Impact Assessment of Integrating AVs in Optimizing Urban Traffic Operations for Sustainable Transportation Planning in Riyadh

Abstract

Integrating autonomous vehicles (AVs) into urban traffic systems presents significant opportunities for optimizing traffic flow, reducing congestion, and enhancing transportation efficiency. This study proposes a comprehensive framework that combines mathematical optimization techniques, policy planning, and AV adoption modeling to improve urban mobility. Using Highway Capacity Manual (HCM) Optimization methods, the research fine-tunes traffic signal timings, dynamically allocates green time, and enhances intersection coordination to maximize throughput. The study evaluates the impact of AV penetration on traffic flow efficiency, congestion reduction, and infrastructure readiness using real-world urban data from Riyadh. The results indicate that AV integration leads to a 40% increase in traffic throughput, a 60% reduction in congestion levels, and a 45% improvement in infrastructure readiness, highlighting the effectiveness of AV-driven traffic optimization strategies. Additionally, policy interventions aimed at reducing legal constraints and increasing societal acceptance contribute to the successful implementation of AV technology. The findings provide a data-driven roadmap for city planners and policymakers, demonstrating how a well-structured AV deployment strategy can significantly enhance urban transportation efficiency.

Keywords

• AVs
• urban planning
• regional planning
• traffic HCM optimization
• sustainable transportation