## 7 Conclusions  
To study the traffic capacity near bus stops under the CAVs environment, two evaluation indexes, FD and FLR, are proposed for the traffic capacity analysis. Through simulation experiments, relationships between FD and FLR and traffic flow are investigated under the influencing factors of bus flow, bus dwell time, bus stop type, and Ra, model fitting is performed of FD-passage capacity and FLR-passage capacity on this basis, and the measured data are used for model validation and evaluation of fitting effect. Additionally, a predictive model is proposed for estimating the capacity of bus stops under the CAVs environment using indicators derived from HV traffic flow conditions. The results show that (i) FD and FLR positively correlate with traffic flow. The indicator FD performs well in the capacity analysis around the bay bus stops but is inappropriate for the curbside bus stop., whereas the indicator FLR can be used to analyze the capacity around both bay and curbside bus stops. (ii) In the absence of actual data from a CAV environment, both FD and FLR at a 0% penetration rate can be utilized to predict the capacity of bus stops under the CAVs environment. (iii) The higher the penetration rate of CAVs, the smaller the impact of the bus failure phenomenon and forced lane change on traffic flow. By evaluating the capacity of the bus stop under the CAVs environment, the capacity of bus stops in the current context and future CAV environments can be rapidly obtained based on relevant data. This allows management authorities to efficiently assess the effectiveness of related optimization measures, contributing to improved traffic efficiency, reduced congestion, and enhanced passenger travel experiences. It is of great significance for the planning department of the public transport system to plan the public transport system, operation, and urban sustainable development. There are two research limitations of this paper: (1) limited applicability of the metrics. The research findings indicate that FLR is applicable for assessing the capacity of all types of bus stops. However, the FD metric does not perform well in evaluating the capacity of curbside bus stops, suggesting a limited applicability of the proposed metrics in this paper. (2) Limited validation of models: due to the limited widespread application of CAVs and CABs in urban areas, we could only validate the relationship models between FD-capacity and FLR-capacity when the CAV penetration rate is 0 using field data. The relationship models for penetration rates of 50% and 100% have not been validated yet. Further validation can be conducted once the necessary conditions for practical validation are met. The research extensions of this paper are in the following four directions: (1) further study the quantitative relationship between two indicators, FD and FLR, and traffic volume, so as to obtain a quantitative prediction model with a broader range of applications. (2) To improve the simulation experiments, especially the simulation of the CAV lane-change model, and further investigate the relationship between CAV permeability and traffic flow stability under the influence of bus stops on this basis. (3) Building upon the existing research, the scope can be expanded to the network level to investigate the traffic flow characteristics near bus stops under the CAVs environment using the NFD approach. (4) In order to make the simulation conditions of the experiment more perfect, we can further improve the nature of the use of each sort of vehicles, and use a more generalized bus dwell time distribution model. Conceptualization, R.L. ; methodology, Q.Y. and T.Q. ; software, T.Q. ; validation, T.Q. and X.X. ; formal analysis, Q.Y. ; investigation, R.L. ; resources, Q.Y. ; data curation, R.L. ; writing--original draft preparation, Q.Y. and T.Q. ; writing--review and editing, X.X. ; visualization, Q.Y., T.Q. and X.X. ; supervision, R.L. ; project administration, R.L. ; funding acquisition, R.L.