Alternative prioritization of freeway incident management using autonomous vehicles in mixed traffic using a type-2 neutrosophic number based decision support system

Abstract

Traffic incident management is combining the assets of authorities to identify, deal with, and manage traffic problems as rapidly as possible while providing the safety of on-scene responders and the traveling public. The advancement of autonomous vehicles is an opportunity for enhancing incident management implementations. This study aims to provide policymakers with four main alternatives to control freeway incidents using autonomous vehicles in mixed traffic. The presented alternatives are namely autonomous vehicles behaving as human-driven vehicles, ones connected, ones using an algorithm for incident management, and ones used in the traditional incident management methodology. The study also aims to introduce an integrated decision-making tool that is comprehendible for policymakers and mobility experts. It is based on the integration of an Entropy-based approach and the complex proportional assessment (COPRAS) method under the type-2 neutrosophic number (T2NN) environment. T2NN can represent uncertainties such as uncertainty, inconsistency, and inconsistency in real-world problems. T2NN-Entropy is presented to reveal the objective importance of evaluation criteria for freeway incident management. T2NN-COPRAS is proposed to order alternatives when deciding on the behavior of autonomous vehicles. The comparative investigation shows the superiority of the T2NN-Entropy-COPRAS model. Its major advantages are high robustness in making real-world multi-criteria decisions due to the triple-normalization backbone, and high flexibility in solving complex decision-making problems. The research findings show that using an algorithm for incident management is the best alternative to solve problems during and after an incident in mixed traffic, while autonomous vehicles that act like human-driven vehicles are the least advantageous.