# The Impact of Automated Vehicles on Road and Intersection Capacity
###### Abstract  
With the rapid development of autonomous driving technology, future road traffic must be composed of autonomous vehicles and artificial vehicles. Although autonomous vehicles have greatly improved road capacity, few studies have involved capacity at signal-controlled intersections, and most of the studies are based on experimental simulation. As such, there is a need to more scientifically analyze the impact of autonomous vehicles on road and intersection capacity. Based on three theories of flow-density relationships, traffic flow equilibrium analysis, and the following model, this paper firstly deduces the flow-density relationship of different vehicle types in a single environment. Secondly, flow-density relationships under different proportions of self-driving vehicles are derived. Through the derivation of these two models, the basic road saturation flow rates under different permeabilities of self-driving vehicles, can be obtained. Based on these results, a revised calculation model for the capacity of signalized intersections with different proportions of autonomous vehicles is proposed, which is essentially to revise the basic saturation flow rate under different permeabilities of autonomous vehicles. By using SUMO 1.15.0 traffic simulation software, the theoretical models are individually tested. The results show that the error rate between the theoretical calculation results and the SUMO simulation results, is less than 16%. This study can provide a basis for the calculation of basic capacity of roads and intersections in a future man-machine hybrid driving environment, and provide theoretical guidance for traffic management and control. autonomous vehicle (AV); capacity; man-machine hybrid driving environment; traffic flow basic map model; following model; SUMO +
Footnote †: journal: Applied  
1350733  
that increased AV penetration will improve traffic flow, reduce average vehicle delays, and increase road capacity by 40% when AV penetration reaches 100% [2]. Similarly, [PERSON] et al. studied the impact of autonomous automated vehicles (AAVs) and cooperative automated vehicles (CAVs) on highway traffic capacity, through VISSIM simulation software. The results show that CAV has a stronger impact on improvement of traffic capacity, regardless of highway section type [3]. [PERSON] et al. studied the impact of intelligent connected vehicles (ICVs) and traditional human-driven vehicles (HVs) on highway capacity in China, and the results showed that, although ICVs significantly improved capacity at high traffic inputs, at low and moderate traffic inputs, the benefits were not obvious [4]. [PERSON] et al. investigated the effect of autonomous vehicles on the macroscopic fundamental diagram (MFD), and showed that as AV penetration increased, so too did the capacity of the road network [5]. [PERSON] et al. investigated and quantified vehicles in road networks in Europe and the United States, and proved, through numerical simulation experiments, that traffic congestion could be reduced by four times under the same traffic flow running speed [6]. [PERSON] et al. proposed a road capacity analysis model in a man-machine hybrid driving environment, by using the improved Markov chain. Through experimental verification, this model can accurately quantify the corresponding traffic capacity [7]. [PERSON] et al. established the expressway as the research object, and found, through simulation experiments, that, for the transitional section with a reduced number of lanes, the traffic capacity of the section could be improved only when the proportion of CACC vehicles was higher than a specific value [8]. In addition, many studies have shown that AVs can improve road capacity [9; 10; 11; 12]. However, some studies suggest that the improvement will not be significant until penetration of autonomous vehicles reaches a certain limit. [PERSON] et al.