Comprehensive Evaluation of the Impacts of Mixed Traffic Conditions on Urban Networks

• Published in: IEEE Access
• Main Authors: Mehmet Nedim Yavuz, Halit Ozen
• Format: Article
• Language: English
• Published: IEEE 2025-01-01
• Subjects: Multi-criteria analysis; mixed traffic; traffic microsimulation; traffic efficiency; traffic emissions; traffic safety
• Online Access: https://ieeexplore.ieee.org/document/10975000/

The emergence of connected and autonomous vehicles (CAVs) has become a focal point in the literature. This study proposes a comprehensive evaluation framework integrating multi-criteria analysis (MCA) methods with traffic microsimulation modeling to assess the impacts of mixed traffic conditions, comprising CAVs and human-driven vehicles (HDVs), on urban networks from various perspectives: traffic efficiency, environmental performance, and traffic safety. To this end, findings obtained from simulations of two real urban networks are employed to evaluate the impacts of penetration rates and different driving behaviors of CAVs. Six penetration rates (15%, 30%, 45%, 60%, 75%, and 90%), and three different driving behaviors of CAVs, namely defensive, normal, and aggressive, are considered in the scope of this study. While the Criteria Importance Through Intercriteria Correlation (CRITIC) method is utilized for determining objective criteria weights, the designed scenarios are scored by means of the Combined Compromise Solution (CoCoSo) method. The findings of the study indicate that defensive driving behavior enhances traffic safety, albeit with trade-offs in reduced traffic efficiency and increased traffic emissions. On the other hand, while aggressive driving behavior improves traffic efficiency and reduces traffic emissions, it also introduces safety risks, particularly at low penetration rates. According to the outcomes of the comprehensive evaluation, scenarios comprising only HDVs lose their dominance beyond 60% and 75% penetration rates, depending on the network. The proposed approach is expected to be effective in assessing the impacts of mixed traffic conditions on urban networks and can provide valuable insights to transportation policymakers and practitioners.