Smart Traffic Management System Using IoT and Machine Learning Approach

Abstract

As we know that due to heavy population in urban areas, our cities are dealing with many problems like pollution, water shortages, traffic jams etc. So, overcome this Situation there is a concept comes in role that is “Smart City”. The main purpose of Smart City is to create a society which can perform effectively and efficiently making effective use of city infrastructures through machine learning and artificial intelligence. It also focuses to optimize city functions and drive economic growth while improving quality of life for its citizens using smart technology. Smart City makes use of Artificial Intelligence, machine learning and Internet of Things (IOT) devices such as connected sensors, lights, and meters to collect and analyze data. The cities then use this data to improve infrastructure, public utilities, services and humans are interact with different devices like Smart homes , smart health , smart vehicles , smart agriculture etc. Machine learning will help the power for control the autonomous vehicles or self-driving vehicles to reduce delays in traffic and to reduce pollution emission by using e-vehicle. IOT based Intelligent Transportation Systems make the exchange of information possible through cooperative systems that broadcast traffic data to enhance road safety. Traffic light assistance systems in particular utilize real-time traffic light timing data by accessing the information directly from the traffic management center. To test the reliability of a traffic light assistant system based on networked inter vehicular interaction with infrastructure, we present in this paper an approach to perform theoretical studies in a lab-controlled scenario. The proposed system retrieves the traffic light timing program within a range in order to calculate the optimal speed while approaching an intersection and shows a recommended velocity based on the vehicle’s current acceleration and speed, phase state of the traffic light, and remaining phase duration. Results show an increase in driving efficiency in the form of improvement of traffic flow, reduced gas emissions, and waiting time at traffic lights after the drivers adjusted their velocity to the speed calculated by the system.