Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer. (25th November 2020)

• Record Type: Journal Article
• Authors: Ragavan, Krishnasamy; Venkatalakshmi, Krishnan; Vijayalakshmi, Kandasamy
• Is Part Of: Computational intelligence. Volume 37:Number 1(2021)
• Page Start: 538
• Page End: 558
• Publication Date: 2020-11-25
• Subjects: ant colony algorithm -- connected vehicles -- intelligent transportation system -- internet of vehicles -- support vector machine -- vehicle traffic congestion; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12424
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: 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.