交通噪声地图的声源反演及修正计算

交通噪声地图是环境噪声管理的重要工具,为了减小大范围噪声地图绘制时产生的预测误差,提出了一种基于监测数据的声源特性反演算法,给出了噪声地图修正计算的详细方法和步骤.该算法利用原始噪声地图的计算结果参与计算来提升修正求解效率,避免对预测模型参数进行直接修改,保证修正区域的计算结果符合预测模型中的声传播规律.在自主研发的噪声地图绘制软件中实现该反演修正算法,并对北京某示范区噪声地图的求解和修正计算来验证算法有效性.6组实验结果分析得出,该算法在监测点位置处的修正误差小于1.1dB,而在非监测点位置处也均对原始预测值进行了不同程度的修正改善,其误差程度与监测点主要声源对监测点的贡献率及监测点的影响范围有关,在监测点控制范围内的预测值误差在2.5dB以下.实验证明该算法能够有效的对交通噪声地图进行修正更新计算,并在保证满足预测模型声传播规律不变的情况下改善噪声地图求解质量.

Sources inversion and correction calculation for traffic noise mapping

Traffic noise map play a significant role in urban environmental noise management. In order to reduce the prediction error for big size noise mapping project, a measurement based inverse approach for traffic noise sources was presented, and the detailed method of noise map correction calculation was introduced step by step. The approach improved correction solving efficiency by mixing original noise mapping calculation result and inverse sources effect, avoided modify the prediction model directly, and ensured that the calculation results in target area can match the noise propagation rules defined by prediction model. Then, the approach mentioned above was implemented as a software package on the noise mapping platform developed by our group. At last, a real traffic noise mapping project in Beijing was introduced to demonstrate the method. By comparing the 6 groups of experimental result, it was showed that the correction error can reach less than 1.1dB on the location of measurement points. On the other points in calculation area, the accuracy of sound levels was improved more or less. The correction error was related to two factors measurement points: the contribution rate of main noise source and the influence area of measurement points. The correction error can reached less than 2.5dB on the points located in influence area of measurement point. The experimental results revealed that this method was suitable to inverse the traffic sources by measurements, generate correction calculation result and improve solving quality of noise map.