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

Sankha Baran Dutta
The University of Manitoba, Winnipeg, Manitoba
The University of Manitoba, 2014

@article{dutta2014gpu,

   title={GPU Accelerated Nature Inspired Methods for Modelling Large Scale Bi-Directional Pedestrian},

   author={Dutta, Sankha Baran},

   year={2014}

}

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Pedestrian movement, although ubiquitous and well-studied, is still not that well under-stood due to the complicating nature of the embedded social dynamics. Interest among researchers in simulating the nature of 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 equal numbers of individuals try to reach the opposite sides of an environment. Two pedestrian movement modeling methods are considered. The reasonableness of these two models in producing better results is compared without increasing the computational complexity. First a Least Effort Model (LEM) is investigated, where agents try to take an optimal path with minimal changes from their intended path as possible. Following this, a modified form of Ant Colony Optimization (ACO) is developed, where individuals are guided by a goal of reaching the other side in a least effort mode as well as being influenced by a pheromone trail left by predecessors. The objective 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 similar interactions of individuals on a 2D grid, GPUs are a well suited computing platform. The main feature of the implementation undertaken here is the data driven parallelism model. The data driven implementation leads to a speedup up to 18x compared to its sequential counterpart running on a single threaded CPU. The number of pedestrians considered in the model ranged from 2K to 100K, representing numbers typical of mass gathering events. A de-tailed analysis is also provided on the throughput of pedestrians across the environment. Compared to LEM model, there is an overall increment of 39.6% in the throughput of agents using the ACO model with a marginal increment of 11% in the computational time. A detailed discussion addresses implementation challenges faced and avoided.
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