## 1 Introduction  
We aim to propose a direction for the mid-to-long-term optimal allocation of vertiports during the initial commercialization phase of urban air mobility (UAM) from a transportation network perspective. The high uncertainty surrounding the characteristics of UAM as a transportation mode, its concept of operations, and public acceptability poses a significant challenge in allocating an optimal vertiport location. This results in the limitation of localized solutions that fail to account for a broader strategic framework [1]. Furthermore, the absence of long-term strategic planning for vertiport placement may lead to substantial opportunity costs, particularly in terms of planned UAM commercialization patterns and synergies with existing transportation systems [2]. To address this issue, we seek to provide insights into the development of a global solution for vertiport allocation from a transportation network perspective as a first step toward resolving these challenges. Traditional location models face difficulties in securing concrete variables required for UAM deployment [3; 4]. The optimal placement of transportation infrastructure is generally premised on maximizing service coverage and minimizing travel distance as objective functions [5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. To solve these objective functions, decision variables and constraints must be defined. From a transportation infrastructure perspective, decision variables should include the characteristics of the mode and network, while constraints must encompass resource limitations, supply-demand balance, and the boundaries of decision variables. At present, UAM lacks the necessary data and realistic parameters to conduct such an analysis effectively. Before developing a concrete location allocation model, a methodological framework for conceptual vertiport location optimization is required. Existing studies addressing vertiport location have primarily relied on scenario-based approaches that assume hypothetical UAM services, questionnaire-based methods such as stated preference (SP) surveys to estimate demand [15; 16; 17; 18; 19; 20]. However, current research on vertiport placement is limited due to the absence of real-world operational data, making it difficult to accurately model real-world conditions [3; 21; 22; 23]. Additionally, given the distinct characteristics of UAM compared to conventional transportation modes, no studies were found within the scope of this research that consider vertiport placement from a transportation network perspective. Therefore, it is crucial to develop a methodological framework for optimizing vertiport locations at a conceptual level. At this stage, it is necessary to explore robust network-based vertiport siting methodologies that can accommodate future uncertainties while contributing to the strategic development of a resilient transportation network. Urban air mobility (UAM) is emerging as a promising mode of future transportation but remains in the early phase of commercialization, with key operational constraints (e.g., vehicle types, flight methods, takeoff and landing standards) not yet fully established [24; 25]. The commercialization of UAM will serve as an inflection point in reshaping urban transportation paradigms as it will significantly influence the structure of transportation networks [26]. Moreover, given the competitive nature of investments in vertiports for new transportation modes the rate of network transformation is accelerating [27; 28]. The increasing uncertainty surrounding the commercialization of autonomous vehicles, user behavior toward UAM, and societal acceptance further complicates the prediction of competitive and cooperative dynamics between different modes of transport [29; 30; 31; 32]. In this context, establishing a directional approach for optimizing vertiport placement from a network perspective is of paramount importance.