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In order to explore the long-term effect of driving-restriction policies on the traffic congestion level and traffic accident rate, we combined system dynamics, the back propagation neural network, and gray system theory (SD-BP-GM integrated algorithm) to construct a dynamic nonlinear function of the private car growth rate. On this basis, an urban traffic congestion management model was constructed, which includes the driving-restriction policies, degree of parking shortage, traffic congestion level, and traffic accident rate. Through simulation, the following results were found: (1) The driving-restriction policies had a dual impact on the traffic congestion level and the traffic accident rate. The short-term effect was significant, but in the long run, the policies induced a new demand for car purchases, further aggravating the degree of parking shortage, traffic congestion level, and traffic accident rate. (2) The single strategy of strengthening publicity guidance, strengthening policy guidance, and strengthening pavement management had different preference effects, while the combination strategy had multiple benefits. (3) Compared with single strategies and driving restriction policies, the cumulative effect of the combined strategies reduced the traffic congestion level by 2.1% and 2.5%, respectively, and also reduced environmental pollution by 1.4% and 2.9%, respectively. [ABSTRACT FROM AUTHOR]

To address the increasingly serious problem of "haze" pollution, this analysis uses the motor vehicle as an example and introduces the strategies of penalties, air pollution charging fees, and subsidies in a successive manner to tackle this issue. In addition, a collaborative strategy model for motor vehicle pollutant emission reduction combined with the system dynamics of the VEMSIM software is constructed. Using a causal loop analysis and simulation analysis, it was found that an air pollution charging fee policy would have a "double dividend" effect, which could achieve the dual goals of congestion alleviation and emission reduction. However, at the same time, it would induce the growth of illegal trips. Although a single penalty policy could effectively reduce the number of illegal trips of vehicles and make up for the deficiency of the air pollution charging fee policy, it would greatly reduce the public transport supply level. Subsidies would significantly improve the public transport supply level, but this policy also showed a certain "weak congestion alleviation" effect and "weak emission reduction" effect. A collaborative strategy that has multiple performances and complementary advantages would overcome the limitations of a single policy. These findings have provide a decision-making basis to improve haze control schemes. [ABSTRACT FROM AUTHOR]

In view of the urban traffic congestion and vehicle exhaust pollution, this paper utilizes the system dynamics method to establish a traffic congestion pricing management model from the perspective of environmental and social benefits. Firstly, the charge policy in Shanghai is introduced to test, validate and simulate the model to get a relatively reasonable range of congestion charges. The results show that this policy reduces the supply level of public transport (SLPT). To this end, this paper further introduces the subsidy mechanism to improve the model and adopts the sensitivity analysis and marginal decreasing (increasing) effects to explore the reasonable subsidy range. Secondly, the combination schemes such as zero subsidy and low charge, zero subsidy and high charge, and high charge and subsidy are conducted with dynamic simulation and comparison to obtain a relatively satisfactory solution. Finally, the implementation effect of the scheme is simulated to obtain the following results: The degree of traffic congestion and amount of $$\hbox {NO}_{\mathrm{X}}$$ emissions decreased by 70.27 and $$19.92\%$$ after the implementation of the policy, respectively. The SLPT increased by approximately $$123.67\%$$ . This verifies the rationality, validity and practicability of the model. [ABSTRACT FROM AUTHOR]