# Operationalizing Dyadic Urban Traffic Interaction Studies: From Theory to Practice
## 2 Background  
### Dyadic Interactions  
Traffic systems are intricate and dynamic, influenced by multiple parameters such as vehicle speed, traffic density, road conditions, and environmental influences, all of which complicate the isolation and study of specific traffic interactions. Historically, the exploration of traffic interaction dynamics primarily relied upon observational methodologies. Notably, [PERSON] utilized ethnographic strategies to study interactions at a Swedish driving school by observing and interviewing participants, recording sessions, and subsequently transcribing and thematically analyzing instances of cooperation among road users [12]. This approach has been paralleled in research addressing various facets of social agent navigation within urban traffic [13], interactions between drivers and buses [14], pedestrian-vehicle dynamics [15; 16; 17], and behaviors at petrol stations [18]. Additionally, [PERSON] and [PERSON] employed ethnographic methods to probe into how AVs are anticipated to interact with pedestrians, bicyclists, and other vehicles in a socially congruent manner, highlighting the methodological challenges in distinguishing such interactions observationally [19]. This is particularly relevant when comparing traffic interaction behaviors across locations with distinct cultures. Scholars studying driving interactions focus on the dynamics betweendrivers and other road users as key elements defining regional driving styles. Sociologist [PERSON], for instance, discusses informal norms that influence driving behaviors, including following distance, merging practices, and the adherence to right-of-way rules, as well as the display of attention or inattention while driving [20]. Extending this viewpoint, [PERSON] et al. propose that certain vehicular accidents should be understood not merely as results of individual risky behaviors but as \"social accidents\", which occur due to misunderstandings and misinterpretations among individuals from different social backgrounds during their interactions [21]. This further contextualizes the need for studying and characterizing traffic interactions, for which the seminal work by [PERSON] et al. [10] provides a solid theoretical foundation. Observational studies and naturalistic data analysis have historically played a crucial role in elucidating insights into traffic interactions. Prominent examples include large-scale driving studies utilizing instrumented vehicles to expand the scope of data by increasing the number of participants, miles driven, and duration of data collection. Notable projects include Virginia Tech's 100 Car Naturalistic Driving Study [22], the SHRP2 Naturalistic Driving Study [23], the University of Minnesota's Field Operational Test of the Teen Driver Support System [24], and the ongoing MIT Autonomous Vehicle Technology Study [25]. These studies collect diverse data forms such as images, videos, and telematic data from instrumented vehicles, creating versatile research datasets used for purposes like accident prediction [26] and analyzing the impact of weather on driving performance [27]. A notable challenge with these multi-use datasets is that they are often compiled without a clear vision of how future researchers might utilize them [28]. Because of this, the analysis of interaction models using such data, as explored by [PERSON] et al. using MIT-AVT data [29], often demands substantial processing to identify and classify interactions due to the complexity and volume of the data [30]. The challenges of isolating findings amidst the sea of confounding factors has lead to growing interest in augmenting naturalistic datasets with more tightly curated simulation datasets collecting interactions under more controlled environments. Simulations are particularly beneficial in the study of AVs, as they allow for control of interactive agents, facilitating analysis of how variations in AV behavior impact human drivers [31].