Mitigation of Stop-and-Go Traffic Waves with Intelligent Vehicles at Low Market Penetration Rates

Stop-and-go traffic patterns sometimes manifest on roadways without any discernible congestion triggers. Such a phenomenon has been observed on homogeneous ring roads without lane changes. With the development of vehicle technology and measurement sensors, multiple researchers have focused on studying the influence of automated vehicles on traffic. In particular, there is a focus on the design of string-stable adaptive cruise control (ACC) strategies to dampen stop-and-go waves. However, there is no systematic comparison among different strategies nor a quantitative analysis of the oscillation reduction at low market penetration rates (MPRs). This paper proposes a framework to evaluate the impact of low MPRs across multiple ACC strategies. Then, through Monte Carlo simulations, our findings indicate that multi-vehicle anticipation technology yields nearly equivalent benefits in mitigating stop-and-go patterns compared to full vehicular connectivity, even at a modest MPR of 1%. In contrast, partial connectivity among vehicles only eliminates ston-and-go waves if the MPR is larger,