Traffic bottlenecks significantly influence the operation efficiency of large-scale road networks. Developing advanced control strategies for bottleneck optimization is a cost-efficient and critical way to deal with network congestion. However, the state-of-the-art studies on network congestion control focus on the topology level, which may fail to relieve congestion by addressing the root cause of bottleneck. This study proposed a hierarchical control framework for alleviating network traffic bottleneck congestion using vehicle trajectory data. First, the bottleneck-related sub-network (BRS) was identified by tracing vehicle trajectories upstream and downstream of the bottleneck based on the traffic flow propagation. Then, a hierarchical control framework was proposed for BRS optimization. Specifically, in the outer layer, i.e., the gating control layer, the multigated intersections in BRS were controlled via a multimemory deep Q-network approach to optimize the network traffic distribution. In the inner layer, i.e., the coordinated control layer, local intersection controllers were coordinated by adjusting the dynamic input and output streams of the bottleneck under the guidance of the outer layer controller, which helps balance the traffic pressure within BRS and avoids congestion transferring in the network. Both simulation and field experiments were conducted to verify the performance of the proposed hierarchical framework. Results reveal that the framework can effectively relieve network traffic congestion with decreased queue length and travel time.