Over the last decades, the number of motor vehicles has increased dramatically in Iran, where different traffic characteristics and urban structures are notable. In the present study, a multilayer perceptron neural network model trained with the Levenberg-Marquardt algorithm was used for predicting the equivalent sound level (LAeq) originating from traffic. Fifty-one samples were collected from different areas of Tehran. Input parameters consisted of total traffic volume per hour, average speed of vehicles, percentage of each category of vehicles, road gradient, density of buildings around the road section and a new parameter named “Building Reflection Factor”. These data were randomly used with 80, 10 and 10 percentiles respectively for training, validation and testing of the Artificial Neural Network (ANN). Results yielded by the ANN model were compared with field measurement data, a proposed regression model and some classical well-known models. Our study indicated that the prediction error of the neural network model was much less than that of the regression model and other classical models. Moreover, a statistical t-test was applied for evaluating the goodness-of-fit of the proposed model and proved that the neural network model is highly efficient in estimating road traffic noise levels.

中文翻译：

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德黑兰城市道路的神经网络噪声预测模型

在过去的几十年里，伊朗的机动车数量急剧增加，不同的交通特征和城市结构是显着的。在本研究中，使用 Levenberg-Marquardt 算法训练的多层感知器神经网络模型用于预测源自交通的等效声级 (LAeq)。从德黑兰的不同地区收集了 51 个样本。输入参数包括每小时总交通量、车辆平均速度、各类车辆的百分比、道路坡度、路段周围建筑物的密度和一个名为“建筑物反射系数”的新参数。这些数据分别以 80、10 和 10 个百分位数随机用于人工神经网络 (ANN) 的训练、验证和测试。ANN 模型产生的结果与现场测量数据、建议的回归模型和一些经典的知名模型进行了比较。我们的研究表明，神经网络模型的预测误差远小于回归模型和其他经典模型的预测误差。此外，统计 t 检验被应用于评估所提出模型的拟合优度，并证明神经网络模型在估计道路交通噪声水平方面是高效的。