This study analyzes the impact of autonomous-vehicle (AV) market-penetration rates on traffic-flow safety using a genetic algorithm. We set up a microscopic traffic-simulation scenario on a 640 m section of the US I-101 freeway using VISSIM, a microscopic traffic-simulation software. The results of analyzing the number of conflicts according to the introduction rate of autonomous vehicles showed that the number of conflicts increased as the introduction rate increased up to 30%, and then decreased from 40% or more. In this study, it was assumed that autonomous vehicles can avoid dangerous situations, so it is judged that this is the result of an increase in the traffic volume of autonomous vehicles and a decrease in the traffic volume of conventional vehicles. When planning an exclusive lane for autonomous vehicles, it is judged that it is desirable to install two exclusive lanes on the left side until the introduction rate of autonomous vehicles reaches 30%. When the introduction rate of autonomous vehicles is 40–90%, 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. Therefore, it is judged that it is desirable to operate a mixed road where autonomous vehicles and conventional vehicles can drive together rather than operating an exclusive lane for autonomous vehicles.

The run-off road crash (RORC) is a representative type of lethal crash. The severity of RORC has increased owing to a combination of factors, such as roadside geometry, traffic conditions, and weather/climatic conditions. In this study, a model for estimating the RORC severity was developed based on various factors, including fixed objects, roadway geometry, traffic conditions, and road traffic environment. To develop the model, the accident data of crashes with roadside fixed objects on highways, as well as information on fixed object-related variables and roadway geometry-related variables, were collected. To improve the model in terms of implementing a close reflection of the real world, a learning method with tree augmented naïve Bayes (TAN), which takes into account the causal links between variables, was applied. The results of the analysis showed that the severity of crashes with roadside fixed objects increased sharply when the vertical slope was ≥4%, the radius of the curve was ≥250 m, the distance between the fixed object and the roadway was less than 3 m, or the density of fixed objects installation was greater than 2 for every 10 m. The proposed model allows for an analysis of sections with a high RORC severity on the roadways in operation and provides improvement measures to reduce the severity of RORC.