# Pedestrian Behavior in Static and Dynamic Virtual Road Crossing Experiments
## 2 Materials and Methods
### Virtual Environment  
Two real-world streets, 25 de Abril street (25A) in Braga and Teixeira de Pascoais street (TP) in Guimaraes, were modeled in two distinct virtual scenarios. The development process for each scenario was similar. Initially, measurements were taken, including the length and width of crosswalks and their markings, road width, sidewalk width, and the dimensions of parking spaces and roads. Next, these scenarios were modeled using Blender 2.79a [42], an open-source 3D computer graphics software that uses Python for scripting. Various architectural details such as buildings, vertical signs, and vegetation were included, and textures for pavements and buildings were added to enhance the realism of the simulation (Figure 1). A similar process was used in previous studies, such as that by [PERSON] et al. [43]. The same two virtual scenarios from previous experimental studies and the method used to model the vehicle's movement were used in this experiment [41]. It involved the use of the data collected with the analysis of the 2 h video recorded in each one of the streets modeled, clustering the trajectories and speeds into three distinct categories: (i) constant speed; (ii) slow down; and (iii) stop, and defining the characteristics of the movement of the virtual vehicle. The ten conditions considered in this study are described in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Experimental Approach** & **Total** \\ \cline{2-3}  & **Static** & **Dynamic** & \\ \hline Age & 24–60 years & 23–57 years & 23–60 years \\  & (M = 39:70; SD = 12:11) & (M = 39:17; SD = 10.44) & (M = 39:88; SD = 11.20) \\ Sex & 46\% Male; & 57\% Male; & 52\% Male; \\  & 54\% Female & 43\% Female & 48\% Female \\ \hline M = mean, SD = standard deviation of age. & & \\ \end{tabular}
\end{table} 
Table 1: Participants’ demographic characteristics. Regarding the sounds used in the experiments, the vehicle emitted sounds recorded from a Kia Ceed SW with a gasoline combustion engine using the static approach. This vehicle was equipped with ContiEcoContact3 195/65-R15 tires, and the recordings were made using a Bruel and Kjaer pulse analyzer type 3560-C combined with a Bruel and Kjaer Head and Torso Simulator (HATS) Type 4128-C with ear simulators Type 4158-C and 4159-C, as described in [PERSON] et al. [43]. Table 3 displays the main characteristics of the audio stimuli used in the static experimental approach. The term dB(A) associated with the acoustic parameters refers to A-weighted sound pressure levels, which express the relative loudness of sounds as perceived by the human ear. The measurement time represents the duration of the stimuli, in seconds, and the dynamic range is the difference between the maximum and the minimum sound levels. These parameters are relevant to compare with the CPX auralized sounds to guarantee that both sound groups are similar. Using the dynamic approach, the vehicle emitted auralized sounds acquired through the Close Proximity (CPX) measurements. The CPX acquisitions were simultaneously performed by the Controlled Pass-By (CPB) ones. The mentioned vehicle was instrumented with two Bruel and Kjaer microphones type 4189 mounted on the back-right w heel and linked to the Bruel and Kjaer Pulse Analyzer type 3560-C with an arrangement following the EN ISO 11819-2 [44] descriptions. The signal captured by the CPX microphones was predominantly tire road noise. The sounds recorded through the CPX method were submitted to an auralization routine that outputs corresponding binaural CPB-like samples. This allowed a subject to hear a sound that appeared to come from the approaching vehicle (that was being observed on the projection screen).