Research on tail gas optimization method based on adaptive dynamic programming*

With the increase in the number of motor vehicles, motor vehicle emissions have become one of the most important factors affecting air quality. For the problem of excessive motor vehicle emissions at signal intersections, this paper proposes a tailpipe optimization method based on adaptive dynamic programming. The proposed method uses back-propagation (BP) neural networks for the model network, execution network and evaluation network. According to the relationship between motor vehicle delay time and tailpipe emission, the tailpipe emission model is established, and then the model network is trained separately, and the optimal weights and thresholds of BP neural network are obtained by using the fast convergence speed and powerful search ability of genetic ant colony fusion algorithm. Finally, the obtained weights and thresholds are brought into the execution network and evaluation network for optimization to achieve the goal of minimizing exhaust emissions at signalized intersections by changing the duration of signal lights. The feasibility and effectiveness of the proposed method are verified by simulation experiments. The experimental results show that the proposed method can significantly reduce vehicle exhaust emissions, resulting in higher fuel efficiency and lower emission levels for vehicles at signalized intersections.