The negative impact of noise on human health is well established and a high percentage of environmental noise is related with traffic sources. In this study, we compared annoyance judgments of real and virtual traffic sounds. Virtual sounds were generated through an auralization software with input from close proximity tyre/road noise measurements and real sounds were recorded through a Head and Torso Simulator. Both groups had sounds generated at two speeds and from three urban pavement surfaces (asphalt concrete, concrete blocks and granite cubes). Under controlled laboratory conditions, participants rated the annoyance of each real and virtual stimulus. It was found that virtual stimuli, based on close proximity tyre/road noise, can be used to assess traffic annoyance, in spite of systematic lower rates than those found for real stimuli. The effects of type of pavement and speed were the same for both conditions (real and virtualized stimulus). Opposed to granite cubes, asphalt concrete had lower annoyance rates for both test speeds and higher rate differences between real and virtual stimuli. Additionally, it was also found that annoyance is better described by Loudness than by LAmax. This evidence is stronger for the virtual stimuli condition than for the real stimuli one. Nevertheless, we should stress that it is possible to accurately predict real annoyance rates from virtual auralized sound samples through a simple transformation model. The methodology developed is clearly efficient and significantly simplifies field procedures, allowing the reduction of experimental costs, a better control of variables and an increment on the accuracy of annoyance ratings.

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The research aimed to establish tyre-road noise models by using a Data Mining approach that allowed to build a predictive model and assess the importance of the tested input variables. The data modelling took into account three learning algorithms and three metrics to define the best predictive model. The variables tested included basic properties of pavement surfaces, macrotexture, megatexture, and unevenness and, for the first time, damping. Also, the importance of those variables was measured by using a sensitivity analysis procedure. Two types of models were set: one with basic variables and another with complex variables, such as megatexture and damping, all as a function of vehicles speed. More detailed models were additionally set by the speed level. As a result, several models with very good tyre-road noise predictive capacity were achieved. The most relevant variables were Speed, Temperature, Aggregate size, Mean Profile Depth, and Damping, which had the highest importance, even though influenced by speed. Megatexture and IRI had the lowest importance. The applicability of the models developed in this work is relevant for trucks tyre-noise prediction, represented by the AVON V4 test tyre, at the early stage of road pavements use. Therefore, the obtained models are highly useful for the design of pavements and for noise prediction by road authorities and contractors.

Abstract. The identification of contributory factors to crash frequencies observed in different highway facilities can aid transportation and traffic management agencies to improve road traffic safety. In spite of the strategic importance of the national Portuguese road network, there are no recent studies concerned with either the identification of contributory factors to road crashes or Crash Prediction Models (CPMs) for this type of roadway. This study presents an initial contribution to this problem by focusing on the national roads NR-14, NR-101 and NR-206, which are located in Northern region of Portugal. They are two-lane single carriageway rural roads. This study analysed the crash frequencies, Average Annual Daily Traffic (AADT) and geometric characteristics of 88 two-lane road segments. The selected segments were 200-m-long and did not cross through urbanized areas. The fixed length of 200 meters corresponds to the road length used in Portugal to define a critical point. Data regarding the annual crash frequency and the AADT were available from 1999 to 2010. Due to the high number of zero-crash records in the initial database, the data were explored to identify the best statistical modelling approach to be adopted. The Generalized Estimating Equations (GEE) procedure was applied to 10 distinctive databases formed by grouping the original data in time and space. The results show that the different observations within each road segment present an exchangeable correlation structure type. This paper also analyses the impact of the sample size on the model's capability of identifying the contributing factors to crash frequencies. The major contributing factors identified for the two-lane highways studied were the traffic volume (expressed in AADT), lane width, vertical sinuosity, and Density of Access Points (DAP). Acceptable CPM was identified for the highways considered, which estimated the total number of crashes for 400-m-long segments for a cumulative period of two years.

Road-traic noise is the most signiicant source of environmental noise. Among the several diferent sources of noise emission from vehicles, tyre/road noise at speeds above 40 km/h is the most prevalent. Its negative impact on health is now beter known and may be mitigated by optimising road surface characteristics. Experimental data linking the characteristics of the road surface to levels of annoyance regarding noise remain scarce. Moreover, assessing annoyance by experimental means using real sounds is complex and could impede study interactions with a wide set of variables. In this chapter, we describe, discuss and present the results of a straightforward method to assess tyre/road noise and related annoyance, based on the virtual sounds made by vehicles, with no interferences.