## 2 Literature Review  
In this section, we briefly review related works on merging area operations. Current research on on-ramp merging mainly focuses on merging control algorithms or strategies at a single-lane freeway merging area. There has been a lot of research on multilane freeway cases. In the case of a multilane mainline merging with a ramp, [PERSON] et al. [11] studied the impact of the Cooperative Adaptive Cruise Control (CACC) vehicle string operation on the capacity of multilane freeway merge bottlenecks. To reduce the computational complexity, [PERSON] et al. [12] converted a multilane merging problem into a decentralized optimal control problem for each CAV. They then used the developed optimal control and barrier function (OCBF) method to solve this optimal control problem. The simulation results of the proposed framework showed significantly better performance. However, in their research work, the optimization of lane changing was not involved. [PERSON] et al. [13] considered lane-changing process risks in an urban freeway off-ramp area, constructed a lane-change risk rank classification model based on the SVM of the partial binary tree structure, and conducted a risk assessment of lane-changing behavior. [PERSON] et al. [14] proposed a collaborative lane-change architecture and protocol considering V2V communication delay for CAVs. [PERSON] et al. [15] took samples from the actual lane-change process of a freeway and formulated a lane-change control strategy based on personalized driving. The simulation results showed that the proposed personalized driving strategy not only respects the personalized operating styles of different drivers but also effectively controls driving risks. [PERSON] et al. [10] presented an online system control algorithm for multilane freeway merging areas based on optimizing vehicles' lane-changing and car-following trajectories. Specifically, the authors adopted a cooperative lane changing control (CLCC) optimization model and a cooperative merging control (CMC) model, and they designed a dynamic moving border point method to coordinate the CLCC and CMC's consecutive execution models. The simulation results verified the effectiveness of the proposed algorithm. Therefore, according to the characteristics of multilane freeways, this research focused on lane-changing operations. According to previous studies [16], the research methods of vehicles passing through a merging area can be divided into centralized and decentralized approaches. This study adopted a centralized control method to control merging vehicles to improve traffic efficiency. In centralized approaches, the traffic controller located in the merging area controls all vehicles' behavior and coordinates them to safely and orderly pass through the freeway ramp area. There have been many works of literature using centralized methods to solve the problem of freeway ramp merging. [PERSON] et al. [17] and [PERSON] et al. [1] described the collaborative merging problem as a constrained optimization problem, where the objective function involves the arrival time at the merging point. The constraints, which were different in each work, were formulated to avoid collisions. In a follow-up work [18], the authors proposed a hierarchical cooperative merging framework based on merging sequence-scheduling strategies and motion-planning methods to study the optimal coordination of CAVs and non-CAVs under mixed traffic conditions. [PERSON] et al. [PERSON] et al. (2019) proposed an optimal strategy based on dynamic programming (DP) to obtain a globally optimal passing order, which reduced computational complexity. In 2019, [PERSON] et al. [PERSON] et al. (2019) presented a cooperative, multi-player, game-based optimization framework and an algorithm to coordinate vehicles and achieve minimum values for global pay-off conditions. After analyzing the merging control zone characteristics, multi-player games were decomposed into multiple two-player games, and the proposed model was validated through simulation. [PERSON] et al.