城市交通管制政策被越来越多地用于应对交通拥堵、排放及可达性问题，但由于城市交通系统的社会技术复杂性，其影响难以评估。近年来，数据可得性和计算能力的提升为交通政策设计提供了基于模型的仿真决策支持新途径。本文提出一种新型仿真范式，用于事前评估交通管制政策的直接与间接影响，涵盖交通状况、交通相关效应及经济可达性等方面。该方法整合了多层城市交通模型，包括反映交通流与排放的物理层，以及捕捉行为响应与政策变化适应性的社会层。利用真实世界数据构建现状情景，将政策备选方案与行为假设作为模型参数，生成多种“如果”情景。通过分析政策干预引起的模拟结果变化，该框架支持对不同情景进行系统性比较。案例研究展示了该方法在评估广泛城市交通限制方案引入影响方面的应用。结果表明，该框架能够探索不同的监管设计与用户响应，支持对城市交通政策进行知情且前瞻性的评估。

Urban traffic regulation policies are increasingly used to address congestion, emissions, and accessibility in cities, yet their impacts are difficult to assess due to the socio-technical complexity of urban mobility systems. Recent advances in data availability and computational power enable new forms of model-driven, simulation-based decision support for transportation policy design. This paper proposes a novel simulation paradigm for the ex-ante evaluation of direct and indirect impacts, spanning traffic conditions, transportation-related effects and economic accessibility. The approach integrates a multi-layer urban mobility model combining a physical layer of mobility flows and emissions with a social layer capturing behavioral responses and adaptation to policy changes. Real-world data are used to instantiate the current as-is scenario, while policy alternatives and behavioral assumptions are encoded as model parameters to generate multiple what-if scenarios. The framework supports systematic comparison across scenarios by analyzing variations in simulated outcomes induced by policy interventions. The proposed approach is illustrated through a case study that aims to assess the impacts of the introduction of broad urban traffic restriction schemes. Results demonstrate the framework's ability to explore alternative regulatory designs and user responses, supporting informed and anticipatory evaluation of urban traffic policies.