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Vehicular Ad-hoc NETworks (VANETs) are developing at a very fast pace toenable smart transportation in urban cities, by designing some mechanisms fordecreasing travel time for commuters by reducing congestion. InefficientTraffic signals and routing mechanisms are the major factors that contribute tothe increase of road congestion. For smoother traffic movement and reducingcongestion on the roads, the waiting time at intersections must be reduced andan optimal path should be chosen simultaneously. In this paper, A GPU assistedPreemptive MACO (GMACO-P) algorithm has been proposed to minimize the totaltravel time of the commuters. GMACO-P is an improvement of MACO-P algorithmthat uses the harnessing the power of the GPU to provide faster computationsfor further minimizing the travel time. The MACO-P algorithm is based on anexisting MACO algorithm that avoid the path with the congestion. The MACO-Palgorithm reduces the average queue length at intersections by incorporatingpreemption that ensures less waiting time. In this paper, GMACO-P algorithm isproposed harnessing the power of GPU to improve MACO-P to further reduce thetravel time. The GMACO-P algorithm is executed with CUDA toolkit 7.5 using Clanguage 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 usingthe proposed GMACO-P algorithm.

GMACO-P: GPU assisted Preemptive MACO algorithm for enabling Smart Transportation