Chiang Mai Journal of Science

     In this paper, we propose a Markov chain model to analyze mixed traffic flows involving human-driven vehicles (HVs) and autonomous vehicles (AVs), specifically examining partial participation in platooning when AV penetration rate and average platoon size are known. Although platooning has the potential to improve traffic efficiency, not all AVs participate due to various constraints such as AV penetration rates, traffic conditions, and vehicle characteristics. Our proposed model defines a state space that encompasses three distinct vehicle types: HVs, AVs that are not in a platoon with the preceding vehicle, and AVs that are in a platoon with the preceding vehicle. We carefully construct state transition probabilities that meticulously capture real-world AV penetration rates and average platoon sizes, with particular attention to the effects of consecutive AVs. Furthermore, we illustrate the model’s practical application in traffic capacity analysis, investigating how AV penetration rates and platoon size influence overall traffic capacity. By providing this theoretical framework, our research provides useful insights for optimizing mixed traffic flow and vehicle coordination in a variety of traffic environments.