  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.
