AN APPROACH FOR REDUCING CO2 EMISSION BY REAL TIME TRAFFIC LIGHT CONTROL USING IOT

Abstract

Intelligent road traffic flow control is one of the major areas of research in transportation and city traffic management in recent times. It is found in studies that most of the pollution is attributed by vehicles waiting at the junctions than driving vehicles. Several past works have emphasized on traffic flow based on intelligent traffic light control, however such methods has failed to reduce the pollution level of the cities because they don’t take into consideration of the pollution being attributed by waiting vehicles or different types of vehicles. For example, a truck would generate higher level of pollution than a car. In order to reduce the city traffic pollution and at control the traffic flow effectively, we have proposed a novel technique of traffic light management based on pollution sensing. The proposed technique is implemented over IoT architecture; it guarantees that the traffic light timing is adjusted based on the observed pollution value by a physical sensor. This work integrates real time pollution sensing mechanism with a traffic simulation framework to provide a comprehensive analysis and proof of the concept. The result shows that the system response to changes in the pollution level with minimum latency and it retains the flow based traffic light control intelligence as a part of the system. The system not only is able to reduce the traffic congestion in city street junctions but at the same time helps in reducing the pollution level and traffic will flow in smooth way.