A dynamic iterative grouping optimization method for emergency vehicle prioritization at on-ramp merging areas under mixed traffic environment

Emergency vehicles (EVs) play an important role in emergency services. Improving the passage efficiency of EVs is crucial for saving lives and preventing property loss. However, existing research on EV prioritization focuses on signalized intersections with explicit timing schemes, whereas studies specifically addressing ramp merging areas remain relatively limited. This paper proposes a dynamic iterative grouping method to address the issue of EV prioritization at on-ramp merging areas under mixed traffic environment. First, considering vehicle types of connected and automated vehicle (CAVs), EVs, and human-driven vehicles, vehicles in the ramp control area are grouped by dynamically adjusting the time headway threshold, and a suboptimal vehicle merging sequence is identified within a subset of the search space to reduce computational complexity. To ensure that EVs reach the desired speed while minimizing the travel time of all vehicles, a merge sequence scheduling model is established, and an iterative optimization algorithm is introduced to calculate the vehicle arrival times corresponding to a specific vehicle merging sequence. Finally, based on the optimized merging sequence, an energy-efficient motion planning model is developed to optimize the longitudinal speed of CAVs. The case study demonstrates that the proposed method can guarantee EV prioritization while mitigating adverse impacts on regular traffic and reducing energy consumption. Furthermore, the robustness of the proposed method is validated via sensitivity analysis with various parameter settings, including the average vehicle arrival rate and CAV market penetration rate.