# Assessing Traffic-Flow Safety at Various Levels of Autonomous-Vehicle Market Penetration
## 2 Related Work  
To analyze traffic safety in a situation where autonomous vehicles and conventional vehicles are mixed, we implemented the driving behavior of autonomous vehicles usingthe Intelligent Driving Model (IDM) and analyzed the mobility and safety of autonomous vehicles as a function of their adoption rate using VISSIM. We found that the mobility of the network improves as the adoption rate of autonomous vehicles increases, but the traffic flow becomes unstable when mixed, increasing the need for safety management [6]. Weather events or infrastructure conditions can cause problems with a vehicle's sensors or communication capabilities, and reduced sensor coverage has been shown to increase both sudden decelerations and the number of vehicle-to-vehicle collisions [7]. Recent studies using VISSIM have proposed the concept of autonomous-vehicle aggressiveness as a traffic operations management strategy to optimize the performance of the road transportation system with the introduction of autonomous vehicles, in terms of accident presence, service level, and vehicle acceptance rate [2; 8], evaluated the potential safety impact of autonomous vehicles, and reported that the frequency and severity of accidents decrease with the introduction of autonomous vehicles [9; 10; 11; 12]. Research on traffic management for vehicles has shown that genetic algorithms are highly effective for calibrating and validating networks [13; 14], and genetic algorithms have been shown to be particularly effective for analyzing traffic characteristics, in terms of safety, operational efficiency, and environmental performance [15; 16]. Models that can represent the driving behavior of autonomous vehicles include the Wiedemann model, the IDM, and the Optimal Velocity Model (OVM). The Wiedemann model is the default driving behavior model used in VISSIM and is based on four driving behaviors: free flow, approach, follow, and brake, while the Wiedemann 99 model is used for continuous flow. The Wiedemann 99 model is employed for simulating uninterrupted traffic flow. While the Wiedemann 74 model is typically utilized for urban road networks (interrupted flow), the Wiedemann 99 model is more suitable for freeway networks (uninterrupted flow). In contrast to the Wiedemann 74 model, which only provides two parameters for calculating average stopping distance and average distance headway, the Wiedemann 99 model allows for a more nuanced representation of car-following behavior. This is achieved through the incorporation of various parameters, including standstill distance (CC0), time headway (CC1), and following threshold (CC3), enabling a more accurate reflection of real-world driving patterns [17]. The IDM is a model designed to maintain a safe following distance based on the cognitive reaction time and acceleration of individual vehicles. While IDMs do a good job of representing differences in perceptual reaction time, they have the limitation that the speed of a particular vehicle may be negative at certain times [18]. OVM models the instability of congested traffic, where each driver reacts to the motion of the vehicle in front and controls acceleration and deceleration to maintain a safe speed, but it does not account for reaction latency [19]. However, it has the limitation of showing unrealistically high acceleration values in some situations [20]. A review of previous studies shows that many studies have been conducted to analyze the safety of traffic flow by the introduction rate of autonomous vehicles in a mixed situation of autonomous vehicles and conventional vehicles using VISSIM, a microscopic traffic simulation, and various models, such as the Wiedemann 99 model, IDM, OVM, etc., have been used to implement the driving behavior of autonomous vehicles. However, in most of the previous studies, the driving behavior of conventional cars was not implemented to reflect the vehicle motion in the analysis section.