An Optimization Method Based on Multivariate Linear Programming for Urban Green Transportation Carbon Emission Control

Abstract

In order to implement the concept of environmental protection and achieve the goal of urban green transportation, this study controls and optimizes the urban transportation carbon emission through the method of multivariate linear programming. This paper proposes a multivariate universe-interval linear programming algorithm, which is applied to the control of transportation carbon emissions. At the same time, the urban traffic carbon emission is measured, and the BP neural network is used to predict the traffic carbon emission, and the urban traffic carbon emission optimization model based on multivariate linear programming is constructed. The practical utility is tested by experiments. Taking the carbon emissions of residents' commuting in Q city as an example, the carbon emissions of Q city after the optimization of the model in this paper were reduced by 860 tons. The per capita carbon emissions of buses, subways, motorcycles and cars all show a decreasing trend. The average values of CO, CO₂ and fuel emissions of this paper's model at traffic flow of 400 pcu/h and 900 pcu/h are much lower than other control methods. The model in this paper has a greater advantage in reducing carbon emissions from transportation.