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首页> 外文会议>2012 Third International Conference on Computing Communication amp; Networking Technologies. >Artificial neural network modeling for road traffic noise prediction
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Artificial neural network modeling for road traffic noise prediction

机译：道路交通噪声预测的人工神经网络建模

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Several attempts have been made by the researchers to predict and model urban road traffic noise mathematically and statistically. There has been a lot of interest in the new techniques for analyzing data. Neural networks offer a new strategy with enormous potential for many tasks in the domain of geospatial planning. ANN technique for modeling provides smaller errors in comparison to other classical methods. Neural networks have been applied to many interesting problems in various areas including road traffic noise prediction. In the present study an attempt has been made to explore the application of neural networks to road traffic noise prediction in Lucknow city, capital of Uttar Pradesh, India. Traffic volume, speed and noise level data were collected at ten selected locations. For development of model, classified traffic volume (Car/Jeep/Van, Scooter/ Motorcycle, LCV/ Minibus, Bus, Truck and 3-Wheeler), traffic speed on both sides of the road were taken as input data. Output was estimated as Leq. Performance of the model was tested by root mean square error (RMSE), mean absolute error (MAE) and coefficient of correlation (R). It was observed that there is no significant difference between observed and predicted noise levels in the present case, indicating the accuracy of model.
机译：研究人员进行了数次尝试，以数学和统计方式预测和建模城市道路交通噪声。人们对用于分析数据的新技术非常感兴趣。神经网络为地理空间规划领域中的许多任务提供了具有巨大潜力的新策略。与其他经典方法相比，用于建模的ANN技术提供的误差较小。神经网络已被应用于包括道路交通噪声预测在内的各个领域的许多有趣问题。在本研究中，已经尝试探索神经网络在印度北方邦首府勒克瑙市的道路交通噪声预测中的应用。在十个选定地点收集了交通量，速度和噪音水平数据。为了开发模型，将分类的交通量（汽车/吉普车/货车，小型摩托车/摩托车，LCV /小巴，公共汽车，卡车和3-轮车），道路两侧的交通速度作为输入数据。估计输出为Leq。通过均方根误差（RMSE），平均绝对误差（MAE）和相关系数（R）来测试模型的性能。据观察，在当前情况下，观察到的噪声水平与预测的噪声水平之间没有显着差异，表明模型的准确性。

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