GMACO-P: GPU assisted Preemptive MACO algorithm for enabling Smart Transportation
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Vehicular Ad-hoc NETworks (VANETs) are developing at a very fast pace to enable smart transportation in urban cities, by designing some mechanisms for decreasing travel time for commuters by reducing congestion. Inefficient Traffic signals and routing mechanisms are the major factors that contribute to the increase of road congestion. For smoother traffic movement and reducing congestion on the roads, the waiting time at intersections must be reduced and an optimal path should be chosen simultaneously. In this paper, A GPU assisted Preemptive MACO (GMACO-P) algorithm has been proposed to minimize the total travel time of the commuters. GMACO-P is an improvement of MACO-P algorithm that uses the harnessing the power of the GPU to provide faster computations for further minimizing the travel time. The MACO-P algorithm is based on an existing MACO algorithm that avoid the path with the congestion. The MACO-P algorithm reduces the average queue length at intersections by incorporating preemption that ensures less waiting time. In this paper, GMACO-P algorithm is proposed harnessing the power of GPU to improve MACO-P to further reduce the travel time. The GMACO-P algorithm is executed with CUDA toolkit 7.5 using C language and the obtained results were compared with existing Dijkstra, ACO, MACO, MACO-P, parallel implementation of the Dijkstra, ACO and MACO algorithms. Obtained results show the significant reduction in the travel time after using the proposed GMACO-P algorithm.
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