# Operationalizing Dyadic Urban Traffic Interaction Studies: From Theory to Practice
## 4 Results: Aspects of Traffic Interaction Study Design Warranting Trade-Offs
### Choice of Scenarios  
[PERSON]'s detailed enumeration of interaction scenarios presents us with a decision point in conducting interaction studies: should we attempt to cover all enumerated scenarios or should we focus in-depth on a select few? Each approach offers distinct benefits and aligns with different research objectives. Broadly studying every possible scenario could provide a comprehensive understanding of traffic behavior across various contexts, emphasizing the universality of space-sharing conflicts. This approach would ideally capture the full spectrum of interaction dynamics, offering a macroscopic view of traffic behavior. However, the feasibility of such an exhaustive study is often limited by factors such as resources, time, funding, simulator sickness, and the sheer complexity of traffic environments.  
On the other hand, focusing on a few selected scenarios in depth allows for a more detailed exploration of specific interactions. While this approach sacrifices some breadth, it enhances depth and may lead to more detailed, context-specific understandings that can inform targeted interventions or policies. Given the impracticality of studying \"every situation\", we suggest in this paper a \"balanced\" approach that involve selecting a representative range of diverse scenarios. This strategy enables researchers to explore how findings from one scenario may transfer to or differ from others, thereby uncovering the relationships between different types of encounters. Understanding these relationships can help to generalize findings and apply them more broadly, even to untested scenarios, thereby maximizing both the reach and the relevance of the research. However, at a more pragmatic level, the right approach is dictated by the technical constraints of the setup for the study, including:  
* _Space Constraint_: Physical or virtual environments where the studies are conducted might have limitations on the nature of the scenarios that can be realistically set up and analyzed due to space restrictions.
* _Time Constraint_: Each scenario requires setup, execution, and analysis time. Covering every possible scenario in an interaction study can thus be exceedingly time-consuming, making it difficult to complete the study within a affordable timeframe. Besides, extensive time commitments can be taxing for participants, potentially affecting their performance and willingness to participate. Furthermore, if participants are compensated by the hour, budget limitations can restrict the total available hours for conducting the study. This financial constraint often necessitates a concise and focused experimental design to stay within budget. Furthermore, in simulated environments, prolonged exposure can lead to various physiological limitations among participants such as simulator sickness (a condition similar to motion sickness), fatigue and cognitive overload, which limits the duration participants can comfortably engage with the simulation.
* _Number of Participants_: Recruiting a sufficient number of participants to cover all scenarios comprehensively can be challenging and costly, especially for more complex interactions that may require a larger and more diverse participant pool.
* _Type of Participants_: Attention need to be paid to the specific demographic of participant, incorporating parameters such as age group and expertise level with a domain and technology (e.g., ADAS system usage, heavy vehicle operation experience, or level of exposure to autonomous driving), aligning with the agents required as actors in the study.
* _Naturalism_: Conducting studies in in the real world ensures that the data and findings approach high ecological validity (e.g., drivers are more likely to exhibit natural driving behaviors). However, this inherently carries risks to the participant and others, and can be resource and cost intensive, and directly dictate the scenarios that are accessible to test in the circumstances.