PaCS: A Parallel Computation Framework for Field-Based Crowd Simulation

Crowd simulation is a convenient method to evaluate pedestrians’ status and their corresponding management strategies in large public spaces. However, the performance of real-time simulation can be limited by the model’s large-scale computational cost. In order to overcome this difficulty, this study proposes PaCS (Parallel Computation for Crowd Simulation), a parallel computation framework for field-based crowd simulation, based on an enhanced status update method and an efficient task assignment strategy. Parallel computing is introduced with synchronous updates, task division and multiprocessing calculation mechanisms. The movement model is split into the smallest and independent computational units. The field model for simulating the crowd movement has also been improved in terms of weighted multi-direction choice and multi-field environment division. The experiments confirmed that the parallel synchronous algorithm has a significant advantage at the computational scale of more than 10,000 pedestrians. The speedup ratio of the parallel approach can be more than 5 times when simulating one million pedestrians. This framework can help to establish the essential methods for multi-modal transportation systems that require fast simulations for a large-scale crowd. It would also help future digital twin systems to evaluate and validate any potential management strategies when applied in metro stations, railway stations, and other transportation hubs.