Shortest Path using Ant Colony Optimization in VANET

VANET is a form of mobile ad-hoc networks (MANET) to provide connections between near vehicles and near fixed equipment. Today VANET used largely function for the public safety, the calm, Travelers Information, Traffic Management, Traffic co-ordination and Assistance, etc. The traffic congestion can happen anytime, anywhere in the traffic. Its condition where the traffic controls fail and may occur accidents, road maintenance, and rule breaking, etc. therefore, to avoid the traffic congestion. Vehicle traffic congestion leads to air pollution, driver irritation, and costs billions of dollars annually in fuel consumption. Finding a proper solution to vehicle congestion is a great challenge due to the dynamic and unpredictable nature of the network topology of vehicular environments, especially in urban areas. Ant algorithms simulate the cooperative behavior of real ants one of the good ways of traffic congestion. It combines the average travel speed predictor of traffic on roads with map segmentation to reduce congestion as much as possible by finding the least congested shortest paths in order on the road to avoid congestion instead of recovering from it. ACO collects real-time traffic data from vehicles and roadside units to predict the average travel speed of road traffic. It utilizes this information to execute an ant-based algorithm on a segmented map resulting in avoidance of congestion. The outcome shows that when the road is congested, path algorithm for active traffic network can be more appropriate for travelers to find the path. The simulation result analysis is NS2 and this analysis is provided in terms of performance metrics, such as a packet delivery ratio, the average end-to-end delay, and throughput. Simulation result shows that the ant colony optimization finding the best path to a nest into food in the model area.