Intersection passing strategies for human-driven and autonomous vehicles in mixed traffic using DEA

Area of Science:

• Traffic Engineering
• Intelligent Transportation Systems
• Operations Research

Background:

• Modern intersections face challenges managing mixed traffic, including human-driven and automated vehicles.
• Optimizing right-of-way allocation is crucial for efficient traffic flow and reduced congestion.

Purpose of the Study:

• To develop and evaluate a multi-objective optimization model for intersection right-of-way in mixed traffic environments.
• To enhance intersection efficiency by minimizing per capita delay, travel time, and traffic volume.

Main Methods:

• Utilized a multi-objective Data Envelopment Analysis (DEA) evaluation for right-of-way optimization.
• Employed the Crossing Efficiency Evaluation Method (CREE) to determine optimal traffic flow order.
• Simulated the proposed strategy using SUMO software against benchmarks like actuated control.

Main Results:

• The proposed optimization strategy significantly reduced per capita delay and travel time.
• Achieved improved traffic flow efficiency compared to traditional actuated control and First-Come, First-Served methods.
• Demonstrated the model's effectiveness in mixed automated and human driving scenarios.

Conclusions:

• The developed right-of-way optimization model effectively enhances intersection performance in mixed traffic.
• The model offers a promising approach for future intelligent transportation system மேம்பாடுகள்.
• Further research can explore adaptive real-time adjustments for dynamic traffic conditions.