During the transition to fully autonomous traffic systems, managing mixed traffic consisting of connected automated vehicles (CAVs) and human-driven vehicles (HDVs) is imperative. Existing macroscopic and microscopic strategies have shown effectiveness in alleviating highway congestion. However, the integration of these strategies for mixed autonomy traffic remains under-explored. This study proposes a hybrid flow and trajectory control (HFTC) strategy that combines a macroscopic control, ramp metering (RM), with a microscopic control, cooperative merging (CM) for CAV trajectory optimization in mixed traffic scenarios. Specifically, the RM control considers CAV-penetration-dependent dynamics to regulate ramp flow, and the CM utilizes a centralized optimization model to enhance CAV merging trajectories. Independently implementing RM or CM proved effective only under heavy or moderate traffic flow, whereas our proposed integrated strategy, HFTC, demonstrated greater adaptability and suitability under various traffic conditions. Additionally, the impacts of CAV penetration rates and traffic flows on performance of different control strategies are thoroughly explored. Simulation results indicate that under low and moderate traffic conditions, microscopic control can be comparable to macroscopic control given sufficient CAV integration, while under heavy traffic flows, macroscopic control cannot be replaced by microscopic control.