A GIS Based Urban Road Traffic Noise Prediction System

The noise pollution problem has attracted people's attention deeply in recent years.The noise pollution has become a contemporary and world problem.Similarthe to the "three discard" of industry, noise pollution is slso a kind of harmful effects which endangers mankind's environment.And the road traffic noise is the main sourc of the noise pollution in the city. So analyzing and predicting the road traffic noise is an effective method not only for decreasing the noise pollution, but also for managementing and programming the transportation of a city. As a strong tool for handling spacial in-formation, the geography information system(GIS) can perform an important function in managing and controlling the road traffic noise pollution. In this research, we integrate GIS with traffic noise prediction and develop a GIS based road traffic noise prediction system.After analyzing the characteristics and the current research situations of the urban road traffic noise, this research choosed the predicton model developed by Bengang Lia et al. This traffic noise prediction system was developed with the GIS component from ESRI-MapObjects 2.2, the VisualBasic 6.0 programming language and the SQL Sever 2000 DBMS from Microsoft, using the object-oriented programming method. This system has the basic functions of a GIS, such as storing, managing, analyzing and displaying the spacial data. At the same time, this system integrates the prediction model with the GIS. So the system has the ability not only to calculate and predicte the urban traffic noise, but also to provide flexible search methods and different ways to display the prediction results. In addition, it also provides simulation to reduce the traffic noise, which provide reference and help for both city layout and traffic noise management.Integrating noise prediction with GIS is a trend in traffic noise prediction research. It solves the probleme such as data management and visualization in traffic noise prediction research before. So this research is representative and applied.