An Optimal Vehicle-Scheduling Model for Signal-Free Intersections Considering Bus Priority in a Connected and...

Area of Science:

• Intelligent Transportation Systems
• Traffic Engineering
• Optimization Algorithms

Background:

• Connected and Automated Vehicles (CAVs) present new challenges for traffic management at signal-free intersections.
• Existing models often oversimplify conflict areas and neglect bus priority, leading to inefficiencies.
• Spatiotemporal sparsity of conflict points requires advanced scheduling solutions.

Purpose of the Study:

• To develop an optimal scheduling model for CAVs at signal-free intersections with bus priority.
• To address limitations in existing models regarding conflict area analysis and bus priority.
• To enhance computational efficiency and accuracy in traffic scheduling.

Main Methods:

• Established an intersection coordinate system and a conflict area analysis model.
• Developed an optimal scheduling model using vehicle entry times and lateral lane changes as decision variables.
• Implemented a search space reduction method and an improved double-layer multi-population Particle Swarm Optimization (PSO) algorithm.

Main Results:

• The proposed method significantly reduces average vehicle delay compared to traditional and other signal-free algorithms.
• It achieves a favorable balance between computational cost and efficiency.
• Incorporating bus priority reduced average per capita delay by 18.95% compared to non-priority scenarios.

Conclusions:

• The developed optimal scheduling model effectively manages CAVs at signal-free intersections.
• The inclusion of bus priority demonstrably enhances overall traffic efficiency and reduces per capita delay.
• The method offers a computationally efficient and accurate solution for intelligent transportation systems.