earticle

영어

Study the road traffic carbon emission and accurately predict the problems, the road traffic carbon emission has the complex systems of chaos and nonlinearity, the traditional method ignores the chaos of the road traffic carbon emission change, and it is so difficult to precisely control the rules of the road traffic carbon emission change that the precision of the of traffic carbon emission prediction is lower. In this study, it proposes the road traffic carbon emission prediction model based on the chaos theory and neural network and improves the prediction precision of the road traffic carbon emission time sequence. First of all, it reconstructs the time sequence data of the road traffic carbon emission change through the space, and sorts out the chaos change rules hidden in the time sequence data and then uses the BP neural network to study and carry out the modeling of the time sequence data of the road traffic carbon emission, and optimize the neural network parameter in order to improve the prediction precision of the road traffic carbon emission time sequence. The simulation result shows that, Chao-BPNN has overcome the deficits of the traditional method and could precisely and comprehensively reflect the change rules of the road traffic carbon emission time sequence, and effectively improved the prediction precision of the road traffic carbon emission.