Short-term Origin-Destination(OD) matrix prediction in metro systems aims to predict the number of passenger demands from one station to another during a short time period. That is crucial for dynamic traffic operations, e.g. route recommendation, metro scheduling. However, existing methods need further improvement due to that they fail to take full use of the real-time traffic information and model the complex spatiotemporal correlation of traffic flows. In this paper, a Multi-View Passenger Flow (MVPF) evolution trend based OD matrix prediction method is proposed. It consists of two components focusing on individual station and cross-station learning. Specifically, the individual station level part uses Gate Recurrent Unit and Extended Graph Attention Networks combined model to learn the high-level spatiotemporal-dependent representation of each station as the roles of origin and destination respectively, by considering multiple views of real-time traffic information (i.e. Inflow, destination allocation of Inflow, Outflow, origin allocations of Outflow). The cross-station part aims to learn passenger mobility pattern from each origin to destination through defining a transition matrix under spatiotemporal context. Compared with state-of-the-art solutions, MVPF increases the OD prediction performance metric of WMAPE by 2.5% on average. The experimental results demonstrate the superiority of MVPF against other competitors. The source code is available at https://github.com/zfrInSIAT/MVPF-code.