Smart Traffic Management System for Metropolitan Cities of Kingdom Using Cutting Edge Technologies. (29th September 2022)

• Record Type: Journal Article
• Authors: Humayun, Mamoona; Afsar, Sadia; Almufareh, Maram Fahaad; Jhanjhi, N. Z.; AlSuwailem, Mashayel
• Other Names: Naranjo Jose E. Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2022(2022)
• Publication Date: 2022-09-29
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2022/4687319
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : The expansion of technology in metropolitan centers draws people to cities, which causes excessive traffic on the roadways during peak hours. This exacerbated the traffic situation, resulting in a delay, a loss of resources, and a waste of time. Like any other metropolitan metropolis, Riyadh, Saudi Arabia, has everyday traffic congestion during business hours. The current traffic management has made many attempts to ease traffic congestion in cities; despite these measures, the problem has not been solved adequately. To handle this road congestion, there is a need to appropriately store the big data collected by traffic sensors and utilize it for efficient traffic management employing cutting-edge technology. This study provides an architecture for a smart traffic management system that uses modern technologies such as the Internet of Things (IoT), cloud computing, 5G, and big data to aid conventional traffic management systems and efficiently handle the stated problem. The proposed technique has the potential to reduce traffic congestion significantly. Our proposed solution encourages mobility by using roadside messaging agents to offer real-time traffic information on traffic congestion and unexpected traffic incidents. Citizens will save time by getting these early warning messages, particularly during peak hours. As part of the suggested method, each signal dashboard gets traffic information. A case study is used in the research to evaluate alternative solutions to traffic congestion. The case study results show that the proposed strategy outperforms the present options.