A System Dynamics-Based Simulation Study on Urban Traffic Congestion Mitigation and Emission Reduction Policies

Urban traffic congestion and carbon emissions pose significant challenges to the sustainable development of megacities. Traditional single-policy interventions often fail to simultaneously mitigate congestion and reduce emissions effectively. This study employs a system dynamics approach to construct a multidimensional dynamic model that analyzes the feedback mechanisms and dynamic interactions of policy variables within the urban traffic system. Furthermore, a TOPSIS multi-criteria decision-making framework is integrated to quantitatively evaluate the overall effectiveness of multiple policy combinations, exploring optimization pathways for achieving synergistic governance. Using Shanghai’s traffic system as a case study, simulation analyses under six policy scenarios reveal significant discrepancies in short- and long-term policy performance. Results demonstrate that traffic congestion, carbon emissions, and environmental pollution are tightly coupled, forming a non-coordinated feedback loop that challenges single-policy solutions. For example, the “two-license-plate restriction” policy reduces traffic congestion by 2.72%, carbon emissions by 10.37%, and pollution by 2.47% compared to the baseline scenario, achieving the highest TOPSIS score of 0.68. The “new energy vehicle promotion” policy significantly contributes to long-term emission reduction; however, its overall effectiveness score is relatively low at 0.5. These findings underscore the need for a systematic approach to urban traffic governance, providing actionable insights for balancing short-term effectiveness and long-term sustainability through dynamic policy integration.