Traffic Road Congestion System Using by the Internet of Vehicles (IoV): A Systematic Literature Review

Abstract

Traffic problems have increased in modern life due to the significant number of vehicles, urbanization, and non-compliance with traffic regulations. Vehicular ad hoc networks (VANETs) have made improvements to the traffic system in the past, playing a crucial role in establishing efficient traffic control systems in large cities. However, VANETs alone cannot effectively address certain problems under specific conditions. Presently, the development of new Internet of Things (IoT) technologies has enabled collaborative and efficient task execution. This technology has been implemented in the transportation system, transforming it into an intelligent transportation system (ITS), known as the Internet of Vehicles (IoV). This study investigates traffic issues within the traditional system and explores the benefits, enhancements, and reasons for improving IoV through a comprehensive Systematic Literature Review (SLR). The SLR approach involved targeted searches using multiple search phrases, including 25 articles published between 2016 and 2023. Furthermore, discussion on the necessary IoV technologies and tools required to establish IoV and address specific traffic challenges. Simulation of Urban Mobility (SUMO) is employed for the design and simulation of road traffic and we aim to contribute to the development of an optimal traffic control system. This paper analyzes two vehicular congestion control models, selects the most optimized and efficient model, and provides evidence for its effectiveness through a Systematic Literature Review (SLR)-based investigation based on its efficient features, in the end, we propose IoV based on vehicular clouds as a superior model, surpassing the capabilities of the traditional model and enhancing the network system.