Autonomous Vehicles, Volume 1

8Traffic Management for Smart City Using Deep Learning

Puja Gupta* and Upendra Singh

Department of Information Technology, S.G.S.I.T.S. Indore, India

Abstract

Recent decades have seen an increase in the population density of cities, and today’s megacities are densely inhabited locations with substantial land use encompassing residential, transit, sanitation and utilities as well as communication infrastructure. In the future, new types of road users will emerge as a consequence of new technology developments. As cities grow in population and highways become more congested, governmental agencies such as the Transportation Department and the National Highway Administration are under increasing pressure to improve their management services through the introduction of more efficient technology. In order to preserve life and establish long-term, expense management methods, the objective is to forecast issues that have never previously been faced. Self-driving vehicles will soon be legal in highly populated major cities where streets will be shared by pedestrians, bicycles, autos, and trucks, further complicating the situation. Adaptations to road and signal widths and timing will be required on a regular basis. Human involvement is still required to count and categorize turning vehicles and pedestrians at intersections, even with the use of traffic monitoring technology. Turning-vehicle counts at traffic junctions may be resolved using our method, which is less invasive, requires ...