Strategies for Coordinated Merging of Vehicles at Ramps in New Hybrid Traffic Environments

Zhizhen Liu (), Xinyue Liu, Qile Li, Zhaolei Zhang, Chao Gao and Feng Tang ()
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Zhizhen Liu: Hunan International Scientific and Technological Innovation Cooperation Base of Advanced Construction and Maintenance Technology of Highway, Changsha University of Science and Technology, Changsha 410114, China
Xinyue Liu: School of Transportation, Changsha University of Science and Technology, Changsha 410114, China
Qile Li: School of Transportation, Changsha University of Science and Technology, Changsha 410114, China
Zhaolei Zhang: School of Transportation, Changsha University of Science and Technology, Changsha 410114, China
Chao Gao: College of Transportation Engineering, Chang’an University, Xi’an 710064, China
Feng Tang: School of Transportation, Changsha University of Science and Technology, Changsha 410114, China

Sustainability, 2025, vol. 17, issue 10, 1-23

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%, NO x emissions by 9.42%, CO 2 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.

Keywords: sustainable transport; intelligent transportation; vehicle merging strategy; new hybrid traffic flow; traffic control method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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