Heterogeneous Algorithm for Efficient-Path Detection and Congestion Avoidance for a Vehicular-Management System

Abstract

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.

Navigation Route Planning for Tourism Intelligent Connected Vehicle Based on the Symmetrical Spatial Clustering and Improved Fruit Fly Optimization Algorithm

“…It prioritized emergency vehicles, public transportation vehicles and other general vehicles in the city, and verified the effectiveness of the constructed route-planning method in different experimental scenarios. Noussaiba et al [6] constructed a heterogeneous algorithm called Ant Colony Optimization with Pheromone Termites (ACO-PT), which combined two state-of-the-art algorithms, namely Pheromone Termites (PT) and Ant Colony Optimization (ACO), to address efficient routing to improve energy efficiency, increase throughput, and shorten end-to-end latency. Wang et al [7] proposed an adaptive adjustment mechanism to address the typical problems of weak global optimization ability, easy falling into local optimization and slow convergence speed in the intelligent vehicle route solving algorithms, and improved the Whale optimization algorithm to enhance its operational ability.…”

“…Our method reflects the fairness of all ICVs and ICV routes, allowing each tourist to enjoy the same POI recommendations and ICV route planning services. Reference [6] focuses on studying the shortest routesearching model based on the improved ant colony algorithm, and has conducted extensive research on algorithm optimization. Our method aims to search for the POIs that best match the interests of tourists, and on this basis, search for the optimal ICV routes connecting the POIs.…”

“…The maximum value is stored at the root node, the second maximum value is stored at the leftmost child node of the lower level, and the minimum value is stored at the rightmost child node of the lower level; Subfigure (5) shows the fifth step of the algorithm: comparing the recommendation degrees of all POIs. The larger value is stored in the upper level or left child node, and the smaller value is stored in the lower level or right child node, so that the recommendation degree of any upper-level child node is greater than that of the lower level, and the recommendation degree of any left child node in any level is larger than that of the right child node; Subfigure (6) shows the sixth step of the algorithm: constructing the second decision tree; Subfigure (7) shows the seventh step of the algorithm: constructing the decision trees for all the natural attributes to form a decision forest.…”

Advances in the Optimization of Vehicular Traffic in Smart Cities: Integration of Blockchain and Computer Vision for Sustainable Mobility

“…Simulation generation was carried out programmatically, with the ability to adjust key parameters, such as sensor location, traffic density, and peak hours. Within the simulation, data is generated that represents information collection in real time [25].…”

Malicious Vehicle Detection Using Layer-Based Paradigm and the Internet of Things

“…The accurate detection of vehicles and the avoidance of malicious vehicles have become highly important. This task can be efficiently performed by utilizing a powerful and reliable object recognition model [ 1 ]. In recent times, researchers have focused on categorizing a single item into multiple categories during image recognition [ 2 ].…”