The development and validation of a model for dynamic traffic noise prediction is presented. The model is composed of a GIS-based traffic microsimulation part coupled with an emission model, and a beamtrace-based 2.5D propagation part, which takes into account multiple reflections and diffractions. The model can be used to analyze the influence of real urban traffic situations (e.g., traffic flow management, road saturation) in the usual equivalent sound level maps. However, it also allows to calculate and visualize statistical noise levels and indicators derived from them. Novel descriptors based on the power spectrum of noise level fluctuations can be obtained. A part of Gentbrugge, Belgium, is taken as a validation area; different traffic demand scenarios are simulated.
Abstract-Applying the current technological possibilities has led to a wide range of traffic monitoring systems. These heterogeneous data sources individually provide a view on the current traffic state, each source having its own properties and (dis)advantages. However, these different sources can be aggregated to create a single traffic state estimation. This paper presents a data fusion algorithm that combines data on the data sample level. The proposed system fuses floating car data with stationary detector data and was implemented on live traffic. Results show the fusion algorithm allows to eliminate individual source bias and alleviates source-specific limitations.
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