# The Impact of Automated Vehicles on Road and Intersection Capacity
###### Abstract
used the Monte Carlo method, that is, the statistical simulation method, to study the influence of autonomous vehicles on highway traffic capacity under two following models, and carried out sensitivity analyses of some parameters in ACC and CACC following models. The results show that the road capacity will reach 30% when the time headway of ACC is 1 s, but it will decrease when the time headway is 1.4 s [13]. Likewise, [PERSON] and [PERSON] have also noted in their paper that traffic flow can only be improved when the penetration rate of ACC or CACC autonomous vehicles on the road, reaches 40% [14]. [PERSON] et al. built a 19 km long 3-lane highway, and the experimental results showed that the traffic flow would be improved only when the penetration rate of self-driving vehicles exceeded 70%, and the traffic flow would decrease slightly when the penetration rate reached 80% [15]. [PERSON] et al. found that road capacity can be improved only when the penetration rate of CACC autonomous vehicles exceeds 85% [16]. For the existing research, the experimental means are obtained through simulation experiments, some from the macro level of the entire road network simulation, others from the micro level of a certain section of road simulation [17; 18; 19; 20; 21; 22]. For autonomous vehicles, as they are still in the testing phase, simulation is still the main research method [23; 24; 25]. In order to more rigorously analyze the impact of AVs on road capacity, this paper deduces the improvement of road capacity by AVs in different proportions, based on three theories of traffic flow-density relationships, a vehicle-following model, and the traffic flow equilibrium state. Existing studies have not analyzed in detail, the impact of AVs on the capacity of signal-controlled intersections. Based on the analysis of road capacity, this paper further extends to intersections, to provide the corresponding basis for signal control in the future man-machine hybrid driving environment. The main research undertaken in this paper is as follows. 1. Based on a basic traffic flow diagram model, traffic flow equilibrium analysis, and a vehicle-following model, a study of the flow-density relationship of homogeneous traffic flow on a single lane road, is proposed. 2. Further, based on the proportional derivation of human-driven vehicles and two kinds of autonomous vehicles, the flow-density relationships of heterogeneous traffic flow on single-lane roads, is obtained. These data are used to analyze the road capacities under different proportions of autonomous vehicles. 3. Based on the traditional signalized intersection capacity calculation model, the revised calculation model of capacity in a mixed environment is obtained, the essence of which is to correct the basic saturated flow rate under different proportions of autonomous vehicles, in order to provide theoretical guidance for future signalized intersection control. 4. Finally, SUMO simulation software was used, to verify the accuracy of the above three models. The structure of this article is as follows. Section 2 introduces the theoretical research methods used in this paper. Section 3 studies the flow-density relationship of single lane road traffic flow in a single environment. Section 4 studies the flow-density relationship of single lane road traffic flow in a mixed environment. In Section 5, the capacity of signalized intersections under a man-machine hybrid driving environment is studied. Section 6 comprises the summary of the article, and the development of future work.