## 1 Introduction
investigated the fundamental diagram model, platoon-following dynamic response, and traffic flow operation at bottlenecks in a mixed traffic flow consisting of HDVs, ACC vehicles, and CACC vehicles. They found that CACC vehicles had a more significant improvement in traffic capacity than ACC vehicles. Automated driving vehicles reduced the time for traffic flow to reach a steady state, effectively reducing congestion at bottlenecks and increasing the critical flow rate at which congestion occurs [20]. [PERSON] and [PERSON] presented an improved cooperative car-following model that considered multiple vehicular gap changes with memory. They found that considering multiple vehicular gap changes with memory can improve traffic flow stability when designing CACC control strategies [21]. [PERSON] et al. simulated travel behavior and traffic flow using POLARIS and investigated the potential impacts of various CAV technologies on traffic at the regional level. They determined the potential range of impacts of different CAV technologies on vehicle miles traveled [22]. [PERSON] et al. proposed a hybrid traffic flow analysis model for highways based on Markov chains. The model considered three key factors: CAV penetration rate, platooning intensity, and mixed traffic flow headway. They evaluated the highway capacity under different headway settings and platooning intensities through numerical experiments and found that the highway capacity does not necessarily increase with an increase in CAV platooning intensity. The specific headway setting needs to be considered [23]. [PERSON] et al. focused on automated electric transportation (AET). They proposed an evaluation framework for the AET system and assessed its benefits through the framework and simulation experiments. The framework enabled the evaluation of the AET system from three perspectives: system capacity, energy savings, and environmental emission reduction. The study found that the AET system has the capability to improve traffic conditions in terms of capacity and reduce fuel consumption and carbon dioxide emissions [24]. [PERSON] proposed a cost model for bus operations considering automation technology. The model calculated the total costs of waiting, boarding, operation, and capital for traditional, semi-automated, and fully automated bus services. Through numerical analysis, the study reveals that semi-automated bus services have limited advantages and are suitable for narrow networks with low demand. On the other hand, fully automated bus services demonstrate significant advantages by reducing operating and waiting costs [25]. Since there are still implementation difficulties in conducting on-road experiments for CAVs, simulation has become the current mainstream research method. One of the main means of simulation is microsimulation for human-driven vehicles and CAVs. [PERSON] et al. attempts to investigate the repercussions of connected automated buses (CABs) in a mixed fleet with connected automated vehicles (CAVs), through a microsimulation modeling exercise [26]. [PERSON] et al. indicated that simulation of urban mobility (SUMO) had been proposed as an alternative for testing autonomous vehicles in their simulation environment and carried out a detailed simulation study by using SUMO to investigate the effect of different percentages of AVs affecting the urban MFD [27]. At the same time, the simulation scenario of SUMO also includes public transportation, bus stops, etc., so it can meet the needs of simulation experiments that include both CAVs and buses. In order to better apply CAVs technology and develop public transportation, some scholars have further explored how mixed traffic flow affects bus capacity. [PERSON] et al. revealed that connected automated buses (CABs) can reduce network-level travel time [26]. [PERSON] quantified the potential benefits and evaluated the impacts of introducing autonomous buses on metropolitan transportation systems [28]. [PERSON] et al.