## 1 Introduction  
Mixed traffic is an environment where a mix of autonomous and manual vehicles are driving; mixed traffic situations will continue until all vehicles are autonomous. In addition, the market penetration rate (MPR) of autonomous vehicles is gradually increasing with the development of autonomous driving system technology. Safety can decrease due to the different driving behaviors of autonomous and manual vehicles. Thus, traffic safety can be affected by the MPR [1]. A proactive safety analysis of mixed traffic is needed for future intelligent transportation systems. Currently, safety analysis is mainly based on virtual-environment-based research due to the lack of real-world driving data. In addition, simulations, which are mainly used for virtual-environment-based research, are utilized in many studies because they can freely represent various situations and current road conditions. It is important to simulate the real-world environment, and implementing autonomous vehicle behavior in simulations is essential. Additionally, studies based on simulation environments lack validation for the indicators selected and explanations for why the indicators used are appropriate.  
Therefore, this study simulates the behavior of autonomous vehicles using real-world autonomous vehicle data to develop an integrated evaluation index that can identify hazardous sections of actual roads. The data were collected from autonomous vehicles driving in an autonomous mobility testbed in Seoul to analyze their driving behavior. The driving risk index (DRI), a deceleration-based event occurrence rate, was computed using these collected autonomous vehicle data; this index defines real-world hazardous sections. The simulation was performed with the same traffic volume, signals, road geometry, number of bus stops, etc., as in a real road environment. Then, simulation-based promising evaluation indicators that can identify real-world hazardous sections were derived through a decision tree (DT) analysis. Vehicle-to-vehicle interaction indicators were adopted as candidate evaluation indicators among the driving safety evaluation indicators and used as input variables in the DT analysis; the DRI-based hazardous road sections were the output variables. The DT analysis revealed promising evaluation indicators that are useful for identifying actual hazardous sections, and these indicators were weighted to develop an integrated evaluation index. In addition, the characteristics of the most hazardous sections were presented based on the integrated evaluation index. The results of this study are expected to proactively identify hazardous sections on actual roads through simulation.  
This study consists of the following steps. The existing studies that have analyzed the driving safety of autonomous vehicles by MPR are reviewed, and the differences among these studies are presented. Next, a methodology for analyzing the driving safety of autonomous vehicles and developing an integrated evaluation index is presented. Promising simulation-based evaluation indicators and integrated evaluation indices are derived by evaluating the driving behavior of real-world autonomous vehicles. Finally, the conclusions of this study and future research issues are presented.