# Pedestrian Behavior in Static and Dynamic Virtual Road Crossing Experiments
## 1 Introduction  
Several studies have been conducted in the last two decades to determine which factors may influence pedestrian behavior and decision-making during interactions with motorized traffic. For instance, when crossing roads, the approaches are diversified and evolved to better-controlled methods, as can be seen in the literature [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]. The data collection method arises as a differentiation feature. Generally, the data collection method in these studies fits within one of the following categories: field observations, surveys, semi-controlled experiments, and virtual reality (VR) simulations [13, 22, 23]. The most commonly used method for collecting data is direct observation of real-life interactions, which typically involves recording videos of the action [24]. Those recordings, however, are limited to the used camera's field of vision and may fail to capture essential parts of the scene, like the trajectory of approaching vehicles. Other alternatives, such as following the trajectory of pedestrians through GPS instrumentation or Bluetooth/Wi-Fi sensors, also have limitations, such as imprecise location, and they do not allow the collection of data regarding other traffic agents involved in the interaction [14, 23]. Surveys are the most often used method to obtain data for qualitative analysis. They are achieved through online or paper questionnaires, face-to-face, or through telephone interviews [25]. While they are a relatively simple way of collecting data, they have thedisadvantage of relying on self-assessment of the respondents, which can be strongly biased and fail to accurately portray their actions in real situations. Additionally, they require large samples of participants to produce meaningful data [23]. Semi-controlled experiments usually analyze factors such as gait dynamics and pedestrian spatial organization along predefined paths [26; 27; 28]. This method has the advantage of partly restricting environment variability, which enables more accurate conclusions to be drawn about the studied phenomena. However, implementing experimental controls in real-world conditions can be difficult from a practical standpoint, even if only partial control is intended. Concurrently, some experiments have been performed using virtual reality simulators in which the test participant visualizes a crossing situation and must choose between going/not going options by clicking a button, e.g., [29; 30; 31; 32; 33], or by actually moving along the virtual crossroad, e.g., [22; 34; 35; 36; 37; 38]. Simulator-based experiments are complicated by the need for physical space to carry them out, and dependence on expensive equipment. However, they have several advantages compared with similar experiments conducted in the real world. They avoid most hurdles required to ensure participants' safety in natural environments while allowing more control over experimental conditions and tasks [39]. However, participants are aware that they are part of a virtual study, which might prevent them from fully experiencing risk and could influence their decisions. Some studies have already begun addressing this issue in virtual experiments [40]. VR-based pedestrian crossing simulators can be divided into two categories: those relying on head-mounted displays (HMDs) or the ones using Cave Automatic Virtual Environments (CAVE) technology [34]. Compared to HMD solutions, projection-based simulators allow the participants greater freedom of movement. By using a power-wall configuration and a motion tracking system to project the intended scenario with a perspective adjusted to the physical location of the participant, this type of simulator allows participants to conduct the act of crossing simply by walking, without the use of instruments such as treadmills or joysticks [34]. The fact that participants can move freely should make the simulator more realistic and immersive.