Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance.

中文翻译：

---

使用改进的蚁群优化器的基于交通视频的智能城市智能交通控制系统

道路交通拥堵在当今世界是一个严重的问题，并且由于城市化和人口增长而发生。交通减少了城市的运输效率，增加了等待时间和旅行时间，还增加了燃料和空气污染的使用。为了克服这些问题，本文提出了一种使用车联网（IoV）的智能交通控制系统。IoV中存在的车辆或节点之间可以相互通信。此技术有助于确定交通强度和到达目的地的最佳路线。本文使用的研究领域是印度泰米尔纳德邦的韦洛尔市。将城市地图分为多个大小相等的部分，然后将蚁群算法（AOC）应用于分离的地图，以找到到达目的地的最佳路线。进一步，支持向量机（SVM）用于计算流量密度并为繁忙流量建模。与现有的路径选择算法相比，该算法在寻找最佳路径方面表现更好。从结果可以看出，所提出的基于IoV的路由选择方法可提供更好的性能。