A Framework for Prioritizing the Connected Vehicle Infrastructure Service in Mixed Autonomy Traffic: A Fuzzy-Analytic Hierarchy Process Approach

Abstract

This study investigates the integration and prioritization of connected vehicle infrastructure services (CVISs) in mixed autonomy traffic systems using a fuzzy–analytic hierarchy process (Fuzzy–AHP). The study aims to enhance operational efficiency in environments where autonomous vehicles (AVs) and human-driven vehicles (HVs) coexist. By evaluating 92 existing services, the research selects and prioritizes 17 critical services that address safety and efficiency challenges. The methodology involves a Fuzzy–AHP analysis to assess service importance and a modified–importance–performance analysis (M–IPA) to categorize services as either specialized or common based on their utility for AVs and HVs. The findings highlight the pivotal roles of emergency management, traffic operation, and pedestrian detection services in improving traffic safety and flow. This study contributes to the theoretical and practical understanding of CVIS implementation, offering a framework for policymakers and engineers to optimize infrastructure in mixed autonomy traffic scenarios.