earticle

영어

Accurate assessment of carbon emission of urban regional transportation is the core of urban low-carbon traffic construction. Traditional carbon emission evaluation methods need a large number of samples and sample data of carbon emission of urban regional transportation is smaller, so the precision will be lower if traditional methods are adopted. This paper proposes particle swarm optimization to optimize support vector machine carbon emission early warning system of urban regional transportation (PSO－SVM) and takes the advantage of small sample data modeling of support vector machine to improve the carbon emission evaluation accuracy of urban regional transportation. Furthermore, this paper takes carbon emission evaluation accuracy of urban regional transportation as modeling target, selects reasonable evaluation index, confirms carbon emission evaluation model structure of urban regional transportation and then optimizes support vector machine (SVM) by adopting particle swarm optimization (PSO) to establish evaluation model and conduct system simulation. Results show that PSO-SVM actually increases the assessment accuracy, having practical application value in urban traffic carbon emission management.