An analysis of the effect of autonomous vehicles on traffic flow

The aim of this thesis is to analyze the impact of autonomous vehicle penetrations into traffic on the basic characteristics of traffic through simulation scenarios via developing of autonomous vehicle definition in terms of traffic Driving characteristics. In this context, firstly the process of development of autonomous vehicles and the current state on lobal scale were described in detail. The highlights, advantages, important equipment and driving characteristics of autonomous vehicles were then shared. Subsequently, future trends in autonomous vehicles were assessed in various ways. Then, for the purpose of determining the driving characteristics of autonomous vehicles in the course of the thesis, firstly, the clustering analysis carried out in the R Studio program has determined the types and acceleration of autonomous vehicles, and the perception reaction time has been obtained with the analyses done in the MATLAB and SPSS program. It has been found that autonomous vehicle acceleration is severe than in conventional vehicles, and the autonomous vehicle perception reaction time is shorter than in conventional vehicles. After determining the driving characteristics of the autonomous vehicle, the resulting 4 autonomous vehicle types were defined in the VISSIM program. With a simulation from a selected traffic segment and data, the penetration rates of 0-10-20-30-40 % autonomous vehicles were analyzed as a separate scenario. In each penetration scenario, each autonomous vehicle type is integrated into the system with equal share respectively. The results showed that as the penetration of autonomous vehicles increased, there were significant improvements in parameters such as average speed in traffic, traffic regularity, distance taken, number of vehicles passing through the traffic section. The average delays are decreasing, the factors causing traffic irregularities are reduced, the distances per vehicle are increasing, and the total travel time is increasing because of the large increase in the number of vehicles passing through the system. However, it appears that this increase is an indirect and partial increase triggered by the large increase in the number of vehicles in the system and the average travel distance. The travel time per vehicle does not increase. On the other hand, the introduction of autonomous vehicles into traffic has indirect effects, such as increased traffic safety, reduced greenhouse gas emissions and saving parking space. Parking space savings also have effects such as reducing time spent by vehicles searching for a parking lot in traffic, thereby contributing to additional traffic regulatory and helping to reduce congestion. As part of the thesis, an analysis was carried out in the direction of the planning of heterogeneous traffic between autonomous vehicles and conventional vehicles, which is predicted to continue for decades with the introduction of autonomous vehicles into the traffic system. In this framework, results have been emerged.