CO2 Trapping Strategy for Urban Road Intersection Based on Distributed Multi-Agent System Considering Neural Network Prediction

To achieve the goal of sustainable development, China has put forward a “dual-carbon” strategy aimed at comprehensively reducing carbon consumption and emissions and providing strong support for building an ecological civilization and a green, low-carbon society. Responding to the national green call and practicing the “dual-carbon” strategy, CO
2
emissions from vehicle exhausts at urban transportation intersections are being researched. Firstly, a distributed multi-intelligence system model was established for urban road intersections in a city to optimize CO
2
capture at urban road intersections by improving the efficiency of pedestrian and vehicle traffic at the intersections. Secondly, the CO
2
concentration detection methods were analyzed for the CO
2
viscosity, and the CO
2
concentration of vehicle exhaust was detected using appropriate methods. Then, a BP neural network prediction model was established by collecting data on CO
2
emissions from automobile exhaust at a city intersection in a winter alpine region, and the prediction model was used to accurately predict the CO
2
emission of automobile exhaust from an intersection in the city. Finally, a series of CO
2
capture strategies were proposed for the CO
2
emission from automobile exhaust at an urban intersection. In this study, beneficial experience is provided for studying carbon emission reduction in other similar regions, contributing an important part to realizing Chinese sustainable development goals.