道路交通噪声描述符的人工神经网络建模

D. Parbat, P. Nagarnaik
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引用次数: 11

摘要

本文以马哈拉施特拉邦维达尔巴地区的印度中部城市亚瓦马特市为例,对人工神经网络建模在道路交通噪声预测中的可行性进行了研究。在不间断和中断的交通流条件下确定了16个地点进行实地研究交通量研究(组成和分类交通量)和噪音水平研究同时进行。采用人工神经网络软件(Elite ANN),该网络采用前馈负反向传播算法,具有三个隐时间元素和三个前一时间元素的权重(Pandharipande & Badhe, 2002)。人工神经网络建模是通过以下输入数据进行的:总交通量、交通构成(公共汽车/卡车、轻型货车、TW、自行车等)的百分比和行车道宽度、接收器与人行道的距离。输出估计为L10, Leq, LNP, TNI和NC。观察到的输入和输出数据通过人工神经网络进行处理和训练,以适应中断和不间断的流动条件。为了提高预测的准确性,进一步对模型进行了线性回归分析。在本例中,观察到观测到的噪声水平与预测的噪声水平之间没有显著差异,表明模型的准确性。
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Artificial Neural Network Modeling of Road Traffic Noise Descriptors
The present paper illustrates study on feasibility of ANN modeling for road traffic noise prediction at Indian Intermediate, Yavatmal city, district place of Vidarbha region in Maharashtra state. Sixteen locations were identified at uninterrupted and interrupted traffic flow conditions for conducting field studies Traffic volume study (composition & classified traffic volume) and noise level study are carried out simultaneously. Artificial neural network software (Elite ANN) is used, the network uses feed forward negative back propagation algorithm with three hidden and three previous time elements of weights (Pandharipande & Badhe, 2002). ANN modeling is performed through input data as- Total traffic, Traffic composition (bus/truck, LCV, TW, bicycle and others) in % and carriageway width, Distance of receiver from pavement. Output is estimated as L10, Leq, LNP, TNI and NC. The observed input and output data is processed and trained through ANN for interrupted and uninterrupted flow condition. To enhance the accuracy of prediction, further this model has been tested by using linear regression analysis between observed and predicted noise levels. In the present case, it is observed that there is no significant difference between the observed and predicted noise levels, indicating the accuracy of model.
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