A mixed capacity analysis and lane management model considering platoon size and intensity of CAVs

Traffic flow will be mixed with connected automated vehicles (CAVs) and human-driven vehicles (HDVs) in the future. The randomness of the spatial distribution of different types of vehicles (i.e., CAVs and HDVs) will not be conducive to the stability and safety of traffic flow, leading to the deterioration of traffic capacity. Therefore, reasonable organization and management of the spatial distribution of vehicles in mixed traffic flow are significant for improving the performance of transportation systems. To effectively organize CAVs and realize the management of automated dedicated lanes, this paper proposes a mixed capacity and lane management model considering platoon size and intensity of CAVs. Firstly, the spatial distribution of different headway types is calculated based on a Markov chain model. Secondly, a single-lane capacity model is developed based on the headway distribution. Then, we analyze the sensibility of the model’s parameters, including market penetration rates, platooning intensity, and platoon size of CAVs. Finally, we investigate the relationship between traffic capacity and lane management. Numerical analyses illustrate that the single-lane capacity is improved by increasing the market penetration rate, platoon size, and platooning intensity of CAVs. Moreover, The insight of the lane management model indicates that optimal lane management is associated with the market penetration rate of CAVs. These findings provide a strategy for the operation and management of dedicated lanes of CAVs in the future. •A single-lane capacity model is developed considering platoon size and platoon intensity.•A lane management model is proposed to illustrate the relationship between road capacity and lane management.•A framework is developed for optimal lane strategy selection and summarized as an optimization model.•An optimal lane management strategy is adopted under different conditions (e.g., market penetration rate, traffic demand).