  Pedestrian movement, although ubiquitous and well-studied, is still not that
well understood due to the complicating nature of the embedded social dynamics.
Interest among researchers in simulating pedestrian movement and interactions
has grown significantly in part due to increased computational and
visualization capabilities afforded by high power computing. Different
approaches have been adopted to simulate pedestrian movement under various
circumstances and interactions. In the present work, bi-directional crowd
movement is simulated where an equal numbers of individuals try to reach the
opposite sides of an environment. Two movement methods are considered. First a
Least Effort Model (LEM) is investigated where agents try to take an optimal
path with as minimal changes from their intended path as possible. Following
this, a modified form of Ant Colony Optimization (ACO) is proposed, where
individuals are guided by a goal of reaching the other side in a least effort
mode as well as a pheromone trail left by predecessors. The basic idea is to
increase agent interaction, thereby more closely reflecting a real world
scenario. The methodology utilizes Graphics Processing Units (GPUs) for general
purpose computing using the CUDA platform. Because of the inherent parallel
properties associated with pedestrian movement such as proximate interactions
of individuals on a 2D grid, GPUs are well suited. The main feature of the
implementation undertaken here is that the parallelism is data driven. The data
driven implementation leads to a speedup up to 18x compared to its sequential
counterpart running on a single threaded CPU. The numbers of pedestrians
considered in the model ranged from 2K to 100K representing numbers typical of
mass gathering events. A detailed discussion addresses implementation
challenges faced and averted.
