System dynamics modeling for improving the policy effect of traffic energy consumption and CO2 emissions

In view of the double superposition effect of the continuous increase in the number of motor vehicles, based on the price regulation strategy, in this article, a psychological decision-making algorithm for motor vehicle trips that considers the frame effect and availability heuristic is established to optimize the number of trips made by different vehicle types. Second, a collaborative pollution control and carbon reduction strategy is optimized from the perspective of improving the efficiency of vehicle scrapping and green travel awareness, as well as the efficiency of staggered shifts. The dynamic simulation results show that the collaborative strategy has a ceiling effect on urban traffic pollution control and carbon reduction. Under the 4H-scenario, the degree of PM pollution (pollution control), amount of CO2 generated by motor vehicles (carbon reduction), total energy consumption of passenger cars (energy conservation), and total energy consumption of trucks (energy conservation) decrease by 37.39%, 40.10%, 31.32%, and 55.96%, respectively. In addition, the degree of traffic congestion and the demand for parking spaces decrease by approximately 83.32% and 40.11%, respectively. The results of this study provide a theoretical basis for optimizing the collaborative urban traffic pollution control and carbon reduction strategy.