A Novel Rough Numbers based Extended MACBETH Method Implementation in the Prioritization of the Connected Autonomous Vehicles

Digital transformation can help to make better use of existing transportation networks that are congested. One solution to the road congestion problem is real-time traffic management, which focuses on enhancing traffic flow conditions. The advantages of real-time traffic management systems have developed significantly as a result of connected autonomous vehicle (CAV) innovations. CAVs can act as enforcers for managing the traffic. This study aims to propose a novel rough numbers-based extended Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) method for prioritizing the real-time traffic management systems. Furthermore, a new approach for defining rough numbers is proposed, based on an improved methodology for defining rough numbers' lower and upper limits. This allows consideration of mutual relations between a set of objects and flexible representation of rough boundary interval depending on the dynamic environmental conditions. In this study, 3 main alternatives are defined for real-time traffic management systems: real-time traffic management, real-time traffic management integrated with CAVs, and real-time traffic management by using CAVs. For these alternatives, 5 main criteria and 18 sub-criteria are defined and then prioritized using the fuzzy multi-criteria decision-making (MCDM) approach. The proposed method's performance is validated through scenario analysis. The findings demonstrate that the proposed method is effective and applicable to real-world conditions.