## 2 Methods
### Social Network Analysis Methodology in Transport-Related Issues
This aspect also aids in understanding the network's topography for those unfamiliar with the operations of specific networks, proving useful for decision-makers. * Targeted interventions, such as identifying key nodes and connections through SNA, can lead to the more effective optimization and management of transport networks. * The integration of data from various sources and perspectives, which is particularly valuable in the context of urban and logistics transport systems, where data multidimensionality is common. * Cost-effectiveness, as SNA is a less expensive research method compared to other analytical techniques that can analyze relationships in networks at both the micro and macro levels. In comparison to other statistical methods, such as Principal Component Analysis (PCA) or hierarchical models, which focus on specific types of relationships or data reduction, SNA offers a unique capability to analyze the structure and complexity of networks by revealing the interdependencies between various factors. PCA, for example, is widely used to reduce the dimensionality of large datasets by identifying key components that explain the most variance [54]. However, it does not provide insight into how individual elements within the data interact with one another. In the case of this study, where multiple technical vehicle factors (e.g., fuel efficiency, battery capacity, vehicle safety) are interconnected and jointly influence decision-making, PCA would not capture the intricate relationships between these factors as effectively as SNA. SNA, by contrast, excels at capturing the complexity of networks, identifying key nodes (factors) that have the greatest influence on the overall system, and revealing how individual factors are interrelated. In the context of this study, SNA allows us to determine which technical features of vehicles--such as fuel consumption, battery efficiency, or safety features--are most central to users' decision-making processes based on metrics like degree centrality, closeness centrality, and betweenness centrality. This is particularly relevant in peer-to-peer car sharing, where multiple factors interact simultaneously to influence user preferences, and the relationships between these factors are crucial for understanding the overall decision-making network. The choice of Social Network Analysis (SNA) in the study of transport networks is attributed to its profound capability to elucidate the structure of networks, its effectiveness in identifying key elements that influence capacity and efficiency, and its adaptability to complex, multi-layered data within transport systems. Additionally, it is important to highlight that among the published articles on the topic, none were found that specifically addressed the application of this method to the realm of peer-to-peer car sharing. This gap in the literature underscores the novelty of the proposed research. SNA, which has not been previously applied to peer-to-peer car sharing, is now being utilized in this study to explore new dimensions of car-sharing networks. This research initiative aims to fill the existing gap by examining the intricate dynamics of peer-to-peer car-sharing systems through the lens of SNA, potentially uncovering unique insights that could redefine best practices and strategies within the sector.