Modelling of congestion carbon emission measurement based on multiple regression analysis for traffic flow

Abstract

Double carbon target has become the national development strategy. Transportation is a key area of energy consumption and greenhouse gas emissions in China, and the task of emission reduction is arduous. China is still in the stage of rapid development of motorization, car ownership continues to increase, exacerbating the urban traffic congestion. Traffic congestion will lead to an increase in energy consumption, thereby increasing vehicle emissions. Therefore, in order to quantify the impact of carbon emissions on urban traffic emission reduction under traffic congestion, the process dynamic detection data of CO2 and CO, HC, and NO from the annual inspection data of gasoline vehicles in Jinan (simple transient working condition method) are used as the basis, and the carbon emissions under traffic congestion are modeled by using the multinomial regression analysis method in combination with different working conditions of traffic congestion. The results show that the number of stops (acceleration and deceleration times) and idling time of vehicles in traffic congestion have a large impact on the emission of gaseous pollutants. Under the idling condition, the emission of various gaseous pollutants presents a constant value; under the deceleration condition, the emission rate of gaseous pollutants is negatively correlated with the vehicle speed; under the acceleration condition, the emission rate of gaseous pollutants is positively correlated with the vehicle speed. when the vehicle is in moderate congestion, its carbon emission is 4.02 times higher than that in smooth traffic, and when the vehicle is in severe congestion, its carbon emission is 7.2 times higher than that in smooth traffic. Reducing traffic congestion can effectively reduce vehicle carbon emissions.