Modeling Metaverse Perceptions for Bolstering Traffic Safety using Novel TrSS-Based OWCM-RAM MCDM Techniques: Purposes and Strategies

Mona Mohamed, Asmaa Elsayed, Marwa Sharawi
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Abstract

The Metaverse has the potential to revolutionize various aspects of human life, including transportation systems. The integration of the Metaverse into intelligent transportation systems has the potential to significantly improve traffic safety in smart cities. By creating a virtual replica of the physical world, the Metaverse can provide a platform for testing new traffic management systems, road designs, and vehicle technologies in a controlled and safe environment before implementing them in the real world. One way to integrate the Metaverse into intelligent transportation systems (ITS) is by enhancing traffic safety. This can be achieved by developing an evaluation model that considers both safety and traffic efficiency. The proposed evaluation methodology encompasses three phases. Firstly, the obligations/criteria, and subsidiary obligations are modeled into nodes within levels based on Tree Soft Sets (TrSSs). Secondly, the Opinion Weight Criteria Method (OWCM) is utilized for generating the weights for obligations and subsidiary obligations. Finally, the Root Assessment Method (RAM) harnesses the generated weights for assessing and ranking alternative approaches to improving traffic safety in smart cities. The utilized techniques are working under the authority of neutrosophic theory to support these techniques in uncertain and ambiguous circumstances. Subsequently, the proposed methodology is tested in a case study that considers three alternative approaches to improving traffic safety in a smart city. The criteria for evaluation include safety and traffic aspects. The results of the case study indicate that the proposed evaluation model effectively ranks the alternative approaches based on their safety and traffic efficiency. This suggests that the Metaverse can be effectively integrated to enhance traffic safety and improve overall transportation efficiency. Overall, the results of the case study suggest that the proposed evaluation model effectively ranks the alternative approaches based on their safety and traffic efficiency. This indicates that the integration of the Metaverse can indeed enhance traffic safety and improve overall transportation efficiency in smart cities. 
利用基于 TrSS 的新型 OWCM-RAM MCDM 技术建立元数据感知模型以加强交通安全:目的与策略
元宇宙有可能彻底改变人类生活的方方面面,包括交通系统。将 "元宇宙 "整合到智能交通系统中,有可能显著改善智能城市的交通安全。通过创建一个物理世界的虚拟复制品,Metaverse 可以提供一个平台,在可控和安全的环境中测试新的交通管理系统、道路设计和车辆技术,然后再在现实世界中实施。将 "元空间 "融入智能交通系统(ITS)的一种方法是加强交通安全。要做到这一点,就必须建立一个同时考虑安全和交通效率的评估模型。建议的评估方法包括三个阶段。首先,基于树状软集(TrSSs),将义务/标准和附属义务建模为层级内的节点。其次,利用意见权重标准法(OWCM)生成义务和附属义务的权重。最后,根评估法 (RAM) 利用生成的权重对备选方法进行评估和排序,以改善智慧城市的交通安全。所使用的技术是在中性理论的指导下工作的,以便在不确定和模糊的情况下支持这些技术。随后,在一项案例研究中对所提出的方法进行了测试,该案例研究考虑了在智慧城市中改善交通安全的三种替代方法。评估标准包括安全和交通方面。案例研究结果表明,所提出的评估模型能有效地根据安全和交通效率对备选方法进行排序。这表明,可以有效整合 Metaverse 来加强交通安全,提高整体交通效率。总体而言,案例研究的结果表明,建议的评估模型能有效地根据安全性和交通效率对备选方法进行排名。这表明,整合 Metaverse 确实可以加强智慧城市的交通安全,提高整体交通效率。
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