Emerging Behavioral Adaptation of Human-Driven Vehicles in Interactions with Automated Vehicles: Insights from a Microsimulation Study

Automated vehicles (AVs) are expected to shape the future of transportation and to improve traffic flow and safety. Studies have focused on AVs effects on traffic flow during the transition to full automation, with few examining their influence on human-driven vehicles (HDVs). This study investigated potential changes in HDVs’ driving behavior induced by the presence of AVs with different driving styles (aggressive vs. cautious) at varying market penetration rates (MPRs) (0%, 25%, 50%, and 75%). First, a driving simulator experiment with 160 people (56 females, 104 males) was conducted to collect HDV trajectory data. Then, a microsimulation model was implemented in VISSIM, where HDV behavioral parameters were calibrated using the driving simulator data. Average time headway (THW), relative velocity (RelVel), average acceleration (Acc), average deceleration (Dec), and lane change frequency (LnCh) were used as behavioral metrics. A two-way ANOVA was applied for analysis. Results showed that higher AVs’ MPRs decreased THW, Acc, and Dec in HDVs, while RelVel increased with cautious AVs and decreased with aggressive AVs. Similar trends were observed for LnCh. These findings highlight the need to consider potential HDVs behavioral adaptation during the transition phase, as neglecting it may lead to inaccurate traffic assessments and ineffective policies.