Modeling of road traffic noise, with an application in urban areas
Approximately 70 percent of total noise pollution in urban areas is caused by road traffic noise. Even though various vehicle groups are the main sources of traffic noise in cities, however, the effect of infrastructure such as noise reflection, absorption, diffraction, etc. may enhance and/or reduce road traffic noise. The exploration of road traffic noise has resulted in the development of models that allows us to predict noise descriptors (traffic noise levels) using explanatory factors. Traffic noise prediction models are required as aids in the design of highways and other roads and sometimes in the assessment of existing or envisaged changes in traffic noise conditions. Traffic noise is a major issue that should be considered during the design and construction of new transportation systems, as well as in improvements of the existing transportation systems. Special noise descriptors including Leq, L10, Lmax, etc. are sometimes required for the assessment of complaints about road traffic noise. Nowadays, the number of vehicles in urban areas especially in the megacities has dramatically increased giving rise to unprecedented noise pollution. Thus, there is a need to develop a general traffic noise model and to assess urban traffic noise (Noise Impact Assessment, NIA).
One of the scientific challenges is the difficulty to design a road traffic noise model including three groups of factors i.e. traffic factors, infrastructure factors, and meteorological factors capable of representing diverse predictor factors (independent variables) as well as various traffic noise descriptors in traffic noise prediction models. Another challenge is the complexity of designing road traffic noise models, considering both linear and nonlinear methods along with their dynamic or static factors. Capability of Path Analysis technique in different scenarios is one of the tools contributing to the challenges of this research project.
The research focuses on the development of traffic noise prediction models for urban roads and highways by employing linear and nonlinear methods. The proposed models are designed to predict road traffic noise and control its side effects. They are promising in minimising the road traffic noise and can be helpful in Environmental Impact Assessment (EIA). In addition, the Path Analysis technique using effective factors of road traffic noise provides insights into the potential benefits of the models. For instance, if the Path Analysis technique reveals that honking is the traffic factor with the highest effect in the model, then transport departments and their policy makers can find a solution to control/minimise the honking. Furthermore, the traffic noise models can be applied in intelligent transportation systems (ITS) in future.
Seyed Shaho Ahmadi Dehrashid
Start/end date: 15 February 2017-15 February 2021