## 2 Related Work  
This article wants to shed light on vertiport airfield operations and show its dynamic sensitivities. For these purposes, a customized Agent-Based Modeling and Simulation (ABMS) framework will be facilitated, which focuses on vertiport airfield operations while including passenger terminal and airspace operations on the system's boundaries. The model and parameter value specifications are described in [14] and the software implementation in the programming language _Python_ is described in [15]. The methodological foundation is laid in [14], which is a publicly available article: it was originally published in a MDPI _Electronics_ Special Issue on \"Urban Air Mobility\" and can be downloaded under [[https://www.mdpi.com/2079-9292/11/7/1071](https://www.mdpi.com/2079-9292/11/7/1071)]([https://www.mdpi.com/2079-9292/11/7/1071](https://www.mdpi.com/2079-9292/11/7/1071)) (accessed on 29 May 2022). Below, we only include a brief summary of the main elements of the model and in order to avoid repetition want to refer the reader to the original article. The advancement of the ABMS method is not an objective in this article, but rather the application of the existing method and the creation of quantified and transferable results. The basic model consists of three types of elements: (1) pads for vertical take-off and landing, which are the interface towards the airspace surrounding the vertiport. (2) Gates for boarding and de-boarding of passengers, which are the interface towards the terminal where the pre-flight passenger processing happens. (3) Stands for parking vehicles during off-peak times. These three elements are connected through (4) taxi lanes. In this virtual environment, two types of agents can move and interact: (I) vehicles and (II) passengers. To simulate one day of operations, the simulation needs four types of inputs: (A) a vertiport layout given by the coordinates of the centers of the three elements described above; (B) a list of plans, which consists of requests of passengers and arrivals of vehicles; (C) an initial population, which are the vehicles parked on the vertiport at the start of the simulation; (D) a list of parameter values defining the length of individual processes occuring on the airfield. Together, inputs A-D make up a scenario. The elements, agents, environment and inputs are depicted in Figure 1. In the visualizations throughout this article, any specific result (e.g., average passenger delay) of one simulation scenario is depicted as a dot. In previous publications [14; 16], the following insights were presented (as depicted in Figure 2), which will be expanded in this work:  
1. Operations on gates (e.g., passenger boarding) can be a bottleneck to operations and, therefore, should be considered in vertiport capacity planning. This diverts from conventional airport planning where the runways are the main limiting factor considered in the capacity planning process (see for example [17; 18]). 2. Increasing/decreasing the time of processes on pads or gates (e.g., approach or boarding time, respectively) has a similar effect as reducing/expanding the number of pads or gates. Both increased process times and reduced number of elements beyond a certain threshold yield an exponential increase in delay. 3. The accumulated daily demand is generally not a reliable indicator for operational efficiency; instead, using the peak-hour demand yields more reliable predictions about delay. Analysis of peak-hour demand is typical for airports, but not for helicports. It  
Figure 1: Depiction of elements, agents and environment of the agent-based simulation framework for vertiport operations; further including inputs needed for one simulation scenario. can, therefore, be assumed that heliports do not operate at capacity limit. Previous simulations showed that the peak-hour demand is the strongest driver of delay. 4. The imbalance of arrivals and departures has a substantial impact on passenger delay.