Developing dynamic speed limit strategies for mixed traffic flow to reduce collision risks at freeway bottlenecks

Area of Science:

• Traffic Engineering
• Intelligent Transportation Systems
• Vehicle Dynamics

Background:

• Connected and automated vehicles (CAVs) offer potential traffic flow improvements due to connectivity and automation.
• Freeway bottlenecks pose significant collision risks, exacerbated by human-driven vehicle behavior.

Purpose of the Study:

• To develop and evaluate dynamic speed limit (DSL) strategies for CAVs to mitigate freeway collision risks.
• To investigate the impact of CAV distribution patterns on the effectiveness of DSL strategies.
• To compare DSL strategies against conventional variable speed limit controls.

Main Methods:

• Development of three DSL strategies based on car-following speed balance and vehicle dynamics.
• Microscopic traffic flow simulations in single-lane and multi-lane freeway scenarios.
• Comparative analysis of CAV distribution patterns (e.g., uniform) and their influence on strategy performance.
• Evaluation of DSL strategy performance against a conventional variable speed limit control.

Main Results:

• CAV-based DSL strategies significantly reduce collision risks with a sufficient CAV penetration rate.
• Uniform distribution of CAVs maximizes the 'moving barrier' effect, enhancing deceleration guidance for human-driven vehicles.
• The DSL strategy based on Min control shows effectiveness in multi-lane scenarios, despite some negative impact from lane-changing behaviors.

Conclusions:

• DSL strategies represent a promising approach for enhancing freeway safety through intelligent vehicle control.
• Optimizing CAV distribution is crucial for maximizing the benefits of moving barrier strategies.
• Further research may be needed to address the impact of complex behaviors like lane-changing in multi-lane environments.