Featured Application: The method discussed in this paper has applications in the context of predicting traffic noise in large urban environments. The system designed by the authors provides an accurate description of traffic noise by relying on measurements of road noise from few monitoring stations appropriately distributed over the zone of interest. A prescription is given of how to choose the location of the noise stations.Abstract: Dynamap, a co-financed project by the European Commission through the Life+ 2013 program, aims at developing a dynamic approach for noise mapping that is capable of updating environmental noise levels through a direct link with a limited number of noise monitoring terminals. Dynamap is based on the idea of finding a suitable set of roads that display similar traffic noise behavior (temporal noise profile over an entire day) so that one can group them together into a single noise map. Each map thus represents a group of road stretches whose traffic noise will be updated periodically, typically every five minutes during daily hours and every hour during night. The information regarding traffic noise will be taken continuously from a small number of monitoring stations (typically 24) appropriately distributed over the urban zone of interest. To achieve this goal, we have performed a detailed analysis of traffic noise data, recorded every second from 93 monitoring stations randomly distributed over the entire urban area of the City of Milan. Our results are presented for a restricted area, the urban Zone 9 of Milan. We have separated the entire set of (about 2000) stretches into six groups, each one represented by a noise map, and gave a prescription for the locations of the future 24 monitoring stations. From our analysis, it is estimated that the mean overall error for each group of stretches (noise map), averaged over the 24 h, is about 2 dB.

The paper provides an empirical analysis of the macroeconomic factors that enhance revenue gap in South Africa using the multivariate cointegration techniques for the period 1965 to 2012. The results from the cointegration analysis indicate that the revenue gap in South Africa is negatively associated with the level of imports while positively related to external debt and underground economy. The former finding is consistent with the notion that imports are subjected to more taxation than domestic activities because of certain features of international trade that tend to make tax evasion difficult. On the other hand, the positive relationship between external debt and tax gap shows that the South African government relies upon external debt to finance its budget deficit resulting from missing revenues. Furthermore, the observed negative effect of the post-apartheid dummy confirms that the tax policy reforms that South Africa introduced following the liberation in 1994 have led to a reduction in missing revenues. The results from the Granger causality test also show that there is a unidirectional causality running from imports and underground economy to revenue gap, while revenue gap on the other hand is found to Granger-cause national income and external debt in South Africa.