# Structured Urban Airspace Capacity Analysis: Four Drone Delivery Cases
###### Abstract
However, the approach caused very conservative analysis results and might disturb the optimality of solutions for the following problems: scheduling and separation management, although these have a computational advantage. As presented in the literature [11; 12; 22; 23; 24], on the development of advanced technology, the free flight-based operation in congested urban airspace can be an option for the UTM system. One of the few options is route network-based operations as described in [13; 25]. Additionally, in the ATM context, opinions on the types of airspace operations diverge into two groups, a reduction in the constraints research [26; 27] and more structured operations-preferred research [28; 29]. Inspired by [13; 25; 30; 31], we investigate strategies for analysing high-density VLL structured and controlled urban airspace capacity, with the aim to support airspace designers, regulators, and policymakers for determining tailored strategies in the consideration of regional characteristics and environment. The benchmark scenarios considered to perform capacity analysis are four different drone delivery operation types (1-to-\(\mathcal{M}\), \(\mathcal{M}\)-to-1, \(\mathcal{N}\)-to-\(\mathcal{M}\), and \(\mathcal{M}\)-to-\(\mathcal{N}\), where \(\mathcal{M},\mathcal{N}\) is a number of arrival or departure points and \(\mathcal{M}>\mathcal{N}\)) in a route network over the roads in a given urban airspace. To overcome the disadvantages of the five two-way route planning used in [13] and perform quantitative capacity analysis, we develop a flight planning algorithm which is an extended version of our previous algorithm [32; 33]. The proposed algorithm that iteratively solves multiple sUASs' flight planning problems consists of an inner loop algorithm and an outer loop algorithm. At each iteration, the inner loop algorithm generates each sUAS' flight route and speed profile by solving the shortest path problem, and the outer loop algorithm determines the departure and arrival sequences. To suggest an arrival and departure algorithm for each operation type, we utilise two different outer loop algorithms, which are the first-come-first-served (FCFS) algorithm and the last-come-first-served (LCFS) algorithm, for allocating the sequences. We utilise the obtained flight plans to analyse the urban airspace by defining four metrics as follows: total flight time; mission completion time; total flight distance; and normalised number of conflicts. For each operation type, we conduct 100 Monte Carlo simulations for both the FCFS algorithm and the LCFS algorithm. From the results, we can either find an appropriate outer loop algorithm for regional characteristics and the cases or reconfigure route networks as is required. We expect that the simulation results and its analysis could provide insight for policymakers, urban airspace designers, and regulators from the perspective of safety, capacity, etc. The structure of this paper is as follows. We first focus on a design of the route network over roads in Section 2. In the following section, we provide details of the flight planning algorithms for the different cases, and describe the metrics to analyse the urban airspace capacity. Note that, in order to avoid extraneous examples to explain the flight planning algorithm in Section 3, we adopt one simulation case in Section 4 to explain it. In Section 4, we perform numerical simulations for each operation type and analyse the results. Finally, the conclusion and discussion are presented in Section 5.