Low-altitude, high-density air traffic is expected to grow in the coming decades with several companies being certified to initiate urban operations for both freight and passenger transport. However, traditional human-centered Air Traffic Control operations (ATCos) are not scalable to handle the increased demand to maintain safe separation between aircraft. Thus, new autonomous solutions to resolve potential conflicts between aircraft must be developed, allowing humans to supervise and understand machine actions. We propose a centralized Deep Reinforcement Learning (DRL)-based framework to provide speed advisories to vehicles, ensuring safe and efficient operations. We used Proximal Policy Optimization (PPO) to train the intelligent controller and show that our framework is capable of handling challenging merging point settings. We evaluated our model extensively using a custom OpenAI Gym environment and proved that it can achieve a 99% success rate in conflict resolution across multiple merging point configurations.