## 1 Introduction  
Noise caused by traffic is a relevant health factor in urban environments, along major transport routes and in the vicinity of airports. Noise, in contrast to sound, can principally not be measured, but has to be assessed. For the most relevant noise sources, objective quantities have been derived that correlate with the annoyance as reported by people. However, these correlations are usually weak. One reason for this is the fact that the describing quantities used so far represent the acoustic situation only in a very simplified manner. A method to further investigate the signal properties relevant to noise is to conduct listening experiments where different stimuli are presented to test persons. Relying on audio recordings allows for little variation of different signal aspects only. A more versatile method with a much higher degree of freedom, as well as full control of the influencing signal parameters is to synthesize the stimuli and, thus, to auralize an acoustical environment. Auralization has been in use for quite some time in architectural acoustics, namely in the fields of room and building acoustics [1; 2; 3], but it has only recently been discovered for environmental acoustical applications. Today, most auralizations are generated based on computer models and digital signal processing. However, between applications, the individual simulation steps mayvary significantly. In room and building acoustical auralizations, it is common to utilize (anechoic) recordings as the source material, whereas in environmental acoustics, it is often required to artificially synthesize the source signals [4; 5; 6; 7; 8]. Furthermore, the sound propagation simulation substantially differs. In room acoustical auralizations, the focus lies on the simulation of many room reflections, in particular specular _vs._ diffuse reflections, and diffraction [2; 3]. In building acoustical auralizations, sound transmission through structures is simulated using sound insulation prediction models [2]. For environmental acoustical auralizations, however, particular emphasis is placed on a detailed simulation of direct sound and ground reflection. Leastwise, in terms of sound reproduction, the same techniques are applicable. Early attempts in auralizations for environmental noise applications have been made by a group at NASA Langley Research Center, where aircraft flyovers have been binaurally rendered based on monaural recordings [9]. In the same period, a synthesis model for the traction noise of electric rail-bound vehicles was developed at the RWTH Aachen University and used to study sound quality [4]. Newer auralization models also try to synthesize the sounds of aircraft [7; 8; 10]. One of these models has already been combined with 3D visualizations to make aircraft noise both heard and seen in immersive virtual reality environments [8]. In the recently-completed Swiss project \"VisAsim\" [11; 12], outdoor auralizations of wind farms were linked to synchronous GIS-based 3D visualizations. Within the Swedish project \"LISTN\" [13; 14] and the European project \"HOSANNA\" [5; 15], tools for the auralization of road traffic noise were developed. The main motivation is to provide more intuitive information about traffic noise scenarios for city planners, noise consultants and decision makers. There is common agreement in the point that information about noise in the form of dB values is difficult to communicate to the public [16]. On the other hand, there is growing interest in the perceptual aspects of noise abatement measures. While in the past, the quality of noise mitigation measures was understood as the A-weighted sound pressure level reduction, the focus shifts to perceptual efficiency [17]. In this respect the optimal measure is not necessarily the one with the highest dB(A) drop, but the one with the highest annoyance reduction. The idea of describing and subsequently improving the acoustical environment with respect to human perception corresponds to the soundscape concept.