Adoption of smart traffic system to reduce traffic congestion in a smart city

Abstract

Cities across the world suffer significantly from traffic congestion. Governments are trying to harness the power of today's computing, networking, and communication technologies to build system that can improve the efficiency of current road traffic and conditions. The study investigated the purpose efficiencies of intelligent system to assess their performance. Considering the findings, it can be said that traffic flow forecasting (TFF) possibilities are numerous, involve a variety of technologies, and can significantly reduce most traffic issues in smart cities. The studies were later evaluated to find similarities, content, benefits, and disadvantages of traffic congestion. By applying the project management tools such as the performance metrics and SQERT model were used to evaluate and prioritize the state-of-the-art methods. A classical model was proposed to improve upon and determine the traffic dangers that affect road users and aggregate the information about traffic from vehicles, traffic lights, and roadside sensors. These on-road sensors (ORS) performance are used for analyses such are vehicle classification, speed calculations, and vehicle counts.