A novel rough numbers based extended MACBETH method for the prioritization of the connected autonomous vehicles in real-time traffic management

Abstract

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 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, three 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. According to the study's findings, real-time traffic management with CAVs is the most advantageous alternative, while real-time traffic management integrated with CAVs is the least advantageous