Effect of urban traffic-restriction policy on improving air quality based on system dynamics and a non-homogeneous discrete grey model

With the acceleration of urbanization, traffic congestion and vehicle exhaust pollution are becoming increasingly serious problems. Focusing on the problem of urban pollution from vehicle exhaust, this study used system dynamics to establish an urban congestion mitigation and emission-reduction management model. Specifically, a nonlinear function that integrates system dynamics and a non-homogeneous discrete grey model (SD-NDGM) was used to construct an algorithm, which improved the accuracy of the model. Thereafter, the mid- and long-term effects of the restriction policy were explored. The main findings from dynamic model simulations were as follows: All types of restrictions alleviated traffic congestion to varying degrees, but “odd and even” restrictions had more obvious effects, with an average annual reduction rate of 43.53% in the number of motor vehicle trips. The driving-restriction policy had a time effect, significantly reducing the number of vehicle trips in the short term. However, it could have negative effects in the long term (e.g., agglomeration effect, emission-reduction paradox), and it does not fundamentally solve traffic and environmental problems. Thus, it could only be used as a phased policy, not a long-term measure. The purchase-restriction policy controlled excessive increases in the number of private cars, but it had little effect in terms of solving environmental problems. Compared with a single policy, the combination of public-transport development and driving-restriction policy not only reduced traffic congestion, air pollution, and air quality health indexes by 29.13%, 52.63%, and 54.63%, respectively, but also improved environmental carrying capacity by 294.26%. A combined approach can therefore be said to have certain benefits for society, health, and the environment.