Agent-based Large Scale Simulation of Pedestrians With Adaptive Realistic Navigation Vector Fields

Abstract

A large scale pedestrian simulation method, implemented with an agent based modelling paradigm, is presented within this paper. It allows rapid prototyping and real-time modifications, suitable for quick generation and testing of the viability of pedestrian movement in urban environments. The techniques described for pedestrian simulation make use of parallel processing through graphics card hardware allowing simulation scales to far exceed those of serial frameworks for agent based modelling. The simulation has been evaluated through benchmarking of the performances manipulating population size, navigation grid, and averaged simulation steps. The results demonstrate that this is a robust and scalable method for implementing pedestrian navigation behaviour. Furthermore an algorithm for generating smooth and realistic pedestrian navigation paths that works well in both small and large spaces is presented. An adaptive smoothing function has been utilised to optimise the path used by pedestrian agents to navigate around in a complex dynamic environment. Optimised and un-optimised vectors maps obtained by applying or not such function are compared, and the results show that the optimized path generates a more realistic flow.