## 1 Introduction
[15] introduced a model for CACC-equipped vehicles that is based on the relative speed, safe deceleration, current acceleration, and the distance between vehicles, to determine the desired acceleration at the next step. By using the microscopic model for simulation of intelligent cruise control (MIXIC) simulation model, the paper focused on the effects of CACC on traffic flow. Its results showed that it is possible to establish a stable and sustainable traffic flow and increase traffic efficiency compared to the traditional scenarios. Much research has also been conducted to group CVs into platoons to enhance vehicle performance. [PERSON] [16] defined a platoon as an application to improve the mobility of vehicles by utilizing DSRC to share information, reducing the distance between vehicles when forming a group. The author also developed PLEXE, a tool combined with the Veins vehicular networking framework, to simulate the platoon system accurately. A simulation model was created with PLEXE to run experiments in scenarios with V2V communication [17]. Two types of experiments were performed to demonstrate the efficiency of the model: controller analysis and join maneuver. However, these models only consider platoons moving in a straight line, not considering the influence at the intersection area or the communication between vehicles and infrastructure. The authors in [18] provided an overview that categorized influencing factors for platoons. The structure of platoon vehicles remains an open and challenging topic. However, the model only includes the desired destination and arrival time as input data used for calculation. Other aspects are omitted due to complexity reasons. In [19], the authors proposed an intra-city transportation service based on connected autonomous platoon systems. The paper aimed to analyze the influence of the platoon system on travel time and develop an algorithm for the behavior of platoons at the roundabout. However, the authors only considered travel time. Other traffic factors of the system have not been fully presented. RVs significantly impacted the platoon formation in mixed traffic compared to the ideal case [20]. Another result of this paper was that platoon formations might not maintain their desired speed, leading to temporary traffic delays. The position of CVs determined car-following models and the formation of the platoon. Simulating a platoon was completely different from simulating a free flow as the platooning vehicles needed to maintain a constant distance between them [21]. This distance can be changed in the case of free-flow vehicles. This paper has proposed a model to analyze the platoon's behavior in the mixed traffic flow. However, this study only focused on analyzing the platoon merging operation. Other papers [22; 23] also developed platoon organization strategies based on local traffic states. CVs could form longer platoons but cause more lane changes in mixed flow. These lane changes did not significantly impact traffic performance at low traffic levels. However, many lane changes could reduce capacity and cause traffic congestion at high traffic levels. Therefore, it was reasonable to propose a platoon organization strategy to enhance platoon formation without reducing traffic performance [24]. Platoons possessed great potential to improve roadway capacity, but there were a number of factors that affected the platoon configuration, such as the inter-platoon gap, intra-platoon gap, platoon size, and wireless communication, as shown in [25]. Platoon size (e.g., number of vehicles in a platoon) is a critical factor affecting model efficiency. Large-sized platoons improved model performance but reduced lateral maneuverability (e.g., lane change or merging) [26]. It was also shown that a high penetration rate improves traffic capacity and stability. Moreover, when the platoon size increased, the mean velocity decreased, and the velocity standard deviation increased, reducing the model's stability.