Train movement prediction simulation is an effective method of enhancing operation safety and efficiency of railway transportation. Train movement data generated by the trains running in a railway network are huge, and on the other hand, the data quantity generated from data processing is also explosively increased for the analysis and decision such as conflict recognition and scheduling optimization, which greatly increases the time cost of prediction simulation. This paper attempts to propose a train movement model driven by the movement authorities (MAs) issued from radio block centers (RBCs) and its parallel simulation algorithm realized on Spark cloud. This paper provides a solution of iterative computing of dynamic process simulation based on cloud. Different from the general big data processing of independent datasets, the resilient distributed datasets are expandable along iterative computing processes. The Dataframe of SparkSQL modules on Apache Spark is employed to handle the problems of usage interdependency of datasets. The parallel simulation is realized by Scala language that is used to build the Spark platform. The simulation results on a high-speed railway network demonstrates that the proposed train movement model and parallel algorithm can achieve theoretical rationality and decrease the time cost to satisfy real-time performance.