The long-term coexistence of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) in mixed traffic flows will continue to significantly impact merging behaviours and operational efficiency in freeway on-ramp merging areas. To improve merging efficiency at freeway on-ramps, this paper proposes a cooperative merging model for mixed traffic. First, the merging points for ramp vehicles are determined based on vehicle interaction characteristics in the merging area. Then, for the dynamic decision-making problem of merging sequences, considering the differences in cooperative merging between CAVs and HDVs, the optimal merging sequence for both single-vehicle and multi-vehicle platoon merging is determined by minimising the global deviation between the actual travel time and the expected minimum travel time. Finally, according to the predetermined merging sequence, a bidirectional speed guidance strategy based on virtual vehicle mapping is designed for both mainline and ramp vehicles to achieve cooperative optimisation of the merging sequence and vehicle trajectories, enabling the smooth merging of ramp vehicles into the mainline. Simulations verify that the proposed method performs better than FIFO and effectively improves merging efficiency under different CAV penetration rates. Particularly in low-to-medium penetration scenarios, it achieves a 25% delay optimisation rate and 22% reduction in travel time, with the optimisation effect improving to varying degrees as penetration rates increase. This establishes a foundation for traffic analysis and cooperative control in freeway on-ramp merging areas under mixed traffic environments.
ZHU et al. (2026) studied this question.