have compared our suggested procedures with the TOPSIS approach and elucidated the diverse features inherent to these operators.