## 2 Literature Review  
### Location-Allocation Planning in Transportation: Traditional Approaches and Network Perspectives  
Traditionally, the location of transportation infrastructure--particularly stops and stations--has been determined primarily through demand-based models and survey-driven analyses. [PERSON] (1994) provided a comprehensive overview of the practical application of stated preference (SP) analysis in mode choice studies, highlighting that demand forecasting based on user behavior data plays a central role in location decision-making [35, 36]. [PERSON] and [PERSON] (1959) demonstrated that socioeconomic variables such as population distribution, employment centers, and commuting patterns have historically been key factors in determining the siting of transportation facilities [37]. In recent years, advancements in GIS-based spatial data, real-time traffic volumes, and mobile-based demand analytics have enabled data-driven approaches to site selection. For example, [PERSON] et al. (2017) conducted a multidimensional analysis of accessibility and efficiency in public transportation systems using spatial network data [38]. Considering the early stage of UAM development, traditional demand-based siting methodologies present inherent limitations when applied to vertiports.  
Research on the characteristics of transportation networks has been continuously conducted. [PERSON] et al. (2019) analyzed the public transportation network of Xi'an, selecting and optimizing the locations of public transport hubs [39]. They identified an imbalance in the network due to the excessively high centrality of certain hub nodes and proposed optimal hub locations to address this limitation. [PERSON] et al. (2021) proposed a conceptual framework for the establishment and operation of global logistics energy hubs and evaluated the efficiency of energy supply chains by utilizing network centrality and connectivity indicators [40]. [PERSON] et al. (2022) employed the WASPAS (weighted aggregated sum product assessment) methodology, which assigns appropriate weights to network centrality measures (degree centrality, betweenness centrality, and closeness centrality) to evaluate their significance and determine the optimal locations for sustainable urban mobility hubs [28]. [PERSON] et al. (2015) conducted a comparative analysis on how different transportation network topologies affect disaster response resilience, demonstrating that structures with higher average degree and cyclicity tend to exhibit greater robustness [41].  
Similarly, [PERSON] et al. (2017), through an analysis of road networks in 40 U.S. cities, argued that efficiency and resilience are independent properties. By developing a model that incorporates network features, such as shortest path-based traffic load, population density, and commuting demand, they empirically showed that some cities, although inefficient under normal conditions, perform more robustly during disasters [42]. These studies suggest that transportation infrastructure siting plays a critical role in shaping network characteristics and highlight the importance of establishing strategic direction for initial vertiport siting, particularly by considering changes in the transportation network.