Bi-level ramp merging coordination for dense mixed traffic conditions

Area of Science:

• Traffic Engineering
• Intelligent Transportation Systems
• Autonomous Vehicle Technology

Background:

• Freeway on-ramp bottlenecks are critical points for traffic congestion, emissions, and safety concerns.
• Mixed traffic environments with Connected and Autonomous Vehicles (CAVs) and Human-Driven Vehicles (HDVs) present unique coordination challenges.
• Optimizing traffic flow at the macroscopic level is crucial for managing complex merging scenarios.

Purpose of the Study:

• To propose a novel bi-level coordination strategy for optimizing freeway on-ramp merging in mixed traffic.
• To enhance overall traffic efficiency, safety, and reduce emissions in congested traffic conditions.
• To address the uncertainties introduced by human drivers and random arrival patterns.

Main Methods:

• A bi-level optimization model integrating fundamental diagrams and shock wave theories for macro-level decisions.
• Micro-level platoon size determination and coordinated trajectory design for merging vehicles.
• Implementation of a receding horizon scheme to manage driver stochasticity.

Main Results:

• The proposed strategy effectively addresses uncertainties in mixed traffic scenarios.
• Substantial improvements in ramp merging efficiency and traffic robustness were observed.
• Significant reductions in collision risk and emissions, particularly under high traffic volumes.

Conclusions:

• The bi-level coordination strategy offers a robust solution for mixed-traffic freeway on-ramp merging.
• The approach demonstrates the potential of CAVs to significantly improve traffic flow dynamics and safety.
• This strategy is particularly beneficial for managing congestion and environmental impact during peak traffic conditions.