Model Based Traffic Light Control For Reducing CO2 Emissions In Vehicles

Generally, vehicles’ stop-and-go driving will consume more fuel and emit more co2 than constant speed of vehicles .To reduce vehicles’ CO2 emissions vehicles’ travel should be smoothed by reducing the stop-and-go times. In this paper, three techniques are proposed to realize dynamic traffic light control for smoothing vehicles’ travel. In technique-1, an intelligent transportation system (ITS) employed for collecting road traffic flow data and calculating the recommended speed. In technique-2, radio antennas are installed near the traffic lights. Traffic flow information can be obtained by wireless communications between the antennas and ITC devices. In technique-3,a branchand-bound based real time traffic light control algorithm is modeled to smooth vehicles’ travels. After smoothing vehicles’ trip, more vehicles can pass intersections with less wait time and reducing shorttimes stops; therefore, CO2 emissions in the vehicles’ can be reduced. Simulation result proves that the proposed scheme performs much better than the adaptive fuzzy light control method: The average waiting time, start-and-stop times, and CO2 emissions are efficiently reduced, and the nonstop passing rate is greatly improved.