How does penetration rate of intelligent eco-driving vehicles affect traffic flow?

Intelligent eco-driving in the Internet of Vehicles (IoV) environment, facilitated by the integration of IoV information and deep learning, has expanded the potential for optimizing the transportation system. Studying how intelligent eco-driving vehicles (IEDVs) interact with traditional vehicles in mixed traffic flow remains a challenging research topic. This paper aims to analyze the impact of IEDV penetration rate on traffic system operation and explore the behavioral interaction mechanisms between heterogeneous vehicles. Accordingly, a reinforcement learning-based eco-driving strategy combining behavior cloning and invalid action masking is proposed. By analyzing the behavioral disturbances exerted by IEDVs on surrounding vehicles, this paper investigates the macroscopic energy consumption and travel efficiency of traffic flow under different penetration levels. Notably, further upgrading and training of intelligent agents become extremely difficult in scenarios fully occupied by highly competitive IEDVs, so the research scope of penetration rate in this work is also restricted by the feasibility of subsequent agent optimization. The results demonstrate that the developed strategy enables IEDVs to achieve 47.61% energy savings with only 4.74% loss in travel timeliness compared with conventional vehicles. As IEDV penetration rises, the overall traffic flow energy consumption gradually declines. Nevertheless, excessively high penetration will trigger traffic system hysteresis and further degrade the comprehensive operational efficiency of the entire traffic flow.