## 5 Conclusions  
Potential UAM demand is expected to lead to thousands of daily operations on a single vertiport. This exceeds the volume of past helicopter operations, which is the most comparable existing mode of transport by far. While vertiport airside-air operations have been studied to some extent, a gap in research has been identified around airside-ground operations on vertiport airfields. Furthermore, in a preceeding publication it was shown that operational dynamics on vertiports can not be neglected [16], which renders past static or system-level analysis of vertiport capacities insufficient. Before UAM can take-off, this gap of knowledge needs to be addressed.  
For this article a custom-tailored ABMS framework [15] was fascilitated to investigate operational dynamics on vertiport airfields. It builds on preliminary insights on the drivers of delay around demand profiles [16] and vertiport layouts [14]. The main contributions of this article are fourfold: (1) The identified drivers of operational inefficiency have been confirmed. Looking at demand-related drivers, it was possible to prove that if the peaks of a demand profile and the imbalance between arrivals and requests stay within certain limits the average passenger delay is guaranteed to be low (below 3 min with a median of 36 s in the baseline scenario; see Section 3.4). (2) The mentioned thresholds between efficient and inefficient operations were quantified for the baseline scenario of 4 pads and 12 gates by using the concept of \"practical capacity\" (see Section 3.1). Defining thresholds of acceptable average delay of 4 min for peak-hour demand yielded \(\mathit{PHCAP}=264\); and of 2 min for the maximum imbalance of arrivals and departures yielded \(\mathit{PICAP}=33\) (see Section 3.4). (3) Looking at layout-related drivers, all of the following have shown  
Figure 13: Graphical display of the interchangeability of demand capacity, number of elements and related processes. (**a**) Design heuristic for pad operations. (**b**) Design heuristic for gate operations.  
high operational sensitivity expressed in exponential increase of delay: number of pads, number of gates, approach and departure time and boarding and de-boarding time. A multi-parameter variation of these factors including a variation of demand showed these sensitivities to hold true over a wide range of values, particularly for highly constrained scenarios as shown in Section 4.2. (4) The insight from 1-3 were aggregated into a design heuristic in order to transfer insights between scenarios and predict operational efficiency based on just a few characteristics (see Section 4.3).  
The claim of this article is that the presented insights will allow to quantify the expected delay on a vertiport by knowing only the following six values:  
1. Peak-hour demand;
2. Maximum imbalance between arrivals and departures;
3. Number of pads;
4. Number of gates;
5. Approach and departure time;
6. Boarding and de-boarding time.  
This design heuristic can be applied in the broader context: Vertiport planners can use the design heuristic to create a vertiport airfield that will match the given constraints in terms of demand, available area and acceptable delay. Furthermore, vehicle designers and regulatory agencies can use the sensitivities presented in this article to optimize their work around processes on pads in particular but also on gates. Lastly, the UAM research community can use insights around delay in at least two ways: first to study its impact on operational procedures on the ground and in the air; second to model the effect of delay on demand and with that on market potential of UAM.