High temporal resolution dynamic traffic noise modelling via traffic flow stochastic disaggregation
提出两种随机分解方法，从聚合交通流数据重建车辆运动细节，实现1秒分辨率的噪声估计，为城市尺度动态噪声暴露评估提供低成本中间方案。
Road traffic noise exposure assessment typically relies on aggregated traffic flow data, which prevents the estimation of high-temporal-resolution noise indicators increasingly recognized as important for health impact studies. To bridge this gap, this research proposes two stochastic disaggregation methods that reconstruct refined vehicle kinematics from aggregated traffic flows, enabling 1-s resolution noise estimation comparable to computationally intensive microscopic traffic modelling chains. Using SUMO microscopic simulation as reference, the disaggregation methods are evaluated in a dense urban area, in Stockholm’s Södermalm Island. The resulting acoustic indicators calculated, including LAeq,1h, LA10,1h, LA1,1h, and LAeq,1s, show estimates comparable to those obtained from the microscopic traffic noise modelling chain. Robustness and sensitivity analyses show that the proposed methods maintain stable performance even with reduced input data granularity. The proposed methods offer a practical intermediate solution between static annual noise maps and detailed microscopic simulations, enabling cost-effective dynamic noise exposure assessment at an urban scale.