| With the rapid development of the urbanization in China,the jeopardy of urban environmental noise pollution is getting obvious,however,due to the complexity of urban scale noise,a timely,comprehensive,and intensive noise monitoring system is needed now to control the environmental noise dynamicly.Cities noise map is a comprehensive analysis and calculation on the sound source data,traffic information and other factors to reflect the noise level of a data map,which is recognized as a relatively effective,inexpensive and comprehensive display of the noise strategic decisions' management tool.In the process of noise simulation,with high cost in drawing and lack of parameters localization,a noise map includes only traffic noise and static noise in a particular moment,so it's of great significance for the noise map's promotion and fine management on city noise to introduce real-time noise,real-time traffic and POI data,and to realize the big data modelingof dynamic noise and visualization.This paper is conducted to research the big date of noise from two aspects including the improvements on the existing noise modeling and monitoring methods based on the noise sensor.The research of urban noise map is based on LUNOS model and neural network method.LUNOS noise model is the basis of static noise.By introducing automaticly noise monitoring data,real-time traffic data and POI data,noise and related big date are associated to model by self-learning with the method of neural network modeling.The research of urban noise map is based on mobile sensors to monitor and Support Vector Machine.Urban environmental noise modeling and noise classification are achieved with the intelligent machine monitoring noise,the introduction of real-time traffic data and POI big data,the integrated use of neural network,spatial similarity analysis,vector machines supported and other big data modeling methods.The paper takes a deeper research on Dalian and Beijing as case studies,and big data modelings of noise were carried out in the two cities respectively.Results of the improved noise modeling based on LUNOS show that by introducing relevant big data,noise has a weak positive correlation with road length,number of vehicles,waiting time and vehicle speed,and a positive correlation with the time passing by,while the congestion level is the most significant variable that influces the noise.By distinguishing the linear regression modeling of congestion level,it's gained that when the congestion level is 1,2,and 3,the R of calibration is0.54,0.77,0.91,the R of validation is 0.53,0.80,0.71 respectively.Through Artificial Neural Network modeling,the optimal network results of the three layers and three neurons structure were finally selected to be noise prediction model.The R of it can reach 0.838,which is much butter than most linear models(when the congestion level is 1,2),but a little bit lower than linear nodels of level 3.The noise research results based on the mobile sensors monitor and spatial similarity mining showstha Hsim function with 8 connected point is the best model,the error of calibration is 9.42 d BA(mean)and 12.14 d BA(var),the error of validation is 9.46 d BA(mean)and 12.19 d BA(Var).Moving time window's R of calibration reaches0.88,tenneurons neural network noise models can better simulate dynamic changes of noise,and reduce the differences when the noise source performs at different times.By the species classification of Support Vector Machines and numbers of decision,it can be achieved to distinguish the regional traffic noise or social noise that are both the main sound source,and it can also observe the main types of noise in the spatial distribution through the method visualization. |
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