# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 5 Case Studies
### Case Study II
#### 5.2.2 Experimental Procedure  
The experiment was conducted in a controlled virtual environment, where a pedestrian wearing a VR headset observed an approaching vehicle driven by a human participant using a regular driving simulator setup with three TV screens. This setup, shown in Figure 8, was designed to assess whether pedestrians could visually perceive speed differences within the virtual environment, thereby validating the realism of the simulation for pedestrian safety studies.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measure** & **Before Critical Moment** & **After Critical Moment** & **Statistical Test (t, _p_)** \\ \hline Critical Distance (m) & 2.30 \(\pm\) 0.12 & N/A \\ \hline Lane Position (m) & 0.94 \(\pm\) 0.09 & 1.14 \(\pm\) 0.08 & \(t=-21.07\), \(p<0.00001\) \\ \hline Heart Rate (bpm) & 69.45 \(\pm\) 2.73 & 72.75 \(\pm\) 2.89 & \(t=-26.94\), \(p<0.00001\) \\ \hline \hline \end{tabular} Note: Lane position is measured within a 2-meter-wide bike lane, where 0 indicates the left edge and 2 indicates the right edge.  
\end{table}
Table 1: Summary of bicyclist responses to passing vehicles.  
At the start of each trial, the driver was positioned at a sufficient distance to ensure that the pedestrian could visually track the vehicle's approach from the moment it entered their field of vision. The scenario was set on a three-lane road, with the pedestrian standing at the edge of the rightmost lane. The pedestrian remained stationary at the roadside throughout the experiment and was instructed to monitor the approaching vehicle and determine the moment they perceived it as unsafe to cross. Participants recorded this decision using a controller, allowing for an objective assessment of gap acceptance behavior.  
Each participant completed two trials, one in which the vehicle approached at 30 mph and another at 50 mph. In both trials, the vehicle traveled in the rightmost lane of a three-lane road. The order of these trials was randomized to prevent order effects, ensuring that participants were not influenced by a fixed speed progression from slow to fast.  
The study measured both behavioral and physiological responses to approaching vehicles. Data were collected from two primary sources:  
* Position and speed data of the vehicle and pedestrian, recorded at a frequency of 0.1 s.
* Pedestrian heart rate measurements, recorded every 1 s, capturing physiological responses to vehicle proximity.  
A key reference point, referred to as the critical moment, was defined as the time at which the vehicle reached its minimum distance from the pedestrian, known as the critical distance. This critical distance represents the closest point of approach between the vehicle and the pedestrian.  
To assess how pedestrians responded to vehicle speed, we computed the following:  
Figure 8: Participants’ perspectives in Case Study II.  
* Gap acceptance, measured as the timestamp when the pedestrian recorded that it was unsafe to cross.
* Average heart rate during the 5 s before and after the critical moment to assess potential stress responses.  
Figure 9 illustrates the relationship between the heart rate and the distance between the driver and the pedestrian, highlighting the critical moment used for analysis. By structuring the analysis around the critical moment, we aimed to quantify how pedestrians mentally and physiologically adapted to different vehicle speeds while also evaluating whether the simulation system provides a valid representation of the visual cues necessary for pedestrian safety research.