Cooperative control strategies to stabilise the freeway mixed traffic stability and improve traffic throughput in an intelligent roadside system environment

Previous studies used an adaptive time gap strategy to stabilise the head-to-tail string stability by connected and automated vehicles (CAVs). However, this strategy can bring an adverse effect on traffic throughput. Moreover, at a lower market penetration rate of CAVs, it is also difficult to implement a larger adaptive time gap to dampen the unstable traffic conditions. To this end, this study proposes cooperative control strategies to improve the mixed traffic throughput and stabilise the head-to-tail string stability in an intelligent road system (IRS) environment. The mixed traffic was composed of autonomous vehicles (AVs) with string instability/stability, and CAVs with low penetration. The feasible stable control parameters of AVs and CAVs are computed and implemented into the cooperative control strategies. The results show that the cooperative control strategies have better performances in improving traffic throughput and stabilising the overall head-to-tail string stability. Moreover, analytical solutions and simulation experiments are conducted to verify the effectiveness of the proposed control strategies subject to exogenous disturbances. The cooperative control strategies can be potentially applied to alleviate the mixed traffic problems, with the advanced connection and automation technologies in an IRS environment.