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For the last fifty years, finding efficient vehicle routes has been studied as a representative logistics problem. In the transportation field, finding the shortest path in a road network is a common problem. VANET presents an innovation opportunity in the transportation field that enables services for intelligent transportation system (ITS) especially communication features. Because of VANET features [1] and despite road obstacles, a route for the shortest path can be established at a given moment. This paper proposes an enhanced algorithm, based on ACO Ant Colony Optimization and related to VANET infrastructure that aims to find the shortest path from the source to destination through the optimal path; in addition, a storage on static nodes is installed in each intersection in a VANET environment and for a specific time.

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Finding reliable and efficient routes is a persistent problem in megacities. To address this problem, several algorithms have been proposed. However, there are still areas of research that require attention. Many traffic-related problems can be resolved with the help of smart cities that incorporate the Internet of Vehicles (IoV). On the other hand, due to rapid increases in the population and automobiles, traffic congestion has become a serious concern. This paper presents a heterogeneous algorithm called ant-colony optimization with pheromone termite (ACO-PT), which combines two state-of-the-art algorithms, pheromone termite (PT) and ant-colony optimization (ACO), to address efficient routing to improve energy efficiency, increase throughput, and shorten end-to-end latency. The ACO-PT algorithm seeks to provide an effective shortest path from a source to a destination for drivers in urban areas. Vehicle congestion is a severe issue in urban areas. To address this issue, a congestion-avoidance module is added to handle potential overcrowding. Automatic vehicle detection has also been a challenging issue in vehicle management. To address this issue, an automatic-vehicle-detection (AVD) module is employed with ACO-PT. The effectiveness of the proposed ACO-PT algorithm is demonstrated experimentally using network simulator-3 (NS-3) and Simulation of Urban Mobility (SUMO). Our proposed algorithm is compared with three cutting-edge algorithms. The results demonstrate that the proposed ACO-PT algorithm is superior to earlier algorithms in terms of energy usage, end-to-end delay, and throughput.

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