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Pedestrian movement, although ubiquitous and well-studied, is still not thatwell understood due to the complicating nature of the embedded social dynamics.Interest among researchers in simulating pedestrian movement and interactionshas grown significantly in part due to increased computational andvisualization capabilities afforded by high power computing. Differentapproaches have been adopted to simulate pedestrian movement under variouscircumstances and interactions. In the present work, bi-directional crowdmovement is simulated where an equal numbers of individuals try to reach theopposite sides of an environment. Two movement methods are considered. First aLeast Effort Model (LEM) is investigated where agents try to take an optimalpath with as minimal changes from their intended path as possible. Followingthis, a modified form of Ant Colony Optimization (ACO) is proposed, whereindividuals are guided by a goal of reaching the other side in a least effortmode as well as a pheromone trail left by predecessors. The basic idea is toincrease agent interaction, thereby more closely reflecting a real worldscenario. The methodology utilizes Graphics Processing Units (GPUs) for generalpurpose computing using the CUDA platform. Because of the inherent parallelproperties associated with pedestrian movement such as proximate interactionsof individuals on a 2D grid, GPUs are well suited. The main feature of theimplementation undertaken here is that the parallelism is data driven. The datadriven implementation leads to a speedup up to 18x compared to its sequentialcounterpart running on a single threaded CPU. The numbers of pedestriansconsidered in the model ranged from 2K to 100K representing numbers typical ofmass gathering events. A detailed discussion addresses implementationchallenges faced and averted.

GPU accelerated Nature Inspired Methods for Modelling Large Scale Bi-Directional Pedestrian Movement