网联环境下混合交通流稳定性分析OACSCD

This study investigates how intelligent connected vehicles(ICVs)affect the stability of mixed traffic flow consisting of ICVs and human-driven vehicles(HDVs).In mixed traffic,HDVs,degraded ICVs,and ICVs are mod-eled using an optimal velocity model,an adaptive cruise control(ACC)model,and a cooperative adaptive cruise control(CACC)model,respectively.Based on these car-following models,traffic stability is numerically compared using transfer-function infinity norm,with explicit consideration of whether HDVs have connectivity.In addition,the predecessor-acceleration gain in the CACC model is further examined through a systematic frequency-domain sensitivity analysis.Subsequently,microscopic traffic simulations are conducted under different ICV market penetra-tion rates.Results show that,when HDVs have connectivity,a larger predecessor-acceleration gain in CACC model significantly improves overall stability.When the predecessor-acceleration gain increases from 0 to 1,the critical ICV penetration rate for stability decreases from 62%to 33%at any speed.In contrast,when HDVs lack connectivi-ty,the critical penetration rate only decreases from 62%to 54%,indicating a limited stability improvement.These findings demonstrate that connectivity of HDVs is a key factor that amplifies the stability benefits of cooperative control in mixed traffic flow.