Year: 2025
Type: article
Abstract: With the advancement of autonomous driving technology, transportation systems are inevitably confronted with mixed traffic flows consisting of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs). Current research has predominantly focused on implementing homogeneous control strategies for ramp merging vehicles in such scenarios, which, however, may result in the oversight of specific requirements in fine-grained traffic scenarios. Therefore, a classified cooperative merging strategy is proposed to address the challenges of microscopic decision-making in hybrid traffic environments where HDVs and CAVs coexist. The optimal cooperating vehicle on the mainline is first selected for the target ramp vehicle based on the principle of minimizing time differences. Three merging strategies—joint coordinated control, partial cooperation, and speed limit optimization—are then established according to the pairing type between the cooperating and ramp vehicles. Optimal deceleration and lane-changing decisions are implemented using the average speed change rate within the control area to achieve cooperative merging. Validation via a SUMO-based simulation platform demonstrates that the proposed strategy reduces fuel consumption by 6.32%, NOx emissions by 9.42%, CO2 emissions by 9.37%, and total delay by 32.15% compared to uncontrolled merging. These results confirm the effectiveness of the proposed strategy in mitigating energy consumption, emissions, and vehicle delays.
Source: Sustainability
DOI: 10.3390/su17104522
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4410401423