Modeling and Analysis of the Mixed Traffic Flow of Automated and Human-Driven Vehicles

Technological advancements in vehicle manufacturing have led to the introduction of Automat-ed Vehicles (AVs) that are expected to revolutionize transportation by improving traffic flow and safety while reducing traffic-related emissions. However, benefiting from these promises depends on the large-scale deployment of these vehicles since at lower Market Penetration Rates (MPR), the effect of these vehicles seems to be minimal. Infrastructural needs, legal frameworks, and tech-nical and technological developments are prerequisites for the large-scale introduction of such technologies. Thus, a transition period to full-scale deployment of high-level AVs is expected dur-ing which Human-Driven Vehicles (HDVs) will share the roadways with their fellow low-level AVs. The coexistence of HDVs and AVs with different functionalities can create a complex traffic environment, where AVs could cause HDVs to adapt their driving behavior, while HDVs, in turn, can hinder the proper implementation of these technologies. The interactions of these two vehicle types during the transition phase have attracted researchers’ attention from both academia and in-dustry due to their critical safety and efficiency challenges. Understanding the dynamics of their interactions helps to develop compatible AVs and take appropriate preparatory actions. An impact assessment of AVs on traffic efficiency during the transition phase was the core focus of this study. In this regard, a microsimulation model method was employed; to accurately model the mixed traffic flow of these two vehicle types, it is essential to simulate the behavior of AVs while considering their potential impacts on the behavior of HDVs. Due to the lack of empirical data about the human drivers’ responses to the presence of AVs with different driving styles, alter-natively, a driving simulator study was conducted to measure the driving behavior of human driv-ers in a virtual environment. To properly evaluate the behavior of human drivers, a relatively large sample size was used. Participants involved in the experiment interacted with AVs of different driv-ing styles at varying penetration rates in car-following events in a highway setting. The spacing and speeding behaviors of the participants were assessed to validate potential changes in their driv-ing behaviors. Afterward, in commonly used microsimulation software such as VISSIM, the mixed traffic flow of AVs and HDVs was modeled, considering the behavioral adaptation of human driv-ers. The effects of AVs on traffic efficiency at different MPRs were evaluated utilizing widely used metrics such as average travel time, average delay, average speed, and capacity. In addition, the influence of the behavioral adaptation of human drivers on these performance metrics was ana-lyzed across various scenarios. The findings of the present study provide more accurate insights into the characteristics of the mixed traffic flow of AVs and HDVs by addressing the behavioral adaptation of human drivers. Practitioners can use the results of the present research to better anticipate the future traffic per-formance, plan roadway infrastructure, develop compatible AVs, and make informed policy deci-sions that maintain a balance between efficiency and safety. Furthermore, training and awareness initiatives may be necessary to help human drivers better adapt their driving behaviors when inter-acting with AVs. Furthermore, the outcomes serve as a base for developing simulation models that account for drivers’ behavior adaptability in the mixed traffic flow scenarios.