# The Impact of Automated Vehicles on Road and Intersection Capacity
## 6 Conclusions and Future Work  
A revised model of intersection capacity calculation, based on saturated flow rate, is proposed in a mixed environment, which is essentially the calibration of the basic saturated flow rates under different automatic driving proportions. Firstly, the flow density relationship of different types of vehicles on a single lane road is proposed, using the basic traffic flow diagram model, traffic flow equilibrium analysis, and the vehicle-following model. Then, the flow density relationship of heterogeneous traffic flows in a single lane is obtained according to the proportional derivation of various vehicles. The results show  
Figure 10: Capacity of the straight lane at the east entrance of the intersection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Number of Lanes** & **Lane Width** & **Load Rate and Slope** & **Green Signal Ratio** \\ \hline value & 1 & 1 & 0.95 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Modified parameter values.  
Figure 9: Signal timing scheme.  
that autonomous vehicles can greatly improve road traffic efficiency. When ACC vehicles are all on the road, their maximum traffic capacity is 1.48 times that of artificial vehicles. When the road is full of CACC vehicles, its maximum capacity is 2.42 times that of manual vehicles. As the proportion of autonomous vehicles increases, so too does road capacity; based on the calculation formula of signalized intersection capacity, the calculation model of intersection capacity under a mixed environment, is obtained. The results show that the mixing of automatic driving and human driving will improve the intersection capacity, but the improvement degree is small, mainly affected by intersection signal control. The three cases are verified by SUMO simulation.  
The essence of the calculation of the capacity of a signalized intersection in a man-machine hybrid driving environment proposed in this paper, is to calibrate the basic saturated flow rate under different proportions of autonomous vehicles, and then calculate it through the method in HCM. However, with the development of artificial intelligence, big data, and other technologies, the research on intersection capacity can also be obtained through traffic flow prediction [33; 34; 35]. Therefore, the authors of this paper will study the following in the future:  
1. The capacity of roads and intersections in a human-machine mixed environment will be studied by means of the methods related to traffic flow prediction;
2. To study the impact of autonomous vehicles on road traffic flow by means of machine learning;
3. The capacity of roads and intersections obtained by the new method will be compared with that obtained by the traditional method.  
Conceptualization, Q.Y. and H.Z.; Data curation, H.Z.; Formal analysis, Q.Y.; Investigation, Q.Y.; Methodology, H.Z.; Project administration, Q.Y.; Resources, Q.Y.; Software, L.W. ([PERSON]); Supervision, L.L.; Validation, L.L. and L.W. ([PERSON]): Visualization, L.W. ([PERSON]); Writing--original draft, H.Z.; Writing--review and editing, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors would like to sincerely thank all of the people who assisted them in completing this paper.  
The authors declare no conflict of interest.