用流模型实现实时城市噪声图生成，精度高且可交互调整
Real-time Prediction of Urban Sound Propagation with Conditioned Normalizing Flows
- 基于条件归一化流构建噪声传播预测模型，支持实时生成256x256地图
- 比传统物理求解器快2000倍以上，非视距区域误差低至0.65 dB
- 适用于城市规划、合规制图及临时施工等需要快速响应的场景
准确高效的都市噪声预测对公共健康与城市监管至关重要，欧盟环境噪声指令要求定期生成战略噪声地图和行动计划，常用于审批、路权分配与施工调度。物理驱动求解器因速度慢，难以支持此类时间敏感的“假设分析”迭代。本文评估条件归一化流（Full-Glow）在256x256城市布局上实时生成符合标准的声压图的能力，单张RTX 4090显卡即可实现每秒生成。在覆盖基准、衍射与反射场景的数据集上，该模型较参考求解器提速超2000倍，非视距区域准确率提升最高达24%；基准非视距情形下达到0.65 dB MAE，结构保真度高。模型能复现衍射与干涉模式，支持源或几何变化下的即时重计算，适用于城市规划、合规制图与运营决策（如临时道路封闭、夜间作业影响评估）。
原文摘要 · Abstract (English)
Accurate and fast urban noise prediction is pivotal for public health and for regulatory workflows in cities, where the Environmental Noise Directive mandates regular strategic noise maps and action plans, often needed in permission workflows, right-of-way allocation, and construction scheduling. Physics-based solvers are too slow for such time-critical, iterative "what-if" studies. We evaluate conditional Normalizing Flows (Full-Glow) for generating for generating standards-compliant urban sound-pressure maps from 2D urban layouts in real time per 256x256 map on a single RTX 4090), enabling interactive exploration directly on commodity hardware. On datasets covering Baseline, Diffraction, and Reflection regimes, our model accelerates map generation by >2000 times over a reference solver while improving NLoS accuracy by up to 24% versus prior deep models; in Baseline NLoS we reach 0.65 dB MAE with high structural fidelity. The model reproduces diffraction and interference patterns and supports instant recomputation under source or geometry changes, making it a practical engine for urban planning, compliance mapping, and operations (e.g., temporary road closures, night-work variance assessments).
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