The quality of a crowd simulation model is determined by its agents' local and global trajectory efficiency. While an agent-based model can accurately handle the local trajectories, global decisions usually are handled by a global path planner. However, most of the global path planning techniques do not consider other agents and their possible paths and the future global flow in the environment. In this paper, we propose a composite system that takes future agent configurations into account via a modified A* algorithm to create a global path plan and combines the global path plan with a local navigation model. We show that the agents using the proposed model intelligently plan their paths based on the dynamic configuration of the environment. In order to balance the performance vs. trajectory quality trade-off, we propose a hierarchical grid structure and discuss its effects on both trajectory quality and computational performance.