# Assessing Traffic-Flow Safety at Various Levels of Autonomous-Vehicle Market Penetration
## 5 Conclusions  
In this study, we analyzed the safety of traffic flow according to the introduction rate of autonomous vehicles using micro-traffic simulation on a 640 m section of the US I-101 highway. In the implementation of VISSIM, the input variables of regular cars were implemented similarly to the actual driving behavior using a genetic algorithm, and the driving behavior of autonomous vehicles was implemented by deriving the input-variable values by reviewing previous studies. The analysis was repeated five times for a total of nine scenarios according to the change in the MPR of autonomous vehicles. When analyzing the number of conflicts between regular cars and autonomous vehicles, we found that the total number of conflicts increased with the adoption rate until the adoption rate of autonomous vehicles reached 30%. But, after 40%, the number of conflicts continuously decreased, as the adoption rate of autonomous vehicles increased. This is likely due to the assumption that autonomous vehicles are able to avoid dangerous situations, so as autonomous-vehicle traffic increases, conventional vehicle traffic decreases. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Mixed-Flow** & **1 Lane (Left)** & **2 Lanes (Left)** & **1 Lane (Right)** &
\begin{tabular}{c} **2 Lanes** \\ **(Right)** \\ \end{tabular} \\ \hline MPR 10\% & 40.44 & 40.45 & 40.45 & 40.45 & 40.46 \\ MPR 20\% & 40.45 & 40.41 & 40.40 & 40.43 & 40.44 \\ MPR 30\% & 40.44 & 40.42 & 40.42 & 40.43 & 40.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Operational efficiency evaluation results by exclusive AV lane operation plan (Unit: km/h). Figure 5: How to operate autonomous-vehicle lanes on US I-101 highway. When analyzing the number of side collision conflicts when the lead vehicle is a conventional vehicle and the autonomous vehicle is a trailing vehicle, the total number of conflicts increased as the adoption rate of autonomous vehicles increased up to 30%. But, after 40%, the number of conflicts continuously decreased as the adoption rate of autonomous vehicles increased. However, when the lead vehicle was an autonomous vehicle, the number of rear-end collisions increased between 30 and 60 percent of autonomous-vehicle adoption. This is likely due to the fact that autonomous vehicles allow for faster acceleration and faster acceleration from a stop compared to conventional vehicles, and these rapid changes in behavior may affect the collision with the trailing conventional vehicle. Furthermore, when analyzing the risk level according to the TTC value, it was found that the number of conflicts between autonomous vehicles and conventional vehicles with a risk level of one accounted for about 49.493.3% of the total number of conflicts, indicating a very high accident risk, and the number of conflicts with a risk level of one continued to increase until the adoption rate of autonomous vehicles reached 30%. Therefore, when the adoption rate of autonomous vehicles is 10-30%, it is considered necessary to consider the implementation of autonomous lanes that separate autonomous vehicles from conventional vehicles. Accordingly, the number of conflicts was analyzed for a total of four autonomous lane design options, and it was found that the number of conflicts was lowest when the autonomous lane was installed with two lanes on the left until the adoption rate of autonomous vehicles reached 30%. Therefore, in the spatial scope of this study, it is recommended to install two lanes on the left until the adoption rate of autonomous vehicles is 30%. When the adoption rate of autonomous vehicles is between 40 and 90 percent, the risk of accidents between autonomous vehicles and conventional vehicles decreases, and the traffic volume of autonomous vehicles is higher than that of conventional vehicles. So, it is recommended to operate mixed-use roads so that autonomous vehicles and conventional vehicles can drive together, instead of operating lanes for autonomous vehicles. This study has the following limitations.