Speed guidance strategy at intersections based on platoon recognition in connected and autonomous vehicles...

Area of Science:

• Traffic Engineering
• Intelligent Transportation Systems
• Autonomous Vehicle Technology

Background:

• Urban traffic congestion and signalized intersection inefficiency lead to increased vehicle energy consumption.
• Connected and Autonomous Vehicles (CAVs) offer potential solutions for traffic flow optimization.
• Understanding the dynamics of vehicle platoons is crucial for developing effective traffic management strategies.

Purpose of the Study:

• To propose and evaluate a speed guidance strategy for CAVs based on platoon recognition.
• To analyze the impact of varying CAV penetration rates, aggregation intensity, and traffic flow distributions.
• To quantify the reductions in fuel consumption, delay time, and vehicle stops.

Main Methods:

• Development of a platoon recognition-based speed guidance strategy for CAVs.
• Definition of six platoon passage scenarios considering obstructions.
• Implementation of three guidance strategies: acceleration, deceleration, and platoon splitting.
• Simulation-based case study using Vissim for performance evaluation.

Main Results:

• The platoon-based speed guidance strategy significantly reduced fuel consumption (by 81%), delay time (by 59%), and stops (by 78%) compared to no guidance.
• Guidance strategy effectiveness increases with CAV penetration rates, becoming more apparent as the rate approaches 0.7.
• At a 0.7 CAV penetration rate, fuel consumption, delay time, and stops were reduced by 40%, 37%, and 23%, respectively.

Conclusions:

• Platoon-based speed guidance is an effective strategy for mitigating traffic congestion and improving efficiency at urban intersections.
• The benefits of this strategy are amplified by higher connected and autonomous vehicle penetration rates.
• Optimizing speed guidance for platooning vehicles is key to realizing the full potential of intelligent transportation systems.