Abstract

A reduction of the available travel lane, commonly referred to as a lane drop, may occur on freeways due to road design, incidents or road maintenance. Lane drops lead to merging and mandatory lane-changing, resulting in various traffic problems, including delays, congestion and safety risks. In mixed traffic environments, the interactions between autonomous vehicles and human-driven vehicles add complexity and alter traffic dynamics in uncertain ways. Understanding the performance of autonomous vehicles is essential for planning and developing a control framework. This study investigates AV performance at a lane-drop bottleneck under varying traffic demands and AV penetration rates, and explores the sensitivity of car-following and lane-changing behavioural parameters. Using PTV VISSIM-COM for microsimulation, three AV driving logics (i.e. cautious, normal, and aggressive) were modelled across four traffic demand levels. Safety performance was assessed using the Surrogate Safety Assessment Model (SSAM) based on surrogate conflicts indicators such as Time-to-Collision (TTC) and Post-Encroachment Time (PET). The results show trade-offs between safety and efficiency across driving logics. Cautious AVs enhance safety and flow stability at low penetration rates but lead to increased delays as penetration rises. Aggressive AVs reduce delays at high penetration rates but increase risk due to higher speed and more changeable behaviour. Normal AVs provide balanced performance across most conditions, particularly in moderate penetration scenarios. The findings emphasize the need for adaptive AV behaviour strategies that respond to real-time traffic composition, AV share and roadway complexity which is a key to achieving safe and efficient traffic systems.