Active lane-changing model based on cellular automata to promote CAV platoon

Abstract To leverage the advantages of Connected and Autonomous Vehicles (CAVs) within mixed traffic environments, the implementation of effective driving strategies for CAVs is crucial for enhancing traffic efficiency and safety. This study introduces an Active Lane-Changing strategy with Platoon Formation (ALC-FP), specifically designed for scenarios involving the temporary closure of the inner lane on a three-lane highway. Recognizing the increased traffic capacity afforded by CAVs traveling in platoons, we hypothesize that CAVs positioned in the middle lane will proactively execute lane changes to the outer lane. This maneuver creates space for vehicles in the inner lane to change lanes concurrently, particularly when the rear vehicle in the target lane is also a CAV, thus allowing for necessary speed adjustments to facilitate the lane change. Utilizing a cellular automata (CA) model, we conducted simulation experiments to assess the efficacy of the ALC-FP strategy in comparison to the traditional Accident Warning Lane-Changing (AW-LC) strategy. The results indicate that in high-density scenarios, the ALC-FP strategy achieved a 76.36% reduction in delays and a 73.8% improvement in safety compared to the AW-LC strategy, particularly at an MPR of 0.8. These findings underscore the potential of ALC-FP to significantly enhance traffic management in mixed traffic systems.