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.

翻译：城市交通法规政策日益被用于缓解交通拥堵、减少排放和改善城市可达性，但由于城市交通系统的社会技术复杂性，其影响难以准确评估。数据可用性和计算能力的进步为交通政策设计提供了新型的模型驱动、基于仿真的决策支持方法。本文提出了一种创新的仿真范式，用于事前评估交通法规的直接与间接影响，涵盖交通状况、交通相关效应及经济可达性等多个维度。该方法整合了多层城市交通模型，将交通流与排放的物理层与捕捉行为响应及政策适应性的社会层相结合。通过真实数据实例化当前基准场景，同时将政策方案与行为假设编码为模型参数以生成多种假设场景。该框架通过分析政策干预引发的模拟结果变异，支持跨场景的系统化比较。本文以评估大规模城市交通限行方案的影响为例，演示了所提方法的应用。结果表明，该框架能够探索不同的法规设计方案与用户响应模式，为城市交通政策提供前瞻性的科学评估依据。