MODELING OF CARBON MONOXIDE CONCENTRATIONS AT URBAN SIGNALIZED INTERSECTIONS USING MULITPLE LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORKS
Keywords:
Vehicles, Carbon Monoxide, traffic dynamics, signalized intersections, MLR, ANNAbstract
Increase in the use of personnel vehicles is evidently decreasing the quality of air in recent days. Pollutants released from vehicles are the major source for air pollution in urban cities. Carbon Monoxide (CO) is one of the dominant pollutants emitted from the transportation which affect the environment unfavorably. The present study attempts to determine the impact of traffic flow parameters such as approach volume (ATV), red time length (RT), cycle time length (CT), average vehicle count stopping during the red time length (AVCR) on Carbon Monoxide Concentrations (COC) at signalized intersections (SI) with a symbolic case study of Tirupathi and Warangal cities of India. The study proposes two models in order to predict CO at SI using Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) methods. It has been seen that the CO levels would range in between 12-35 ppm at signalized intersections. The linear increase in COC with increase of RT, CT, ATV, and AVCR was observed due to the changes in acceleration and deceleration of the vehicles at the intersections. The models validation illustrates that the estimated COC are constantly following the observed COC under the same sets of traffic and roadway conditions. The results of the study depicted that ANN model had performed well than MLR model in predicting the CO values with an MAPE value of 0.09%. The results recommend Pollution Control Board (PCB) officials and traffic control authorities to implement necessary actions for improving the air quality within city under consideration.
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