A Stability Analysis of Mixed Traffic Flow in Connected Environment

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 modeled 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 penetration 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 connectivity, 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.