Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia.
Environ Monit Assess. 2019 Feb 26;191(3):190. doi: 10.1007/s10661-019-7333-3.
This study proposes a neural network (NN) model to predict and simulate the propagation of vehicular traffic noise in a dense residential area at the New Klang Valley Expressway (NKVE) in Shah Alam, Malaysia. The proposed model comprises of two main simulation steps: that is, the prediction of vehicular traffic noise using NN and the simulation of the propagation of traffic noise emission using a mathematical model. First, the NN model was developed with the following selected noise predictors: the number of motorbikes, the sum of vehicles, car ratio, heavy vehicle ratio (e.g. truck, lorry and bus), highway density and a light detection and ranging (LiDAR)-derived digital surface model (DSM). Subsequently, NN and its hyperparameters were optimised by a systematic optimisation procedure based on a grid search approach. The noise propagation model was then developed in a geographic information system (GIS) using five variables, namely road geometry, barriers, distance, interaction of air particles and weather parameters. The noise measurement was conducted continuously at 15-min intervals and the data were analysed by taking the minimum, maximum and average values recorded during the day. The measurement was performed four times a day (i.e. morning, afternoon, evening, and midnight) over two days of the week (i.e. Sunday and Monday). An optimal radial basis function NN was used with 17 hidden layers. The learning rate and momentum values were 0.05 and 0.9, respectively. Finally, the accuracy of the proposed method achieved 78.4% with less than 4.02 dB (A) error in noise prediction. Overall, the proposed models were found to be promising tools for traffic noise assessment in dense urban areas.
本研究提出了一种神经网络 (NN) 模型，用于预测和模拟马来西亚雪兰莪州新巴生谷快速公路 (NKVE) 密集住宅区的车辆交通噪声传播。所提出的模型由两个主要模拟步骤组成：即使用 NN 预测车辆交通噪声和使用数学模型模拟交通噪声排放的传播。首先，使用以下选定的噪声预测器开发了 NN 模型：摩托车数量、车辆总数、汽车比例、重型车辆比例（如卡车、货车和公共汽车）、高速公路密度和激光雷达 (LiDAR) 衍生的数字表面模型 (DSM)。随后，通过基于网格搜索方法的系统优化程序对 NN 及其超参数进行了优化。然后在地理信息系统 (GIS) 中使用五个变量（即道路几何形状、障碍物、距离、空气粒子相互作用和天气参数）开发噪声传播模型。噪声测量以 15 分钟的间隔连续进行，数据通过记录白天的最小值、最大值和平均值进行分析。测量每天进行四次（即早上、下午、晚上和午夜），每周进行两天（即周日和周一）。使用具有 17 个隐藏层的最优径向基函数 NN。学习率和动量值分别为 0.05 和 0.9。最后，所提出的方法的准确性达到了 78.4%，噪声预测的误差小于 4.02dB(A)。总体而言，所提出的模型被发现是密集城市地区交通噪声评估的有前途的工具。
