# Enhancing Freeway Traffic Capacity: The Impact of Autonomous Vehicle Platooning Intensity
## 1 Introduction  
Compared to human-driven vehicles (HVs), connected and autonomous vehicles (CAVs) are considered to improve traffic capacity due to their superiority, with short headway, quick reaction time, lower error rate, etc. Rapid progress in computing, robotics, and artificial intelligence has made it possible to develop vehicles that are almost ready for road usage. As a result, significant endeavors have been made in the regulatory, legal, and insurance sectors to address the implications of integrating such vehicles into everyday life. However, accurately predicting and understanding the potential impacts of these advancements on transportation supply, mobility, and the demand for these services over time has proven to be a challenging task, making it difficult to develop precise planning tools [1]. On the other hand, the energy drive form is also important. The driving efficiency of electric-driven vehicles is indeed generally higher than that of gas or oil fuel-driven vehicles. However, it is important to note that the production efficiency of electric traction is typically lower compared to gas or oil fuels [2]. When considering the popularity of CAVs, energy consumption and environmental factors should also be taken into consideration. Advances in material and immaterial technologies, such as CAV technology, are enabling the implementation of the Mobility as a Service paradigm, which offers an opportunity to address the mobility challenges related to sustainability [3]. Microscopic simulation is widely used in most studies to investigate the impact of CAV technology on traffic capacity [4; 5; 6; 7; 8; 9; 10; 11]. For example, [PERSON]. [9] proposed an agent-based microscopic simulation model to estimate the impacts of AVs on the capacity of a multi-lane highway system. [PERSON]. [12] examined the effect on highway capacity of varying AV market penetrations using microscopic simulation methods. Little theoretical research hasbeen conducted to provide a systematic formulation in a macroscopic way. [PERSON]. [13; 14; 15] propose a general methodology that combines both empirical experiments and theoretical models to construct a fundamental diagram to reflect the full spectrum of traffic flow characteristics. Investigating the extent to which the network fundamental diagram profile changes based on the control strategy is of significant importance [16]. Ref. [15] also derives the fundamental diagram for mixed HV and CAV traffic, considering the stochastic headway. A few field tests have been conducted recently, providing practical support for theoretical research [17]. The majority of the existing studies conclude that CAVs are beneficial for traffic [18; 19; 20; 5; 10; 21]. According to research conducted by [5], the capacity of the pipeline undergoes a 90% increase under an all-CAVs scenario compared to an all-HVs scenario. Additionally, [PERSON]. [10] demonstrated that increasing the market penetration rate (MPR) can lead to a reduction in both mean spacing variance and mean speed variance, resulting in decreased congestion duration and improved traffic throughput. However, it is important to note that the increase in road capacity is not always guaranteed with the rise of CAVs. Research by Ref. [22] indicates that in heterogeneous traffic flow, the impact on road capacity is not significant at low penetration rates of CAVs. Refs. [23; 24] also show that the increase in delay in the network with the rise in the penetration rate of CAVs can lead to an unstable pattern. As CAVs and HVs are likely to coexist for a significant period, the problem caused by the heterogeneous traffic may be more complex compared to the all-CAVs or all-HVs situations. Therefore, understanding the complexities of this heterogeneous traffic scenario is critical. The improvement of road traffic capacity with the maturation of CAV technology and increasing penetration rate is a key issue in autonomous vehicle discussions.